SYSTEM AND METHODS FOR PROCESSING ANALYTE SENSOR DATA
First Claim
1. A method for determining a stability of substantially continuous glucose sensor, the method comprising:
- receiving a data stream from a continuous glucose sensor, including one or more sensor data points; and
determining a level of stability of the continuous glucose sensor, wherein the determining a level stability is performed in real-time.
1 Assignment
0 Petitions

Accused Products

Abstract
Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Udo Hoss, Gary A. Stafford
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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32 Claims
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1. A method for determining a stability of substantially continuous glucose sensor, the method comprising:
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receiving a data stream from a continuous glucose sensor, including one or more sensor data points; and determining a level of stability of the continuous glucose sensor, wherein the determining a level stability is performed in real-time. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system for determining a stability of continuous glucose sensor, comprising:
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a sensor data module operatively connected to a continuous glucose sensor that receives a data stream comprising a plurality of time spaced sensor data points from the glucose sensor; and a processor module programmed to determine a level of stability of the continuous glucose sensor in real-time. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32)
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1 Specification
This application is a division of U.S. application Ser. No. 10/633,367 filed Aug. 1, 2003, which is incorporated by reference herein in its entirety, and which is hereby made a part of this specification.
The present invention relates generally to systems and methods for analyte sensor data processing. Particularly, the present invention relates to retrospectively and/or prospectively initiating a calibration, converting sensor data, updating the calibration, evaluating received reference and sensor data, and evaluating the calibration for the analyte sensor.
Diabetes mellitus is a disorder in which the pancreas cannot create sufficient insulin (Type I or insulin dependent) and/or in which insulin is not effective (Type 2 or non-insulin dependent). In the diabetic state, the victim suffers from high blood sugar, which may cause an array of physiological derangements (e.g., kidney failure, skin ulcers, or bleeding into the vitreous of the eye) associated with the deterioration of small blood vessels. A hypoglycemic reaction (low blood sugar) may be induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake.
Conventionally, a diabetic person carries a self-monitoring blood glucose (SMBG) monitor, which typically comprises uncomfortable finger pricking methods. Due to the lack of comfort and convenience, a diabetic will normally only measure his or her glucose level two to four times per day. Unfortunately, these time intervals are so far spread apart that the diabetic will likely find out too late, sometimes incurring dangerous side effects, of a hyper- or hypo-glycemic condition. In fact, it is not only unlikely that a diabetic will take a timely SMBG value, but the diabetic will not know if their blood glucose value is going up (higher) or down (lower) based on conventional methods, inhibiting their ability to make educated insulin therapy decisions.
Systems and methods are needed that accurately provide estimated glucose measurements to a diabetic patient continuously and/or in real time so that they may proactively care for their condition to safely avoid hyper- and hypo-glycemic conditions. Real time and retrospective estimated glucose measurements require reliable data processing in order to provide accurate and useful output to a patient and/or doctor.
Similarly, systems and methods are needed that accurately provide substantially continuous estimated analyte measurements for a variety of known analytes (e.g., oxygen, salts, protein, and vitamins) to provide prospective and/or retrospective data analysis and output to a user.
Accordingly, systems and methods are provided for retrospectively and/or prospectively calibrating a sensor, initializing a sensor, converting sensor data into calibrated data, updating and maintaining a calibration over time, evaluating received reference and sensor data for clinical acceptability, and evaluating the calibration statistical acceptability, to ensure accurate and safe data output to a patient and/or doctor.
In a first embodiment a method is provided for initializing a substantially continuous analyte sensor, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including two or more reference data points; providing at least two matched data pairs by matching reference analyte data to substantially time corresponding sensor data; forming a calibration set including the at least two matching data pairs; and determining a stability of the continuous analyte sensor.
In an aspect of the first embodiment, the step of determining the stability of the substantially continuous analyte sensor includes waiting a predetermined time period between about one minute and about six weeks.
In an aspect of the first embodiment, the step of determining the stability of the substantially continuous analyte sensor includes evaluating at least two matched data pairs.
In an aspect of the first embodiment, the step of determining the stability of the substantially continuous analyte sensor includes evaluating one of pH, oxygen, hypochlorite, interfering species, correlation of matched pairs, R-value, baseline drift, baseline offset, and amplitude.
In an aspect of the first embodiment, the method further includes providing one of an audible, visual, or tactile output to a user based on the stability of the sensor.
In an aspect of the first embodiment, the step of providing output based on the stability of the sensor includes indicating at least one of a numeric estimated analyte value, a directional trend of analyte concentration, and a graphical representation of an estimated analyte value.
In an aspect of the first embodiment, the step of receiving sensor data includes receiving sensor data from a substantially continuous glucose sensor.
In an aspect of the first embodiment, the step of receiving sensor data includes receiving sensor data from an implantable glucose sensor.
In an aspect of the first embodiment, the step of receiving sensor data includes receiving sensor data from subcutaneously implantable glucose sensor.
In an aspect of the first embodiment, the step of receiving reference data includes receiving reference data from a self-monitoring blood glucose test.
In an aspect of the first embodiment, the step of receiving reference data includes downloading reference data via a cabled connection.
In an aspect of the first embodiment, the step of receiving reference data includes downloading reference data via a wireless connection.
In an aspect of the first embodiment, the step of receiving reference data from a reference analyte monitor includes receiving within a receiver internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the first embodiment, the step of forming a calibration set includes evaluating at least one matched data pair using inclusion criteria.
In an aspect of the first embodiment, the step of receiving sensor data includes receiving sensor data that has been algorithmically smoothed.
In an aspect of the first embodiment, the step of receiving sensor data includes algorithmically smoothing the received sensor data.
In an aspect of the first embodiment, the step of forming a calibration set includes including in the calibration set between one and six matched data pairs.
In an aspect of the first embodiment, the step of forming a calibration set includes including six matched data pairs.
In an aspect of the first embodiment, the step of forming a calibration set further includes determining a value for n, where n is greater than one and represents the number of matched data pairs in the calibration set.
In an aspect of the first embodiment, the step of determining a value for n is determined as a function of the frequency of the received reference data points and signal strength over time.
In a second embodiment, a system is provided for initializing a continuous analyte sensor, including: a sensor data module operatively connected to a continuous analyte sensor that receives a data stream including a plurality of time spaced sensor data points from the analyte sensor; a reference input module adapted to obtain reference data from a reference analyte monitor, including one or more reference data points; a processor module that forms one or more matched data pairs by matching reference data to substantially time corresponding sensor data and subsequently forms a calibration set including the one or more matched data pairs; and a start-up module associated with the processor module programmed to determine the stability of the continuous analyte sensor.
In an aspect of the second embodiment, the sensor data module is adapted to wirelessly receive sensor data points from the sensor.
In an aspect of the second embodiment, the start-up module is programmed to wait a predetermined time period between six hours and six weeks.
In an aspect of the second embodiment, the start-up module is programmed to evaluate at least two matched data pairs.
In an aspect of the second embodiment, the start-up module is programmed to evaluate one of pH, oxygen, hypochlorite, interfering species, correlation of matched pairs, R-value, baseline drift, baseline offset, and amplitude.
In an aspect of the second embodiment, the system further includes an output control module associated with the processor module and programmed to control output of sensor data.
In an aspect of the second embodiment, the output control module indicates at least one of a numeric estimated analyte value, a directional trend of analyte concentration, and a graphical representation of an estimated analyte value.
In an aspect of the second embodiment, the sensor data module is configured to receive sensor data from substantially the continuous glucose sensor.
In an aspect of the second embodiment, the sensor data module is configured to receive sensor data from an implantable glucose sensor.
In an aspect of the second embodiment, the sensor data module is configured to receive sensor data from subcutaneously implantable glucose sensor.
In an aspect of the second embodiment, the reference input module is configured to receive reference data from a self-monitoring blood glucose test.
In an aspect of the second embodiment, the reference input module is configured to download reference data via a cabled connection.
In an aspect of the second embodiment, the reference input module is configured to download reference data via a wireless connection.
In an aspect of the second embodiment, the system further includes a reference analyte monitor integral with the system and wherein the reference input module is configured to receive an internal communication from the reference analyte monitor.
In an aspect of the second embodiment, the processor module includes programming to evaluate at least one matched data pair using inclusion criteria.
In an aspect of the second embodiment, the reference input module is configured to receive sensor data that has been algorithmically smoothed.
In an aspect of the second embodiment, the reference input module is configured to algorithmically smooth the received sensor data.
In an aspect of the second embodiment, the calibration set includes between one and six matched data pairs.
In an aspect of the second embodiment, the calibration set includes six matched data pairs.
In an aspect of the second embodiment, the calibration set includes n matched data pairs, where n is greater than one.
In an aspect of the second embodiment, n is a function of the frequency of the received reference data points and signal strength over time.
In a third embodiment, a computer system is provided for initializing a continuous analyte sensor, the computer system including: a sensor data receiving module that receives sensor data from the substantially continuous analyte sensor via a receiver, including one or more sensor data points; a reference data receiving module that receives reference data from a reference analyte monitor, including one or more reference data points; a data matching module that forms one or more matched data pairs by matching reference data to substantially time corresponding sensor data; a calibration set module that forms a calibration set including at least one matched data pair; and a stability determination module that determines the stability of the continuous analyte sensor.
In an aspect of the third embodiment, the stability determination module includes a system for waiting a predetermined time period.
In an aspect of the third embodiment, the stability determination module evaluates at least two matched data pairs.
In an aspect of the third embodiment, the stability determination module evaluates one of pH, oxygen, hypochlorite, interfering species, correlation of matched pairs, R-value, baseline drift, baseline offset, and amplitude.
In an aspect of the third embodiment, the computer system further includes an interface control module that provides output to the user based on the stability of the sensor.
In an aspect of the third embodiment, the output from the interface control module includes at least one of a numeric estimated analyte value, an indication of directional trend of analyte concentration, and a graphical representation of an estimated analyte value.
In an aspect of the third embodiment, the reference data receiving module is adapted to receive sensor data from a substantially continuous glucose sensor.
In an aspect of the third embodiment, the reference data receiving module is adapted to receive sensor data from an implantable glucose sensor.
In an aspect of the third embodiment, the reference data receiving module is adapted to receive sensor data from a subcutaneously implantable glucose sensor.
In an aspect of the third embodiment, the reference data receiving module is adapted to receive sensor data from a self-monitoring blood glucose test.
In an aspect of the third embodiment, the reference data receiving module is adapted to receive sensor data from a cabled connection.
In an aspect of the third embodiment, the reference data receiving module is adapted to download reference data via a wireless connection.
In an aspect of the third embodiment, the reference data receiving module is adapted to receive reference data from an internal reference analyte monitor that is housed integrally the computer system.
In an aspect of the third embodiment, the calibration set module evaluates at least one matched data pair using inclusion criteria.
In an aspect of the third embodiment, the sensor data receiving module is adapted to receive sensor data that has been algorithmically smoothed.
In an aspect of the third embodiment, the computer system further includes a data smoothing module that smoothes the received sensor data.
In an aspect of the third embodiment, the calibration set module includes between one and six matched data pairs.
In an aspect of the third embodiment, the calibration set module includes six matched data pairs.
In an aspect of the third embodiment, the calibration set includes n number of matched data pairs, where n is greater than one.
In an aspect of the third embodiment, n is a function of the frequency of the received reference data points and signal strength over time.
In a fourth embodiment, method is provided for initializing a substantially continuous analyte sensor, the method including: receiving sensor data from a substantially continuous analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including one or more reference data points; forming one or more matched data pairs by matching reference data to substantially time corresponding sensor data; forming a calibration set including at least one matched data pair; determining stability of continuous analyte sensor; and outputting information reflective of the sensor data once a predetermined level of stability has been determined.
In a fifth embodiment, a system is provided for initializing a continuous analyte sensor, including: a sensor data module operatively linked to a continuous analyte sensor and configured to receive one or more sensor data points from the sensor; a reference input module adapted to obtain one or more reference data points; and a processor module associated with the sensor data module and the input module and programmed to match reference data points with time-matched sensor data points to form a calibration set including at least one matched data pair; and a start-up module associated with the processor module programmed to determine the stability of the continuous analyte sensor and output information reflective of the sensor data once a predetermined level of stability has been determined.
In a sixth embodiment, a computer system is provided for initializing a continuous analyte sensor, the system including: a sensor data receiving module that receives sensor data including one or more sensor data points from the substantially continuous analyte sensor via a receiver; a reference data receiving module for receiving reference data from a reference analyte monitor, including one or more reference data points; a data matching module for forming one or more matched data pairs by matching reference data to substantially time corresponding sensor data; a calibration set module for forming a calibration set including at least one matched data pair; a stability determination module for evaluating the stability of the continuous analyte sensor; and an interface control module that outputs information reflective of the sensor data once a predetermined level of stability has been determined.
In a seventh embodiment, a method for initializing a glucose sensor, the method including: receiving sensor data from the glucose sensor, including one or more sensor data points; receiving reference data from a reference glucose monitor, including one or more reference data points; forming one or more matched data pairs by matching reference data to substantially time corresponding sensor data; determining whether the glucose sensor has reached a predetermined level of stability.
In an eighth embodiment, a system is provided for initializing a continuous analyte sensor, including: a sensor data module operatively linked to a continuous analyte sensor and configured to receive one or more sensor data points from the sensor; a reference input module adapted to obtain one or more reference data points; and a processor module associated with the sensor data module and the input module and programmed to match reference data points with time-matched sensor data points to form a calibration set including at least one matched data pair; and a stability module associated with the processor module programmed to determine the stability of the continuous analyte sensor.
In a ninth embodiment, a method is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including one or more reference data points; and evaluating the clinical acceptability at least one of the reference and sensor analyte data using substantially time corresponding reference or sensor data, wherein the at least one of the reference and sensor analyte data is evaluated for deviation from its substantially time corresponding reference or sensor data and clinical risk associated with that deviation based on the glucose value indicated by at least one of the sensor and reference data.
In an aspect of the ninth embodiment, the method further includes providing an output through a user interface responsive to the clinical acceptability evaluation.
In an aspect of the ninth embodiment, the step of providing an output includes alerting the user based on the clinical acceptability evaluation.
In an aspect of the ninth embodiment, the step of providing an output includes altering the user interface based on the clinical acceptability evaluation.
In an aspect of the ninth embodiment, the step of altering the user interface includes at least one of providing color-coded information, trend information, directional information (e.g., arrows or angled lines), and/or fail-safe information.
In an aspect of the ninth embodiment, the step of evaluating the clinical acceptability includes using one of a Clarke Error Grid, a mean absolute difference calculation, a rate of change calculation, a consensus grid, and a standard clinical acceptance test.
In an aspect of the ninth embodiment, the method further includes requesting additional reference data if the clinical acceptability evaluation determines clinical unacceptability.
In an aspect of the ninth embodiment, the method further includes repeating the clinical acceptability evaluation step for the additional reference data.
In an aspect of the ninth embodiment, the method further includes a step of matching reference data to substantially time corresponding sensor data to form a matched pair after the clinical acceptability evaluation step.
In a tenth embodiment, a system is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the method including: means for receiving a data stream from an analyte sensor, a plurality of time-spaced sensor data points; means for receiving reference data from a reference analyte monitor, including one or more reference data points; and means for evaluating the clinical acceptability of at least one of the reference and sensor analyte data using substantially time corresponding reference and sensor data, wherein the at least one of the reference and sensor analyte data is evaluated for deviation from its substantially time corresponding reference or sensor data and clinical risk associated with that deviation based on the glucose value indicated by at least one of the sensor and reference data.
In an aspect of the tenth embodiment, the system further includes means for providing an output based through a user interface responsive to the clinical acceptability evaluation.
In an aspect of the tenth embodiment, the means for providing an output includes means for alerting the user based on the clinical acceptability evaluation.
In an aspect of the tenth embodiment, the means for providing an output includes means for altering the user interface based on the clinical acceptability evaluation.
In an aspect of the tenth embodiment, the means for altering the user interface includes at least one of providing color-coded information, trend information, directional information (e.g., arrows or angled lines), and/or fail-safe information.
In an aspect of the tenth embodiment, the means for evaluating the clinical acceptability includes using one of a Clarke Error Grid, a mean absolute difference calculation, a rate of change calculation, a consensus grid, and a standard clinical acceptance test.
In an aspect of the tenth embodiment, the system further includes means for requesting additional reference data if the clinical acceptability evaluation determines clinical unacceptability.
In an aspect of the tenth embodiment, the system further includes means for repeated the clinical acceptability evaluation for the additional reference data.
In an aspect of the tenth embodiment, the system further includes means for matching reference data to substantially time corresponding sensor data to form a matched data pair after the clinical acceptability evaluation.
In an eleventh embodiment, a computer system is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the computer system including: a sensor data receiving module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference data receiving module that receives reference data from a reference analyte monitor, including one or more reference data points; and a clinical acceptability evaluation module that evaluates at least one of the reference and sensor analyte data using substantially time corresponding reference and sensor data, wherein the at least one of the reference and sensor analyte data is evaluated for deviation from its substantially time corresponding reference or sensor data and clinical risk associated with that deviation based on the glucose value indicated by at least one of the sensor and reference data.
In an aspect of the eleventh embodiment, the computer system further includes an interface control module that controls the user interface based on the clinical acceptability evaluation.
In an aspect of the eleventh embodiment, the interface control module alerts the user based on the clinical acceptability evaluation.
In an aspect of the eleventh embodiment, the interface control module alters the user interface based on the clinical acceptability evaluation.
In an aspect of the eleventh embodiment, the interface control module alters the user interface by providing at least one of providing color-coded information, trend information, directional information (e.g., arrows or angled lines), and/or fail-safe information.
In an aspect of the eleventh embodiment, the clinical acceptability evaluation module uses one of a Clarke Error Grid, a mean absolute difference calculation, a rate of change calculation, a consensus grid, and a standard clinical acceptance test to evaluate clinical acceptability.
In an aspect of the eleventh embodiment, the interface control module that requests additional reference data if the clinical acceptability evaluation determines clinical unacceptability.
In an aspect of the eleventh embodiment, the interface control module evaluates the additional reference data using clinical acceptability evaluation module.
In an aspect of the eleventh embodiment, the computer system further includes a data matching module that matches clinically acceptable reference data to substantially time corresponding clinically acceptable sensor data to form a matched pair.
In a twelfth embodiment, a method is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including one or more reference data points; evaluating the clinical acceptability at least one of the reference and sensor analyte data using substantially time corresponding reference and sensor data, wherein the at least one of the reference and sensor analyte data is evaluated for deviation from its substantially time corresponding reference or sensor data and clinical risk associated with that deviation based on the glucose value indicated by at least one of the sensor and reference data; and providing an output through a user interface responsive to the clinical acceptability evaluation.
In an thirteenth embodiment, a method is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including one or more reference data points; and evaluating the clinical acceptability at least one of the reference and sensor analyte data using substantially time corresponding reference and sensor data, including using one of a Clarke Error Grid, a mean absolute difference calculation, a rate of change calculation, and a consensus grid.
In an fourteenth embodiment, a computer system is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the computer system including: a sensor data module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference input module that receives reference data from a reference analyte monitor, including one or more reference data points; a clinical module that evaluates at least one of the reference and sensor analyte data using substantially time corresponding reference and sensor data, wherein the at least one of the reference and sensor analyte data is evaluated for deviation from its substantially time corresponding reference or sensor data and clinical risk associated with that deviation based on the glucose value indicated by at least one of the sensor and reference data; and an interface control module that controls the user interface based on the clinical acceptability evaluation.
In an fifteenth embodiment, a computer system is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the computer system including: a sensor data module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference input module that receives reference data from a reference analyte monitor, including one or more reference data points; and a clinical module that evaluates at least one of the reference and sensor analyte data with substantially time corresponding reference and sensor data, wherein the clinical module uses one of a Clarke Error Grid, a mean absolute difference calculation, a rate of change calculation, a consensus grid, and a standard clinical acceptance test to evaluate clinical acceptability.
In an sixteenth embodiment, a computer system is provided for evaluating clinical acceptability of at least one of reference and sensor analyte data, the computer system including: a sensor data module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor via a receiver; a reference input module that receives reference data from a reference analyte monitor, including one or more reference data points; and a clinical module that uses a Clarke Error Grid to evaluate the clinical acceptability at least one of the reference and sensor analyte data using substantially time corresponding reference and sensor data; and a fail-safe module that controls the user interface responsive to the clinical module evaluating clinical unacceptability.
In an seventeenth embodiment, a method is provided for evaluating clinical acceptability of at least one of reference and sensor glucose data, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference glucose monitor, including one or more reference data points; evaluating the clinical acceptability at least one of the reference and sensor glucose data using substantially time corresponding reference and sensor data, wherein the at least one of the reference and sensor analyte data is evaluated for deviation from its substantially time corresponding reference or sensor data and clinical risk associated with that deviation based on the glucose value indicated by at least one of the sensor and reference data; and a fail-safe module that controls the user interface responsive to the clinical module evaluating clinical unacceptability.
In an eighteenth embodiment, a method is provided for maintaining calibration of a substantially continuous analyte sensor, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including two or more reference data points; providing at least two matched data pairs by matching reference analyte data to substantially time corresponding sensor data; forming a calibration set including the at least two matching data pairs; creating a conversion function based on the calibration set; converting sensor data into calibrated data using the conversion function; subsequently obtaining one or more additional reference data points and creating one or more new matched data pairs; evaluating the calibration set when the new matched data pair is created, wherein evaluating the calibration set includes at least one of 1) ensuring matched data pairs in the calibration set span a predetermined time range, 2) ensuring matched data pairs in the calibration set are no older than a predetermined value, 3) ensuring the calibration set has substantially distributed high and low matched data pairs over the predetermined time range, and 4) allowing matched data pairs only within a predetermined range of analyte values; and subsequently modifying the calibration set if such modification is required by the evaluation.
In an aspect of the eighteenth embodiment, the step of evaluating the calibration set further includes at least one of evaluating a rate of change of the analyte concentration, evaluating a congruence of respective sensor and reference data in the matched data pairs, and evaluating physiological changes.
In an aspect of the eighteenth embodiment, the step of evaluating the calibration set includes evaluating only the new matched data pair.
In an aspect of the eighteenth embodiment, the step of evaluating the calibration set includes evaluating all of the matched data pairs in the calibration set and the new matched data pair.
In an aspect of the eighteenth embodiment, the step of evaluating the calibration set includes evaluating combinations of matched data pairs from the calibration set and the new matched data pair.
In an aspect of the eighteenth embodiment, the step of receiving sensor data includes receiving a data stream from a long-term implantable analyte sensor.
In an aspect of the eighteenth embodiment, the step of receiving sensor data includes receiving a data stream that has been algorithmically smoothed.
In an aspect of the eighteenth embodiment, the step of receiving sensor data stream includes algorithmically smoothing the data stream.
In an aspect of the eighteenth embodiment, the step of receiving reference data includes downloading reference data via a cabled connection.
In an aspect of the eighteenth embodiment, the step of receiving reference data includes downloading reference data via a wireless connection.
In an aspect of the eighteenth embodiment, the step of receiving reference data from a reference analyte monitor includes receiving within a receiver internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the eighteenth embodiment, the reference analyte monitor includes self-monitoring of blood analyte.
In an aspect of the eighteenth embodiment, the step of creating a conversion function includes linear regression.
In an aspect of the eighteenth embodiment, the step of creating a conversion function includes non-linear regression.
In an aspect of the eighteenth embodiment, the step of forming a calibration set includes including in the calibration set between one and six matched data pairs.
In an aspect of the eighteenth embodiment, the step of forming a calibration set includes including six matched data pairs.
In an aspect of the eighteenth embodiment, the step of forming a calibration set further includes determining a value for n, where n is greater than one and represents the number of matched data pairs in the calibration set.
In an aspect of the eighteenth embodiment, the step of determining a value for n is determined as a function of the frequency of the received reference data points and signal strength over time.
In an aspect of the eighteenth embodiment, the method further includes determining a set of matching data pairs from the evaluation of the calibration set and re-forming a calibration set.
In an aspect of the eighteenth embodiment, the method further includes repeating the step of re-creating the conversion function using the re-formed calibration set.
In an aspect of the eighteenth embodiment, the method further includes converting sensor data into calibrated data using the re-created conversion function.
In a nineteenth embodiment, a system is provided for maintaining calibration of a substantially continuous analyte sensor, the system including: means for receiving a data stream from an analyte sensor, a plurality of time-spaced sensor data points; means for receiving reference data from a reference analyte monitor, including two or more reference data points; means for providing two or more matched data pairs by matching reference analyte data to substantially time corresponding sensor data; means for forming a calibration set including at least two matched data pair; means for creating a conversion function based on the calibration set; means for converting sensor data into calibrated data using the conversion function; subsequently obtaining one or more additional reference data points and creating one or more new matched data pairs; means for evaluating the calibration set when the new matched data pair is created, wherein evaluating the calibration set includes at least one of 1) ensuring matched data pairs in the calibration set span a predetermined time range, 2) ensuring matched data pairs in the calibration set are no older than a predetermined value, 3) ensuring the calibration set has substantially distributed high and low matched data pairs over the predetermined time range, and 4) allowing matched data pairs only within a predetermined range of analyte values; and means for modifying the calibration set if such modification is required by the evaluation.
In an aspect of the nineteenth embodiment, the means for evaluating the calibration set further includes at least one of means for evaluating a rate of change of the analyte concentration, means for evaluating a congruence of respective sensor and reference data in matched data pairs; and means for evaluating physiological changes.
In an aspect of the nineteenth embodiment, the means for evaluating the calibration set includes means for evaluating only the one or more new matched data pairs.
In an aspect of the nineteenth embodiment, the means for evaluating the calibration set includes means for evaluating all of the matched data pairs in the calibration set and the one or more new matched data pairs.
In an aspect of the nineteenth embodiment, the means for evaluating the calibration set includes means for evaluating combinations of matched data pairs from the calibration set and the one or more new matched data pair.
In an aspect of the nineteenth embodiment, the means for receiving sensor data includes means for receiving sensor data from a long-term implantable analyte sensor.
In an aspect of the nineteenth embodiment, the means for receiving sensor data includes means for receiving sensor data that has been algorithmically smoothed.
In an aspect of the nineteenth embodiment, the means for receiving sensor data includes means for algorithmically smoothing the receiving sensor data.
In an aspect of the nineteenth embodiment, the means for receiving reference data includes means for downloading reference data via a cabled connection.
In an aspect of the nineteenth embodiment, the means for receiving reference data includes means for downloading reference data via a wireless connection.
In an aspect of the nineteenth embodiment, the means for receiving reference data from a reference analyte monitor includes means for receiving within a receiver internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the nineteenth embodiment, the means for receiving reference data includes means for receiving from a self-monitoring of blood analyte.
In an aspect of the nineteenth embodiment, the means for creating a conversion function includes means for performing linear regression.
In an aspect of the nineteenth embodiment, the means for creating a conversion function includes means for performing non-linear regression.
In an aspect of the nineteenth embodiment, the means for forming a calibration set includes including in the calibration set between one and six matched data pairs.
In an aspect of the nineteenth embodiment, the means for forming a calibration set includes including in the calibration set six matched data pairs.
In an aspect of the nineteenth embodiment, the means for forming a calibration set further includes determining a value for n, where n is greater than one and represents the number of matched data pairs in the calibration set.
In an aspect of the nineteenth embodiment, the means for determining a value for n is determined as a function of the frequency of the received reference data points and signal strength over time.
In an aspect of the nineteenth embodiment, the system further includes means for determining a set of matching data pairs from the evaluation of the calibration set and re-forming a calibration set.
In an aspect of the nineteenth embodiment, the system further includes the means for repeating the set of creating the conversion function using the re-formed calibration set.
In an aspect of the nineteenth embodiment, the system further includes means for converting sensor data into calibrated data using the re-created conversion function.
In a twentieth embodiment, a computer system is provided for maintaining calibration of a substantially continuous analyte sensor, the computer system including: a sensor data receiving module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference data receiving module that receives reference data from a reference analyte monitor, including two or more reference data points; a data matching module that forms two or more matched data pairs by matching reference data to substantially time corresponding sensor data; a calibration set module that forms a calibration set including at least two matched data pairs; a conversion function module that creates a conversion function using the calibration set; a sensor data transformation module that converts sensor data into calibrated data using the conversion function; and a calibration evaluation module that evaluates the calibration set when the new matched data pair is provided, wherein evaluating the calibration set includes at least one of 1) ensuring matched data pairs in the calibration set span a predetermined time period, 2) ensuring matched data pairs in the calibration set are no older than a predetermined value, 3) ensuring the calibration set has substantially distributed high and low matched data pairs over a predetermined time range, and 4) allowing matched data pairs only within a predetermined range of analyte values, wherein the conversion function module is programmed to re-create the conversion function of such modification is required by the calibration evaluation module.
In an aspect of the twentieth embodiment, the evaluation calibration module further evaluates at least one of a rate of change of the analyte concentration, a congruence of respective sensor and reference data in matched data pairs; and physiological changes.
In an aspect of the twentieth embodiment, the evaluation calibration module evaluates only the new matched data pair.
In an aspect of the twentieth embodiment, the evaluation calibration module evaluates all of the matched data pairs in the calibration set and the new matched data pair.
In an aspect of the twentieth embodiment, the evaluation calibration module evaluates combinations of matched data pairs from the calibration set and the new matched data pair.
In an aspect of the twentieth embodiment, the sensor data receiving module receives the data stream from a long-term implantable analyte sensor.
In an aspect of the twentieth embodiment, the sensor data receiving module receives an algorithmically smoothed data stream.
In an aspect of the twentieth embodiment, the sensor data receiving module includes programming to smooth the data stream.
In an aspect of the twentieth embodiment, the reference data receiving module downloads reference data via a cabled connection.
In an aspect of the twentieth embodiment, the reference data receiving module downloads reference data via a wireless connection.
In an aspect of the twentieth embodiment, the reference data receiving module receives within a receiver internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the twentieth embodiment, the reference data receiving module receives reference data from a self-monitoring of blood analyte.
In an aspect of the twentieth embodiment, the conversion function module includes programming that performs linear regression.
In an aspect of the twentieth embodiment, the conversion function module includes programming that performs non-linear regression.
In an aspect of the twentieth embodiment, the calibration set module includes in the calibration set between one and six matched data pairs.
In an aspect of the twentieth embodiment, the calibration set module includes in the calibration set six matched data pairs.
In an aspect of the twentieth embodiment, the calibration set module further includes programming for determining a value for n, where n is greater than one and represents the number of matched data pairs in the calibration set.
In an aspect of the twentieth embodiment, the programming for determining a value for n determines n as a function of the frequency of the received reference data points and signal strength over time.
In an aspect of the twentieth embodiment, data matching module further includes programming to re-form the calibration set based on the calibration evaluation.
In an aspect of the twentieth embodiment, the conversion function module further includes programming to re-create the conversion function based on the re-formed calibration set.
In an aspect of the twentieth embodiment, the sensor data transformation module further including programming for converting sensor data into calibrated using the re-created conversion function.
In a twenty-first embodiment, a method is provided for maintaining calibration of a glucose sensor, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including two or more reference data points; providing at least two matched data pairs by matching reference analyte data to substantially time corresponding sensor data; forming a calibration set including the at least two matching data pairs; creating a conversion function based on the calibration set; subsequently obtaining one or more additional reference data points and creating one or more new matched data pairs; and evaluating the calibration set when the new matched data pair is created, wherein evaluating the calibration set includes at least one of 1) ensuring matched data pairs in the calibration set span a predetermined time range, 2) ensuring matched data pairs in the calibration set are no older than a predetermined value, 3) ensuring the calibration set has substantially distributed high and low matched data pairs over the predetermined time range, and 4) allowing matched data pairs only within a predetermined range of analyte values.
In a twenty-second embodiment, a computer system is provided for maintaining calibration of a glucose sensor, the computer system including: a sensor data module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference input module that receives reference data from a reference analyte monitor, including two or more reference data points; a processor module that forms two or more matched data pairs by matching reference data to substantially time corresponding sensor data and subsequently forms a calibration set including the two or more matched data pairs; and a calibration evaluation module that evaluates the calibration set when the new matched data pair is provided, wherein evaluating the calibration set includes at least one of 1) ensuring matched data pairs in the calibration set span a predetermined time period, 2) ensuring matched data pairs in the calibration set are no older than a predetermined value, 3) ensuring the calibration set has substantially distributed high and low matched data pairs over a predetermined time range, and 4) allowing matched data pairs only within a predetermined range of analyte values, wherein the conversion function module is programmed to re-create the conversion function of such modification is required by the calibration evaluation module.
In a twenty-third embodiment, a method is provided for evaluating the quality of a calibration of an analyte sensor, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including two or more reference data points; providing at least two matched data pairs by matching reference analyte data to substantially time corresponding sensor data; forming a calibration set including the at least two matching data pairs; creating a conversion function based on the calibration set; receiving additional sensor data from the analyte sensor; converting sensor data into calibrated data using the conversion function; and evaluating the quality of the calibration set using a data association function.
In an aspect of the twenty-third embodiment, the step of receiving sensor data includes receiving a data stream that has been algorithmically smoothed.
In an aspect of the twenty-third embodiment, the step of receiving sensor data includes algorithmically smoothing the data stream.
In an aspect of the twenty-third embodiment, the step of receiving sensor data includes receiving sensor data from a substantially continuous glucose sensor.
In an aspect of the twenty-third embodiment, the step of receiving sensor data includes receiving sensor data from an implantable glucose sensor.
In an aspect of the twenty-third embodiment, the step of receiving sensor data includes receiving sensor data from a subcutaneously implantable glucose sensor.
In an aspect of the twenty-third embodiment, the step of receiving reference data includes receiving reference data from a self-monitoring blood glucose test.
In an aspect of the twenty-third embodiment, the step of receiving reference data includes downloading reference data via a cabled connection.
In an aspect of the twenty-third embodiment, the step of receiving reference data includes downloading reference data via a wireless connection.
In an aspect of the twenty-third embodiment, the step of receiving reference data from a reference analyte monitor includes receiving within a receiver internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the twenty-third embodiment, the step of evaluating the quality of the calibration set based on a data association function includes performing one of linear regression, non-linear regression, rank correlation, least mean square fit, mean absolute deviation, and mean absolute relative difference.
In an aspect of the twenty-third embodiment, the step of evaluating the quality of the calibration set based on a data association function includes performing linear least squares regression.
In an aspect of the twenty-third embodiment, the step of evaluating the quality of the calibration set based on a data association function includes setting a threshold of data association.
In an aspect of the twenty-third embodiment, the step of evaluating the quality of the calibration set based on data association includes performing linear least squares regression and wherein the step of setting a threshold hold includes an R-value threshold of 0.79.
In an aspect of the twenty-third embodiment, the method further includes providing an output to a user interface responsive to the quality of the calibration set.
In an aspect of the twenty-third embodiment, the step of providing an output includes displaying analyte values to a user dependent upon the quality of the calibration.
In an aspect of the twenty-third embodiment, the step of providing an output includes alerting the dependent upon the quality of the calibration.
In an aspect of the twenty-third embodiment, the step of providing an output includes altering the user interface dependent upon the quality of the calibration.
In an aspect of the twenty-third embodiment, the step of providing an output includes at least one of providing color-coded information, trend information, directional information (e.g., arrows or angled lines), and/or fail-safe information.
In a twenty-fourth embodiment, a system is provided for evaluating the quality of a calibration of an analyte sensor, the system including: means for receiving a data stream from an analyte sensor, a plurality of time-spaced sensor data points; means for receiving reference data from a reference analyte monitor, including two or more reference data points; means for providing two or more matched data pairs by matching reference analyte data to substantially time corresponding sensor data; means for forming a calibration set including at least two matched data pair; means for creating a conversion function based on the calibration set; means for converting sensor data into calibrated data using the conversion function; means for evaluating the quality of the calibration set based on a data association function.
In an aspect of the twenty-fourth embodiment, the means for receiving sensor data includes means for receiving sensor data that has been algorithmically smoothed.
In an aspect of the twenty-fourth embodiment, the means for receiving sensor data includes means for algorithmically smoothing the receiving sensor data.
In an aspect of the twenty-fourth embodiment, the means for receiving sensor data includes means for receiving sensor data from substantially continuous glucose sensor.
In an aspect of the twenty-fourth embodiment, the means for receiving sensor data includes means for receiving sensor data from an implantable glucose sensor.
In an aspect of the twenty-fourth embodiment, the means for receiving sensor data includes means for receiving sensor data from subcutaneously implantable glucose sensor.
In an aspect of the twenty-fourth embodiment, the means for receiving reference data includes means for receiving reference data from a self-monitoring blood glucose test.
In an aspect of the twenty-fourth embodiment, the means for receiving reference data includes means for downloading reference data via a cabled connection.
In an aspect of the twenty-fourth embodiment, the means for receiving reference data includes means for downloading reference data via a wireless connection.
In an aspect of the twenty-fourth embodiment, the means for receiving reference data from a reference analyte monitor includes means for receiving within a receiver internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the twenty-fourth embodiment, the means for evaluating the quality of the calibration set includes means for performing one of linear regression, non-linear regression, rank correlation, least mean square fit, mean absolute deviation, and mean absolute relative difference.
In an aspect of the twenty-fourth embodiment, the means for evaluating the quality of the calibration set includes means for performing linear least squares regression.
In an aspect of the twenty-fourth embodiment, the means for evaluating the quality of the calibration set includes means for setting a threshold of data association.
In an aspect of the twenty-fourth embodiment, the means for evaluating the quality of the calibration set includes means for performing linear least squares regression and wherein the means for setting a threshold hold includes an R-value threshold of 0.71.
In an aspect of the twenty-fourth embodiment, the system further includes means for providing an output to a user interface responsive to the quality of the calibration set.
In an aspect of the twenty-fourth embodiment, the means for providing an output includes means for displaying analyte values to a user dependent upon the quality of the calibration.
In an aspect of the twenty-fourth embodiment, the means for providing an output includes means for alerting the dependent upon the quality of the calibration.
In an aspect of the twenty-fourth embodiment, the means for providing an output includes means for altering the user interface dependent upon the quality of the calibration.
In an aspect of the twenty-fourth embodiment, the means for providing an output includes at least one of providing color-coded information, trend information, directional information (e.g., arrows or angled lines), and/or fail-safe information.
In a twenty-fifth embodiment, a computer system is provided for evaluating the quality of a calibration of an analyte sensor, the computer system including: a sensor data receiving module that receives a data stream including a plurality of time spaced sensor data points from a substantially continuous analyte sensor; a reference data receiving module that receives reference data from a reference analyte monitor, including two or more reference data points; a data matching module that forms two or more matched data pairs by matching reference data to substantially time corresponding sensor data; a calibration set module that forms a calibration set including at least two matched data pairs; a conversion function module that creates a conversion function using the calibration set; a sensor data transformation module that converts sensor data into calibrated data using the conversion function; and a quality evaluation module that evaluates the quality of the calibration set based on a data association function.
In an aspect of the twenty-fifth embodiment, the sensor data receiving module receives sensor data that has been algorithmically smoothed.
In an aspect of the twenty-fifth embodiment, the computer system further includes a data smoothing module that algorithmically smoothes sensor data received from the sensor data receiving module.
In an aspect of the twenty-fifth embodiment, the sensor data receiving module is adapted to receive sensor data from substantially continuous glucose sensor.
In an aspect of the twenty-fifth embodiment, the sensor data receiving module is adapted to receive sensor data from an implantable glucose sensor.
In an aspect of the twenty-fifth embodiment, the sensor data receiving module is adapted to receive sensor data from subcutaneously implantable glucose sensor.
In an aspect of the twenty-fifth embodiment, the reference data receiving module is adapted to receive reference data from a self-monitoring blood glucose test.
In an aspect of the twenty-fifth embodiment, the reference data receiving module is adapted to download reference data via a cabled connection.
In an aspect of the twenty-fifth embodiment, the reference data receiving module is adapted to download reference data via a wireless connection.
In an aspect of the twenty-fifth embodiment, the reference data receiving module is adapted to receive reference data from a reference analyte monitor integral with the receiver.
In an aspect of the twenty-fifth embodiment, the quality evaluation module performs one of linear regression, non-linear regression, rank correlation, least mean square fit, mean absolute deviation, and mean absolute relative difference to evaluate calibration set quality.
In an aspect of the twenty-fifth embodiment, the quality evaluation module performs linear least squares regression.
In an aspect of the twenty-fifth embodiment, the quality evaluation module sets a threshold for the data association function.
In an aspect of the twenty-fifth embodiment, the quality evaluation module performs linear least squares regression and wherein the threshold of the data association function includes an R-value threshold of at least 0.79.
In an aspect of the twenty-fifth embodiment, the computer system further includes an interface control module that controls the user interface based on the quality of the calibration set.
In an aspect of the twenty-fifth embodiment, the interface control module displays analyte values to a user dependent upon the quality of the calibration set.
In an aspect of the twenty-fifth embodiment, the interface control module alerts the user based upon the quality of the calibration set.
In an aspect of the twenty-fifth embodiment, the interface control module alters the user interface based upon the quality of the calibration set.
In an aspect of the twenty-fifth embodiment, the interface control module provides at least one of color-coded information, trend information, directional information (e.g., arrows or angled lines), and/or fail-safe information.
In a twenty-sixth embodiment, a method is provided for evaluating the quality of a calibration of an analyte sensor, the method including: receiving a data stream from an analyte sensor, including one or more sensor data points; receiving reference data from a reference analyte monitor, including two or more reference data points; providing at least two matched data pairs by matching reference analyte data to substantially time corresponding sensor data; forming a calibration set including the at least two matching data pairs; creating a conversion function based on the calibration set; receiving additional sensor data from the analyte sensor; converting sensor data into calibrated data using the conversion funct