APNEA RISK EVALUATION SYSTEM - AUTOMATED PREDICTION OF RISK FOR PERIOPERATIVE COMPLICATIONS

• US 20080146893A1
• Filed: 12/12/2007
• Published: 06/19/2008
• Est. Priority Date: 12/13/2006
• Status: Active Grant

• Current Assignee: Watermark Medical, Inc. (BioSerenity SAS)
• Original Assignee: Advanced Brain Monitoring
• Inventors: Berka, Chris, Levendowski, Daniel J., Westbrook, Philip R.
• Granted Patent: US 8,333,696 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system for predicting risk for perioperative complications is described including a user device for receiving a set of risk factors to determine perioperative complications for a patient including patient data useful to determine the likelihood of obstructive sleep apnea. The system also includes an acquisition module to receive data from an obstructive sleep apnea sleep study of the patient. Further a determination module can determine the severity of obstructive sleep apnea for the patient. The system can also include an analysis module having a predictive model that incorporates one or more prediction equations for predicting perioperative complications derived from one or more databases having multiple patient data relevant to predict perioperative complications. The analysis module can be configured to apply the one or more prediction equations to the set of risk factors and the severity of obstructive sleep apnea for the patient in order to identify perioperative complication risks of that patient and to generate a perioperative complications report for the patient.

Claims

1. A system for predicting risk for perioperative complications comprising:

a user device for receiving a set of risk factors to determine perioperative complications for a patient including patient data useful to determine the likelihood and severity of sleep apnea;

an acquisition module to receive data from a sleep apnea sleep study of the patient;

a determination module to determine the severity of sleep apnea for the patient; and

an analysis module having a predictive model that incorporates one or more prediction equations for predicting perioperative complications derived from one or more databases having multiple patient data relevant to predict perioperative complications, the analysis module configured to apply the one or more prediction equations to the set of risk factors and the severity of sleep apnea for the patient in order to identify and report the likelihood of perioperative complication risks of that patient.

2. A system of claim 1, the set of risk factors are acquired from questionnaire responses that are analyzed to determine if a patient is in need of a sleep apnea sleep study and the likelihood of that patient having mild, moderate or severe sleep apnea.

3. The system of claim 1, wherein the determination module is configured to score the data from the sleep study to determine the sleep apnea severity.

4. The system of claim 1, wherein the sleep study data is analyzed automatically and written to a sleep study database.

5. The system of claim 1, wherein the sleep study is conducted while the patient'"'"'s head or body position is recorded.

6. The system of claim 1, further comprising an updating module to facilitate evaluation of the sets of risk factors, including surgical risk factors, in order to optimize the predicted likelihood of perioperative complications.

7. The system of claim 1, wherein the perioperative complications report comprises recommendations to reduce the frequency and severity of the perioperative complications.

8. The system of claim 1, wherein the one or more databases comprises a population database.

10. The system of claim 1, further comprising an updating module to incorporate the set of risk factors, including surgical risk factors, and sleep study data of the patient into the predictive model in order to optimize the predictive model.

11. The system of claim 1, further configured to provide recommendations to reduce the frequency and severity of the perioperative complications.

12. The system of claim 1, wherein the risk factors are selected from the group comprising patient data, type and invasiveness of the surgery, type and dose of sedative or narcotic, likelihood of sleep apnea, level of sleep apnea severity, type of analgesics to be administered and type of anesthesia.

13. The system of claim 1, wherein the risk factors are written to a patient database.

14. The system of claim 1, further comprising an output device configured to output the report of the likelihood of perioperative complication risks of the patient.

15. The system of claim 1, wherein the user device is selected from the group consisting of a portable device, handheld device, a computer system, a web based application and a wireless device.

16. The system of claim 1, wherein a statistical analysis is used to determine the relationship and interactions between perioperative complications and the set of risk factors.

9. A system for predicting risk for perioperative complications comprising:

a user device for receiving a set of risk factors to determine perioperative complications for a patient;

a server comprising,

an analysis module having a predictive model that incorporates one or more prediction equations for perioperative complications derived from one or more databases having multiple patient data relevant to predict perioperative complications, the analysis module configured to apply the one or more prediction equations to the set of risk factors in order to identify and report the likelihood of perioperative complication risks of that patient.

17. A system for predicting risk for perioperative complications comprising:

a user device for receiving a set of risk factors to determine perioperative complications for a patient; and

a processing unit having a predictive model that incorporates one or more prediction equations for perioperative complications derived from one or more databases having multiple patient data relevant to predict perioperative complications, the processing unit configured to apply the one or more prediction equations to the set of risk factors in order to identify and report the likelihood of perioperative complication risks of that patient.

19. The method of claim 17, wherein the perioperative complications report comprises recommendations to reduce the frequency and severity of the perioperative complications.

20. The method of claim 17, further comprising incorporating the set of risk factors for the patient, including surgical risk factors, and sleep study data of the patient into the predictive model in order to optimize the predictive model.

18. A method for prediction of risk for perioperative complications comprising:

inputting a set of risk factors to determine perioperative complications into a user device;

processing data from one or more databases having multiple patient data relevant to predict perioperative complications by, incorporating in an analysis module having a predictive model, one or more prediction equations derived from the data in the one or more databases and by applying the one or more prediction equations to the set of risk factors in order to predict perioperative complications risks for a given patient;

identifying the likelihood of perioperative complications risks for a patient based on the processing; and

generating a report of the likelihood of perioperative complications risk.