Precision dialysis monitoring and synchonization system

• US 20150149096A1
• Filed: 11/26/2014
• Published: 05/28/2015
• Est. Priority Date: 11/27/2013
• Status: Active Grant

• Current Assignee: Mozarc Medical US LLC
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Soykan, Orhan
• Granted Patent: US 10,595,775 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• 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 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A device, system and related methods for monitoring a mammal with heart failure, kidney disease or both, to make predictions about the likelihood of a life threatening ventricular arrhythmia. The device, system and related methods can have one or more sensors in electronic communication with a processor, the sensors determining one or more physiological parameters of a patient, and communicating the physiological parameter to the processor, and the processors using an algorithm to determine the probability of a ventricular arrhythmia based on the physiological parameters.

Claims

1. A device, comprising:

one or more sensors in electronic communication with a medical device processor, the sensors obtaining one or more physiological parameters of a patient and communicating the one or more physiological parameters to a medical device processor; and

the medical device processor using an algorithm to determine a probability of a ventricular arrhythmia based on the physiological parameters.

2. The device of claim 1, wherein the algorithm, for each of the one or more physiological parameters of a patient, calculates an individual hazard estimate given by y₁(t)=h₁(t){circle around (×

)}x₁(t), wherein h₁(t)=k₁e^k2t, t is time, y₁(t) is the individual hazard estimate at time t, k₁ and k₂ are constants for the given parameter, x₁(t) is the physiological parameter at time t, and {circle around (×

)} is a convolution operator.

3. The device of claim 2, wherein the convolution operator is defined by [h₁ {circle around (×

)}x₁](n)=Σ

<sub>m=−

∞</sub>

^+∞

h₁(m)x₁(n−

m), wherein n is time, h₁(n)=k₁e^k2n, x₁(n) is the physiological parameter at time n, and k₁ and k₂ are constants for the given parameter.

4. The device of claim 3 wherein the algorithm further calculates a total hazard function:

f(t)=Σ

_m=1ⁿy_m(t), wherein f(t) is the total hazard at time t, y_m(t) is an individual hazard estimate for parameter m at time t, and n is the number of physiological parameters used.

5. The device of claim 3 wherein the algorithm further calculates a total hazard function:

6. The device of claim 5, wherein the device is configured to allow for entering actual results at time t for the patient into the medical device processor, and the algorithm further calculates a total error as the difference between the total hazard and the actual results at time t, and the algorithm further adjusts each of the coefficients to minimize the error.

7. The device of claim 1, wherein the physiological parameters are any one of selected from the group consisting of tissue impedance, number of spontaneous ventricular tachycardia and ventricular fibrillation events per day, time spent in atrial fibrillation, and dialysis status data.

8. A method for determining the probability of a life threatening ventricular arrhythmia, comprising the steps of:

obtaining one or more physiological parameters of a patient from one or more sensors;

communicating the physiological parameters to a medical device processor;

wherein the medical device processor utilizes an algorithm to determine a probability of a ventricular arrhythmia.

9. The method of claim 8, wherein the physiological parameters are tissue impedance, number of spontaneous ventricular tachycardia and ventricular fibrillation events per day, time spent in atrial fibrillation, and information about the dialysis status data.

10. The method of claim 8 wherein at least one of the physiological parameters is obtained from a sensor that is a component of an implantable medical device.

11. The method of claim 8 wherein the medical device processor further comprises a signaling mechanism to signal if the probability of ventricular arrhythmia is greater than a pre-set value.

12. The method of claim 8, wherein the algorithm, for each of the one or more physiological parameters of a patient, calculates an individual hazard estimate given by y₁(t)=h₁(t){circle around (×

)}x₁(t), wherein h₁(t)=k₁e^k2t, t is time, y₁(t) is the individual hazard estimate at time t, k₁ and k₂ are constants for the given parameter, x₁(t) is the physiological parameter at time t, and {circle around (×

)} is a convolution operator.

13. The method of claim 12, wherein the convolution operator is defined by [h₁ {circle around (×

)}x₁](n)=Σ

<sub>m=−

∞</sub>

^+∞

h₁(m)x₁(n−

m), wherein n is time, h₁(n)=k₁e^k2n, x₁(n) is the physiological parameter at time n, and k₁ and k₂ are constants for the given parameter.

14. The method of claim 13 wherein the algorithm further calculates a total hazard function:

f(t)=Σ

_m=1ⁿy_m(t), wherein f(t) is the total hazard at time t, y_m(t) is an individual hazard estimate for parameter m at time t, and n is the number of physiological parameters used.

15. The method of claim 13 wherein the algorithm further calculates a total hazard function:

16. The method of claim 15, further comprising the step of entering actual results at time t for the patient into the medical device processor, and the algorithm further calculates a total error as the difference between the total hazard and the actual results at time t, and the algorithm further adjusts each of the coefficients to minimize the error.

17. The method of claim 16, wherein the algorithm further utilizes an adaptive filter to alter the coefficients for each patient periodically.

18. The method of claim 17 wherein the algorithm utilizes the adaptive filter to alter the coefficients for each patient every 14 days.

19. The method of claim 9 wherein the information about the dialysis status data is entered manually.

20. The method of claim 9 wherein the information about the dialysis status data is communicated to the medical device processor from an electronic medical record.

21. The method of claim 15 further comprising the step of entering actual results at time t for a group of patients into the medical device processor, and the algorithm further calculates a total error as the difference between the total hazard and the actual results for the group of patients at time t, and the algorithm further adjusts each the coefficients for each patient to minimize the error.