SYSTEMS AND METHODS FOR DIGITAL PREDICTIVE DISEASE EXACERBATION AND PRE-EMPTIVE TREATMENT

• US 20160089089A1
• Filed: 09/24/2015
• Published: 03/31/2016
• Est. Priority Date: 09/25/2014
• Status: Abandoned Application

• Current Assignee: Aedio, Inc.
• Original Assignee: Aedio, Inc.
• Inventors: Kakkar, Rahul, Collier, Cyan, Sanganee, Hitesh
• Application Number:
  US14/863,876
• Publication Number:
  US 20160089089A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• 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 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0823 Detecting or evaluating cough events
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• 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: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

The system described herein collects patient data passively and non-passively via onboard and external sensors, and combines the data with past clinical history to generate digital biomarkers. The collected data can also be further combined with other data generating systems to more accurately predict disease exacerbations. The system monitors the digital biomarkers in real-time, and can detect a change in the disease state prior to clinical decompensation and suggest pre-emptive intervention. The system enables a patient to be treated early in the clinical timeline when the disease exacerbation is at the subclinical level rather than waiting until the disease exacerbation reaches the clinical level. Acting when the exacerbation is at the subclinical level enables preemptive treatment rather than reactive treatment, which is often more cost effective while improving clinical outcomes. The system is able to make the predictions by detecting subclinical changes in digital biomarkers that are generated from respiratory, cardiac, patient reported symptoms, user behaviors, and environmental triggers.

Claims

1. A system to detect a disease exacerbation, the system comprising:

a wearable device configured to couple to a patient, the wearable device comprising;

a pulse sensor configured to measure a pulse of the patient by transmitting a light signal toward the patient and receiving a reflection of the light signal transmitted back from the patient;

a breath sensor configured to measure a breath of the patient;

a wireless module configured to communicate data comprising the breath and pulse measurements of the patient detected by the wearable device;

a server configured to receive the data comprising the breath and pulse measurements from the wireless module, the server comprising a prediction engine, wherein the prediction engine is configured to;

generate a digital biomarker as a function of the breath and pulse measurements, the digital biomarker measuring a disease state; and

determine if the digital biomarker crosses a corresponding threshold.

2. The system of claim 1, further comprising a DSP engine configured to analyze the breath measurement to determine an inspiration to expiration ratio.

3. The system of claim 1, further comprising a DSP engine configured to analyze the breath measurement to determine a breath rate.

4. The system of claim 1, wherein wearable device further comprises a first microphone and a second microphone to acoustically record the breath of the patient.

5. The system of claim 4, wherein the breath measurement is acoustically recorded tracheal breath sounds.

6. The system of claim 1, wherein the DSP engine is configured to detect at least one of a cough, a wheeze, an apnea condition, and a use of an inhaler in the data.

7. The system of claim 1, wherein the predictive agent is configured to incorporate a past clinical history into the digital biomarker.

8. The system of claim 1, wherein the digital biomarker comprises a time series.

9. The system of claim 1, wherein the threshold defines an exacerbation point.

10. The system of claim 1, wherein the predictive agent is configured to generate an alarm signal responsive to determining that the digital biomarker crossed the corresponding threshold.

11. A method to detect a disease exacerbation, the method comprising:

measuring, with a pulse sensor of a wearable device, a pulse of a patient by transmitting a light signal toward the patient and receiving a reflection of the light signal transmitted back from the patient;

measuring, with a breath sensor of the wearable device, a breath of the patient;

transmitting, by a wireless module of the wearable device, data comprising the breath and pulse measurements of the patient detected by the wearable device;

receiving, by a server, the breath and pulse measurements from the wireless module;

generating, by a prediction engine of the server, a digital biomarker as a function of the breath and pulse measurements, the digital biomarker measuring a disease state; and

determining, by the prediction engine of the server, if the digital biomarker crosses a corresponding threshold.

12. The method of claim 11, further comprising analyzing the breath measurement to determine an inspiration to expiration ratio.

13. The method of claim 11, further comprising analyzing the breath measurement to determine a breath rate.

14. The method of claim 11, further comprising measuring the breath of the patient with a first microphone and a second microphone.

15. The method of claim 11, further comprising measuring tracheal breath sounds.

16. The method of claim 11, further comprising detecting at least one of a cough, a wheeze, an apnea condition, and a use of an inhaler in the data.

17. The method of claim 11, further comprising incorporating a past clinical history into the digital biomarker.

18. The method of claim 11, wherein the digital biomarker comprises a time series.

19. The method of claim 11, wherein the threshold defines an exacerbation point.

20. The method of claim 11, further comprising generating an alarm signal responsive to determining that the digital biomarker crossed the corresponding threshold.