# US 2019/0167204 A1 · Worsening heart failure detection based on patient demographic clusters

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- Number: US 2019/0167204 A1
- Document ID: US20190167204A1
- Status: Published application
- Kind: A1
- Application number: 16/180,937
- Filed: 2018-11-05
- Published: 2019-06-06
- Earliest claimed: 2017-12-06
- Assignees: Cardiac Pacemakers, Inc.
- Applicants: Cardiac Pacemakers, Inc.
- Inventors: Jianjun Yuan; Qi An; Rezwan Ahmed; Pramodsingh Hirasingh Thakur
- Web page: https://patentgrep.com/patent/US20190167204A1

## CPC

- 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}
- A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)}
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 5/0031: {Implanted circuitry}
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/0537: Measuring body composition by impedance, e.g. tissue hydration or fat content
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/0826: {Detecting or evaluating apnoea events (sleep apnoea A61B5/4818)}
- A61B 5/086: {by impedance pneumography}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Systems and methods for monitoring patients for risk of worsening heart failure (WHF) are discussed. A patient management system includes a receiver circuit to receive a heart failure phenotype of the patient including patient demographic information, The system may include a classifier circuit to classify the patient into one of a plurality of phenotypes based on the received heart failure phenotype. The plurality of phenotypes are each represented by multi-dimensional categorized demographics. A detector circuit may detect a WHF event from a physiologic signal using the classified phenotype. The system may include a therapy circuit to deliver or adjust a heart failure therapy in response to the detected WHF event.

## Claims

### Claim 1

A system for detecting worsening heart failure (WHF) in a patient, comprising:
a signal receiver configured to receive a physiologic signal from the patient;
a phenotype receiver configured to receive a heart failure phenotype of the patient including patient demographic information; and
a processor circuit, including:
a classifier circuit configured to classify the patient into one of a plurality of phenotypes based on the received heart failure phenotype, the plurality of phenotypes each represented by multi-dimensional categorized demographics; and
a detector circuit configured to detect a WHF event using the sensed physiologic signal and the classified phenotype.

### Claim 2 (depends on 1)

The system of claim 1, wherein the plurality of phenotypes each further include medical history information.

### Claim 3 (depends on 1)

The system of claim 1, wherein the plurality of phenotypes each further include medication information.

### Claim 4 (depends on 1)

The system of claim 1, wherein the received heart failure phenotype further includes medical history or medication information of the patient, and the classifier circuit is configured to classify the patient into one of the plurality of phenotypes in response to a change in the medical history or medication of the patient.

### Claim 5 (depends on 1)

The system of claim 1, comprising a storage device configured to store a correspondence between the plurality of phenotypes and the corresponding multi-dimensional categorized demographics, wherein the classifier circuit is configured to classify the patient into one of the plurality of phenotypes using the stored correspondence.

### Claim 6 (depends on 1)

The system of claim 1, wherein the classifier circuit is configured to determine similarity metrics between the received heart failure phenotype and each of the plurality of phenotypes, and to classify the patient into one of the plurality of phenotypes based on the similarity metrics.

### Claim 7 (depends on 1)

The system of claim 1, wherein the classifier circuit is configured to compute a patient phenotype score using a combination of numerical values respectively assigned to the received patient demographic information, and to classify the patient into one of the plurality of phenotypes based on the computed patient phenotype score.

### Claim 8 (depends on 1)

The system of claim 1, wherein the detector circuit is configured to identify a detection algorithm based on the classified phenotype, and to detect the WHF event using the identified detection algorithm and the sensed physiologic signal.

### Claim 9 (depends on 1)

The system of claim 1, wherein the detector circuit is configured to compute a composite signal metric using the sensed physiologic signal, and to detect the WHF event using the composite signal metric.

### Claim 10 (depends on 9)

The system of claim 9, wherein the detector circuit is configured to adjust a threshold value based on the classified phenotype threshold value, and to detect the WHF event using a comparison of the composite signal metric to the adjusted threshold value.

### Claim 11 (depends on 9)

The system of claim 9, wherein the detector circuit is configured to:
generate a plurality of signal metrics from the sensed physiologic signal;
assign weight factors to the plurality of signal metrics based on the classified phenotype; and
compute the composite signal metric using a weighted combination of the plurality of the signal metrics respectively scaled by the assigned weight factors.

### Claim 12 (depends on 11)

The system of claim 11, wherein the detector circuit is configured to assign weight factors including to:
increase a weight factor to a respiration rate metric if the classified phenotype includes an attribute of significant shortness of breath;
increase a weight factor to a heart rate metric if the classified phenotype includes an attribute of palpitation; or
increase a weight factor to a total thoracic impedance metric if the classified phenotype includes an attribute of edema.

### Claim 13 (depends on 1)

The system of claim 1, comprising a therapy circuit configured to generate and deliver a heart failure therapy in response to the detection of the WHF event.

### Claim 14

A method for detecting worsening heart failure (WHF) in a patient using a medical system, comprising:
receiving a physiologic signal from the patient;
receiving a heart failure phenotype of the patient including patient demographic information; and
classifying the patient into one of a plurality of phenotypes based on the received heart failure phenotype, the plurality of phenotypes each represented by multi-dimensional categorized demographics; and
detecting a WHF event using the sensed physiologic signal and the classified phenotype.

### Claim 15 (depends on 14)

The method of claim 14, wherein the received heart failure phenotype further includes medical history or medication information of the patient, and the classifier circuit is configured to classify the patient into one of the plurality of phenotypes in response to a change in the medical history or medication of the patient.

### Claim 16 (depends on 14)

The method of claim 14, comprising determining similarity metrics between the received heart failure phenotype and each of the plurality of phenotypes, wherein classifying the patient into one of the plurality of phenotypes is based on the similarity metrics.

### Claim 17 (depends on 14)

The method of claim 14, comprising computing a patient phenotype score using the received heart failure phenotype, wherein classifying the patient into one of the plurality of phenotypes is based on the computed patient phenotype score.

### Claim 18 (depends on 14)

The method of claim 14, comprising computing a composite signal metric using the sensed physiologic signal, and wherein detecting the WHF event is based on the composite signal metric.

### Claim 19 (depends on 18)

The method of claim 18, comprising adjusting a threshold value based on the classified phenotype threshold value, wherein detecting the WHF event includes using a comparison of the composite signal metric to the adjusted threshold value.

### Claim 20 (depends on 18)

The method of claim 18, comprising:
generating a plurality of signal metrics from the sensed physiologic signal; and
assigning weight factors to the plurality of signal metrics based on the classified phenotype;
wherein computing the composite signal metric includes a weighted combination of the plurality of the signal metrics respectively scaled by the assigned weight factors.