Systems and methods for personalized cardiovascular analyses

Patent metadata

Field	Value
Publication number	US-11837364-B2
Application number	US-202017118951-A
Country	US
Kind code	B2
Filing date	Dec 11, 2020
Priority date	Dec 20, 2019
Publication date	Dec 5, 2023
Grant date	Dec 5, 2023

Abstract

Systems and methods for performing personalized cardiovascular analyses are provided. A method includes building, using a modeling and simulation computing device, a patient-specific model, storing, using the modeling and simulation computing device, the patient-specific model in a database, receiving, at the modeling and simulation computing device, remote monitoring data from at least one remote monitoring data source, and receiving, at the modeling and simulation computing device, clinical data from at least one clinical data source. The method further includes updating, using the modeling and simulation computing device, the patient-specific model using the remote monitoring data and the clinical data, performing, using the modeling and simulation computing device, at least one simulation on the updated patient-specific model, and outputting, from the modeling and simulation computing device, at least one output based on the at least one simulation.

First claim

What is claimed is:

1. A computer-implemented method for performing personalized cardiovascular analyses, the method comprising:
building, using a modeling and simulation computing device, a patient-specific model, wherein the patient-specific model represents a circulatory system of a particular patient;
storing, using the modeling and simulation computing device, the patient-specific model in a database;
receiving, at the modeling and simulation computing device, remote monitoring data from at least one remote monitoring data source, the remote monitoring data including at least actual hemodynamic data for the particular patient;
receiving, at the modeling and simulation computing device, clinical data from at least one clinical data source;
updating, using the modeling and simulation computing device, the patient-specific model using the remote monitoring data and the clinical data, wherein updating the patient-specific model comprises varying parameters of the patient-specific model until the patient-specific model replicates the actual hemodynamic data for the particular patient;
performing, using the modeling and simulation computing device, at least one simulation on the updated patient-specific model that replicates the actual hemodynamic data for the particular patient to simulate operation of a ventricular assist device in the particular patient, wherein the at least one simulation includes simulating operation of the ventricular assist device in the particular patient while adjusting at least one of a pulse type, a medication, and a fluid volume;
outputting, from the modeling and simulation computing device, at least one output based on the at least one simulation;
analyzing, using the modeling and simulation computing device, the updated patient-specific model using machine learning;
generating, using the modeling and simulation computing device, based on the analysis, an adjusted operating parameter for the ventricular assist device; and
controlling operation of an implanted device by transmitting a control signal including the adjusted operating parameter to the implanted device, the control signal causing the implanted device to adjust operation and begin operating at the adjusted operating parameter.

2. The method of claim 1 , further comprising displaying a recommendation including the adjusted operating parameter.

3. The method of claim 1 , wherein building a patient-specific model comprises:
generating a database including a plurality of anonymized patient models; and
selecting one of the plurality of anonymized patient models as the patient-specific model.

4. A computing device for performing personalized cardiovascular analyses, the computing device comprising:
a memory device; and
a processor communicatively coupled to the memory device, the processor configured to:
build a patient-specific model, wherein the patient-specific model represents a circulatory system of a particular patient;
store the patient-specific model in the memory device;
receive remote monitoring data from at least one remote monitoring data source, the remote monitoring data including at least actual hemodynamic data for the particular patient;
receive clinical data from at least one clinical data source;
update the patient-specific model using the remote monitoring data and the clinical data, wherein to update the patient-specific model, the processor is configured to vary parameters of the patient-specific model until the patient-specific model replicates the actual hemodynamic data for the particular patient;
perform at least one simulation on the updated patient-specific model that replicates the actual hemodynamic data for the particular patient to simulate operation of a ventricular assist device in the particular patient, wherein the at least one simulation includes simulating operation of the ventricular assist device in the particular patient while adjusting at least one of a pulse type, a medication, and a fluid volume;
output an output based on the at least one simulation;
analyze the updated patient-specific model using machine learning;
generate, based on the analysis, an adjusted operating parameter for the ventricular assist device; and
control operation of an implanted device by transmitting a control signal including the adjusted operating parameter to the implanted device, the control signal causing the implanted device to adjust operation and begin operating at the adjusted operating parameter.

5. The computing device of claim 4 , wherein the processor is further configured to output a recommendation including the adjusted operating parameter.

6. The computing device of claim 4 , wherein to build a patient-specific model, the processor is configured to
generate a database including a plurality of anonymized patient models; and
select one of the plurality of anonymized patient models as the patient-specific model.

7. Non-transitory computer-readable media having computer-executable instructions thereon, wherein when executed by a processor of a computing device, cause the processor of the computing device to:
build a patient-specific model, wherein the patient-specific model represents a circulatory system of a particular patient;
store the patient-specific model in a database;
receive remote monitoring data from at least one remote monitoring data source, the remote monitoring data including at least actual hemodynamic data for the particular patient;
receive clinical data from at least one clinical data source;
update the patient-specific model using the remote monitoring data and the clinical data, wherein to update the patient-specific model, the instructions cause the process to vary parameters of the patient-specific model until the patient-specific model replicates the actual hemodynamic data for the particular patient;
perform at least one simulation on the updated patient-specific model that replicates the actual hemodynamic data for the particular patient to simulate operation of a ventricular assist device in the particular patient, wherein the at least one simulation includes simulating operation of the ventricular assist device in the particular patient while adjusting at least one of a pulse type, a medication, and a fluid volume;
output an output based on the at least one simulation;
analyze the updated patient-specific model using machine learning;
generate, based on the analysis, an adjusted operating parameter for the ventricular assist device; and
control operation of an implanted device by transmitting a control signal including the adjusted operating parameter to the implanted device, the control signal causing the implanted device to adjust operation and begin operating at the adjusted operating parameter.

8. The non-transitory computer-readable media of claim 7 , wherein the instructions further cause the processor to output a recommendation including the adjusted operating parameter.

Assignees

• Tc1 Llc

Inventors

• Lee Eric
• Harjes Daniel I

Classifications

• G16H50/00 Primary

  ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics · CPC title

• G16H10/60

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H40/67

  for remote operation · CPC title

• G16H50/20 Primary

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H70/20

  relating to practices or guidelines · CPC title