Systems and methods for estimating hemodynamic forces acting on plaque and monitoring risk
Computer-implemented methods are disclosed for estimating values of hemodynamic forces acting on plaque or lesions. One method includes: receiving one or more patient-specific parameters of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion; constructing a patient-specific geometric model of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion, using the received one or more patient-specific parameters; estimating, using one or more processors, the values of hemodynamic forces at one or more points on the patient-specific geometric model, using the patient-specific parameters and geometric model by measuring, deriving, or obtaining one or more of a pressure gradient and a radius gradient; and outputting the estimated values of hemodynamic forces to an electronic storage medium. Systems and computer readable media for executing these methods are also disclosed.
1. A computer-implemented method for monitoring hemodynamic risk of a patient, the method comprising:
acquiring a patient-specific model of blood flow through at least a portion of a patient's vasculature;
obtaining a current patient-specific parameter of the patient;
determining a current hemodynamic characteristic of the patient based on the patient-specific model and the current patient-specific parameter of the patient;
obtaining a maximum allowable hemodynamic characteristic of the patient;
comparing the current hemodynamic characteristic with the maximum allowable hemodynamic characteristic;
determining, based on the comparing, whether the current hemodynamic characteristic includes an abnormal value; and
in response to determining that the current hemodynamic characteristic includes an abnormal value, producing a warning indicative of the abnormal value.
2. The computer-implemented method of claim 1 , wherein the current patient-specific parameter of the patient is acquired during exercise by the patient.
3. The computer-implemented method of claim 1 , wherein the current patient-specific parameter of the patient is acquired by a mobile device associated with the patient.
4. The computer-implemented method of claim 1 , wherein the patient-specific model is a geometric model.
5. The computer-implemented method of claim 1 , wherein the current patient-specific parameter includes a biophysical characteristic or a hemodynamic characteristic.
6. The computer-implemented method of claim 1 , wherein determining the current hemodynamic characteristic of the patient includes:
determining a simplified hemodynamic characteristic for at least a first portion of the patient-specific model; and
based on the determined simplified hemodynamic characteristic, determining a hemodynamic characteristic for a second portion of the patient-specific model, for which the simplified hemodynamic characteristic is unknown.
7. The computer-implemented method of claim 6 , wherein determining the hemodynamic characteristic for the second portion of the patient-specific model includes employing a machine learning algorithm trained, based on patient-specific parameters of other patients and hemodynamic characteristics associated with one or more portions of patient-specific models of the other patients, to determine the hemodynamic characteristic for various portions of a patient-specific geometric model.
8. The computer-implemented method of claim 1 , wherein obtaining the maximum allowable hemodynamic characteristic of the patient includes:
Obtaining a patient-specific exercise-induced physiological stress characteristic for the patient; and
based on the patient-specific exercise-induced physiological stress characteristic, determining the maximum allowable hemodynamic characteristic of the patient.
9. The computer-implemented method of claim 8 , wherein:
determining the maximum allowable hemodynamic characteristic of the patient includes inputting the patient-specific exercise-induced physiological stress characteristic into a trained machine learning algorithm; and
the trained machine learning algorithm is obtained based on one or more patient-specific parameters of a vascular system from each of a plurality of individuals with known values of hemodynamic forces.
10. The computer-implemented method of claim 8 , wherein obtaining the patient-specific exercise-induced physiological stress characteristic includes performing an exercise test on the patient.
11. The computer-implemented method of claim 8 , wherein the patient-specific exercise-induced physiological stress characteristic is determined based on patient medical records.
12. The computer-implemented method of claim 1 , wherein the warning includes a signal provided by a device associated with the patient or a physician.
13. The computer-implemented method of claim 1 , further comprising obtaining, for the patient, a geometric characteristic, a plaque characteristic, or a coronary dynamics characteristic.
14. The computer-implemented method of claim 1 , wherein the patient-specific model of blood flow includes blood flow through one or more of: a coronary vascular model, a cerebral vascular model, a peripheral vascular model, a hepatic vascular model, a renal vascular model, or a visceral vascular model.
15. The computer-implemented method of claim 1 , further comprising acquiring a digital representation of a patient-specific parameter using imaging, scanning, or measuring modalities.
16. A system for monitoring hemodynamic risk in a patient, the system comprising:
a data storage device storing instructions for monitoring hemodynamic risk in a patient; and
a processor operatively connected to the data storage device and configured to execute the instructions to perform a method, including:
acquiring a patient-specific model of blood flow through at least a portion of a patient's vasculature;
obtaining a current patient-specific parameter of the patient;
determining a current hemodynamic characteristic of the patient based on the patient-specific model and the current patient-specific parameter of the patient;
obtaining a maximum allowable hemodynamic characteristic of the patient;
comparing the current hemodynamic characteristic with the maximum allowable hemodynamic characteristic;
determining, based on the comparing, whether the current hemodynamic characteristic includes an abnormal value; and
in response to determining that the current hemodynamic characteristic includes an abnormal value, producing a warning indicative of the abnormal value.
17. The system of claim 16 , wherein the current patient-specific parameter of the patient is acquired during exercise by the patient.
18. The system of claim 16 , wherein the current patient-specific parameter of the patient is acquired by a mobile device associated with the patient.
19. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for a method for monitoring hemodynamic risk in a patient, the method comprising:
acquiring a patient-specific model of blood flow through at least a portion of a patient's vasculature;
obtaining a current patient-specific parameter of the patient;
determining a current hemodynamic characteristic of the patient based on the patient-specific model and the current patient-specific parameter of the patient;
obtaining a maximum allowable hemodynamic characteristic of the patient;
comparing the current hemodynamic characteristic with the maximum allowable hemodynamic characteristic;
determining, based on the comparing, whether the current hemodynamic characteristic includes an abnormal value; and
in response to determining that the current hemodynamic characteristic includes an abnormal value, producing a warning indicative of the abnormal value.
20. The non-transitory computer readable medium of claim 19 , wherein the current patient-specific parameter of the patient is acquired during exercise by the patient.