BACKGROUND

Ischemic heart disease is a condition caused by coronary arteries that are narrowed. Narrowing of the lumen of coronary arteries means less blood and oxygen reaches the heart muscle (‘myocardium’). When more oxygen is needed, such as while exercising, sometimes the heart cannot meet the body's metabolic demand. An insufficiency of oxygen caused by ischemic heart disease can produce chest pain, known as angina pectoris, and ischemic myocardial events such as acute coronary syndrome (ACS) or acute myocardial infarction (‘heart attack’). According to data from NHANES 2007 to 2010 (U.S. National Heart Lung and Blood Institute NHLBI tabulation), the overall prevalence for the ischemic cardiac event resulting in death of myocardial muscle tissue, termed acute myocardial infarction or AMI, is 2.9% in U.S. adults greater than 20 years of age. AMI prevalence is 4.2% for men and 1.7% for women. Patients who have experienced one or more ischemic myocardial events in the past are at the greatest risk of incurring additional ischemic myocardial events subsequently. While there have been attempts to provide a technological solution through decision support systems, these systems have significant drawbacks and cannot provide the reliability and accuracy of the systems and processes proposed in the present disclosure.

SUMMARY

Systems, methods and computer-readable media are provided for predicting an occurrence of ischemic myocardial events based on a time series of monitored vital signs values measured from a patient, and in some instances, for providing advanced notice to clinicians or caregivers when such an ischemic event is forecasted or modifying treatment for the patient, according to the predicted likelihood. In particular, an ischemic heart disease management system is provided for determining a likelihood of near-term future significant myocardial ischemia in persons with coronary artery disease. Embodiments of the disclosure described herein may provide a forecasted risk for future significant myocardial ischemia within a time horizon comprising a future time interval. In one embodiment, the future time interval is from 30 min to approximately 4 hours into the future, and may be dependent on the frequency of vital signs measurements.

A catastrophe-theoretic approach is provided, for determining the predicted ischemic myocardial event, which does not require patient measurements to be acquired on a regular or periodic basis. In one aspect, a vital signs time series is determined for a candidate patient. From the time series, a linear model and cusp catastrophe model is calculated, and goodness-of-fit measures are determined. A likelihood of future myocardial ischemia occurrence is then determined within a future time interval, based on whether threshold for the smoothed cusp model is transgressed or, alternately, based on whether a threshold for the ratio of linear-to-cusp model values is exceeded. Based on the determined likelihood, a set of one or more actions may be initiated. One action comprises generating a notification that may be emitted or otherwise communicated to a provider clinician(s) responsible for the care of the patient. Another action that may be initiated, based on the determined likelihood, comprises a recommendation for modifying a care plan or treatment procedure associated with the patient. Yet another action that may be initiated, based on the determined likelihood, comprises automatically modifying computer code executed in a healthcare software program for treating the patient, thereby transforming the program at runtime.

Accordingly, one aim of embodiments of this disclosure is to improve upon conventional industry practice. The improvement relates to deriving accurate predictive capabilities from moderate-frequency, potentially-aperiodic time series such as accrue with continuous monitoring in acute care environments. In this way, embodiments described herein overcome deficiencies in the prior art as they are robust (a) against temporary sensor artifacts or intermittent gaps or failures to perform periodic measurements, (b) against delays in uploading or synchronizing newly acquired patient physiological values with historical vital signs time series measured in the patient, and (c) against non-stationarity in the time series, such as may arise during periods when the patient's health deviates from predominant patterns, due to health conditions or physiologic phenomena that alter autonomic sympathetic-parasympathetic tone and neuroendocrine regulation, such as when the patient has systemic inflammatory response syndrome (SIRS) or sepsis. Moreover, forecasting one or more event occurrences within a near-term future interval, such as a 4-hour time horizon, provides a valuable benefit. Such a timeframe is long enough that an actionable occurrence is quite likely for many patients, but not so long that the risk of the event is omnipresent such that alert signals become annoying or a cause of “alert fatigue”.

