Abstract
Introduction
The myocardial-ischemic-injury-index (MI3) is a promising machine learned algorithm that predicts the likelihood of myocardial infarction in patients with suspected acute coronary syndrome. Whether this algorithm performs well in unselected patients or predicts recurrent events is unknown.
Methods
In an observational analysis from a multi-centre randomised trial, we included all patients with suspected acute coronary syndrome and serial high-sensitivity cardiac troponin I measurements without ST-segment elevation myocardial infarction. Using gradient boosting, MI3 incorporates age, sex, and two troponin measurements to compute a value (0–100) reflecting an individual's likelihood of myocardial infarction, and estimates the negative predictive value (NPV) and positive predictive value (PPV). Model performance for an index diagnosis of myocardial infarction, and for subsequent myocardial infarction or cardiovascular death at one year was determined using previously defined low- and high-probability thresholds (1.6 and 49.7, respectively).
Results
In total 20,761 of 48,282 (43%) patients (64±16 years, 46% women) were eligible of whom 3,278 (15.8%) had myocardial infarction. MI3 was well discriminated with an area under the receiver-operating-characteristic curve of 0.949 (95% confidence interval 0.946–0.952) identifying 12,983 (62.5%) patients as low-probability (sensitivity 99.3% [99.0–99.6%], NPV 99.8% [99.8–99.9%]), and 2,961 (14.3%) as high-probability (specificity 95.0% [94.7–95.3%], PPV 70.4% [69–71.9%]). At one year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability compared to low-probability patients (17.6% [520/2,961] versus 1.5% [197/12,983], P<0.001).
Conclusions
In unselected consecutive patients with suspected acute coronary syndrome, the MI3 algorithm accurately estimates the likelihood of myocardial infarction and predicts probability of subsequent adverse cardiovascular events.
Performance of MI3 at example thresholds
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): Medical Research Council
Dickkopf-1 as a Novel Predictor Is Associated with Risk Stratification by GRACE Risk Scores for Predictive Value in Patients with Acute Coronary Syndrome: A Retrospective Research
