Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations

Figures

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  Fig. 1. Positive predictive value of the sex-specific 99th percentile cardiac troponin threshold in the derivation cohort across patient subgroups. Data are presented as a central estimate with 95% CIs based on the Clopper–Pearson method. eGFR, estimated glomerular filtration rate.

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  Fig. 2. Diagnostic performance of the CoDE-ACS score in the external validation cohort using the presentation troponin concentration alone. a, Receiver-operating characteristic curve illustrating the discrimination of the CoDE-ACS for myocardial infarction. b, Calibration of the CoDE-ACS score with the observed proportion of patients with myocardial infarction. The dashed line represents perfect calibration. Each point represents 100 patients. Patients are grouped as low (<3), intermediate (3–60) or high probability (≥61) of myocardial infarction. The darker shaded area represents the 95% CI, while the lighter shaded area represents the 99% CI. AUC, area under curve.

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  Fig. 3. External validation of the performance of the CoDE-ACS pathway in 10,286 patients with possible myocardial infarction. Diagnostic performance of CoDE-ACS models in 10,286 patients from seven international cohorts. Sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) with 95% CIs of the CoDE-ACS scores were used to identify patients as low probability (<3) or high probability (≥61) of myocardial infarction at presentation and after serial troponin testing if required.

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  Fig. 4. Diagnostic performance of the CoDE-ACS score in the external validation cohort for identifying patients as having a low or high probability of myocardial infarction across patient subgroups. Data are presented as a central estimate with 95% CIs based on the Clopper–Pearson method. a, Negative predictive value of the low-probability CoDE-ACS score using the presentation troponin concentration alone across patient subgroups. b, Positive predictive value of the high-probability CoDE-ACS score using the presentation troponin concentration alone across patient subgroups. eGFR, estimated glomerular filtration rate.

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  Fig. 5. Cumulative incidence of cardiac death and all-cause mortality as stratified by the CoDE-ACS score at presentation in the external validation cohort. a,b, Data for cardiac death (a) and all-cause mortality (b).

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  Extended Data Fig. 1. Flow diagram illustrating the populations used to train CoDE-ACS models in patients with and without myocardial injury. 1Lancet. 2015 Dec 19;386(10012):2481-8. 2Lancet. 2018 Sep 15;392(10151):919–928.

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  Extended Data Fig. 2. Negative predictive value of the 5 ng/L risk stratification threshold at presentation in the derivation cohort across patient subgroups. Data are presented as a central estimate with 95% confidence intervals based on the Clopper-Pearson method.

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  Extended Data Fig. 3. Importance permutation rank of the features in the XGBoost model. (a) In patients without myocardial injury. (b) In patients with myocardial injury.

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  Extended Data Fig. 4. Diagnostic performance of CoDE- ACS scores at presentation in the derivation cohort across patient subgroups. Data are presented as a central estimate with 95% confidence intervals based on the Clopper-Pearson method. (a) CoDE-ACS low probability score of less than 3. (b) CoDE-ACS high probability score of 61 or more.

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  Extended Data Fig. 5. Diagnostic performance of CoDE- ACS scores on serial troponin testing in the derivation cohort across patient subgroups. Data are presented as a central estimate with 95% confidence intervals based on the Clopper-Pearson method. (a) CoDE-ACS low probability score of less than 3. (b) CoDE-ACS high probability score of 61 or more.

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  Extended Data Fig. 6. Diagnostic performance of CoDE- ACS in the external validation cohort using serial troponin results. (a) Receiver-operating- characteristic (ROC) curve illustrating discrimination of the CoDE-ACS for myocardial infarction. (b) Calibration of the CoDE-ACS score with the observed proportion of patients with myocardial infarction. The dashed line represents perfect calibration. Each point represents 100 patients. Patients are grouped as low- (<3), intermediate- (3 to 60) or high-probability (≥61) of myocardial infarction. The darker shaded area represents the 95% confidence interval, while the lighter shaded area the 99% confidence interval.

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  Extended Data Fig. 7. Diagnostic performance of CoDE- ACS scores on serial troponin testing in the external validation cohort across patient subgroups. Data are presented as a central estimate with 95% confidence intervals based on the Clopper-Pearson method. (a) CoDE-ACS low probability score of less than 3. (b) CoDE-ACS high probability score of 61 or more.