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Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.

ABSTRACT: Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0-100) that corresponds to an individual's probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947-0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers.

SUBMITTER: Doudesis D

PROVIDER: S-EPMC10202804 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.

Doudesis Dimitrios D Lee Kuan Ken KK Boeddinghaus Jasper J Bularga Anda A Ferry Amy V AV Tuck Chris C Lowry Matthew T H MTH Lopez-Ayala Pedro P Nestelberger Thomas T Koechlin Luca L Bernabeu Miguel O MO Neubeck Lis L Anand Atul A Schulz Karen K Apple Fred S FS Parsonage William W Greenslade Jaimi H JH Cullen Louise L Pickering John W JW Than Martin P MP Gray Alasdair A Mueller Christian C Mills Nicholas L NL

Nature medicine 20230511 5

Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0-100) that corresponds ...[more]

PMID: 37169863

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Project description:Glucose is a universally available inexpensive biomarker, which is increased as part of the physiological stress response to acute myocardial infarction (AMI) and may therefore help in its early diagnosis. To test this hypothesis, glucose, high-sensitivity cardiac troponin (hs-cTn) T, and hs-cTnI were measured in consecutive patients presenting with acute chest discomfort to the emergency department (ED) and enrolled in a large international diagnostic study (NCT00470587). Two independent cardiologists centrally adjudicated the final diagnosis using all clinical data, including serial hs-cTnT measurements, cardiac imaging and clinical follow-up. The primary diagnostic endpoint was index non-ST-segment elevation MI (NSTEMI). Prognostic endpoints were all-cause death, and cardiovascular (CV) death or future AMI, all within 730-days. Among 5639 eligible patients, NSTEMI was the adjudicated final diagnosis in 1051 (18.6%) patients. Diagnostic accuracy quantified using the area under the receiver-operating characteristics curve (AUC) for the combination of glucose with hs-cTnT and glucose with hs-cTnI was very high, but not higher versus that of hs-cTn alone (glucose/hs-cTnT 0.930 [95% CI 0.922-0.937] versus hs-cTnT 0.929 [95% CI 0.922-0.937]; glucose/hs-cTnI 0.944 [95% CI 0.937-0.951] versus hs-cTnI 0.944 [95% CI 0.937-0.951]). In early-presenters, a dual-marker strategy (glucose < 7 mmol/L and hs-cTnT < 5/hs-cTnI < 4 ng/L) provided very high and comparable sensitivity to slightly lower hs-cTn concentrations (cTnT/I < 4/3 ng/L) alone, and possibly even higher efficacy. Glucose was an independent predictor of 730-days endpoints. Our results showed that a dual marker strategy of glucose and hs-cTn did not increase the diagnostic accuracy when used continuously. However, a cutoff approach combining glucose and hs-cTn may provide diagnostic utility for patients presenting ≤ 3 h after onset of symptoms, also providing important prognostic information.

Project description:Objective To evaluate how selection of patients for high sensitivity cardiac troponin testing affects the diagnosis of myocardial infarction across different healthcare settings.Design Prospective study of three independent consecutive patient populations presenting to emergency departments.Setting Secondary and tertiary care hospitals in the United Kingdom and United States.Participants High sensitivity cardiac troponin I concentrations were measured in 8500 consecutive patients presenting to emergency departments: unselected patients in the UK (n=1054) and two selected populations of patients in whom troponin testing was requested by the attending clinician in the UK (n=5815) and the US (n=1631). The final diagnosis of type 1 or type 2 myocardial infarction or myocardial injury was independently adjudicated.Main outcome measures Positive predictive value of an elevated cardiac troponin concentration for a diagnosis of type 1 myocardial infarction.Results Cardiac troponin concentrations were elevated in 13.7% (144/1054) of unselected patients, with a prevalence of 1.6% (17/1054) for type 1 myocardial infarction and a positive predictive value of 11.8% (95% confidence interval 7.0% to 18.2%). In selected patients, in whom troponin testing was guided by the attending clinician, the prevalence and positive predictive value were 14.5% (843/5815) and 59.7% (57.0% to 62.2%) in the UK and 4.2% (68/1631) and 16.4% (13.0% to 20.3%) in the US. Across both selected patient populations, the positive predictive value was highest in patients with chest pain, with ischaemia on the electrocardiogram, and with a history of ischaemic heart disease.Conclusions When high sensitivity cardiac troponin testing is performed widely or without previous clinical assessment, elevated troponin concentrations are common and predominantly reflect myocardial injury rather than myocardial infarction. These observations highlight how selection of patients for cardiac troponin testing varies across healthcare settings and markedly influences the positive predictive value for a diagnosis of myocardial infarction.

Project description:BackgroundAlthough the 99th percentile is the recommended diagnostic threshold for myocardial infarction, some guidelines also advocate the use of higher troponin thresholds to rule in myocardial infarction at presentation. It is unclear whether the magnitude or change in troponin concentration can differentiate causes of myocardial injury and infarction in practice.MethodsIn a secondary analysis of a multicenter randomized controlled trial, we identified 46 092 consecutive patients presenting with suspected acute coronary syndrome without ST-segment-elevation myocardial infarction. High-sensitivity cardiac troponin I concentrations at presentation and on serial testing were compared between patients with myocardial injury and infarction. The positive predictive value and specificity were determined at the sex-specific 99th percentile upper reference limit and rule-in thresholds of 64 ng/L and 5-fold of the upper reference limit for a diagnosis of type 1 myocardial infarction.ResultsTroponin was above the 99th percentile in 8188 patients (18%). The diagnosis was type 1 or type 2 myocardial infarction in 50% and 14% and acute or chronic myocardial injury in 20% and 16%, respectively. Troponin concentrations were similar at presentation in type 1 (median [25th-75th percentile] 91 [30-493] ng/L) and type 2 (50 [22-147] ng/L) myocardial infarction and in acute (50 [26-134] ng/L) and chronic (51 [31-130] ng/L) myocardial injury. The 99th percentile and rule-in thresholds of 64 ng/L and 5-fold upper reference limit gave a positive predictive value of 57% (95% CI, 56%-58%), 59% (58%-61%), and 62% (60%-64%) and a specificity of 96% (96%-96%), 96% (96%-96%), and 98% (97%-98%), respectively. The absolute, relative, and rate of change in troponin concentration were highest in patients with type 1 myocardial infarction (P<0.001 for all). Discrimination improved when troponin concentration and change in troponin were combined compared with troponin concentration at presentation alone (area under the curve, 0.661 [0.642-0.680] versus 0.613 [0.594-0.633]).ConclusionsAlthough we observed important differences in the kinetics, cardiac troponin concentrations at presentation are insufficient to distinguish type 1 myocardial infarction from other causes of myocardial injury or infarction in practice and should not guide management decisions in isolation. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01852123.

Dataset Information

Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.

Publications

Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.

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