Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations

Abstract Background Machine learning approaches are increasingly being explored for use in healthcare systems, but there is a trade-off between increased accuracy and decreased explainability with more complex models. We aimed to evaluate the diagnostic performance for acute myocardial infarction (AMI) or death within 30 days of index visit. Machine learning models were trained using demographic factors, ECG and blood markers, and to compare them to a single high sensitivity TnT (hs-cTnT) value. Methods Using records from 9519 ED patients from two hospitals in Skåne, Sweden, we created machine learning models based on both logistic regression and artificial neural networks. Inputs in the models varied and included sex and age, first hs-cTnT value at the ED, glucose, creatinine, hemoglobin, and ECG signal data. The models were adapted to meet the following criteria for safe rule-out of 30-day myocardial infarction or death: Negative predictive value (NPV) >99.5% and sensitivity >99%. For rule-in of myocardial infarction or death, a positive predictive value (PPV) of >70% was set. The models were then compared to the performance of a first hs-cTnT <5 ng/L for rule-out, and >51 ng/L for rule-in. The patient population was split by arrival date and models were trained on the initial 50% of patients. Thresholds were selected from the subsequent 25%, and tests were performed on the final 25% (2379 patients). Results The best model, a convolutional neural network, identified 1309 (55%) patients for rule-out and 125 (5.3%) for rule-in, with the required NPV, sensitivity and PPV. In comparison, a single hs-cTnT value identified 1123 (47.2%) patients for rule-out and 158 (6.6%) for rule-in, but failed to reach the required sensitivity and PPV levels. Conclusions These results indicate that more complex models are able to safely identify a large proportion of patients for early rule-out or rule-in without the need for serial troponin tests. In future studies attempts should be made to improve the explainability of these models. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): VR; grant no. 2019-00198

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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Prospective validation of a 1-hour algorithm to rule-out and rule-in acute myocardial infarction using a high-sensitivity cardiac troponin T assay

Canadian Medical Association Journal ◽

10.1503/cmaj.141349 ◽

2015 ◽

Vol 187 (8) ◽

pp. E243-E252 ◽

Cited By ~ 137

Author(s):

Tobias Reichlin ◽

Raphael Twerenbold ◽

Karin Wildi ◽

Maria Rubini Gimenez ◽

Nathalie Bergsma ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Cardiac Troponin ◽

Troponin T ◽

High Sensitivity ◽

Cardiac Troponin T ◽

Rule Out

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Correction: Rapid Rule-out of Acute Myocardial Infarction With a Single High-Sensitivity Cardiac Troponin T Measurement Below the Limit of Detection

Annals of Internal Medicine ◽

10.7326/l17-0538 ◽

2017 ◽

Vol 167 (7) ◽

pp. 528 ◽

Cited By ~ 1

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Cardiac Troponin ◽

Limit Of Detection ◽

Troponin T ◽

High Sensitivity ◽

Cardiac Troponin T ◽

Rule Out

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Biochemical Markers of Acute Myocardial Infarction: Strategies for Improving Their Clinical Usefulness

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/000456329803500308 ◽

1998 ◽

Vol 35 (3) ◽

pp. 393-399 ◽

Cited By ~ 10

Author(s):

Ian A Katz ◽

Les Irwig ◽

John D Vinen ◽

Lyn March ◽

Lindsay E Wyndham ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Chest Pain ◽

Troponin T ◽

Mass Measurement ◽

World Health ◽

Likelihood Ratios ◽

Significant Independent Predictor ◽

Activity Measurements

We investigated the early diagnostic utility, including incremental value, of the serum cardiac markers creatine kinase (CK), CK-MB (mass and activity measurements), cardiac troponin T, and myoglobin in the diagnosis of acute myocardial infarction (AMI) in patients presenting to a major teaching hospital with chest pain and non-diagnostic electrocardiographs (ECG). The reference diagnosis of acute myocardial infarction was made by a single, independent cardiologist using World Health Organization criteria. CK and CK-MB mass were the only significant predictors of AMI at presentation to the Emergency Department. Logistic regression analysis revealed that CK did not significantly predict ( P = 0·23) myocardial infarction once CK-MB mass was in the model. Using test results on follow up, in addition to presentation CK-MB mass, change in CK-MB mass was the only other significant independent predictor of AMI. Likelihood ratios for various levels of the significant markers in the logistic regression are given. In conclusion, CK-MB mass measurement was the only useful serum cardiac marker for the diagnosis of AMI in patients presenting with chest pain with non-diagnostic ECGs.

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Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry

10.1101/540369 ◽

2019 ◽

Author(s):

Rohan Khera ◽

Julian Haimovich ◽

Nate Hurley ◽

Robert McNamara ◽

John A Spertus ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Chest Pain ◽

High Risk ◽

Hospital Mortality ◽

Gradient Descent ◽

Machine Learning Methods ◽

C Statistic

ABSTRACTIntroductionAccurate prediction of risk of death following acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making. Contemporary machine-learning may improve risk-prediction by identifying complex relationships between predictors and outcomes.Methods and ResultsWe studied 993,905 patients in the American College of Cardiology Chest Pain-MI Registry hospitalized with AMI (mean age 64 ± 13 years, 34% women) between January 2011 and December 2016. We developed and validated three machine learning models to predict in-hospital mortality and compared the performance characteristics with a logistic regression model. In an independent validation cohort, we compared logistic regression with lasso regularization (c-statistic, 0.891 [95% CI, 0.890-0.892]), gradient descent boosting (c-statistic, 0.902 [0.901-0.903]), and meta-classification that combined gradient descent boosting with a neural network (c-statistic, 0.904 [0.903-0.905]) with traditional logistic regression (c-statistic, 0.882 [0.881-0.883]). There were improvements in classification of individuals across the spectrum of patient risk with each of the three methods; the meta-classifier model – our best performing model - reclassified 20.9% of individuals deemed high-risk for mortality in logistic regression appropriately as low-to-moderate risk, and 8.2% of deemed low-risk to moderate-to-high risk based consistent with the actual event rates.ConclusionsMachine-learning methods improved the prediction of in-hospital mortality for AMI compared with logistic regression. Machine learning methods enhance the utility of risk models developed using traditional statistical approaches through additional exploration of the relationship between variables and outcomes.

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Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database (Preprint)

10.2196/preprints.27405 ◽

2021 ◽

Author(s):

Arom Choi ◽

Min Joung Kim ◽

Ji Min Sung ◽

Hyuk-Jae Chang ◽

Jayong Lee ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Statistical Methods ◽

Prediction Models ◽

National Database ◽

Type Model ◽

Prehospital Management ◽

Extreme Gradient Boosting

BACKGROUND Since acute myocardial infarction (AMI) is a leading cause of mortality worldwide, the accurate evaluation of risk factors of AMI at prehospital stage provides appropriate prehospital management and rapid transportation to the most appropriate hospital for treatment. Prediction of AMI derived from national database can accelerate early recognition and timely management to improve the survival rate. OBJECTIVE This study was conducted to develop and compare the efficacy of models for the prediction of AMI at the prehospital stage based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms. METHODS From among patients transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018, the patients aged >15 years in the EMS cardiovascular registry were enrolled. Two datasets were constructed according to the hierarchical structure of the EMS cardiovascular registry. For each dataset, several predictive models for AMI were derived and compared using conventional statistical methods and machine learning. RESULTS In total, 184,577 patients (Dataset 1) in the EMS cardiovascular registry were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at the prehospital stage (as assessed by paramedics). Between the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model, and exhibited a superior discriminative ability (P = 0.02). The models that used extreme gradient boosting and multilayer perceptron yielded a higher predictive performance than the model derived based on conventional logistic regression for all analyses that used both datasets. Each machine learning algorithm yielded different classification lists regarding the 10 most important features. CONCLUSIONS This study demonstrates that prediction models, which use nationwide prehospital data and are developed with appropriate structures, can improve the identification of patients who need timely AMI management.

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Serial high-sensitivity cardiac troponin T measurements to rule out acute myocardial infarction and a single high baseline measurement for swift rule-in: A systematic review and meta-analysis

European Heart Journal Acute Cardiovascular Care ◽

10.1177/2048872618819421 ◽

2019 ◽

Vol 9 (1) ◽

pp. 14-22 ◽

Cited By ~ 5

Author(s):

M Arslan ◽

A Dedic ◽

E Boersma ◽

EA Dubois

Keyword(s):

Myocardial Infarction ◽

Systematic Review ◽

Acute Myocardial Infarction ◽

Cardiac Troponin ◽

Troponin T ◽

Meta Analysis ◽

High Sensitivity ◽

Cardiac Troponin T ◽

Single Baseline ◽

Rule Out

Aims: The purpose of this study was to determine (a) the ability of serial high-sensitivity cardiac troponin T measurements to rule out acute myocardial infarction and (b) the ability of a single high baseline high-sensitivity cardiac troponin T measurement to rule in acute myocardial infarction in patients presenting to the emergency department with acute chest pain. Methods and results: Embase, Medline, Cochrane, Web of Science and Google scholar were searched for prospective cohort studies that evaluated parameters of diagnostic accuracy of serial high-sensitivity cardiac troponin T to rule out acute myocardial infarction and a single baseline high-sensitivity cardiac troponin T value>50 ng/l to rule in acute myocardial infarction. The search yielded 21 studies for the systematic review, of which 14 were included in the meta-analysis, with a total of 11,929 patients and an overall prevalence of acute myocardial infarction of 13.0%. For rule-out, six studies presented the sensitivity of serial measurements <14 ng/l. This cut-off classified 60.1% of patients as rule-out and the summary sensitivity was 96.7% (95% confidence interval: 92.3–99.3). Three studies presented the sensitivity of a one-hour algorithm with a baseline high-sensitivity cardiac troponin T value<12 ng/l and delta 1 hour <3 ng/l. This algorithm classified 60.2% of patients as rule-out and the summary sensitivity was 98.9% (96.4–100). For rule-in, six studies reported the specificity of baseline high-sensitivity cardiac troponin T value>50 ng/l. The summary specificity was 94.6% (91.5–97.1). Conclusion: Serial high-sensitivity cardiac troponin T measurement strategies to rule out acute myocardial infarction perform well, and a single baseline high-sensitivity cardiac troponin T value>50 ng/l to rule in acute myocardial infarction has a high specificity.

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