Confounding factors, including sex, age, and renal dysfunction, affect high-sensitivity cardiac troponin T (hs-cTnT) concentrations and the acute myocardial infarction (AMI) diagnosis. This study assessed the effects of these confounders through logistic regression models and evaluated the diagnostic performance of an optimized, integrated prediction model.
This retrospective study included a primary derivation cohort of 18,022 emergency department (ED) patients at a US medical center and a validation cohort of 890 ED patients at a Canadian medical center. Hs-cTnT was measured with 0/3 h sampling. The primary outcome was index AMI diagnosis. Logistic regression models were optimized to predict AMI using delta hs-cTnT and its confounders as covariates. The diagnostic performance of model cutoffs was compared to that of the hs-cTnT delta thresholds. Serial logistic regressions were carried out to evaluate the relationship between covariates.
The area under the curve of the best-fitted model was 0.95. The model achieved a 90.0% diagnostic accuracy in the validation cohort. The optimal model cutoff yielded comparable performance (90.5% accuracy) to the optimal sex-specific delta thresholds (90.3% accuracy), with 95.8% agreement between the two diagnostic methods. Serial logistic regressions revealed that delta hs-cTnT played a more predominant role in AMI prediction than its confounders, among which sex is more predictive of AMI (total effect coefficient 1.04) than age (total effect coefficient 0.05) and eGFR (total effect coefficient −0.008).
The integrated prediction model incorporating confounding factors does not outperform hs-cTnT delta thresholds. Sex-specific hs-cTnT delta thresholds remain to provide the highest diagnostic accuracy.
Research funding: This work was partially supported by the United States National Institute of Environmental Health Sciences at the National Institutes of Health (Grant T32ES007271). The study sponsors had no role in the study design; in the collection, analysis, or interpretation of the data; in the writing of the report; or in the decision to submit the article for publication.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Peter Kavsak has received grants and provision of study materials from Roche Diagnostics, Abbott Laboratories, Beckman Coulter, Ortho Clinical Diagnostics, Randox Laboratories, and Siemens Healthcare Diagnostics. He has received consulting fees or honoraria from Abbott Laboratories, Beckman Coulter, Quidel, Roche Diangostics, Siemens Healthcare Diagnostics, and Thermo Fisher Scientific. He has received support for attending meetings from Randox Laboratories. Peter Kavsak and Andrew Worster have a pending patent application filed by McMaster University as inventors in the acute cardiovascular biomarker field. Andrew Worster has received research grant from the Canadian Institute of Health Research. Xueya Cai has received research grant from the National Institutes of Health. No other financial relationships were declared.
Informed consent: Not applicable.
Ethical approval: This retrospective study consisted of a primary cohort approved by the University of Rochester Institutional Review Board and an external validation cohort with previous approval by the Hamilton Integrated Research Ethics Board.
Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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