Why the study?
Do machine learning predictive models improve the differential diagnosis of hypertrophic cardiomyopathy in patients with severe myocardial hypertrophy?
Population
1169 patients with severe myocardial hypertrophy and a preliminary diagnosis of hypertrophic cardiomyopathy…
Design
Cohort, Blinding validation
Key result
A logistic regression machine learning model reduced the hypertrophic cardiomyopathy misdiagnosis risk in patients with questionable diagnosis to 31%.
Authors
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May support ML-assisted HCM diagnosis in severe LVH; leaves open need for prospective validation before practice change.
Observational (n=1,169)
Do machine learning predictive models improve the differential diagnosis of hypertrophic cardiomyopathy in patients with severe myocardial hypertrophy?
A logistic regression-based machine learning model can aid in the differential diagnosis of severe left ventricular hypertrophy and reduce the risk of misdiagnosing hypertrophic cardiomyopathy.
Zaĭtsev et al. (2024) conducted an observational in Hypertrophic cardiomyopathy (n=1,169). Machine learning predictive models was evaluated on Accuracy of detecting hypertrophic cardiomyopathy. A logistic regression machine learning model reduced the hypertrophic cardiomyopathy misdiagnosis risk in patients with questionable diagnosis to 31%.
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