Abstract Body (Do not enter title and authors here): INTRODUCTION
Artificial intelligence electrocardiograms are considered efficient for estimating ejection fraction in heart failure patients. However, whether this method is a potential tool to diagnose peripartum cardiomyopathy has not been thoroughly explored.
RESEARCH QUESTIONS
Are electrocardiogram-Base AI models able to predict peripartum cardiomyopathy?
AIMS
We conducted a meta-analysis and systematic review to evaluate the accuracy of electrocardiogram-base artificial intelligence models to predict peripartum cardiomyopathy.
METHODS
We searched PubMed, Embase and Cochrane. We computed true positives, true negatives, false positives and false negatives events to estimate pooled sensitivity, specificity and area under the curve under random mode. We used R 4.3.1 to perform statistics.
RESULTS
We identified 3 studies of data from 4 different datasets, including 425 patients evaluated for peripartum cardiomyopathy. The mean age ranged from 29 to 33 years. Multiparity ranged from 20.58% to 36.94%. Black population ranged from 58.13% to 62.6% Chronic hypertension ranged from 1.56% to 9.5%. Gestational hypertension ranged from 28% to 32.8% .The AI enabled electrocardiogram data yielded areas under the receiver operator of 0.900, sensitivity of 0.841(0.749-0.903), and specificity of 0.840(0.714-0.917) to predict for peripartum cardiomyopathy.
CONCLUSIONS
In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity for the diagnosis of peripartum cardiomyopathy.