The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis.

INTRODUCTION

To perform a meta-analysis to discover the performance of ML algorithms in identifying Congenital long QT syndrome (LQTS).

METHODS

The searched databases included Cochrane, EMBASE, Web of Science, and PubMed. Our study considered all English-language studies that reported the detection of LQTS using ML algorithms. Quality was assessed using QUADAS-2 and QUADAS-AI tools. The bivariate mixed effects models were used in our study. Based on genotype data for LQTS, we performed a subgroup analysis.

RESULTS

Out of 536 studies, 8 met all inclusion criteria. The pooled area under the receiving operating curve (SAUROC) for detecting LQTS was 0.95 (95% CI: 0.31-1.00); sensitivity was 0.87 (95% CI: 0.83-0.90), and specificity was 0.91 (95% CI: 0.88-0.93). Additionally, diagnostic odd ratio (DOR) was 65 (95% CI: 39-109). The positive likelihood ratio (PLR) was 9.3 (95% CI: 7.0-12.3) and the negative likelihood ratio (NLR) was 0.14 (95% CI: 0.11-0.20), with very low heterogeneity (= 16%).

DISCUSSION

We found that machine learning can be used to detect features of rare cardiovascular disease like LQTS, thus increasing our understanding of intelligent interpretation of ECG. To improve ML performance in the classification of LQTS subtypes, further research is required.

SYSTEMATIC REVIEW REGISTRATION

identifier PROSPERO CRD42022360122.