LAUSR

BACKGROUND Acquired long QT syndrome (aLQTS) is often associated with poor clinical outcomes. OBJECTIVE The present study examined the important predictors for all-cause mortality of aLQTS patients by applying both random survival forest (RSF) and non-negative matrix factorization (NMF) analyses. METHODS Clinical characteristics and manually measured electrocardiographic (ECG) parameters were initially entered into the RSF model. Subsequently, latent variables identified using NMF were entered into the RSF as additional variables. The primary outcome was the all-cause mortality. RESULTS A total of 327 aLQTS patients were included. The RSF model identified 16 predictive factors with positive variable importance (VIMP) values including cancer, potassium, RR interval, calcium, age, JT interval, diabetes mellitus, QRS duration, QTp interval, chronic kidney disease, QTc interval, hypertension, QT interval, female, JTc interval and cerebral hemorrhage. Increasing the number of latent features between ECG indices, which incorporated from n=0 to n=4 by NMF, maximally improved the prediction ability of the RSF-NMF model (c-statistic: 0.77 vs. 0.89). CONCLUSION Cancer, potassium and calcium can predict the all-cause mortality of aLQTS patients and so were ECG indicators including JTc and QRS. The present RSF-NMF model significantly improved mortality prediction.