Multi‐parametric system for risk stratification in mitral regurgitation: A multi-task Gaussian prediction approach

Background: We hypothesized that a multiparametric approach incorporating medical co-morbidity information, electrocardiographic P-wave indices, echocardiographic assessment, neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) calculated from laboratory data, can improve risk stratification in mitral regurgitation (MR).
Methods: Patients diagnosed with mitral regurgitation between 1st March 2005 and 30th October 2018 from a single center were retrospectively analyzed. Outcomes analyzed were incident atrial fibrillation (AF), transient ischemic attack (TIA)/stroke and mortality.
Results: This study cohort included 706 patients, of whom 171 had normal inter-atrial conduction, 171 had partial IAB, 86 had advanced inter-atrial block (IAB) and 266 had AF at baseline. Logistic regression analysis showed that age, hypertension and mean P-wave duration (PWD) were significant predictors of new onset AF. Low left ventricular ejection fraction (LVEF), abnormal P wave terminal force in V1 (PTFV1) predicted TIA/stroke. Age, smoking, hypertension, diabetes mellitus, hypercholesterolaemia, ischemic heart disease, urea, creatinine, NLR, PNI, left atrial diameter (LAD), LVEF, IAB, baseline AF predicted all-cause mortality. A multi-parametric approach (one-point for each variable, optimum cut-off 5.5, odds ratio: 5.01, 95% CI: 3.30-7.62) provided a moderate predictive value for mortality (area under the curve = 0.69, 95% CI: 0.64-0.74). A multi-task Gaussian process learning model demonstrated significant improvement in risk stratification compared to logistic regression and a decision tree method.
Conclusions: A multi-parametric approach incorporating multi-modality clinical data improves risk stratification in mitral regurgitation. Multi-task machine learning can significantly improve overall risk stratification performance.