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

Gary Tse, Jiandong Zhou, Sharen Lee, Yingzhi Liu, Keith Sai Kit Leung, Rachel Wing Chuen Lai, Anthony Burtman, Carly Wilson, Tong Liu, Ka Hou Christien Li, Ishan Lakhani, Qingpeng Zhang

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Background: We hypothesized that a multi-parametric approach incorporating medical comorbidity 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 1 March 2005 and 30 October 2018 from a single centre were retrospectively analysed. Outcomes analysed 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, 257 had 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, secondary mitral regurgitation, urea, creatinine, NLR, PNI, left atrial diameter (LAD), left ventricular end-diastolic dimension, LVEF, pulmonary arterial systolic pressure, IAB, baseline AF and heart failure predicted all-cause mortality. 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.

Original languageEnglish
Article numbere13321
JournalEuropean Journal of Clinical Investigation
Volume50
Issue number11
DOIs
Publication statusPublished - 1 Nov 2020
Externally publishedYes

Keywords

  • P-wave
  • inter-atrial block
  • mitral regurgitation
  • neutrophil
  • prognostic nutritional index

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