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

TitleMulti-parametric system for risk stratification in mitral regurgitation: A multi-task Gaussian prediction approach
Authors
Keywordsinter-atrial block
mitral regurgitation
neutrophil
P-wave
prognostic nutritional index
Issue Date2020
Citation
European Journal of Clinical Investigation, 2020, v. 50, n. 11, article no. e13321 How to Cite?
AbstractBackground: 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.
Persistent Identifierhttp://hdl.handle.net/10722/330650
ISSN
2023 Impact Factor: 4.4
2023 SCImago Journal Rankings: 1.270
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTse, Gary-
dc.contributor.authorZhou, Jiandong-
dc.contributor.authorLee, Sharen-
dc.contributor.authorLiu, Yingzhi-
dc.contributor.authorLeung, Keith Sai Kit-
dc.contributor.authorLai, Rachel Wing Chuen-
dc.contributor.authorBurtman, Anthony-
dc.contributor.authorWilson, Carly-
dc.contributor.authorLiu, Tong-
dc.contributor.authorLi, Ka Hou Christien-
dc.contributor.authorLakhani, Ishan-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:12:47Z-
dc.date.available2023-09-05T12:12:47Z-
dc.date.issued2020-
dc.identifier.citationEuropean Journal of Clinical Investigation, 2020, v. 50, n. 11, article no. e13321-
dc.identifier.issn0014-2972-
dc.identifier.urihttp://hdl.handle.net/10722/330650-
dc.description.abstractBackground: 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.-
dc.languageeng-
dc.relation.ispartofEuropean Journal of Clinical Investigation-
dc.subjectinter-atrial block-
dc.subjectmitral regurgitation-
dc.subjectneutrophil-
dc.subjectP-wave-
dc.subjectprognostic nutritional index-
dc.titleMulti-parametric system for risk stratification in mitral regurgitation: A multi-task Gaussian prediction approach-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/eci.13321-
dc.identifier.pmid32535888-
dc.identifier.scopuseid_2-s2.0-85089255616-
dc.identifier.volume50-
dc.identifier.issue11-
dc.identifier.spagearticle no. e13321-
dc.identifier.epagearticle no. e13321-
dc.identifier.eissn1365-2362-
dc.identifier.isiWOS:000558010300001-

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