Association of NPAC score with survival after acute myocardial infarction

Eur J Clin Investigation

Lee³

et al. 2020

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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 multitask Gaussian process learning model demonstrated significant improvement in risk stratification compared to logistic regression and a decision tree method.

Section: Discussionmentioning

confidence: 99%

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

Tse

Eur J Clin Investigation

Lee³

et al. 2020

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“…The details of patients are linked to a territory‐wide Clinical Data Analysis and Reporting System, with each patient identifiable with a unique reference identifier, enabling access to comprehensive medical records. This has previously been used by local teams for conducting research studies 15–17 …”

Section: Methodsmentioning

confidence: 99%

“…This has previously been used by local teams for conducting research studies. [15][16][17] The following clinical details were obtained: age, gender, smoking status, diabetes mellitus, hypercholesterolaemia, coronary artery disease, hypertension, baseline atrial fibrillation (AF), and anti-hypertensive and anti-coagulation medications. Complete blood count was performed using automated haematology analysers.…”

Section: Study Population Baseline Characteristics and Laboratory Imentioning

confidence: 99%

Multi‐modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%

Tse

Woo³

et al. 2020

ESC Heart Failure

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Aims Heart failure (HF) involves complex remodelling leading to electrical and mechanical dysfunction. We hypothesized that machine learning approaches incorporating data obtained from different investigative modalities including atrial and ventricular measurements from electrocardiography and echocardiography, blood inflammatory marker [neutrophil-to-lymphocyte ratio (NLR)], and prognostic nutritional index (PNI) will improve risk stratification for adverse outcomes in HF compared to logistic regression. Methods and results Consecutive Chinese patients referred to our centre for transthoracic echocardiography and subsequently diagnosed with HF, between 1 January 2010 and 31 December 2016, were included in this study. Two machine learning techniques, multilayer perceptron and multi-task learning, were compared with logistic regression for their ability to predict incident atrial fibrillation (AF), transient ischaemic attack (TIA)/stroke, and all-cause mortality. This study included 312 HF patients [mean age: 64 (55-73) years, 75% male]. There were 76 cases of new-onset AF, 62 cases of incident TIA/stroke, and 117 deaths during follow-up. Univariate analysis revealed that age, left atrial reservoir strain (LARS) and contractile strain (LACS) were significant predictors of new-onset AF. Age and smoking predicted incident stroke. Age, hypertension, type 2 diabetes mellitus, chronic kidney disease, mitral or aortic regurgitation, P-wave terminal force in V1, the presence of partial inter-atrial block, left atrial diameter, ejection fraction, global longitudinal strain, serum creatinine and albumin, high NLR, low PNI, and LARS and LACS predicted all-cause mortality. Machine learning techniques achieved better prediction performance than logistic regression. Conclusions Multi-modality assessment is important for risk stratification in HF. A machine learning approach provides additional value for improving outcome prediction.

“…Comprehensive medical data were accessed from the healthcare database (Clinical Management System, CMS) that is linked to a territory-wide Clinical Data Analysis and Reporting System (CDARS) with unique reference identifier for each patient. Our team and other teams have previously used this system for epidemiological studies [20-22]. Clinical details including patient age, gender, blood pressure, smoking status, hypertension, diabetes mellitus, hypercholesterolemia, ischemic heart disease were extracted with the system.…”

Section: Methodsmentioning

confidence: 99%

Predicting Stroke and Mortality in Mitral Regurgitation: A Gradient Boosting Approach

Lee²,

Liu³

et al. 2021

Preprint

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IntroductionWe hypothesized that an interpretable gradient boosting machine (GBM) model considering comorbidities, P-wave and echocardiographic measurements, can better predict mortality and cerebrovascular events in mitral regurgitation (MR).MethodsPatients from a tertiary center were analyzed. The GBM model was used as an interpretable statistical approach to identify the leading indicators of high-risk patients with either outcome of CVAs and all-cause mortality.ResultsA total of 706 patients were included. GBM analysis showed that age, systolic blood pressure, diastolic blood pressure, plasma albumin levels, mean P-wave duration (PWD), MR regurgitant volume, left ventricular ejection fraction (LVEF), left atrial dimension at end-systole (LADs), velocity-time integral (VTI) and effective regurgitant orifice were significant predictors of TIA/stroke. Age, sodium, urea and albumin levels, platelet count, mean PWD, LVEF, LADs, left ventricular dimension at end systole (LVDs) and VTI were significant predictors of all-cause mortality. The GBM demonstrates the best predictive performance in terms of precision, sensitivity c-statistic and F1-score compared to logistic regression, decision tree, random forest, support vector machine, and artificial neural networks.ConclusionGradient boosting model incorporating clinical data from different investigative modalities significantly improves risk prediction performance and identify key indicators for outcome prediction in MR.