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  • Oxford University Press (OUP)  (3)
  • Lee, Seung-Pyo  (3)
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  • Oxford University Press (OUP)  (3)
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  • 1
    Online Resource
    Online Resource
    Data-driven mortality risk prediction of severe degenerative mitral regurgitation patients undergoing mitral valve surgery
    Oxford University Press (OUP) ; 2023
    In:  European Heart Journal - Cardiovascular Imaging Vol. 24, No. 9 ( 2023-08-23), p. 1156-1165
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), Vol. 24, No. 9 ( 2023-08-23), p. 1156-1165
    Abstract: The outcomes of mitral valve replacement/repair (MVR) in severe degenerative mitral regurgitation (MR) patients depend on various risk factors. We aimed to develop a risk prediction model for post-MVR mortality in severe degenerative MR patients using machine learning. Methods and results Consecutive severe degenerative MR patients undergoing MVR were analysed (n = 1521; 70% training/30% test sets). A random survival forest (RSF) model was constructed, with 3-year post-MVR all-cause mortality as the outcome. Partial dependency plots were used to define the thresholds of each risk factor. A simple scoring system (MVR-score) was developed to stratify post-MVR mortality risk. At 3 years following MVR, 90 patients (5.9%) died in the entire cohort (59 and 31 deaths in the training and test sets). The most important predictors of mortality in order of importance were age, haemoglobin, valve replacement, glomerular filtration rate, left atrial dimension, and left ventricular (LV) end-systolic diameter. The final RSF model with these six variables demonstrated high predictive performance in the test set (3-year C-index 0.880, 95% confidence interval 0.834–0.925), with mortality risk increased strongly with left atrial dimension & gt;55 mm, and LV end-systolic diameter & gt;45 mm. MVR-score demonstrated effective risk stratification and had significantly higher predictability compared to the modified Mitral Regurgitation International Database score (3-year C-index 0.803 vs. 0.750, P = 0.034). Conclusion A data-driven machine learning model provided accurate post-MVR mortality prediction in severe degenerative MR patients. The outcome following MVR in severe degenerative MR patients is governed by both clinical and echocardiographic factors.
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jead077
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2638345-7
    detail.hit.zdb_id: 2647943-6
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Derivation and validation of a mortality risk prediction model using global longitudinal strain in patients with acute heart failure
    Oxford University Press (OUP) ; 2020
    In:  European Heart Journal - Cardiovascular Imaging Vol. 21, No. 12 ( 2020-12-01), p. 1412-1420
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), Vol. 21, No. 12 ( 2020-12-01), p. 1412-1420
    Abstract: To develop a mortality risk prediction model in patients with acute heart failure (AHF), using left ventricular (LV) function parameters with clinical factors. Methods and results In total, 4312 patients admitted for AHF were retrospectively identified from three tertiary centres, and echocardiographic parameters including LV ejection fraction (LV-EF) and LV global longitudinal strain (LV-GLS) were measured in a core laboratory. The full set of risk factors was available in 3248 patients. Using Cox proportional hazards model, we developed a mortality risk prediction model in 1859 patients from two centres (derivation cohort) and validated the model in 1389 patients from one centre (validation cohort). During 32 (interquartile range 13–54) months of follow-up, 1285 patients (39.6%) died. Significant predictors for mortality were age, diabetes, diastolic blood pressure, body mass index, natriuretic peptide, glomerular filtration rate, failure to prescribe beta-blockers, failure to prescribe renin–angiotensin system blockers, and LV-GLS; however, LV-EF was not a significant predictor. Final model including these predictors to estimate individual probabilities of mortality had C-statistics of 0.75 [95% confidence interval (CI) 0.73–0.78; P  & lt; 0.001] in the derivation cohort and 0.78 (95% CI 0.75–0.80; P  & lt; 0.001) in the validation cohort. The prediction model had good performance in both heart failure (HF) with reduced EF, HF with mid-range EF, and HF with preserved EF. Conclusion We developed a mortality risk prediction model for patients with AHF incorporating LV-GLS as the LV function parameter, and other clinical factors. Our model provides an accurate prediction of mortality and may provide reliable risk stratification in AHF patients.
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jez300
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2638345-7
    detail.hit.zdb_id: 2647943-6
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Left ventricular global longitudinal strain as a prognosticator in hypertrophic cardiomyopathy with a low-normal left ventricular ejection fraction
    Oxford University Press (OUP) ; 2023
    In:  European Heart Journal - Cardiovascular Imaging Vol. 24, No. 10 ( 2023-09-26), p. 1374-1383
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), Vol. 24, No. 10 ( 2023-09-26), p. 1374-1383
    Abstract: The aim of this study was to investigate the prognostic utility of left ventricular (LV) global longitudinal strain (LV-GLS) in patients with hypertrophic cardiomyopathy (HCM) and an LV ejection fraction (LVEF) of 50–60%. Methods and results This retrospective cohort study included 349 patients with HCM and an LVEF of 50–60%. The primary outcome was a composite of cardiovascular death, including sudden cardiac death (SCD) and SCD-equivalent events. The secondary outcomes were SCD/SCD-equivalent events, cardiovascular death (including SCD), and all-cause death. The final analysis included 349 patients (mean age 59.2 ± 14.2 years, men 75.6%). During a median follow-up of 4.1 years, the primary outcome occurred in 26 (7.4%), while the secondary outcomes of SCD/SCD-equivalent events, cardiovascular death, and all-cause death occurred in 15 (4.2%), 20 (5.7%), and 34 (9.7%), respectively. After adjusting for age, atrial fibrillation, ischaemic stroke, LVEF, and left atrial volume index, absolute LV-GLS (%) was independently associated with the primary outcome [adjusted hazard ratio (HR) 0.88, 95% confidence interval (CI) 0.788–0.988, P = 0.029]. According to receiver operating characteristic analysis, 10.5% is an optimal cut-off value for absolute LV-GLS in predicting the primary outcome. Patients with an absolute LV-GLS ≤ 10.5% had a higher risk of the primary outcome than those with an absolute LV-GLS & gt; 10.5% (adjusted HR 2.54, 95% CI 1.117–5.787, P = 0.026). Absolute LV-GLS ≤ 10.5% was an independent predictor for each secondary outcome (P & lt; 0.05). Conclusions LV-GLS was an independent predictor of a composite of cardiovascular death, including SCD/SCD-equivalent events, in patients with HCM and an LVEF of 50–60%. Therefore, LV-GLS can help in risk stratification in these patients.
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jead177
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2638345-7
    detail.hit.zdb_id: 2647943-6
    Location Call Number Limitation Availability
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    Online Resource
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