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Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling.
JACC Adv
January 2025
Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, Germany. Electronic address:
Background: Patients with severe tricuspid regurgitation (TR) typically present with heterogeneity in the extent of cardiac dysfunction and extra-cardiac comorbidities, which play a decisive role for survival after transcatheter tricuspid valve intervention (TTVI).
Objectives: This aim of this study was to create a survival tree-based model to determine the cardiac and extra-cardiac features associated with 2-year survival after TTVI.
Methods: The study included 918 patients (derivation set, n = 631; validation set, n = 287) undergoing TTVI for severe TR.
Machine learning-derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patients.
JTCVS Open
December 2024
Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio.
Objective: To develop a model for preoperatively predicting postcardiotomy cardiogenic shock (PCCS) in patients with poor left ventricular (LV) function undergoing cardiac surgery.
Methods: From the Society of Thoracic Surgeons Adult Cardiac Database, 11,493 patients with LV ejection fraction ≤35% underwent isolated on-pump surgery from 2018 through 2019, of whom 3428 experienced PCCS. In total, 68 preoperative clinical variables were considered in machine-learning algorithms trained and optimized using scikit-learn software.
Effect of a Machine Learning-Derived Early Warning Tool With Treatment Protocol on Hypotension During Cardiac Surgery and ICU Stay: The Hypotension Prediction 2 (HYPE-2) Randomized Clinical Trial.
Crit Care Med
November 2024
Department of Intensive Care, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: Cardiac surgery is associated with perioperative complications, some of which might be attributable to hypotension. The Hypotension Prediction Index (HPI), a machine-learning-derived early warning tool for hypotension, has only been evaluated in noncardiac surgery. We investigated whether using HPI with diagnostic guidance reduced hypotension during cardiac surgery and in the ICU.
View Article and Find Full Text PDFA Machine Learning-derived Risk Score Improves Prediction of Outcomes After LVAD Implantation: An Analysis of the INTERMACS Database.
J Card Fail
October 2024
Cardiology Department, University of California San Diego, La Jolla, California.
Background: Significant variability in outcomes after left ventricular assist device (LVAD) implantation emphasize the importance of accurately assessing patients' risk before surgery. This study assesses the Machine Learning Assessment of Risk and Early Mortality in Heart Failure (MARKER-HF) mortality risk model, a machine learning-based tool using 8 clinical variables, to predict post-LVAD implantation mortality and its prognostic enhancement over the Interagency Registry of Mechanically Assisted Circulatory Support (INTERMACS) profile.
Methods: Analyzing 25,365 INTERMACS database patients (mean age 56.
Clinical Utility of Machine Learning-Derived Vocal Biomarkers in the Management of Heart Failure.
Circ Rep
August 2024
Division of Cardiology, Yokohama City University, Graduate School of Medicine Yokohama Japan.
Background: This study aimed to systematically evaluate voice symptoms during heart failure (HF) treatments and to exploratorily extract HF-related vocal biomarkers.
Methods And Results: This single-center, prospective study longitudinally acquired 839 audio files from 59 patients with acute decompensated HF. Patients' voices were analyzed along with conventional HF indicators (New York Heart Association [NYHA] class, presence of pulmonary congestion and pleural effusion on chest X-ray, and B-type natriuretic peptide [BNP]) and GOKAN scores based on the assessment of a cardiologist.
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