Observational studyBMC cardiovascular disorders2021
The multivariable prognostic models for severe complications after heart valve surgery.
Observational study in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- A systematic review of cardiac surgery clinical prediction models that include intra-operative variables.Perfusion · 2025Pooled it
- Impact of FloTrac/EV1000-guided intraoperative hemodynamic optimization on postoperative outcomes in cardiac valve surgery: a randomized controlled trial.Scientific reports · 2026Trial
- The Role of Markers of Myocardial Damage in Predicting Postoperative Multiple Organ Dysfunction Syndrome and 30-Day Mortality in Patients Undergoing Heart Valve Surgery.Journal of clinical medicine · 2026Article
- Association between lactic acidosis and multiple organ dysfunction syndrome after cardiopulmonary bypass.PeerJ · 2024Article
- A novel interpretative tool for early prediction of low cardiac output syndrome after valve surgery: online machine learning models.Annals of medicine · 2023Article
- The Role of the N-Terminal of the Prohormone Brain Natriuretic Peptide in Predicting Postoperative Multiple Organ Dysfunction Syndrome.Journal of clinical medicine · 2022Article
- Preoperative Prediction Models for 30-Day All-Cause Mortality After Mitral Valve Repair in Dogs: A Single-Center Retrospective Cohort Study.Journal of veterinary internal medicineArticle
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo provide multivariable prognostic models for severe complications prediction after heart valve surgery, including low cardiac output syndrome (LCOS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS).
methodsWe developed multivariate logistic regression models to predict severe complications after heart valve surgery using 930 patients collected retrospectively from the first affiliated hospital of Sun Yat-Sen University from January 2014 to December 2015. The validation was conducted using a retrospective dataset of 713 patients from the same hospital from January 2016 to March 2017. We considered two kinds of prognostic models: the PRF models which were built by using the preoperative risk factors only, and the PIRF models which were built by using both of the preoperative and intraoperative risk factors. The least absolute shrinkage selector operator was used for developing the models. We assessed and compared the discriminative abilities for both of the PRF and PIRF models via the receiver operating characteristic (ROC) curve.
resultsCompared with the PRF models, the PIRF modes selected additional intraoperative factors, such as auxiliary cardiopulmonary bypass time and combined tricuspid valve replacement. Area under the ROC curves (AUCs) of PRF models for predicting LCOS, AKI-rH and MODS are 0.565 (0.466, 0.664), 0.688 (0.62, 0.757) and 0.657 (0.563, 0.751), respectively. As a comparison, the AUCs of the PIRF models for predicting LOCS, AKI-rH and MODS are 0.821 (0.747, 0.896), 0.78 (0.717, 0.843) and 0.774 (0.7, 0.847), respectively.
conclusionsAdding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valve surgery.
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