Adding intraoperative factors to preoperative risk models significantly improved the prediction of low cardiac output syndrome after heart valve surgery, increasing the AUC from 0.565 to 0.821.
Observational (n=1,643)
No
Does adding intraoperative factors to preoperative risk factors improve the prediction of severe complications after heart valve surgery?
Incorporating intraoperative factors significantly improves the predictive accuracy of prognostic models for severe complications following heart valve surgery.
Absolute Event Rate: 0.821% vs 0.565%
p-value: p=<0.01
BACKGROUND: To 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). METHODS: We 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. RESULTS: Compared 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. CONCLUSIONS: Adding the intraoperative factors can increase the predictive power of the prognostic models for severe complications prediction after heart valve surgery.
Liu et al. (Mon,) conducted a observational in Heart valve surgery (n=1,643). Preoperative and intraoperative risk factors (PIRF) model vs. Preoperative risk factors (PRF) model was evaluated on Prediction of Low Cardiac Output Syndrome (LCOS) (Area Under the Curve) (95% CI 0.747-0.896, p=<0.01). Adding intraoperative factors to preoperative risk models significantly improved the prediction of low cardiac output syndrome after heart valve surgery, increasing the AUC from 0.565 to 0.821.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: