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BMC Cardiovascular Disorders
Published in: BMC Cardiovascular Disorders 1/2021

01-12-2021 | Tricuspid Valve Replacement | Research

The multivariable prognostic models for severe complications after heart valve surgery

Authors: Yunqi Liu, Jiefei Xiao, Xiaoying Duan, Xingwei Lu, Xin Gong, Jiantao Chen, Mai Xiong, Shengli Yin, Xiaobo Guo, Zhongkai Wu

Published in: BMC Cardiovascular Disorders | Issue 1/2021

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Abstract

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.
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Metadata
Title
The multivariable prognostic models for severe complications after heart valve surgery
Authors
Yunqi Liu
Jiefei Xiao
Xiaoying Duan
Xingwei Lu
Xin Gong
Jiantao Chen
Mai Xiong
Shengli Yin
Xiaobo Guo
Zhongkai Wu
Publication date
01-12-2021

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