scholarly journals The multivariable prognostic models for severe complications after heart valve surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yunqi Liu ◽  
Jiefei Xiao ◽  
Xiaoying Duan ◽  
Xingwei Lu ◽  
Xin Gong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Valve ◽  
Kidney Injury ◽  
Roc Curves ◽  
Multiple Organ ◽  
Valve Surgery ◽  
Prognostic Models ◽  
Heart Valve Surgery ◽  
Low Cardiac Output Syndrome ◽  
Logistic Regression Models

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.

scholarly journals The Prognostic Multivariate Models For Severe Complications After Heart Valve Surgery

2021 ◽  
Author(s):  
Yunqi Liu ◽  
Jiefei Xiao ◽  
Xiaoying Duan ◽  
Xingwei Lu ◽  
Xin gong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Characteristic Curve ◽  
Kidney Injury ◽  
Multiple Organ ◽  
Prognostic Models ◽  
Multivariate Models ◽  
Low Cardiac Output Syndrome ◽  
Logistic Regression Models ◽  
Valvular Surgery ◽  
Preoperative Risk Factors

Abstract Background: To provide prognostic multivariate models for severe complications prediction after heart valvular surgery, such as low cardiac output syndrome (LOCS), acute kidney injury requiring hemodialysis (AKI-rH) and multiple organ dysfunction syndrome (MODS).Methods: We developed multivariate logistic regression models to predict the severe complications after heart valvular 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 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. The area under the curves (AUCs) of the PRF models for predicting LOCS, 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 valvular surgery.

Risk factors of postpericardiotomy syndrome after heart valve surgery

2021 ◽  
Vol 14 (4) ◽  
pp. 308
Author(s):  
L.N. Ivanova ◽  
V.I. Boltenkova ◽  
E.V. Ivanova ◽  
E.P. Evseev
Keyword(s):  
Risk Factors ◽  
Heart Valve ◽  
Valve Surgery ◽  
Heart Valve Surgery ◽  
Postpericardiotomy Syndrome

Frequency, Risk Factors and Prognosis of Postoperative Hyperbilirubinemia after Heart Valve Surgery

Cardiology ◽  
2012 ◽  
Vol 122 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Hiroyuki Nishi ◽  
Taichi Sakaguchi ◽  
Shigeru Miyagawa ◽  
Yasushi Yoshikawa ◽  
Satsuki Fukushima ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Valve ◽  
Valve Surgery ◽  
Heart Valve Surgery

scholarly journals Repeat heart valve surgery: Risk factors for operative mortality

2001 ◽  
Vol 122 (5) ◽  
pp. 913-918 ◽  
Author(s):  
J.Mark Jones ◽  
Hugh O'Kane ◽  
Dennis J. Gladstone ◽  
Mazin A.I. Sarsam ◽  
Gianfranco Campalani ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Valve ◽  
Operative Mortality ◽  
Valve Surgery ◽  
Heart Valve Surgery

scholarly journals Risk Factors for Mortality in Patients with Alcoholic Hepatitis and Assessment of Prognostic Models: A Population-Based Study

2015 ◽  
Vol 29 (3) ◽  
pp. 131-138 ◽  
Author(s):  
Jack XQ Pang ◽  
Erin Ross ◽  
Meredith A Borman ◽  
Scott Zimmer ◽  
Gilaad G Kaplan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alcoholic Hepatitis ◽  
Roc Curves ◽  
International Normalized Ratio ◽  
Population Based ◽  
Meld Score ◽  
Prognostic Models ◽  
Stepwise Logistic Regression ◽  
Predictive Values ◽  
Logistic Regression Models

BACKGROUND: Severe alcoholic hepatitis (AH) is associated with a substantial risk for short-term mortality.OBJECTIVES: To identify prognostic factors and validate well-known prognostic models in a Canadian population of patients hospitalized for AH.METHODS: In the present retrospective study, patients hospitalized for AH in Calgary, Alberta, between January 2008 and August 2012 were included. Stepwise logistic regression models identified independent risk factors for 90-day mortality, and the discrimination of prognostic models (Model for End-stage Liver Disease [MELD] and Maddrey discriminant function [DF]) were examined using areas under the ROC curves.RESULTS: A total of 122 patients with AH were hospitalized during the study period; the median age was 49 years (interquartile range [IQR] 42 to 55 years) and 60% were men. Median MELD score and Maddrey DF on admission were 21 (IQR 18 to 24) and 45 (IQR 26 to 62), respectively. Seventy-three percent of patients received corticosteroids and/or pentoxifylline, and the 90-day mortality was 17%. Independent predictors of mortality included older age, female sex, international normalized ratio, MELD score and Maddrey DF (all P<0.05). For discrimination of 90-day mortality, the areas under the ROC curves of the prognostic models (MELD 0.64; Maddrey DF 0.68) were similar (P>0.05). At optimal cut-offs of ≥22 for MELD score and ≥37 for Maddrey DF, both models excluded death with high certainty (negative predictive values 90% and 96%, respectively).CONCLUSIONS: In patients hospitalized for AH, well-known prognostic models can be used to predict 90-day mortality, particularly to identify patients with a low risk for death.

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