The Society of Thoracic Surgeons 2018 Adult Cardiac Surgery Risk Models: Part 2—Statistical Methods and Results (original) (raw)
Related papers
The Society of Thoracic Surgeons 2008 Cardiac Surgery Risk Models: Introduction
The Annals of Thoracic Surgery, 2009
Surgeons National Adult Cardiac Surgery Database (STS NCD) was developed nearly 2 decades ago. Since its inception, the number of participants has grown dramatically, patient acuity has increased, and overall outcomes have consistently improved. To adjust for these and other changes, all STS risk models have undergone periodic revisions. This report provides a detailed description of the 2008 STS risk model for coronary artery bypass grafting surgery (CABG).
The Annals of thoracic surgery, 2018
The last published version of the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database (ACSD) risk models were developed in 2008 based on patient data from 2002 to 2006 and have been periodically recalibrated. In response to evolving changes in patient characteristics, risk profiles, surgical practice, and outcomes, STS has now developed a set of entirely new risk models for adult cardiac surgery. New models were estimated for isolated coronary artery bypass grafting surgery (CABG, n = 439,092), isolated aortic or mitral valve surgery (n = 150,150), and combined valve + CABG (n = 81,588) procedures. The development set was based on July 2011 to June 2014 STS-ACSD data; validation was performed using July 2014 to December 2016 data. Separate models were developed for operative mortality, stroke, renal failure, prolonged ventilation, reoperation, composite major morbidity or mortality, and prolonged or short postoperative length of stay. Because of its low occurrence rate...
Comparison of Risk Scores for Prediction of Complications following Aortic Valve Replacement
Heart, Lung and Circulation, 2015
Aortic valve replacement (AVR) is the recommended treatment for severe symptomatic aortic valve disease as prognosis is significantly improved when compared to medical treatment [1,2]. Risk models play an important role in stratification as well as decision-making for the optimal treatment modality in high-risk patients, whether it be AVR, transcatheter aortic valve implantation (TAVI) or conservative medical therapy [1,3,4]. Although several studies have Background Risk models play an important role in stratification of patients for cardiac surgery, but their prognostic utilities for post-operative complications are rarely studied. We compared the EuroSCORE, EuroSCORE II, Society of Thoracic Surgeon's (STS) Score and an Australasian model (Aus-AVR Score) for predicting morbidities after aortic valve replacement (AVR), and also evaluated seven STS complications models in this context. Methods We retrospectively calculated risk scores for 620 consecutive patients undergoing isolated AVR at Auckland City Hospital during 2005-2012, assessing their discrimination and calibration for post-operative complications. Results Amongst mortality scores, the EuroSCORE was the best at discriminating stroke (c-statistic 0.845); the EuroSCORE II at deep sternal wound infection (c=0.748); and the STS Score at composite morbidity or mortality (c=0.666), renal failure (c=0.634), ventilation>24 hours (c=0.732), return to theatre (c=0.577) and prolonged hospital stay >14 days post-operatively (c=0.707). The individual STS complications models had a marginally higher c-statistic (c=0.634-0.846) for all complications except mediastinitis, and had good calibration (Hosmer-Lemeshow test P-value 0.123-0.915) for all complications. Conclusion The STS Score was best overall at discriminating post-operative complications and their composite for AVR. All STS complications models except for deep sternal wound infection had good discrimination and calibration for post-operative complications.
Validation and Refinement of Mortality Risk Models for Heart Valve Surgery
The Annals of Thoracic Surgery, 2005
Background. The Northern New England Cardiovascular Disease Study Group (NNE) recently published risk models for hospital mortality after heart valve surgery. The Providence Health System Cardiovascular Study Group (PHS) has been collecting similar heart valve data for 8 years, providing an ideal opportunity to both validate the NNE risk models and attempt to produce an improved model, by using some different modeling techniques. Methods. From 1997 to 2004, 3,324 patients aged 30 to 95 years underwent aortic valve replacement (AVR), and 1,596 underwent mitral valve replacement or repair (MVRR) at one of nine PHS medical centers. We used area under the receiver operating characteristic curve (c-index) to measure model discrimination, and Hosmer-Lemeshow statistic (H-L) to measure calibration. We modified the NNE models by ungrouping continuous variables, seeking optimal transformations of continuous variables, and imputing missing values by multiple regression. Results. The prevalence and the lethality of risk factors were similar in PHS and NNE patients. The NNE models fit PHS patients well: c-index (95% confidence interval) ؍ 0.75 (0.70 to 0.80) for AVR and 0.81 (0.76 to 0.86) for MVRR; H-L ؍ 3.95 (p ؍ 0.861) for AVR and 7.10 (p ؍ 0.526) for MVRR. A single PHS model performed slightly better for both positions: c-index ؍ 0.79 (0.75 to 0.83) for AVR and 0.84 (0.80 to 0.88) for MVRR; H-L ؍ 2.75 (p ؍ 0.949) for AVR and 12.21 (p ؍ 0.142) for MVRR. Conclusions. The NNE models for aortic and mitral valve surgery were successfully validated using PHS patients. Using some different statistical approaches to modeling, we produced a new, unified model for both positions.
Comparison of 19 pre-operative risk stratification models in open-heart surgery
European Heart Journal, 2006
Aims To compare 19 risk score algorithms with regard to their validity to predict 30-day and 1-year mortality after cardiac surgery. Methods and results Risk factors for patients undergoing heart surgery between 1996 and 2001 at a single centre were prospectively collected. Receiver operating characteristics (ROC) curves were used to describe the performance and accuracy. Survival at 1 year and cause of death were obtained in all cases. The study included 6222 cardiac surgical procedures. Actual mortality was 2.9% at 30 days and 6.1% at 1 year. Discriminatory power for 30-day and 1-year mortality in cardiac surgery was highest for logistic (0.84 and 0.77) and additive (0.84 and 0.77) European System for Cardiac Operative Risk Evaluation (EuroSCORE) algorithms, followed by Cleveland Clinic (0.82 and 0.76) and Magovern (0.82 and 0.76) scoring systems. None of the other 15 risk algorithms had a significantly better discriminatory power than these four. In coronary artery bypass grafting (CABG)-only surgery, EuroSCORE followed by New York State (NYS) and Cleveland Clinic risk score showed the highest discriminatory power for 30-day and 1-year mortality. Conclusion EuroSCORE, Cleveland Clinic, and Magovern risk algorithms showed superior performance and accuracy in open-heart surgery, and EuroSCORE, NYS, and Cleveland Clinic in CABG-only surgery. Although the models were originally designed to predict early mortality, the 1-year mortality prediction was also reasonably accurate.
The Annals of Thoracic Surgery, 2004
Risk models play a vital role in monitoring health care performances. Despite extensive research and widespread use of risk models in cardiac surgery, there are methodologic problems. We reviewed the methodology used for risk models for short-term mortality. The findings suggest that many risk models are developed in an ad hoc manner. Important aspects such as selection of risk factors, handling of missing values, and size of the data used for model development are not dealt with adequately. Methodologic details presented in publications are often sparse and unclear. Model development and validation processes are not always linked to the clinical aim of the model, which may affect their clinical validity. We make some suggestions in this review for improvement in methodology and reporting.
Interactive CardioVascular and Thoracic Surgery, 2013
The aim of this study was to compare additive and logistic European System for Cardiac Operative Risk Evaluation (EuroSCORE), EuroSCORE II and the Society of Thoracic Surgeons (STS) models in calculating mortality risk in a Turkish cardiac surgical population. METHODS: The current patient population consisted of 428 patients who underwent isolated coronary artery bypass grafting (CABG) between 2004 and 2012, extracted from the TurkoSCORE database. Observed and predicted mortalities were compared for the additive/logistic EuroSCORE, EuroSCORE II and STS risk calculator. The area under the receiver operating characteristics curve (AUC) values were calculated for these models to compare predictive power. RESULTS: The mean patient age was 74.5 ± 3.9 years at the time of surgery, and 35.0% were female. For the entire cohort, actual hospital mortality was 7.9% (n = 34; 95% confidence interval [CI] 5.4-10.5). However, the additive EuroSCORE-predicted mortality was 6.4% (P = 0.23 vs observed; 95% CI 6.2-6.6), logistic EuroSCORE-predicted mortality was 7.9% (P = 0.98 vs observed; 95% CI 7.3-8.6), EuroSCORE II-predicted mortality was 1.7% (P = 0.00 vs observed; 95% CI 1.6-1.8) and STS predicted mortality was 5.8% (P = 0.10 vs observed; 95% CI 5.4-6.2). The mean predictive performance of the analysed models for the entire cohort was fair, with 0.7 (95% CI 0.60-0.79). AUC values for additive EuroSCORE, logistic EuroSCORE, EuroSCORE II and STS risk calculator were 0.70 (95% CI 0.60-0.79), 0.70 (95% CI 0.59-0.80), 0.72 (95% CI 0.62-0.81) and 0.62 (95% CI 0.51-0.73), respectively. CONCLUSIONS: EuroSCORE II significantly underestimated mortality risk for Turkish cardiac patients, whereas additive and logistic EuroSCORE and STS risk calculators were well calibrated.
Risk stratification in heart surgery: comparison of six score systems
European Journal of Cardio-Thoracic Surgery, 2000
Objective: Risk scores have become an important tool in patient assessment, as age, severity of heart disease, and comorbidity in patients undergoing heart surgery have considerably increased. Various risk scores have been developed to predict mortality after heart surgery. However, there are signi®cant differences between scores with regard to score design and the initial patient population on which score development was based. It was the purpose of our study to compare six commonly used risk scores with regard to their validity in our patient population. Methods: Between September 1, 1998 and February 28, 1999, all adult patients undergoing heart surgery with cardiopulmonary bypass in our institution were preoperatively scored using the initial Parsonnet, Cleveland Clinic, French, Euro, Pons, and Ontario Province Risk (OPR) scores. Postoperatively, we registered 30-day mortality, use of mechanical assist devices, renal failure requiring hemodialysis or hemo®ltration, stroke, myocardial infarction, and duration of ventilation and intensive care stay. Score validity was assessed by calculating the area under the ROC curve. Odds ratios were calculated to investigate the predictive relevance of risk factors. Results: Follow-up was able to be completed in 504 prospectively scored patients. Receiver operating characteristics (ROC) curve analysis for mortality showed the best predictive value for the Euro score. Predictive values for morbidity were considerably lower than predictive values for mortality in all of the investigated score systems. For most risk factors, odds ratios for mortality were substantially different from ratios for morbidity. Conclusions: Among the investigated scores, the Euro score yielded the highest predictive value in our patient population. For most risk factors, predictive values for morbidity were substantially different from predictive values for mortality. Therefore, development of speci®c morbidity risk scores may improve prediction of outcome and hospital cost. Due to the heterogeneity of morbidity events, future score systems may have to generate separate predictions for mortality and major morbidity events. q
Cardiac Surgery Risk Models: A Position Article
The Annals of Thoracic Surgery, 2004
Differences in medical outcomes may result from disease severity, treatment effectiveness, or chance. Because most outcome studies are observational rather than randomized, risk adjustment is necessary to account for case mix. This has usually been accomplished through the use of standard logistic regression models, although Bayesian models, hierarchical linear models, and machine-learning techniques such as neural networks have also been used. Many factors are essential to insuring the accuracy and usefulness of such models, including selection of an appropriate clinical database, inclusion of critical core variables, precise definitions for predictor variables and endpoints, proper model development, validation, and audit. Risk models may be used to assess the impact of specific predictors on outcome, to aid in patient counseling and treatment selection, to profile provider quality, and to serve as the basis of continuous quality improvement activities.