Cardiac surgery risk modeling for mortality: a review of current practice and suggestions for improvement (original) (raw)
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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.
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).
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.
European Journal of Cardio-Thoracic Surgery, 2010
Population-specific risk models are required to build consumer and provider confidence in clinical service delivery, particularly when the risks may be life-threatening. Cardiac surgery carries such risks. Currently, there is no model developed on the Australian cardiac surgery population and this article presents a novel risk prediction model for the Australian cohort with the aim to provide a guide for the surgeons and patients in assessing preoperative risk factors for cardiac surgery. This study aims to identify preoperative risk factors associated with 30-day mortality following cardiac surgery for an Australian population and to develop a preoperative model for risk prediction. All patients (23016) undergoing cardiac surgery between July 2001 and June 2008 recorded in the Australian Society of Cardiac and Thoracic Surgeons (ASCTS) database were included in this analysis. The data were divided randomly into model creation (13810, 60%) and model validation (9206, 40%) sets. The model was developed on the creation set and then validated on the validation set. The bootstrap sampling and automated variable selection methods were used to develop several candidate models. The final model was selected from this group of candidate models by using prediction mean square error (MSE) and Bayesian Information Criteria (BIC). Using a multifold validation, the average receiver operating characteristic (ROC), p-value for Hosmer-Lemeshow chi-squared test and MSE were obtained. Risk thresholds for low-, moderate- and high-risk patients were defined. The expected and observed mortality for various risk groups were compared. The multicollinearity and first-order interaction effect between clinically meaningful risk factors were investigated. A total of 23016 patients underwent cardiac surgery and the 30-day mortality rate was 3.2% (728 patients). Independent predictors of mortality in the model were: age, sex, the New York Heart Association (NYHA) class, urgency of procedure, ejection fraction estimate, lipid-lowering treatment, preoperative dialysis, previous cardiac surgery, procedure type, inotropic medication, peripheral vascular disease and body mass index (BMI). The model had an average ROC 0.8223 (95% confidence interval (CI): 0.8118-0.8227), p-value 0.8883 (95% CI: 0.8765-0.90) and MSE 0.0251 (95% CI: 0.02515-0.02516). The validation set had observed mortality 3.0% (95% CI: 2.7-3.3%) and predicted mortality 2.9% (95% CI: 2.6-3.2%). The low-risk group (additive score 0-3) had 0.6% observed mortality (95% CI: 0.3-0.9%) and 0.5% predicted mortality (95% CI: 0.2-0.8%). The moderate-risk group (additive score 4-9) had 1.7% observed mortality (95% CI: 1.2-2.2%) and 1.4% predicted mortality (95% CI: 1.0-1.8%). The observed mortality for the high-risk group (additive score 9 plus) was 6.7% (95% CI: 5.8-7.6%) and the expected mortality was 6.7% (95% CI: 5.8-7.6%). A preoperative risk prediction model for 30-day mortality was developed for the Australian cardiac surgery population.
The importance of independent risk-factors for long-term mortality prediction after cardiac surgery
European Journal of Clinical Investigation, 2006
Background The purpose of the present study was to determine independent predictors for long-term mortality after cardiac surgery. The European System for Cardiac Operative Risk Evaluation (EuroSCORE) was developed to score in-hospital mortality and recent studies have shown its ability to predict long-term mortality as well. We compared forecasts based on EuroSCORE with other models based on independent predictors.
Subjective Versus Statistical Model Assessment of Mortality Risk in Open Heart Surgical Procedures
surgical procedures Subjective versus statistical model assessment of mortality risk in open heart http://ats.ctsnetjournals.org/cgi/content/full/67/3/635 on the World Wide Web at: The online version of this article, along with updated information and services, is located Print ISSN: 0003-4975; eISSN: 1552-6259. Southern Thoracic Surgical Association. Background. The aim of this study was to compare the predictive accuracy for open heart surgical mortality between a statistical model based on collection of clinical data and surgeons' subjective risk assessment.
Archives of Surgery, 1998
To compare the performance of several riskscoring models to predict surgical mortality following open heart surgery. Design: A prospective observational study. Setting: Seven tertiary cardiac centers (3 private and 4 public and teaching hospitals) in Catalonia (Spain).
Cardiac surgery risk-stratification models
Cardiovascular Journal of Africa, 2012
Risk models are widely used to predict outcomes after cardiac surgery. Not only is risk modelling applied in the assessment of the relative impact of specific risk factors on surgical outcomes, but also in patient counselling, the selection of treatment options, comparison of postoperative results, and quality-improvement programmes. At least 19 risk-stratification models exist for open-heart surgery. The focus of risk models was originally on pre-operative prediction of mortality. However, major morbidity is in general more common than mortality and the ability to predict only operative mortality is not an adequate method of determining surgical outcome. Multiple intra-and postoperative variables have been excluded in the majority of models and the possible effect of their future inclusion remains to be seen. The unique patient population of sub-Saharan Africa requires a unique risk model that reflects the patient population and levels of care.