A first postoperative day predictive score of mortality for cardiac surgery (original) (raw)
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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.
Prediction of mortality in intensive care unit cardiac surgical patients☆☆☆
European Journal of Cardio-Thoracic Surgery, 2010
Objectives: The purpose of this study was to develop a specific postoperative score in intensive care unit (ICU) cardiac surgical patients for the assessment of organ dysfunction and survival. To prove the reliability of the new scoring system, we compared its performance to existing ICU scores. Methods: This prospective study consisted of all consecutive adult patients admitted after cardiac surgery to our ICU over a period of 5.5 years. Variables were evaluated using the patients of the first year who stayed in ICU for at least 24 h. The reproducibility was then tested in two validation sets using all patients. Performance was assessed with the Hosmer-Lemeshow (HL) goodness-of-fit test and receiver operating characteristic (ROC) curves and compared with the Acute Physiology and Chronic Health Evaluation (APACHE II) and Multiple Organ Dysfunction Score (MODS). The outcome measure was defined as 30-day mortality. Results: A total of 6007 patients were admitted to the ICU after cardiac surgery. Mean HL values for the new score were 5.8 (APACHE II, 11.3; MODS, 9.7) for the construction set, 7.2 (APACHE II, 8.0; MODS, 4.5) for the validation set I and 4.9 for the validation set II. The mean area under the ROC curve was 0.91 (APACHE II, 0.86; MODS, 0.84) for the new score in the construction set, 0.88 (APACHE II, 0.84; MODS, 0.84) in the validation set I and 0.92 in the validation set II. Conclusions: Most of general ICU scoring systems use extensive data collection and focus on the first day of ICU stay. Despite this fact, general scores do not perform well in the prediction of outcome in cardiac surgical patients. Our new 10-variable risk index performs very well, with calibration and discrimination very high, better than general severity systems, and it is an appropriate tool for daily risk stratification in ICU cardiac surgery patients. Thus, it may serve as an expert system for diagnosing organ failure and predicting mortality in ICU cardiac surgical patients. #
The Journal of Thoracic and Cardiovascular Surgery, 2011
Objective: Age, preoperative creatinine value, and ejection fraction are easily arranged in the age, creatinine, ejection fraction score to predict operative mortality in elective cardiac operations, as recently shown. We validate the age, creatinine, ejection fraction score in a large multicentric study.
2020
Introduction: The number of non-cardiac major surgeries carried out has significantly increased in recent years to around 200 million procedures carried out annually. Approximately 30% of patients submitted to non-cardiac surgery present some form of cardiovascular comorbidity. In emergency situations, with less surgery planning time and greater clinical severity, the risks become even more significant. The aim of this study is to determine the incidence and clinical outcomes in patients with cardiovascular disease submitted to non-cardiac surgical procedures in a single cardiovascular referral center. Methods: This is a prospective cohort study of patients with cardiovascular disease submitted to non-cardiovascular surgery. All procedures were carried out by the same surgeon, between January 2006 and January 2018. Results: 240 patients included were elderly, 154 were male (64%), 8 patients presented two diagnoses. Of the resulting 248 procedures carried out, 230 were emergency (92.8%). From the data obtained it was possible to estimate the day from which the occurrence of mortality is less probable in the postoperative phase. Conclusion: Our research evaluated the epidemiological profile of the surgeries and we were able to estimate the survival and delimit the period of greatest risk of mortality in these patients. The high rate of acute mesenteric ischemia was notable, a serious and frequently fatal condition.
Preoperative risk prediction and intraoperative events in cardiac surgery
European Journal of Cardio-Thoracic Surgery, 2002
Objective: To examine the relationship between preoperative risk prediction and intraoperative events. Methods: A total of 3118 patients operated in 1999 and 2000 at our institution were analysed, all of whom had their EuroSCORE collected prospectively. The intraoperative variables studied were consultant or trainee operating, long bypass time, long ischaemic time, return on bypass in theatre and use of intraaortic balloon pump at the end of the procedure. The outcomes are reported as hospital mortality, prolonged length of stay in the intensive therapy unit (pLOS-ITU, .48 h) and death or pLOS-ITU. Risk models were constructed by logistic regression for predicting these three outcomes. Results: With the exception of prolonged cross-clamp time, all variables analysed were independently predictive of a negative outcome. Trainee operating had an apparent protective effect. All risk models performed well. The area under the receiver operating characteristic (ROC) curve (95% CI) increased from 0.857 (0.81, 0.90) for EuroSCORE to 0.874 (0.83, 0.92) for the risk of death model. Similarly, the area under the ROC curve for the pLOS-ITU model increased from 0.687 (0.642, 0.732) to 0.734 (0.691, 0.777) and for the death or pLOS-ITU model from 0.717 (0.677, 0.756) to 0.757 (0.719, 0.795). Conclusions: Knowledge of adverse intraoperative events enhances preoperative risk prediction. This type of analysis could be used for identifying 'near miss' outcomes in adult cardiac surgery. q
PLoS ONE, 2010
Background: Operative mortality risk in cardiac surgery is usually assessed using preoperative risk models. However, intraoperative factors may change the risk profile of the patients, and parameters at the admission in the intensive care unit may be relevant in determining the operative mortality. This study investigates the association between a number of parameters at the admission in the intensive care unit and the operative mortality, and verifies the hypothesis that including these parameters into the preoperative risk models may increase the accuracy of prediction of the operative mortality. Methodology: 929 adult patients who underwent cardiac surgery were admitted to the study. The preoperative risk profile was assessed using the logistic EuroSCORE and the ACEF score. A number of parameters recorded at the admission in the intensive care unit were explored for univariate and multivariable association with the operative mortality.
European Journal of Cardio-Thoracic Surgery, 2014
OBJECTIVES: Age, creatinine, ejection fraction (ACEF) score is a simplified algorithm for prediction of mortality after elective cardiac surgery. Although mainly conceived for elective cardiac surgery, no information is available on its performance in non-elective surgery and on comparison with the new EuroSCORE II. This study was undertaken to compare the performance of ACEF score and EuroSCORE II within classes of urgency. METHODS: Complete data on 13 871 consecutive patients who underwent major cardiac surgery in a 6-year period were retrieved from three prospective institutional databases. Discriminatory power was assessed using the c-index and h with Delong, bootstrap and Venkatraman methods. Calibration was evaluated with calibration curves and associated statistics. RESULTS: The in-hospital mortality rate was 2.5%. The discriminatory power of ACEF score within elective and non-elective surgery was similar (area under the curve (AUC) 0.71, 95% confidence interval (CI) 0.67-0.74 and AUC 0.68, 95% CI 0.62-0.73, respectively) but significantly lower than that of EuroSCORE II (AUC 0.80, 95% CI 0.77-0.83 for elective surgery; AUC 0.82, 95% CI 0.78-0.85 for non-elective surgery). The calibration patterns were different in the two subgroups, but the summary statistics underscored a miscalibration in both of them (U-statistic and Spiegelhalter Z-test P-values <0.05). Even the calibration of EuroSCORE II was insufficient, although it was demonstrated to be well calibrated in the first tertile of predicted risk. CONCLUSIONS: This study demonstrated that the performance of ACEF score in predicting in-hospital mortality in elective and nonelective cardiac surgery is comparable. Nonetheless, it is not as satisfactory as the new EuroSCORE II, as its discrimination is significantly lower and it is also miscalibrated.
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.
Journal of Cardiac Surgery, 2009
Background and aim of the study: Predictive models for the length of stay (LOS) in the intensive care unit (ICU) following cardiac surgery have been developed in the last decade. These risk models use different endpoint and risk factor definitions. This review discusses the need for a uniform multi-institutional risk scoring system for a prolonged ICU LOS. Methods: The MEDLINE database was searched for studies assessing the prognostic value of clinical variables predicting ICU LOS. Information on study design, patient population, extended ICU LOS definition, and predictors was retrieved. Results: There is no consensus on the definition of a prolonged ICU LOS. This is mainly because some studies take the continuous variables of "days in the intensive care unit" and try to make it dichotomous when actually the LOS should be analyzed as a "continuous variable." We also report a cardiac surgeon-related component. The most important risk factors were: increased age, no elective surgery, type of cardiac surgery, low left ventricular ejection fraction, recent myocardial infarction, history of pulmonary disease, history of renal disease, and reoperation/reexploration. Conclusions: There is a need for the development of a multi-institutional risk scoring system for prolonged ICU LOS following cardiac surgery. This predictive model could aid in quality assessment, practice improvement, patient counseling, and decision making. In order to develop this risk model, uniformed and standardized definitions are needed.