Preoperative risk stratification models fail to predict hospital cost of cardiac surgery patients

Abstract

BackgroundPreoperative risk stratification models have previously been suggested to predict cardiac surgery unit costs. However, there is a lack of consistency in their reliability in this field. In this study we aim to test the correlation between the values of six commonly known preoperative scoring systems and evaluate their reliability at predicting unit costs of cardiac surgery patients.MethodsOver a period of 14 months all consecutive adult patients undergoing cardiac surgery on cardiopulmonary bypass were prospectively classified using six preoperative scoring models (EuroSCORE, Parsonnet, Ontario, French, Pons and CABDEAL). Transplantation patients were the only patients we excluded. Total hospital costs for each patient were calculated independently on a daily basis using the bottom up method. The full unit costs were calculated including preoperative diagnostic tests, operating room cost, disposable materials, drugs, blood components as well as costs for personnel and fixed hospital costs. The correlation between hospital cost and the six models was determined by linear regression analysis. Both Spearman’s and Pearson’s correlation coefficients were calculated from the regression lines. An analysis of residuals was performed to determine the quality of the regression.ResultsA total of 887 patients were operated on for CABG (n = 608), valve (n = 142), CABG plus valve (n = 100), thoracic aorta (n = 33) and ventricular assist devices (n = 4). Mean age of the patients was 68.3±9.9 years, 27.6% were female. 30-day mortality rate was 4.1%. Correlation between the six models and hospital cost was weak (Pearson’s: r < 0.30; Spearman’s: r < 0.40).ConclusionThe risk stratification models in this study are not reliable at predicting total costs of cardiac surgical patients. We therefore do not recommend their use for this purpose.

Serum creatinine role in predicting outcome after cardiac surgery beyond acute kidney injury

“…So, we probably are unable to accurately predict morbidity and cost of care by the available risk models. The other major flaw is the lack of a consensus definition of time span for mortality by these models [137] .…”

“…The critical role of creatinine as a strong predictor has been incorporated in the different mortality risk scores that are currently in use for cardiac surgery patients [3,49,137] . Known risk models have employed a wide range of risk factors from only three to dozens [3,4] .…”

Post-surgery survival and associated factors for cardiac patients in Ethiopia: applications of machine learning, semi-parametric and parametric modelling

“…The study demonstrated the feasibility of using serum creatinine as an outcome indicator in post-cardiac surgery [53]. The critical role of creatinine as a strong predictor has been incorporated in the different mortality risk scores currently used for cardiac surgery patients [54][55][56]. Creatinine is a chemical waste product.…”

Cardiac patients’ surgery outcome and associated factors in Ethiopia: application of machine learning

“…Creatinine value is crucial in predicting death [ 36 , 39 , 40 ]. Given its significance, the role of creatinine as a powerful predictor has been incorporated into various mortality risk assessment tools currently used for patients undergoing cardiac surgery [ 41 – 43 ]. Creatinine test is conducted to evaluate the functioning of the kidneys, as they are responsible for removing creatinine from the body.…”