OBJECTIVE To determine the incremental hospital cost and mortality associated with the development of postoperative acute kidney injury (AKI) and with other associated postoperative complications. SUMMARY BACKGROUND DATA Each year 1.5 million patients develop a major complication after surgery. Postoperative AKI is one of the most common postoperative complications and is associated with an increase in hospital mortality and decreased survival for up to 15 years after surgery. METHODS In a single-center cohort of 50,314 adult surgical patients undergoing major inpatient surgery we applied risk-adjusted regression models for cost and mortality using postoperative AKI and other complications as the main independent predictors. We defined AKI using consensus RIFLE criteria. RESULTS The prevalence of AKI was 39% among 50,314 patients with available serum creatinine. Patients with AKI were more likely to have postoperative complications and had longer lengths of stay in the intensive care unit and the hospital. The risk-adjusted average cost of care for patients undergoing surgery was $42,600 for patients with any AKI compared to $26,700 for patients without AKI. The risk-adjusted 90-day mortality was 6.5% for patients with any AKI compared to 4.4% for patients without AKI. Serious postoperative complications resulted in increased cost of care and mortality for all patients, but the increase was much larger for those patients with any degree of AKI. CONCLUSIONS Hospital costs and mortality are strongly associated with postoperative AKI, are correlated with the severity of AKI, and are much higher for patients with other postoperative complications in addition to AKI.

ObjectiveTo compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury.DesignRetrospective single center cohort study of adult surgical patients admitted between 2000 and 2010.Patients50,318 adult patients undergoing major surgery.MeasurementsWe evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury. We assessed the impact of feature reduction techniques on predictive performance. Model performance was determined using the area under the receiver operating characteristic curve, accuracy, and positive predicted value. The results were reported based on a 70/30 cross validation procedure where the data were randomly split into 70% used for training the model and the 30% for validation.Main ResultsThe areas under the receiver operating characteristic curve for different models ranged between 0.797 and 0.858 for acute kidney injury and between 0.757 and 0.909 for severe sepsis. Logistic regression, generalized additive model, and support vector machines had better performance compared to Naïve Bayes model. Generalized additive models additionally accounted for non-linearity of continuous clinical variables as depicted in their risk patterns plots. Reducing the input feature space with LASSO had minimal effect on prediction performance, while feature extraction using principal component analysis improved performance of the models.ConclusionsGeneralized additive models and support vector machines had good performance as risk prediction model for postoperative sepsis and AKI. Feature extraction using principal component analysis improved the predictive performance of all models.

Objective Calculate mortality risk that accounts for both severity and recovery of postoperative kidney dysfunction using the pattern of longitudinal change in creatinine. Summary Background Data Although the importance of renal recovery after acute kidney injury (AKI) is increasingly recognized, the complex association that accounts for longitudinal creatinine changes and mortality is not fully described. Methods We used routinely collected clinical information for 46,299 adult patients undergoing major surgery to develop a multivariable probabilistic model optimized for non-linearity of serum creatinine time series that calculates the risk function for ninety-day mortality. We performed a 70/30 cross validation analysis to assess the accuracy of the model. Results All creatinine time series exhibited nonlinear risk function in relation to ninety-day mortality and their addition to other clinical factors improved the model discrimination. For any given severity of AKI, patients with complete renal recovery, as manifested by the return of the discharge creatinine to the baseline value, experienced a significant decrease in the odds of dying within ninety days of admission compared to patients with partial recovery. Yet, for any severity of AKI even complete renal recovery did not entirely mitigate the increased odds of dying as patients with mild AKI and complete renal recovery still had significantly increased odds for dying compared to patients without AKI (odds ratio 1,48 (95% confidence interval 1.30-1.68). Conclusions We demonstrate the nonlinear relationship between both severity and recovery of renal dysfunction and ninety-day mortality after major surgery. We have developed an easily applicable computer algorithm that calculates this complex relationship.