Abstract
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.
Highlights
Postoperative complications are significant sources of morbidity and mortality leading to a multi-fold increase in costs and adverse long-term consequences [1]
We evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury
Logistic regression, generalized additive model, and support vector machines had better performance compared to Naïve Bayes model
Summary
Postoperative complications are significant sources of morbidity and mortality leading to a multi-fold increase in costs and adverse long-term consequences [1]. Postoperative sepsis and acute kidney injury (AKI) are well-recognized risk factors for short and long term morbidity and mortality after surgery [2,3,4,5,6,7]. There is an increasing interest in predicting the probability of postoperative complications in order to improve risk stratification prior to surgery and to allow timely use of preventive therapies during surgery and anesthesia. Assessment of this risk requires timely, accurate and dynamic synthesis of the large amount of clinical information in the preoperative period. While the majority of existing preoperative AKI risk scores are limited to cardiac surgery and have modest accuracy [11, 12], tools for preoperative risk stratification for severe sepsis are missing
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