Risk stratification in deep brain stimulation surgery: Development of an algorithm to predict patient discharge disposition with 91.9% accuracy.

Clinical decision making is susceptible to biases and can be improved with the application of predictive models and decision support systems (DSS). The purpose of this study was to develop a predictive risk stratification model and DSS that could accurately predict whether a patient was likely to be of high- or low-acuity discharge disposition (DD) status subsequent to DBS surgery. Data were collected for 135 DBS patients by reviewing medical records. Multivariate logistic regression was applied to develop the predictive algorithm. The two significant predictive models showed good fit and were calibrated by using AUROC curve analysis. The model predicting DD in all patients (n = 135) yielded a predictive accuracy of 91.9% (AUROC = 0.825, p < .001). The model predicting DD in Parkinson's Disease patients (n = 91) yielded a predictive accuracy of 89.0% (AUROC = 0.853, p < .001). Age was a significant predictor of DD across all patients (OR = 1.11, p < .05) and BMI was a significant predictor of DD amongst Parkinson's Disease patients (OR = 1.16, p < .05). It is possible to accurately predict the DD of DBS patients using routinely collected preoperative variables. The predictive algorithms were applied in the form of a model-driven DSS to assist in improving clinical decision making and patient safety.