Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score

Abstract

Background: A substantial proportion of patients undergoing inpatient surgery each year is at risk for postoperative institutionalization and loss of independence. Reliable individualized preoperative prediction of adverse discharge can facilitate advanced care planning and shared decision making.

Methods: Using hospital registry data from previously home-dwelling adults undergoing inpatient surgery, we retrospectively developed and externally validated a score predicting adverse discharge. Multivariable logistic regression analysis and bootstrapping were used to develop the score. Adverse discharge was defined as in-hospital mortality or discharge to a skilled nursing facility. The model was subsequently externally validated in a cohort of patients from an independent hospital.

Results: In total, 106 164 patients in the development cohort and 92 962 patients in the validation cohort were included, of which 16 624 (15.7%) and 7717 (8.3%) patients experienced adverse discharge, respectively. The model was predictive of adverse discharge with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI 0.87-0.88) in the development cohort and an AUC of 0.86 (95% CI 0.86-0.87) in the validation cohort.

Conclusion: Using preoperatively available data, we developed and validated a prediction instrument for adverse discharge following inpatient surgery. Reliable prediction of this patient centered outcome can facilitate individualized operative planning to maximize value of care.

Keywords: patient discharge; risk assessment; skilled nursing facilities; surgical procedures.