• Maximilian Hammer, Friederike C Althoff, Katharina Platzbecker, Luca J Wachtendorf, Bijan Teja, Dana Raub, Maximilian S Schaefer, Karuna Wongtangman, Xinling Xu, Timothy T Houle, Matthias Eikermann, and Kadhiresan R Murugappan.
• Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center (BIDMC), Harvard Medical School, Boston, MA, USA.
• Acta Anaesthesiol Scand. 2021 May 1; 65 (5): 607-617.

BackgroundA 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.MethodsUsing 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.ResultsIn 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.ConclusionUsing 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.© 2021 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

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