TY - JOUR
T1 - Discharge Prediction for Patients Undergoing Inpatient Surgery
T2 - Development and validation of the DEPENDENSE score
AU - Hammer, Maximilian
AU - Althoff, Friederike C.
AU - Platzbecker, Katharina
AU - Wachtendorf, Luca J.
AU - Teja, Bijan
AU - Raub, Dana
AU - Schaefer, Maximilian S.
AU - Wongtangman, Karuna
AU - Xu, Xinling
AU - Houle, Timothy T.
AU - Eikermann, Matthias
AU - Murugappan, Kadhiresan R.
N1 - Publisher Copyright:
© 2021 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - patient discharge
KW - risk assessment
KW - skilled nursing facilities
KW - surgical procedures
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U2 - 10.1111/aas.13778
DO - 10.1111/aas.13778
M3 - Article
C2 - 33404097
AN - SCOPUS:85099474688
SN - 0001-5172
VL - 65
SP - 607
EP - 617
JO - Acta Anaesthesiologica Scandinavica
JF - Acta Anaesthesiologica Scandinavica
IS - 5
ER -