Predicting Length of Stay and Discharge Destination for Surgical Patients

Discharge planning is important to prevent surgical site infections, reduce costs, and improve the hospitalization experience. The identification of early variables that can predict a longer-than-expected length of stay or the need for a discharge with additional needs can improve this process. A cohort study was conducted in the largest hospital of Northern Italy, collecting discharge records from January 2017 to January 2020 and pre-admission visits in the last three months. Socio-demographic and clinical data were collected. Linear and logistic regression models were fitted. The main outcomes were the length of stay (LOS) and discharge destination. The main predictors of a longer LOS were the need for additional care at discharge (+10.76 days), hospitalization from the emergency department (ED) (+5.21 days), and age (+0.04 days per year), accounting for clinical variables ( p p < 0.001). No additional findings came from pre-admission forms. Discharge difficulties seem to be related mainly to age and hospitalization procedures: those factors are probably masking underlying social risk factors that do not show up in patients with planned admissions

Acute care · Cohort · Discharge planning · Emergency department · Health care · Hospital discharge · Intensive care medicine · Logistic regression · Masking (illustration · Medical emergency · Retrospective cohort study · Cardiac, Anesthesia and Surgical Outcomes · Demography · Enhanced Recovery After Surgery · Hip and Femur Fractures · Medicine · Nursing · Emergency Medicine · Internal Medicine · Surgery