Predicting the length of stay at admission for emergency general surgery patients a cohort study

Abstract

IntroductionPredicting length of stay (LOS) is beneficial to patients and the health service. When a prolonged LOS is predicted, it gives the opportunity for focused therapies and allocation of resources to reduce this period. In emergency general surgery (EGS) there has been limited investigation of variables that may be important predictors of LOS. This study examines social characteristics alongside measures of severity of acute illness and co-morbidities in an adult EGS population to establish their contribution to LOS. MethodsData were collected prospectively from patients at admission including medical variables, demographics, and therapeutic requirements. The length of hospital admission was measured, and multiple regression analysis was used to identify variables which predicted the LOS. ResultsData were collected from 105 patients. The regression model gave an R2 of 0.34, p = 0.0006. Barthal index (measure of independence in activities of daily living) was a significant predictor of LOS [logworth 1.649, p0.02243]. Housing status and Level of social support both correlated in one-way analysis with LOS. ConclusionThere are non-surgical variables, measurable at admission which are of significant value in predicting LOS of EGS patients. This warrants further investigation through a larger study to better quantify the contributions of these variables, and establish potential early interventions to reduce the LOS.