[Risk Factors for Delayed Discharge Due to Social Factors: A Retrospective Study].

INTRODUCTION

The hospital setting faces a rate of bed occupation by patients whose discharge is limited by other factors apart from clinical needs. This urges the need for an early identification of the patients at risk of delayed discharge due to social factors in order to reduce expenses and to add value that converts itself into the patient health. The aim of this study was to identify the demographic and clinical factors that may be associated with delayed discharge.

MATERIAL AND METHODS

Demographic and clinical comorbidity data on 582 patients of an internal medicine ward from a tertiary hospital center during the years 2018 and 2019 was analyzed. A binomial logistic regression model was used, adjusted for sex, age, and length of clinical stay, in order to identify potential risk factors associated with delayed discharge.

RESULTS

A total of 473 patients admitted in the internal medicine ward throughout the two years of study were included. Ninety-four (19%) of these patients had their discharge delayed beyond their clinical needs; sixty-four (68%) of these were females. The most representative age was between 75 - 89 years old (45.7%). The characteristics that significantly differed between both non-delayed and delayed discharge were female sex (OR 2.84, 95% CI 1.65 - 4.90, p-value < 0.05), prolonged clinical stay (OR 2.64, 95% CI 1.60 - 4.937, p-value < 0.05) and diabetes mellitus (OR 1.87, 95% CI 1.08 - 3.23, p-value < 0.05). Besides these, the presence of heart failure (OR 0.52, 95% CI 0.27 - 0.99, p-value < 0.05) and chronic kidney disease (OR 0.34, 95% CI 0.14 - 0.86, p-value < 0.05) were associated with a lower risk of delayed discharge.

CONCLUSION

Female sex, a prolonged clinical stay and diabetes mellitus were associated with a higher risk of delayed discharge, while heart failure and chronic kidney disease were associated with a reduced risk. These findings create a basis for a possible future multicentre study aimed at creating a clinical prediction rule to stratify the risk of delayed hospital discharge in the Portuguese population.