Abstract
Background
consideration of the first hospital re-admission only and failure to take account of previous hospital stays, which are the two significant limitations when studying risk factors for hospital re-admission. The objective of the study was to use appropriate statistical models to analyse the impact of previous hospital stays on the risk of hospital re-admission among older patients.
Methods
an exhaustive analysis of hospital discharge and health insurance data for a cohort of patients participating in the PAERPA (‘Care Pathways for Elderly People at Risk of Loss of Personal Independence’) project in the Hauts de France region of France. All patients aged 75 or over were included. All data on hospital re-admissions via the emergency department were extracted. The risk of unplanned hospital re-admission was estimated by applying a semiparametric frailty model, the risk of death by applying a time-dependent semiparametric Cox regression model.
Results
a total of 24,500 patients (median [interquartile range] age: 81 [77–85]) were included between 1 January 2015 and 31 December 2017. In a multivariate analysis, the relative risk (95% confidence interval [CI]) of hospital re-admission rose progressively from 1.8 (1.7–1.9) after one previous hospital stay to 3.0 (2.6–3.5) after five previous hospital stays. The relative risk [95%CI] of death rose slowly from 1.1 (1.07–1.11) after one previous hospital stay to 1.3 (1.1–1.5) after five previous hospital stays.
Conclusion
analyses of the risk of hospital re-admission in older adults must take account of the number of previous hospital stays. The risk of death should also be analysed.
Variable selection with group LASSO approach: Application to Cox regression with frailty model
