Quality of life after hospitalization predicts one-year readmission risk in a large Swiss cohort of medical in-patients

Springer Science and Business Media LLC - Tập 30 - Trang 1863-1871 - 2021
Tristan Struja1,2, Daniel Koch1, Sebastian Haubitz1, Beat Mueller1,3, Philipp Schuetz1,3, Timo Siepmann2,4
1Medical University Clinic, Kantonsspital Aarau, Aarau, Switzerland
2Division of Health Care Sciences, Dresden International University, Dresden, Germany
3Medical Faculty of the University of Basel, Basel, Switzerland
4Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

Tóm tắt

Estimating the probability of readmission following hospitalization using prediction scores can be complex. Quality of life (QoL) may provide an easy and effective alternative. Secondary analysis of the prospective “TRIAGE” cohort. All medical in-patients admitted to a Swiss tertiary care institution (2016–2019) ≥18 years with a length of stay of ≥2 days (23,309 patients) were included. EQ-5D VAS, EQ-5D index, and Barthel index were assessed at a single telephone interview 30-day after admission. Patients lost to follow-up were excluded. Readmission was defined as a non-elective hospital stay at our institution >24 h within 1 year after discharge and assessed using area under the curve (AUC) analysis with adjustment for confounders. 12,842 patients (43% females, median age 68, IQR 55–78) were included. Unadjusted discrimination was modest at 0.59 (95% CI 0.56–0.62) for EQ-5D VAS. Partially adjusted discrimination (for gender) was identical. Additional adjustment for insurance, Charlson comorbidity index, length of stay, and native language increased the AUC to 0.66 (95% CI 0.63–0.69). Results were robust irrespective of time to event (12, 6 or 3 months). A cut-off in the unadjusted model of EQ-5D VAS of 55 could separate cases with a specificity of 80% and a sensitivity of 30%. QoL at day 30 after admission can predict one-year readmission risk with similar precision as more intricate tools. It might help for identification of high-risk patients and the design of tailored prevention strategies.

Tài liệu tham khảo

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