ArticleBMC geriatrics2026
Development and internal validation of a 30-day readmission risk model for older adults using Swiss electronic health record data: a retrospective cohort study.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnplanned 30-day readmissions in discharged older adults often reflect unmet needs and fragmented care transitions. Existing prediction models frequently lack geriatric specificity, clinical interpretability, and applicability to decentralised European healthcare systems. This study aimed to develop a clinically interpretable risk model for older adults using routine discharge data. Findings may provide a basis for targeted interventions.
methodsWe conducted a retrospective cohort study using electronic health record (EHR) data from a Swiss multi-site hospital network. Adults aged ≥ 65 years discharged alive in 2024 were eligible, excluding planned readmission, oncology/palliative cases, in-hospital deaths, and records with missing predictors. Of 15,635 discharges, 12,814 met inclusion criteria. 9,429 (60.3%) were retained for complete-case analysis. A multivariable logistic regression model with backward selection predicted unplanned 30-day readmission. Model performance was evaluated by AUC, calibration, decision curve analysis (DCA), and bootstrap validation, with a Fine-Gray model addressing competing risk of mortality.
resultsAmong 9,429 patients (mean age 78.0 years; 45.5% female), 1,845 (19.6%) had an unplanned 30-day readmission. Independent predictors were polypharmacy (OR 1.83), home care (OR 1.61), male sex (OR 1.42), low fall risk (vs. high; OR 1.41), moderate care intensity (vs. high; OR 1.30), comorbidity count (OR 1.12/diagnosis), younger age (OR 0.99/year), and shorter stay (OR 0.98/day). The model showed an AUC of 0.716 (95% CI 0.706-0.726), but calibration was poor, with risk overestimation at low predicted probabilities. At a 10% threshold, sensitivity was 7.5%, specificity 98.1%, positive predictive value 49.5%, and negative predictive value 81.4%. Compared with LACE index (AUC 0.707, 95% CI 0.694-0.720), performance improved modestly but significantly (p < 0.01).
conclusionsNearly one in five older adults was readmitted within 30 days, underscoring the burden of rehospitalisation and its link to quality-of-care. The model identified clinically relevant, EHR-available predictors, including medical complexity and care dependency. Despite acceptable discrimination, calibration was suboptimal and sensitivity low, limiting immediate clinical application. Future refinement and external validation may enhance utility. This model may provide a pragmatic starting point for risk-stratified discharge planning research and guiding transitional care interventions.
trial registrationStudy protocol was registered on the Open Science Framework (OSF) ( https://osf.io/vkte2/ ; 10 May 2025).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.