ArticleBMC health services research2025
Multimorbidity measurement strategies for predicting hospital visits.
Article in BMC health services research, 2025. 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMultimorbidity is a major challenge for healthcare systems. While multiple methods exist to measure it from electronic health records (EHRs), their relative performance for predicting hospital utilization is unclear. STUDY DESIGN AND
methodsWe conducted a retrospective cohort study using 15 years of EHR data (2007–2022) from a Portuguese hospital, including 925,508 patients and 9·7 M visits. Three phenotyping strategies were compared: a multi-source rule-based dictionary, Clinical Classification Software Refined (CCSR) mapping, and drug-based mapping. Five multimorbidity indices were evaluated: Charlson Comorbidity Index (ChCI), Elixhauser, Multimorbidity Weighted Index (MWI), RxRisk, and disease counts. Outcomes included emergency department (ED) visits, hospital admissions, unplanned admissions, and readmissions over 30–365 days, modeled with logistic regression (LR) and XGBoost.
resultsBest performance was achieved with XGBoost (AUROC 0.671–0.681 for ED visits, 0.663–0.668 for admissions, 0.773–0.781 for unplanned admissions, 0.798–0.848 for readmissions), with AUPRC patterns consistent with AUROC rankings. In LR models comparing indices, multimorbidity improved AUROC by 0.024–0.150 over demographics. MWI performed best for ED visits and unplanned admissions, while ChCI led for 30-day admissions (AUROC 0.613) and readmissions (AUROC 0.734). Combining multimorbidity with healthcare utilization features yielded the highest discrimination (AUROC 0.596–0.777), with readmissions most predictable. Multimorbidity ranked among the top predictors across outcomes.
conclusionsMulti-source phenotyping enhances chronic condition detection and prediction. Weighted indices offer modest benefits over disease counts, with optimal choice depending on the prediction task. Multimorbidity adds consistent value alongside utilization measures, supporting its routine use in risk stratification to improve population health management.
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.