Counting co-occurring diseases to predict mortality is as accurate as multimorbidity indices: an external validation study.
Background A systematic review recommended seven multimorbidity indices for predicting mortality. However, their performance has not been assessed in a head-to-head comparison. We externally validated these indices and determined their performance compared to counting co-occurring diseases. Setting...
| Published in: | Age & Ageing Vol. 55; no. 6; pp. 1 - 10 |
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| Main Authors: | , , , , , , , , , |
| Format: | Article |
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Oxford University Press / USA
Jun2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=195099708&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195099708 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Jun2026 vid: 55 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 195099708 10.1093/ageing/afag150 ppf: 1 ppct: 9 formats: tig: atl: Counting co-occurring diseases to predict mortality is as accurate as multimorbidity indices: an external validation study. aug: au: Velek, Premysl Splinter, Marije J Stirland, Lucy Luik, Annemarie Bindels, Patrick J Stricker, Bruno Schepper, Evelien I T de Munster, Barbara C van Ruiter, Rikje Brusselle, Guy affil: Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The NetherlandsDepartment of General Practice, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands Division of Psychiatry, The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK Department of General Practice, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands University Medical Center Groningen, University Center of Geriatric Medicine, University of Groningen, Groningen, The Netherlands Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The NetherlandsInternal Medicine, Maasstad Hospital, Rotterdam, Zuid-Holland, The Netherlands Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The NetherlandsRespiratory Medicine, University Hospital Ghent, Gent, Oost-Vlaanderen, BelgiumRespiratory Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, The Netherlands su: Netherlands Causes of death Mortality risk factors Predictive tests Risk assessment Independent living Research funding Logistic regression analysis Descriptive statistics Longitudinal method Comparative studies Data analysis software Comorbidity Nosology sug: subj: Causes of death Netherlands Mortality risk factors Predictive tests Risk assessment Independent living Research funding Logistic regression analysis Descriptive statistics Longitudinal method Comparative studies Data analysis software Comorbidity Nosology keyword: aged calibration cohort study community copyrightHolder:British Geriatrics Society copyrightYear:2026 external validation follow-up https://dx.doi.org/10.1093/ageing/afag150 inLanguage:en mortality multimorbidity non-communicable diseases older people publisher:Oxford University Press sameAs:https://pubmed.ncbi.nlm.nih.gov/42248799/ aged calibration cohort study community copyrightHolder:British Geriatrics Society copyrightYear:2026 external validation follow-up https://dx.doi.org/10.1093/ageing/afag150 inLanguage:en mortality multimorbidity non-communicable diseases older people publisher:Oxford University Press sameAs:https://pubmed.ncbi.nlm.nih.gov/42248799/ ab: Background A systematic review recommended seven multimorbidity indices for predicting mortality. However, their performance has not been assessed in a head-to-head comparison. We externally validated these indices and determined their performance compared to counting co-occurring diseases. Setting Within the prospective Rotterdam Study in the Netherlands, we constructed seven specific sub-cohorts, selected from 14 926 community-dwelling older adults to match the target population of the selected multimorbidity indices. Methods We calculated prediction scores according to the indices' original methods and used these as predictors in logistic regression models with all-cause mortality as outcome. We assessed their performance and compared it to four benchmark models fitted on the same index-specific samples. These models were based on (i) age and sex; (ii) counts of co-occurring diseases, age and sex; (iii) counts of co-occurring diseases associated with mortality, age and sex; and (iv) individual diseases as separate predictors, age and sex. Results The total population sizes of the seven sub-cohorts ranged from 2409 to 9045 participants. The mean age of the populations ranged from 59.4 to 77.0 years; the proportion of women ranged from 56.0% to 61.8% (excluding single-sex indices). The absolute risk for mortality ranged from 0.9% to 13%. Discriminative performance of the indices and corresponding count models was nearly identical across all indices (maximum difference in C-statistic: 0.06), yet higher than age-and-sex models. Absolute accuracy of the prediction scores was similar across all models (maximum improvement in Brier score: 4%). Calibration was poor in four out of seven indices, all of which had a follow-up time of 2 years or less. Conclusion Counting co-occurring diseases is as accurate in predicting all-cause mortality in the general population as using multimorbidity indices. These findings imply that counting diseases is the more practical and reliable way of providing prognosis to patients with multimorbidity in a population of community-dwelling adults. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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