ArticleHealth science reports2023
Prediction models of all-cause mortality among older adults in nursing home setting: A systematic review and meta-analysis.
Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models.Age and ageing · 2024Pooled it
- Article
- The Moderating Role of Digital Literacy in the Relationship Between Cognitive Function and Instrumental Activities of Daily Living Among Community-Dwelling Older Adults.Healthcare (Basel, Switzerland) · 2026Article
- An Updated Mortality and Community Discharge Prognostic Model for Older Adults Admitted to Skilled Nursing Facilities for Post-Acute Care.Journal of the American Medical Directors Association · 2026Article
- Machine learning prediction of survival in centenarians after age 100: a retrospective, population-based cohort study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Observational
- Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review.JMIR aging · 2023Article
- Prediction models of all-cause mortality among older adults in nursing home setting: A systematic review and meta-analysis.Health science reports · 2023Article
- Exploring the feasibility of vital signs-based mortality risk prediction in a care facility setting.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Few studies have meta-analyzed different prognostic models developed for older adults, especially nursing home residents. We aimed to systematically review and meta-analyze the performance of all published models that predicted all-cause mortality among older nursing home residents. Methods: We systematically searched PubMed and EMBASE from the databases' inception to January 1, 2020 to capture studies developing and/or validating a prognostic/prediction model for all-cause mortality among nursing home residents. We then carried out both qualitative and quantitative analyses evaluating these models' risks of bias and applicability. Results: The systematic search yielded 23,975 articles. We identified 28 indices that predicted the risk of all-cause mortality from 14 days to 39 months among older adults in nursing homes. The most used predictors were age, sex, body weight, swallowing problem, congestive heart failure, shortness of breath, body mass index, and activities of daily living. Of the 28 indices, 8 (29%) and 3 (11%) were internally and externally validated, respectively. None of the indices was validated in more than one cohort. Of the 28 indices, 22 (79%) reported the C-statistic, while only 6 (6%) reported the 95% confidence interval for the C statistic in the development cohorts. In the validation cohorts, 11 (39%) reported the C-statistic and 8 (29%) reported the 95% confidence interval. The meta-analyzed C statistic for all indices is 0.733 (95% prediction interval: 0.669-0.797). All studies/indices had high risks of bias and high concern for applicability according to PROBAST. Conclusion: We identified 28 indices for predicting all-cause mortality among older nursing home residents. The overall quality of evidence was low due to a high degree of bias and poor reporting of model performance statistics. Before any prediction model could be recommended in routine care, future research is needed to rigorously validate existing prediction models and evaluate their applicability and develop new prediction models.
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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.