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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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