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Updated: Jun 25, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Clinical prognostic factors for older people: A systematic review and meta-analysis
Nicola Veronese1, Anna Fazzari1, Maria Armata1
1Department of Internal Medicine, Geriatrics Section, University of Palermo, Palermo, Italy.
Objective:
To explore the accuracy and precision of prognostic tools used in older people in predicting mortality, hospitalization, and nursing home admission across different settings and timings.
Design:
Systematic review and meta-analysis of prospective and retrospective studies.
Data Sources:
A systematic search from database inception until 01st February 2023 was run in Medline, Embase, Cinhal, Cochrane Library.
Eligibility Criteria:
Studies were eligible if they reported accuracy (area under the curve [AUC]) and/or precision (C-index) for the prognostic index in relation to any of the following outcomes: mortality, hospitalization, and nursing home admission.
Data Extraction And Synthesis:
Two independent reviewers extracted data. Data were pooled using a random effects model. The risk of bias was assessed with the Quality in Prognosis Studies (QUIPS) tool. If more than three studies for the same setting and time were available, a meta-analysis was performed and evaluated using the GRADE tool; other data were reported descriptively.
Results:
Among 16,082 studies initially considered, 159 studies with a total of 2398856 older people (mean age: 78 years) were included. The majority of the studies was carried out in hospital or medical wards. In the community setting, only two tools (Health Assessment Tool and the Multidimensional Prognostic Index, MPI) had good precision for long-term mortality. In emergency department setting, Barthel Index had an excellent accuracy in predicting short-term mortality. In medical wards, the MPI had a moderate certainty of the evidence in predicting short-term mortality (13 studies; 11,787 patients; AUC=0.79 and 4 studies; 3915 patients; C-index=0.82). Similar findings were available for MPI when considering longer follow-up periods. When considering nursing home and surgical wards, the literature was limited. The risk of bias was generally acceptable; observed bias was mainly owing to attrition and confounding.
Conclusions:
Several tools are used to predict poor prognosis in geriatric patients, but only those derived from a multidimensional evaluation have the characteristics of precision and accuracy.
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