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Updated: Jun 1, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Characterizing predictive models of mortality for older adults and their validation for use in clinical practice
Lilian Minne1, Jeroen Ludikhuize, Sophia E J A de Rooij
1Department of Medical Informatics, Academic Medical Center, Amsterdam, the Netherlands. L.Minne@amc.uva.nl
Objectives:
To systematically identify and characterize prognostic models of mortality for older adults, their reported potential use, and the actual level of their (external) validity.
Design:
The Scopus database until January 2010 was searched for articles that developed and validated new models or validated existing prognostic models of mortality or survival in older adults.
Setting:
All domains of health care.
Participants:
Adults aged 50 and older.
Measurements:
Study and model characteristics were summarized, including the model's development method and degree of validation, data types used, and outcomes.
Results:
One hundred three articles describing 193 models in 10 domains and mostly originating from the United States were included. These domains were mostly secondary or tertiary care settings (54%) such as intensive care (7%) or geriatric units (8%). Half of the studies (50%) were not disease specific. Heart failure-related diseases (9%) and pneumonia (9%) constituted the major disease-specific subgroups. Most studies (67%) reported support of clinical individual (treatment) decisions as use of prognostic models, but only 34% were externally validated, and only four models (2%) were validated in more than two studies. Most studies (68%) developed at least one new model, but they did not often go beyond addressing their apparent validation (49%).
Conclusion:
Although prognostic models are regularly developed to support clinical individual decisions and could be useful for this purpose, their use is premature. Because clinical credibility and evidence of external validity build trust in prognostic models, both require much more consideration to enhance model acceptance in the future.
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