Validity of three risk prediction models for dementia or cognitive impairment in Australia

Gopisankar M. Geethadevi, Roseanne Peel, J. Simon Bell, Amanda J. Cross, Stephen Hancock, Jenni Ilomaki, Titus Tang, John Attia, Johnson George

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Abstract

BACKGROUND: no studies have compared the predictive validity of different dementia risk prediction models in Australia. 

OBJECTIVES: (i) to investigate the predictive validity of the Australian National University-Alzheimer's Disease Risk Index (ANU-ADRI), LIfestyle for BRAin Health (LIBRA) Index and cardiovascular risk factors, ageing and dementia study (CAIDE) models for predicting probable dementia/cognitive impairment in an Australian cohort. (ii) To develop and assess the predictive validity of a new hybrid model combining variables from the three models. 

METHODS: the Hunter Community Study (HCS) included 3,306 adults aged 55-85 years with a median follow-up of 7.1 years. Probable dementia/cognitive impairment was defined using Admitted Patient Data Collection, dispensing of cholinesterase inhibitors or memantine, or a cognitive test. Model validity was assessed by calibration and discrimination. A hybrid model was developed using deep neural network analysis, a machine learning method. 

RESULTS: 120 (3.6%) participants developed probable dementia/cognitive impairment. Mean calibration by ANU-ADRI, LIBRA, CAIDE and the hybrid model was 19, 0.5, 4.7 and 3.4%, respectively. The discrimination of the models was 0.65 (95% CI 0.60-0.70), 0.65 (95% CI 0.60-0.71), 0.54 (95% CI 0.49-0.58) and 0.80 (95% CI 0.78-0.83), respectively. 

CONCLUSION: ANU-ADRI and LIBRA were better dementia prediction tools than CAIDE for identification of high-risk individuals in this cohort. ANU-ADRI overestimated and LIBRA underestimated the risk. The new hybrid model had a higher predictive performance than the other models but it needs to be validated independently in longitudinal studies.

Original languageEnglish
Article numberafac307
Number of pages9
JournalAge and Ageing
Volume51
Issue number12
DOIs
Publication statusPublished - Dec 2022

Keywords

  • cognitive impairment
  • dementia risk
  • older people
  • prognostic models
  • risk assessment
  • risk prediction

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