Zhang Y, Li Y, Song S, Li Z, Lu M, Shan G. Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.
J Alzheimers Dis 2024;
101:147-157. [PMID:
39121117 DOI:
10.3233/jad-240285]
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Abstract
Background
Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time.
Objective
It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data.
Methods
The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model.
Results
We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients.
Conclusions
Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.
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