External Validation of the Charlson Comorbidity Index-based Model for Survival Prediction in Thai Patients Diagnosed

Nida Buawangpong1,2, Phichayut Phinyo1,3,4, Chaisiri Angkurawaranon1,2

  • 1Department of Family Medicine, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Rd., Chiang Mai, 50200, Thailand.

BMC Geriatrics
|August 12, 2024
PubMed

Insights

The Charlson Comorbidity Index (CCI) shows fair ability but poor calibration for predicting survival in Thai dementia patients. Updating the model did not significantly improve its accuracy, suggesting other factors are needed.

Area of Science:

  • Gerontology
  • Neurology
  • Public Health

Background:

  • The Charlson Comorbidity Index (CCI) is widely used for mortality prediction.
  • Its effectiveness in dementia patient populations is under-researched.
  • This study evaluates the CCI's predictive performance in Thai dementia patients.

Purpose of the Study:

  • To examine the predictive capability of the CCI-based model for survival in Thai patients with dementia.
  • To validate the CCI model using external data.
  • To assess and improve model performance through updating and recalibration.

Main Methods:

  • External validation using retrospective data from 702 Thai dementia patients (2006-2012).
  • Data included demographics, dementia type, and comorbidities.
  • Performance assessed using Harrell's C-statistic and calibration plots; model updating was performed.

Main Results:

  • The CCI-based model achieved a Harrell's C-statistic of 0.58, indicating fair discriminative ability.
  • The model demonstrated poor external calibration.
  • Model updating yielded modest improvements in discrimination but did not significantly enhance calibration.

Conclusions:

  • The CCI-based model has limitations in predicting survival for Thai dementia patients due to fair discrimination and poor calibration.
  • Model updating and recalibration did not substantially improve predictive accuracy.
  • Incorporating additional prognostic factors is recommended for better survival prediction in this population.
Abstract

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