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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.
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.
Background:
The Charlson Comorbidity Index (CCI) is commonly employed for predicting mortality. Nonetheless, its performance has rarely been evaluated in patients with dementia. This study aimed to examine the predictive capability of the CCI-based model for survival prediction in Thai patients diagnosed with dementia.
Methods:
An external validation study was conducted using retrospective data from adults with dementia who had visited the outpatient departments at Maharaj Nakorn Chiang Mai Hospital between 2006 and 2012. The data obtained from electronic medical records included age, gender, date of dementia diagnosis and death, types of dementia, and comorbidities at the time of dementia diagnosis. The discriminative ability and calibration of the CCI-based model were estimated using Harrell's C Discrimination Index and visualized with calibration plot. As the initial performance did not meet satisfaction, model updating and recalibration were performed.
Results:
Of 702 patients, 56.9% were female. The mean age at dementia diagnosis was 75.22 (SD 9.75) year-old. During external validation, Harrell's C-statistic of the CCI-based model was 0.58 (95% CI, 0.54-0.61). The model showed poor external calibration. Model updating was subsequently performed. All updated models demonstrated a modest increase in Harrell's C-statistic. Temporal recalibration did not significantly improve the calibration of any of the updated models.
Conclusion:
The CCI-based model exhibited fair discriminative ability and poor calibration for predicting survival in Thai patients diagnosed with dementia. Despite attempts at model updating, significant improvements were not achieved. Therefore, it is important to consider the incorporation of other influential prognostic factors.
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