08. Comparing novel machine-learning derived weights versus standard weights for the Charlson Comorbidity Index in predicting mortality for autistic older adults

The Charlson Comorbidity Index (CCI) was developed in 1987 to predict one-year mortality of breast cancer patients based on 19 weighted conditions, providing a valuable tool for assessing patient risk in clinical and research settings. The CCI was updated over the years, including accommodating changes in coding systems like ICD-9 and ICD-10 and improving predictive accuracy. The CCI provides important foundational work for the field. Yet, this index is not ideally equipped to predict mortality in certain patient populations, like autistic older adults, who experience a higher prevalence of co-occurring conditions than the general population. We aimed to develop autism-specific weights for the 19 conditions in the CCI to better quantify the risk of mortality for autistic older adults. We hypothesized that the novel autism-specific weights for the CCI would have greater predictive validity than the established CCI weights, developed for the general population. We used a 100% sample of national Medicare claims from the years 2013-2021. We leveraged a machine learning optimization technique called stochastic hill climbing, where the weights of the 12 updated CCI conditions were randomly shifted to optimize a weighted mean for quantifying mortality risk. We then compared the predictive validity of the new autism-specific comorbidity weights to the established CCI weights through the area under the curve (AUC) of a logistic regression model with mortality as the outcome and the weighted mean as the predictor. We found the AUCs were similar for predicting mortality using the novel autism-specific weights (AUC=0.68) and the established CCI weights (AUC=0.67) among autistic older adults. These findings may suggest that additional health conditions not currently captured by the CCI, or patient demographics, may need to be added to the CCI to better predict mortality in autistic older adults. Future studies on developing an autism-specific mortality risk index are warranted.

Autism

Mortality

Machine learning

Older adults

Charlson 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025