Decline in Explanatory Power of Trauma Mortality Models With Age: Varying Contribution of Glasgow Coma Scale, Injury Severity Score, Comorbidities, and Frailty

INTRODUCTION

Adjusting for confounding variables is critical for objective comparison of outcomes. The explanatory power of variables used in adjusted models for injury and their relative utility across age groups has not been well-defined. This study aimed to assess the explanatory power of covariates commonly adjusted in injury research and their relative performance across age groups.

METHODS

Inpatients 18-100 y (2017-2022) were selected from 90 hospital trauma registries. Patients were grouped into sequential 5-y age blocks. Mortality was defined as the proportion of patients "expired + hospice". Dominance analysis was used to determine the average contribution (McFadden's R2) for covariates commonly included in multivariable logistic regressions.

RESULTS

Three hundred seventeen-thousand one hundred thirty-six patients were included (51.1% male, mean age: 63, mean injury severity score [ISS]: 9.8, mean Glasgow Coma Scale: 14.3, 93.5% blunt). Total explanatory power (McFadden's R2) for mortality was highest in youngest age group (52.7% in 18-24 group) and decreased with age, with the lowest R2 (19.6%) in 95-100 group. Regardless of age, the Glasgow Coma Scale was the most important covariate (R2 ranging from 9.0% to 20.4%). At age 18-24 y, ISS was a more dominant contributor than Elixhauser Score, but beyond 55 y, Elixhauser Score became more dominant than ISS.

CONCLUSIONS

The explanatory power of adjustment models including common covariates is limited and varies significantly across age groups, decreasing linearly with age. Adjusting for outcomes using these covariates may limit objective comparisons especially for older adults. Additional research is needed to identify covariates that enhance the explanatory power of adjustment models to allow for more objective comparisons across all ages.

Explaining the Variance in Cardiovascular Health Indicators among Asian Americans: A Comparison of Demographic, Socioeconomic, and Ethnicity

The Asian American (AA) population is the fastest-growing major racial group in the U.S. Typically treated as a monolith in research, disaggregated data show disproportionate cardiovascular disease (CVD) burden among certain AA ethnic groups. This analysis aimed to identify which factors explain variance in cardiovascular health among AA ethnic groups. We analyzed pooled 2010-2018 National Health Interview Survey data from Chinese, Asian Indian, Filipino, and Other Asian adults in the U.S. CVD outcomes of interest were coronary heart disease, heart attack, and stroke. Covariates included sociodemographic characteristics, CVD-related health conditions, and behaviors. Variance explained by sociodemographic, health behaviors, and health conditions were calculated based on the adjusted R-squared from a series of five models for each CVD health outcome. Of the 10,353 AA adults in the sample, 53% identified as female. Compared to the aggregate AA sample and the other ethnic groups, Filipinos had a higher burden of any CVD outcome (5.9%), particularly for coronary heart disease (4.0%). The combination of all predictors explained at most 13% of variance, with sociodemographic characteristics accounting for at least half of the variance explained among all participants. Health behaviors explained a greater amount of additional variance for all CVD outcomes among Asian Indians, including an additional 3.1% for stroke. Inversely, existing health conditions were significant predictors of CVD for all AA ethnic groups compared to Asian Indians. There is heterogeneity in CVD outcomes and related risk factors in AA ethnic groups, emphasizing the need for culturally tailored prevention and intervention strategies.