Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes...

To quantify differences between (1) stratifying patients by predicted disease onset risk alone and (2) stratifying by predicted disease onset risk and severity of downstream outcomes. We perform a case study of predicting sepsis.

We performed a retrospective analysis using observational data from Michigan Medicine at the University of Michigan (U-M) between 2016 and 2020 and the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2012. We measured the correlation between the estimated sepsis risk and the estimated effect of sepsis on mortality using Spearman's correlation. We compared patients stratified by sepsis risk with patients stratified by sepsis risk and effect of sepsis on mortality.

The U-M and BIDMC cohorts included 7282 and 5942 ICU visits; 7.9% and 8.1% developed sepsis, respectively. Among visits with sepsis, 21.9% and 26.3% experienced mortality at U-M and BIDMC. The effect of sepsis on mortality was weakly correlated with sepsis risk (U-M: 0.35 [95% CI: 0.33-0.37], BIDMC: 0.31 [95% CI: 0.28-0.34]). High-risk patients identified by both stratification approaches overlapped by 66.8% and 52.8% at U-M and BIDMC, respectively. Accounting for risk of mortality identified an older population (U-M: age = 66.0 [interquartile range-IQR: 55.0-74.0] vs age = 63.0 [IQR: 51.0-72.0], BIDMC: age = 74.0 [IQR: 61.0-83.0] vs age = 68.0 [IQR: 59.0-78.0]).

Predictive models that guide selective interventions ignore the effect of disease on downstream outcomes. Reformulating patient stratification to account for the estimated effect of disease on downstream outcomes identifies a different population compared to stratification on disease risk alone.

Models that predict the risk of disease and ignore the effects of disease on downstream outcomes could be suboptimal for stratification.