Abstract
Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable patient populations, such as those of the PRospective, Observational, Multi-center Major Trauma Transfusion study. Ten US level I trauma centers enrolled a total of 1,245 trauma patients who survived at least 30 minutes after admission and received at least one unit of red blood cells. Outcomes included death, multiple organ failure, substantial bleeding, and transfusion of blood products. The centers involved were classified as either large or small-volume based on the number of massive transfusion patients enrolled during the study period. We focused on estimation of parameters inspired by causal inference, specifically estimated impacts on patient outcomes related to the volume of the trauma hospital that treated them. We defined this association as the change in mean outcomes of interest that would be observed if, contrary to fact, subjects from large-volume sites were treated at small-volume sites (the effect of treatment among the treated). We estimated this parameter using three different methods, some of which use data-adaptive machine learning tools to derive the outcome models, minimizing residual confounding by reducing model misspecification. Differences between unadjusted and adjusted estimators sometimes differed dramatically, demonstrating the need to account for differences in patient characteristics in clinic comparisons. In addition, the estimators based on robust adjustment methods showed potential impacts of hospital volume. For instance, we estimated a survival benefit for patients who were treated at large-volume sites, which was not apparent in simpler, unadjusted comparisons. By removing arbitrary modeling decisions from the estimation process and concentrating on parameters that have more direct policy implications, these potentially automated approaches allow methodological standardization across similar comparativeness effectiveness studies.
Highlights
Trauma is a leading cause of death worldwide and is an increasing global health burden, rapidly eclipsing HIV/AIDS, vaso-occlusive and infectious diseases [1,2], and understanding the underlying mechanisms and improving the treatment of trauma is of paramount clinical and public health interest
Our approach described here utilizes two major advances in statistical methodology: (1) targeted data adaptive machine learning tools for modeling clinical outcomes and the distribution of patients across different trauma centers, given a potentially large number of covariates, and (2) using the resulting models to estimate parameters that reflect how the site characteristic of interest affects the distribution of outcomes among the patients. The purpose of this analysis was to present statistical methodology that can be available as an automated, data-adaptive statistical learner targeted towards the estimation of the impact of center-level characteristics on patient outcomes; we demonstrate the potential of these approaches on data from the PRospective, Observational, Multi-center Major Trauma Transfusion (PROMMTT) study [7]
Though this does not show the differences in the joint distribution of the explanatory variables, there appear to be important bivariate differences among them; the large hospitals serve a more racially diverse, younger group of patients who have more penetrating injuries, less baseline use of anticoagulants, and higher platelet and hemoglobin counts observed in the emergency department (ED)
Summary
Trauma is a leading cause of death worldwide and is an increasing global health burden, rapidly eclipsing HIV/AIDS, vaso-occlusive and infectious diseases [1,2], and understanding the underlying mechanisms and improving the treatment of trauma is of paramount clinical and public health interest. Confounding is introduced by the considerable heterogeneity among patients across trauma units, including differences in age, gender, injury types and severity. Impacts of treatment decisions are often highly confounded by current patient health states. The very measures aimed at saving lives and improving outcomes often prevent clean separation of patient cohorts and make causal relationships challenging and sometimes impossible to discern. Missing data is very prevalent because of the primacy of patient treatment over collecting data. Randomized controlled trials (RCTs), which minimize between-group differences and isolate a variable or treatment to be studied, are optimum, but are rare in trauma due to issues surrounding methodological complexity, issues with informed consent and expense
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