Format

Send to

Choose Destination
PLoS One. 2015 Aug 21;10(8):e0136438. doi: 10.1371/journal.pone.0136438. eCollection 2015.

Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study.

Author information

1
Division of Biostatistics, University of California, Berkeley, California, United States of America.
2
Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.
3
Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
4
Division of Clinical and Translational Sciences, Department of Internal Medicine, Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
5
Division of Trauma, Critical Care and Acute Care Surgery, School of Medicine, Oregon Health & Science University, Portland, Oregon, United States of America.
6
Division of Trauma and General Surgery, Department of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
7
Division of Trauma and Critical Care, Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America.
8
Division of Trauma and Critical Care, Department of Surgery, School of Medicine, University of Washington, Seattle, Washington, United States of America.
9
Division of Trauma/Critical Care, Department of Surgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States of America.
10
Division of Trauma, Department of Surgery, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America.
11
Division of Burn/Trauma/Critical Care, Department of Surgery, Medical School, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, United States of America.

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.

PMID:
26296088
PMCID:
PMC4546674
DOI:
10.1371/journal.pone.0136438
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center