Frequency and Predictors of Trauma Transfer Futility to a Rural Level I Trauma Center.

INTRODUCTION

Transfer of trauma patients whose injuries are deemed unsurvivable, often results in early death or transition to comfort care and could be considered misuse of health care resources. This is particularly true where tertiary care resources are limited. Identifying riskfactors for and predicting futile transfers could reduce this impact and help to optimize triage and management.

METHODS

A retrospective study of interfacility trauma transfers to a single rural Level I rauma center from 2014 to 2019. Futility was defined as death, hospice, or declaration of comfort measures within 48 h of transfer without procedural or radiographic intervention at the accepting center. Multiple logistic regressions identified independent predictors of futile transfers. The predictive power of Mechanism,Glasgow coma scale, Age, and Arterial pressure (MGAP), an injury severity score based on Mechanism, Glasgow coma scale, Age, and systolic blood Pressure, were evaluated.

RESULTS

Of the 3368 trauma transfers, 37 (1.1%) met criteria as futile. Futile transfers occurred among patients who were significantly older with falls as the most common mechanism. Age, Glasgow coma scale, systolic blood Pressure and Injury Severity Score were significant (P < 0.05) independent predictors of futile transfer. MGAP had a high predictive power area under the receiver operating characteristic (AUROC 0.864, 95% confidence interval 0.803-0.925) for futility.

CONCLUSIONS

A small proportion (1.1%) of transfers to a rural Level I trauma center met criteria for futility. Predictive tools, such as MGAP scoring, can provide objective criteria for evaluation of transfer necessity and prompt care pathways that involve pre-transfer communications, telemedicine, and/or patient centered goals of care discussions. Such tools could be used in conjunction with a more granular assessment regarding potential operational barriers to reduce futile transfers and to enhance optimization of resource utilization in low-resource service areas.