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Prospective validation of a hospital triage predictive model to decrease undertriage: an EAST multicenter study. | LitMetric

AI Article Synopsis

  • - The study aimed to optimize and validate a trauma triage prediction model (NEI-6) used to identify patients needing emergency interventions, addressing issues like undertriage and overtriage rates in trauma care systems.
  • - Researchers collected and analyzed data from 14,421 patients for model training, and then validated it with 2,476 patients, finding undertriage at 9.1% and overtriage at 53.7%, with varying rates based on injury type and demographics.
  • - The NEI-6 model successfully approaches target undertriage rates, enhances consistency in trauma team activation across centers, and performs effectively for traditionally underserved populations.

Article Abstract

Background: Tiered trauma team activation (TTA) allows systems to optimally allocate resources to an injured patient. Target undertriage and overtriage rates of <5% and <35% are difficult for centers to achieve, and performance variability exists. The objective of this study was to optimize and externally validate a previously developed hospital trauma triage prediction model to predict the need for emergent intervention in 6 hours (NEI-6), an indicator of need for a full TTA.

Methods: The model was previously developed and internally validated using data from 31 US trauma centers. Data were collected prospectively at five sites using a mobile application which hosted the NEI-6 model. A weighted multiple logistic regression model was used to retrain and optimize the model using the original data set and a portion of data from one of the prospective sites. The remaining data from the five sites were designated for external validation. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to assess the validation cohort. Subanalyses were performed for age, race, and mechanism of injury.

Results: 14 421 patients were included in the training data set and 2476 patients in the external validation data set across five sites. On validation, the model had an overall undertriage rate of 9.1% and overtriage rate of 53.7%, with an AUROC of 0.80 and an AUPRC of 0.63. Blunt injury had an undertriage rate of 8.8%, whereas penetrating injury had 31.2%. For those aged ≥65, the undertriage rate was 8.4%, and for Black or African American patients the undertriage rate was 7.7%.

Conclusion: The optimized and externally validated NEI-6 model approaches the recommended undertriage and overtriage rates while significantly reducing variability of TTA across centers for blunt trauma patients. The model performs well for populations that traditionally have high rates of undertriage.

Level Of Evidence: 2.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11086287PMC
http://dx.doi.org/10.1136/tsaco-2023-001280DOI Listing

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