Literature DB >> 28598904

Deaths and high-risk trauma patients missed by standard trauma data sources.

Craig D Newgard1, Rongwei Fu, E Brooke Lerner, Mohamud Daya, Dagan Wright, Jonathan Jui, N Clay Mann, Eileen Bulger, Jerris Hedges, Lynn Wittwer, David Lehrfeld, Thomas Rea.   

Abstract

BACKGROUND: Trauma registries are used to evaluate and improve trauma care, yet potentially miss certain trauma deaths and high-risk patients. We estimated the number of missed deaths and high-risk trauma patients using commonly available sources of trauma data and resulting bias in quality metrics for field trauma triage.
METHODS: This was a preplanned secondary analysis of a population-based prospective cohort of injured patients transported by 44 emergency medical services agencies to 28 hospitals in seven Northwest counties from January 1, 2011 to December 31, 2011 and followed through hospitalization. We used a stratified probability sampling design for 17,633 patients, weighted to represent all 53,487 injured patients transported by emergency medical services. We compared patients meeting National Trauma Data Bank (NTDB) criteria (weighted n = 5,883), all injured patients presenting to major trauma centers (weighted n = 16,859), and all admitted patients (weighted n = 18,433), to the full sample. Outcomes included in-hospital mortality, Injury Severity Score (ISS) of 16 or higher, and critical resource use within 24 hours.
RESULTS: Among 53,487 injured patients, there were 520 emergency department and in-hospital deaths, 1,745 with ISS of 16 or higher, and 923 requiring early critical resources. Compared to the full cohort, the NTDB cohort missed 62.1% of deaths, 39.2% of patients with ISS of 16 or higher, and 23.8% requiring early critical resources, especially older adults injured by falls and admitted to nontrauma hospitals. The admission cohort missed the fewest patients-23.3% of deaths, 10.5% with an ISS of 16 or higher, and 13.1% requiring early resources. Compared to triage sensitivity in the full cohort (66.2%), sensitivity estimates ranged from 63.6% (all admissions) to 93.4% (NTDB). Compared to triage specificity in the full cohort (87.8%), estimates ranged from 36.4% (NTDB) to 77.3% (all admissions).
CONCLUSION: Common sources of trauma data miss substantial numbers of trauma deaths and high-risk trauma patients and can generate biased estimates for trauma system quality metrics. LEVEL OF EVIDENCE: Epidemiologic, level III.

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Mesh:

Year:  2017        PMID: 28598904     DOI: 10.1097/TA.0000000000001616

Source DB:  PubMed          Journal:  J Trauma Acute Care Surg        ISSN: 2163-0755            Impact factor:   3.313


  7 in total

1.  Comparison of Injured Older Adults Included in vs Excluded From Trauma Registries With 1-Year Follow-up.

Authors:  Craig D Newgard; Aaron Caughey; K John McConnell; Amber Lin; Elizabeth Eckstrom; Denise Griffiths; Susan Malveau; Eileen Bulger
Journal:  JAMA Surg       Date:  2019-09-18       Impact factor: 14.766

2.  Crash Telemetry-Based Injury Severity Prediction is Equivalent to or Out-Performs Field Protocols in Triage of Planar Vehicle Collisions.

Authors:  Katherine He; Peng Zhang; Stewart C Wang
Journal:  Prehosp Disaster Med       Date:  2019-07-19       Impact factor: 2.040

3.  Comparative analysis of traumatic esophageal injury in pediatric and adult populations.

Authors:  Alexander A Xu; Janis L Breeze; Carl-Christian A Jackson; Jessica K Paulus; Nikolay Bugaev
Journal:  Pediatr Surg Int       Date:  2019-05-10       Impact factor: 1.827

4.  Toward an all-inclusive trauma system in Central South Ontario: development of the Trauma-System Performance Improvement and Knowledge Exchange (T-SPIKE) project.

Authors:  Paul T Engels; Angela Coates; Russell D MacDonald; Mahvareh Ahghari; Michelle Welsford; Tim Dodd; Katie Turcotte; Jeffrey D Doyle; Arthur M Eugenio; Jason P Green; J Eric Irvine; Paul J Lysecki; Simerpreet K Sandhanwalia; Sunjay V Sharma
Journal:  Can J Surg       Date:  2021-03-15       Impact factor: 2.089

5.  Development and validation of a novel prediction model to identify patients in need of specialized trauma care during field triage: design and rationale of the GOAT study.

Authors:  Rogier van der Sluijs; Thomas P A Debray; Martijn Poeze; Loek P H Leenen; Mark van Heijl
Journal:  Diagn Progn Res       Date:  2019-06-20

6.  Profiling the Expression of Circulating Acute-Phase Proteins, Cytokines, and Checkpoint Proteins in Patients with Severe Trauma: A Pilot Study.

Authors:  Shao-Chun Wu; Cheng-Shyuan Rau; Pao-Jen Kuo; Fu-Yuan Shih; Hui-Ping Lin; Yi-Chan Wu; Ting-Min Hsieh; Hang-Tsung Liu; Ching-Hua Hsieh
Journal:  J Inflamm Res       Date:  2021-08-06

7.  The Network of miRNA-mRNA Interactions in Circulating T Cells of Patients Following Major Trauma - A Pilot Study.

Authors:  Cheng-Shyuan Rau; Pao-Jen Kuo; Hui-Ping Lin; Chia-Jung Wu; Yi-Chan Wu; Peng-Chen Chien; Ting-Min Hsieh; Hang-Tsung Liu; Chun-Ying Huang; Ching-Hua Hsieh
Journal:  J Inflamm Res       Date:  2022-09-22
  7 in total

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