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Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and 79,664 Accidents

Author

Listed:
  • Antonio Palazón-Bru

    (Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain)

  • María José Prieto-Castelló

    (Department of Pathology and Surgery, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain)

  • David Manuel Folgado-de la Rosa

    (Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain)

  • Ana Macanás-Martínez

    (Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain)

  • Emma Mares-García

    (Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain)

  • María de los Ángeles Carbonell-Torregrosa

    (Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain)

  • Vicente Francisco Gil-Guillén

    (Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain
    These authors contributed equally to this work.)

  • Antonio Cardona-Llorens

    (Department of Pathology and Surgery, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain
    These authors contributed equally to this work.)

  • Dolores Marhuenda-Amorós

    (Department of Pathology and Surgery, Miguel Hernández University, San Juan de Alicante, 03550 Alicante, Spain
    These authors contributed equally to this work.)

Abstract

Predictive factors for fatal traffic accidents have been determined, but not addressed collectively through a predictive model to help determine the probability of mortality and thereby ascertain key points for intervening and decreasing that probability. Data on all road traffic accidents with victims involving a private car or van occurring in Spain in 2015 (164,790 subjects and 79,664 accidents) were analyzed, evaluating 30-day mortality following the accident. As candidate predictors of mortality, variables associated with the accident (weekend, time, number of vehicles, road, brightness, and weather) associated with the vehicle (type and age of vehicle, and other types of vehicles in the accident) and associated with individuals (gender, age, seat belt, and position in the vehicle) were examined. The sample was divided into two groups. In one group, a logistic regression model adapted to a points system was constructed and internally validated, and in the other group the model was externally validated. The points system obtained good discrimination and calibration in both the internal and the external validation. Consequently, a simple tool is available to determine the risk of mortality following a traffic accident, which could be validated in other countries.

Suggested Citation

  • Antonio Palazón-Bru & María José Prieto-Castelló & David Manuel Folgado-de la Rosa & Ana Macanás-Martínez & Emma Mares-García & María de los Ángeles Carbonell-Torregrosa & Vicente Francisco Gil-Guillé, 2020. "Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and ," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9518-:d:464703
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    References listed on IDEAS

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    2. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    3. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
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