Literature DB >> 33411262

The value of trauma patients' centralization: an analysis of a regional Italian Trauma System performance with TMPM-ICD-9.

Paola Fugazzola1, Vanni Agnoletti2, Silvia Bertoni3, Costanza Martino2, Matteo Tomasoni4, Federico Coccolini5, Emiliano Gamberini2, Emanuele Russo2, Luca Ansaloni6.   

Abstract

BACKGROUND: In recent years, many studies showed that the Trauma Mortality Probability Model (TMPM-ICD-9) had better calibration compared to other ICD-9-based models and to the ones based to the Abbreviated Injury Scale (AIS). The study aims to assess the validity of TMPM-ICD-9 in predicting injury severity in an Italian region and, through this model, to assess the performances of the Trauma Systems SIAT Romagna.
METHODS: Administrative data of trauma patients admitted in the Trauma System of SIAT Romagna, in Northern Italy, from 2014 to 2018 were obtained. The XISS, an indirect indicator of Injury Severity Score (ISS) and the TMPM-POD (Probability of Death) were calculated from ICD-9-CM codes. Only patients with XISS > 15 were included. Student t-test, Mann-Whitney test and Chi-square test were used for univariate analyses, while logistic regression for multivariate analyses.
RESULTS: 3907 trauma patients with XISS > 15 were included. The Hub hospital (HUB) received 47.1% of these patients. Patients treated in HUB had higher TMPM-POD than in SPOKE + PST (mean TMPM-POD ± SD: HUB 0.093 ± 0.091, SPOKE + PST 0.082 ± 0.90, p < 0.027), but only age and sex were significant risk factors for centralization at multivariate analyses. Higher age (73.1 ± 21.2 vs 66.9 ± 21.2, p < 0.001), higher XISS (16(9) vs 16(4), p < 0.001) and higher TMPM-POD (0.15 ± 0.14 vs 0.08 ± 0.08, p < 0.001) resulted significant risk factors for mortality at multivariate analysis. Lower age, higher XISS and lower Trauma Centers (TC) level were significant risk factors for splenectomy at multivariate analysis. The splenectomy rate was 1.3% in HUB and of 2.2% in SPOKE + PST (Risk Ratio = 0.4, p = 0.002).
CONCLUSIONS: Present analysis proved the validity of TMPM-ICD-9 in predicting mortality of trauma patients in an Italian region. Furthermore, the usefulness of data extracted from an administrative database to assess the performance of a TS and the importance of an adequate centralization process have emerged. Even with a higher TMPM-POD and with the same mortality rate, HUB showed a higher spleen salvage rate compared to SPOKE + PST. However, thanks to this model, an improvable centralization process in SIAT Romagna was found in the study period. Probably, an enhanced centralization would have improved the spleen salvage rate, which is an important quality indicator in the evaluation of the performance of the TS.

Entities:  

Keywords:  Centralization; TMPM; TMPM-ICD-9; Trauma; Trauma system

Year:  2021        PMID: 33411262     DOI: 10.1007/s11739-020-02611-w

Source DB:  PubMed          Journal:  Intern Emerg Med        ISSN: 1828-0447            Impact factor:   3.397


  10 in total

1.  Is the TMPM-ICD9 revolution in trauma risk-adjustment compatible with imperfect administrative coding?

Authors:  Stefano Di Bartolomeo; Chiara Ventura; Massimiliano Marino; Arturo Chieregato; Giorgio Gambale; Andrea Fabbri; Annalisa Volpi; Rossana De Palma
Journal:  Accid Anal Prev       Date:  2011-06-08

2.  Epidemiology of major trauma.

Authors:  K Søreide
Journal:  Br J Surg       Date:  2009-07       Impact factor: 6.939

3.  Easy way to learn standardization : direct and indirect methods.

Authors:  N N Naing
Journal:  Malays J Med Sci       Date:  2000-01

Review 4.  Performance of International Classification of Diseases-based injury severity measures used to predict in-hospital mortality: A systematic review and meta-analysis.

Authors:  Mathieu Gagné; Lynne Moore; Claudia Beaudoin; Brice Lionel Batomen Kuimi; Marie-Josée Sirois
Journal:  J Trauma Acute Care Surg       Date:  2016-03       Impact factor: 3.313

5.  A trauma mortality prediction model based on the ICD-10-CM lexicon: TMPM-ICD10.

Authors:  Turner M Osler; Laurent G Glance; Alan Cook; Jeffrey S Buzas; David W Hosmer
Journal:  J Trauma Acute Care Surg       Date:  2019-05       Impact factor: 3.313

6.  Evaluation of the survival benefit of Trauma-Centre care in the Italian setting.

Authors:  Stefano Di Bartolomeo; Massimiliano Marino; Chiara Ventura; Rossana De Palma
Journal:  Injury       Date:  2013-03-13       Impact factor: 2.586

7.  A comparison of the Injury Severity Score and the Trauma Mortality Prediction Model.

Authors:  Alan Cook; Jo Weddle; Susan Baker; David Hosmer; Laurent Glance; Lee Friedman; Turner Osler
Journal:  J Trauma Acute Care Surg       Date:  2014-01       Impact factor: 3.313

8.  TMPM-ICD9: a trauma mortality prediction model based on ICD-9-CM codes.

Authors:  Laurent G Glance; Turner M Osler; Dana B Mukamel; Wayne Meredith; Jacob Wagner; Andrew W Dick
Journal:  Ann Surg       Date:  2009-06       Impact factor: 12.969

9.  Effect of regional trauma centralization on volume, injury severity and outcomes of injured patients admitted to trauma centres.

Authors:  D Metcalfe; O Bouamra; N R Parsons; M O Aletrari; F E Lecky; M L Costa
Journal:  Br J Surg       Date:  2014-07       Impact factor: 6.939

10.  Association between volume of severely injured patients and mortality in German trauma hospitals.

Authors:  M T Zacher; K-G Kanz; M Hanschen; S Häberle; M van Griensven; R Lefering; V Bühren; P Biberthaler; S Huber-Wagner
Journal:  Br J Surg       Date:  2015-07-07       Impact factor: 6.939

  10 in total

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