Literature DB >> 30633101

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

Turner M Osler1, Laurent G Glance, Alan Cook, Jeffrey S Buzas, David W Hosmer.   

Abstract

BACKGROUND: Outcome prediction models allow risk adjustment required for trauma research and the evaluation of outcomes. The advent of ICD-10-CM has rendered risk adjustment based on ICD-9-CM codes moot, but as yet no risk adjustment model based on ICD-10-CM codes has been described.
METHODS: The National Trauma Data Bank provided data from 773,388 injured patients who presented to one of 747 trauma centers in 2016 with traumatic injuries ICD-10-CM codes and Injury Severity Score (ISS). We constructed an outcome prediction model using only ICD-10-CM acute injury codes and compared its performance with that of the ISS.
RESULTS: Compared with ISS, the TMPM-ICD-10 discriminated survivors from non-survivors better (ROC TMPM-ICD-10 = 0.861 [0.860-0.872], ROC [reviever operating curve] ISS = 0.830 [0.823-0.836]), was better calibrated (HL [Hosmer-Lemeshow statistic] TMPM-ICD-10 = 49.01, HL ISS = 788.79), and had a lower Akaike information criteria (AIC TMPM-ICD10 = 30579.49; AIC ISS = 31802.18).
CONCLUSIONS: Because TMPM-ICD10 provides better discrimination and calibration than the ISS and can be computed without recourse to Abbreviated Injury Scale coding, the TMPM-ICD10 should replace the ISS as the standard measure of overall injury severity for data coded in the ICD-10-CM lexicon. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, level II.

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Year:  2019        PMID: 30633101     DOI: 10.1097/TA.0000000000002194

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


  5 in total

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

Authors:  Paola Fugazzola; Vanni Agnoletti; Silvia Bertoni; Costanza Martino; Matteo Tomasoni; Federico Coccolini; Emiliano Gamberini; Emanuele Russo; Luca Ansaloni
Journal:  Intern Emerg Med       Date:  2021-01-07       Impact factor: 3.397

2.  A traumatic injury mortality prediction (TRIMP) based on a comprehensive assessment of abbreviated injury scale 2005 predot codes.

Authors:  Muding Wang; Guohu Zhang; Degang Cong; Yunji Zeng; Wenhui Fan; Yi Shen
Journal:  Sci Rep       Date:  2021-11-05       Impact factor: 4.379

3.  Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases.

Authors:  Kristof Anetta; Ales Horak; Wojciech Wojakowski; Krystian Wita; Tomasz Jadczyk
Journal:  J Pers Med       Date:  2022-05-25

4.  Traumatic injury mortality prediction (TRIMP-ICDX): A new comprehensive evaluation model according to the ICD-10-CM codes.

Authors:  Guohu Zhang; Muding Wang; Degang Cong; Yunji Zeng; Wenhui Fan
Journal:  Medicine (Baltimore)       Date:  2022-08-05       Impact factor: 1.817

5.  Geospatial characteristics of non-motor vehicle and assault-related trauma events in greater Phoenix, Arizona.

Authors:  Alan Cook; Robin Harris; Heidi E Brown; Edward Bedrick
Journal:  Inj Epidemiol       Date:  2020-06-15
  5 in total

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