Evidence map›Paper›PMID 38839627›Full record

ArticleUnfallchirurgie (Heidelberg, Germany)2024

[A retrospective identification of severely injured patients using ICD 10 diagnoses codes : Part of the project "Quality of life and ability to work after severe trauma" (LeAf Trauma)].

Anne Neubert, Sebastian Hempe, Veronika Gontscharuk, Carina Jaekel, Joachim Windolf, Erwin Kollig, Catharina Gäth, LeAf Trauma Study Group, Dan Bieler

Abstract readEnglish Abstract
In one paragraph

Article in Unfallchirurgie (Heidelberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Anne NeubertKlinik für Orthopädie und Unfallchirurgie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland. Anne.neubert@med.uni-duesseldorf.de.
Sebastian HempeKlinik für Orthopädie und Unfallchirurgie, Wiederherstellungs- und Handchirurgie, Bundeswehrzentralkrankenhaus Koblenz, Koblenz, Deutschland.
Veronika GontscharukInstitut für Versorgungsforschung und Gesundheitsökonomie, Medizinische Fakultät und Universitätsklinikum Heine-Heine-Universität Düsseldorf, Düsseldorf, Deutschland.
Carina JaekelKlinik für Orthopädie und Unfallchirurgie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland.
Joachim WindolfKlinik für Orthopädie und Unfallchirurgie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland.
Erwin KolligKlinik für Orthopädie und Unfallchirurgie, Wiederherstellungs- und Handchirurgie, Bundeswehrzentralkrankenhaus Koblenz, Koblenz, Deutschland.
Catharina GäthKlinik für Orthopädie und Unfallchirurgie, Wiederherstellungs- und Handchirurgie, Bundeswehrzentralkrankenhaus Koblenz, Koblenz, Deutschland.
LeAf Trauma Study Group
Dan BielerKlinik für Orthopädie und Unfallchirurgie, Wiederherstellungs- und Handchirurgie, Bundeswehrzentralkrankenhaus Koblenz, Koblenz, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to continuous improvements in treatment, more and more severely and seriously injured patients are surviving. The complexity of the injury patterns of these patients means that they are difficult to map in routine data. AIM OF THE WORK: The aim of the data exploration was to identify ICD 10 diagnoses that show an association with an injury severity score (ISS) ≥ 16 and could therefore be used to operationalize severely injured patients in routine data. MATERIAL AND

methodsThe coded four-digit ICD 10 S diagnoses and the calculated ISS of trauma patients from the Armed Forces Central Hospital Koblenz (BwZKrhs) and the University Hospital Düsseldorf (UKD) were analyzed using statistical association measures (phi and Cramer's V), linear regressions and machine learning methods (e.g., random forest).

resultsThe S diagnoses of facial, head, thoracic and pelvic injuries, associated with an ISS ≥ 16 were identified. Some S diagnoses showed an association with an ISS ≥ 16 in only 1 of the 2 datasets. Likewise, facial, head, thoracic and pelvic injuries were found in the subgroup of 18-55-year-old patients. DISCUSSION: The current evaluations show that it is possible to identify ICD 10 S diagnoses that have a significant association with an ISS ≥ 16. According to the annual report of the trauma register of the German Society for Trauma Surgery (TR-DGU®), injuries with an abbreviated injury scale (AIS) ≥ 3 are particularly common in the head and thoracic regions.

Indexed as

Injury Severity ScoreInternational Classification of DiseasesAdolescentAdultFemaleGermanyHumansMaleMiddle AgedQuality of LifeRetrospective StudiesWounds and InjuriesYoung AdultHealth insurance dataInjury Severity ScoreMachine LearningPolytraumaRetrospective analyses

Identifiers

PMID38839627
PMCPMC11341631

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.