Evidence map›Paper›PMID 20622665›Full record

ArticleAnnals of surgery2010

Development and validation of the mortality risk for trauma comorbidity index.

Hilaire J Thompson, Frederick P Rivara, Avery Nathens, Jin Wang, Gregory J Jurkovich, Ellen J Mackenzie

Open access · greenAbstract readMulticenter StudyValidation Study
In one paragraph

Article in Annals of surgery, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.

  1. Pooled it
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  13. Inpatient mortality after orthopaedic surgery.International orthopaedics · 2015
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Hilaire J ThompsonDepartment of Biobehavioral Nursing and Health Systems, Harborview Injury Prevention and Research Center, The University of Washington School of Nursing, Seattle, WA, USA.
Frederick P Rivara
Avery Nathens
Jin Wang
Gregory J Jurkovich
Ellen J Mackenzie
University of Washington · USHarborview Injury Prevention and Research Center · USJohns Hopkins University · USUniversity of Toronto · CA

Funding

Institute for Translational Health Science (KL2)KL2RR025015 · NCRR · UNIVERSITY OF WASHINGTON · PI DISIS, MARY L. · 2007 to 2011
$9.3M
UW Multidisciplinary Clinical Research Training (RMI)K12RR023265 · NCRR · UNIVERSITY OF WASHINGTON · PI PROBSTFIELD, JEFFREY LYNN · 2005 to 2007
$6.1M
NCRR NIH HHS 5 K12 RR023265-03NCRR NIH HHS K12 RR023265NCRR NIH HHS KL2 RR025015NINR NIH HHS L30 NR010534PHS HHS R49/CCR316840
6 · The paper itself

Abstract

objectiveThe aim of this study was to develop and validate a comorbidity index to predict the risk of mortality associated with chronic health conditions following a traumatic injury. SUMMARY BACKGROUND DATA: Currently available comorbidity adjustment tools do not account for certain chronic conditions, which may influence outcome following traumatic injury or they have not been fully validated for trauma. Controlling for comorbidity in trauma patients is becoming increasingly important as the population ages and elderly patients are more active, as well as to adjust for bias in trauma mortality studies.

methodsCohort study using data from the National Study on the Costs and Outcome of Trauma. Subject pool (N = 4644/Weighted Number = 14,069) was randomly divided in half; the first half of subjects was used to derive the risk scale, the second to validate the instrument. To construct the Mortality Risk Score for Trauma (MoRT), univariate analysis and odds ratios were performed to determine relative risk of mortality at hospital discharge comparing those persons with a comorbid condition to those without. Conditions significantly associated with mortality (P < 0.05) were included in the multivariate model. The variables in the final model were used to build the MoRT. The predictive ability of the MoRT and the Charlson Comorbidity Index (CCI) for discharge and 1-year mortality were estimated using the c-statistic in the validation sample.

resultsSix comorbidity factors were independently associated with the risk of mortality and formed the basis for the MoRT: severe liver disease, myocardial infarction, cerebrovascular disease, cardiac arrhythmias, dementia, and depression. The MoRT had a similar overall discrimination as the CCI for mortality at hospital discharge in injured adults (c-statistic: 0.56 vs. 0.56) although neither by itself performed well. The addition of age and gender improved the predictive ability of the MoRT (0.59; 95% CI: 0.56, 0.62) and the CCI (0.59; 0.56, 0.62). Similar results were seen at 1-year postinjury. The further addition of Injury Severity Score significantly improved the predictive ability of the MoRT (0.77, 95% CI: 0.74, 0.79) and the CCI (0.77, 95% CI: 0.75, 0.80).

conclusionsThe MoRTs primary advantage over current instruments is its parsimony, containing only 6 items. In the present study, the comorbid conditions found to be predictive of mortality had some overlap with the CCI, but this study identified 2 novel predictors: cardiac arrhythmias and depression. Inclusion and reporting of these items within trauma registries would therefore be an important step to allow further validation and use of the MoRT.

Indexed as

ComorbidityRisk AssessmentAdultChi-Square DistributionConfidence IntervalsFemaleHumansInjury Severity ScoreMalePredictive Value of TestsPrevalenceProspective StudiesRisk FactorsUnited StatesWounds and Injuries

Identifiers

PMID20622665
PMCPMC3039002
OpenAlexW1964048028

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.