SciELO - Scientific Electronic Library Online

 
vol.106 issue10 author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • On index processSimilars in Google

Share


SAMJ: South African Medical Journal

On-line version ISSN 2078-5135
Print version ISSN 0256-9574

Abstract

SPENCE, R T et al. Injury Severity Score coding: Data analyst v. emerging m-health technology. SAMJ, S. Afr. med. j. [online]. 2016, vol.106, n.10, pp.1037-1041. ISSN 2078-5135.  http://dx.doi.org/10.7196/samj.2016.v106i10.10597.

BACKGROUND. The cost of Abbreviated Injury Scale (AIS) coding has limited its utility in areas of the world with the highest incidence of trauma. We hypothesised that emerging mobile health (m-health) technology could offer a cost-effective alternative to the current gold-standard AIS mechanism in a high-volume trauma centre in South Africa. METHODS. A prospectively collected sample of consecutive patients admitted following a traumatic injury that required an operation during a 1-month period was selected for the study. AISs and Injury Severity Scores (ISSs) were generated by clinician-entered data using an m-health application (ISS eTHR) as well as by a team of AIS coders at Vancouver General Hospital, Canada (ISS VGH). Rater agreements for ISSs were analysed using Bland-Altman plots with 95% limits of agreement (LoA) and kappa statistics of the ISSs grouped into ordinal categories. Reliability was analysed using a two-way mixed-model intraclass correlation coefficient (ICC). Calibration and discrimination of univariate logistic regression models built to predict in-hospital complications using ISSs coded by the two methods were also compared. RESULTS. Fifty-seven patients were managed operatively during the study period. The mean age of the cohort was 27.2 years (range 14 - 62), and 96.3% were male. The mechanism of injury was penetrating in 93.4% of cases, of which 52.8% were gunshot injuries. The LoA fell within -8.6 - 9.4. The mean ISS difference was 0.4 (95% CI -0.8 - 1.6). The kappa statistic was 0.53. The ICC of the individual ISS was 0.88 (95% CI 0.81 - 0.93) and the categorical ISS was 0.81 (95% CI 0.68 - 0.87). Model performance to predict in-hospital complications using either the ISS eTHR or the ISS VGH was equivalent. CONCLUSIONS. ISSs calculated by the eTHR and gold-standard coding were comparable. Emerging m-health technology provides a cost-effective alternative for injury severity scoring.

        · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License