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South African Journal of Surgery

On-line version ISSN 2078-5151
Print version ISSN 0038-2361

Abstract

OSHUN, N  and  HARDCASTLE, T C. Validation of the mortality prediction equation for damage control surgery in an independent severe trauma population. S. Afr. j. surg. [online]. 2015, vol.53, n.3-4, pp.1-3. ISSN 2078-5151.

BACKGROUND: A prediction model was developed in Cape Town which utilised age, preoperative lowest pH and lowest core body temperature to derive an equation for the purpose of predicting mortality in damage control surgery. It was shown to reliably predict death despite damage control surgery. The equation derivation dataset and the validation set showed the equation to have 100% positive predictive value (PPV) for both datasets and 24% sensitivity. The aim of the study was to validate the prediction model in an independent dataset from a prospective trauma registry. METHOD: Retrospective analysis of an ethics-approved prospectively collected database and electronic medical records was performed on trauma patients undergoing damage control surgery at the Inkosi Albert Luthuli Central Hospital, Durban, between 2007 and 2013. Age, lowest preoperative core body temperature and the pH of the patients were analysed using the previously derived equation. The output from the equation was then classified as a prediction of death, based on the obtained values, and then compared to the actual outcome of whether the patients survived or died. RESULT: A total of 48 patient records were analysed in the study. Twenty-nine patients in the cohort died. The equation predicted mortality in only four cases, of whom three died and one survived (75% PPV and 10% sensitivity). The unexpected survivor reduced the PPV to 75%, compared to 100% PPV achieved in the original study. CONCLUSION: The results of this study were inconsistent with those of the original study, and the 0.500 cut-off value used in the equation yielded PPV and sensitivity which were relatively non-clinically useful for the average patient in this cohort.

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