Predicting survival in thermal injury: A systematic review of methodology of composite prediction models

Hussain, Atif; Choukairi, Fouzia; Dunn, Ken

doi:10.1016/j.burns.2012.12.010

Cited by 88 publications

(53 citation statements)

References 116 publications

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“…Several previous studies using a multivariable logistic regression model proposed various prognostic scoring systems, which showed a better predictive value than the Baux score [6-9, [23][24][25][26][27][28][29][30][31][32][33][34][35]. However, no one score can claim to be the most accurate in evaluation of the entire burn population [6].…”

Section: Discussionmentioning

confidence: 98%

Validation of the prognostic burn index: A nationwide retrospective study

Tagami

Matsui

Fushimi

et al. 2015

Burns

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Section: Discussionmentioning

confidence: 98%

Validation of the prognostic burn index: A nationwide retrospective study

Tagami

Matsui

Fushimi

et al. 2015

Burns

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“…We estimated injury severity using age, Total Burn Surface Area (TBSA), presence of inhalation injury, and presence of third degree burns, which are based on the same items presented in the ABSI (Abbreviated Burn Severity Index) [12]. This index has been tested in different centers [13,14] and has demonstrated good accuracy to predict mortality in burn patients [15]. History of previous comorbidities including clinical comorbidities, psychiatric illness and drug abuse were listed if reported in medical records and were recorded in terms of presence or absence and type.…”

Section: Methodsmentioning

confidence: 99%

Intentional injuries and patient survival of burns: A 10-year retrospective cohort in southern Brazil

Duarte

Neumann

Weber

2015

Burns

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“…However, mortality remains an important indicator of quality of life after burn care, and many models have been developed to predict the risk [5,[9][10][11][12][13][14] and to provide a baseline. The timely identification of risk factors in such models might help clinicians to reduce the mortality after burns further.…”

Section: Introductionmentioning

confidence: 99%

Inclusion of coexisting morbidity in a TBSA% and age based model for the prediction of mortality after burns does not increase its predictive power

Pompermaier

Steinvall

Fredrikson

et al. 2015

Burns

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Introduction: Several models for predicting mortality have been developed for patients with burns, and the most commonly used are based on age and total body surface area (TBSA%). They often show good predictive precision as depicted by high values for area under the receiver operating characteristic curves (AUC). However the effect of coexisting morbidity on such prediction models has not to our knowledge been thoroughly examined. We hypothesised that adding it to a previously published model (based on age, TBSA%, full thickness burns, gender, and need for mechanical ventilation) would further improve its predictive power. Methods: We studied 772 patients admitted during the period 1997-2008 to the Linkoping University Hospital, National Burn Centre with any type of burns. We defined coexisting morbidity as any of the medical conditions listed in the Charlson list, as well as psychiatric disorders or drug or alcohol misuse. We added coexisting medical conditions to the model for predicting mortality (age, TBSA%, and need for mechanical ventilation) to determine whether it improved the model as assessed by changes in deviances between the models. Results: Mean (SD) age and TBSA% was 35 (26) years and 13 (17) %, respectively. Among 725 patients who survived, 105 (14%) had one or more coexisting condition, compared with 28 (60%) among those 47 who died. The presence of coexisting conditions increased with age (p < 0.001) among patients with burns. The AUC of the mortality prediction model in this study, based on the variables age, TBSA%, and need for mechanical ventilation was 0.980 (n = 772); after inclusion of coexisting morbidity in the model, the AUC improved only marginally, to 0.986. The model was not significantly better either. Conclusion: Adding coexisting morbidity to a model for prediction of mortality after a burn based on age, TBSA%, and the need for mechanical ventilation did not significantly improve its predictive value. This is probably because coexisting morbidity is automatically adjusted for by age in the original model. (C) 2015 Elsevier Ltd and ISBI. All rights reserved

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Predicting survival in thermal injury: A systematic review of methodology of composite prediction models

Cited by 88 publications

References 116 publications

Validation of the prognostic burn index: A nationwide retrospective study

Validation of the prognostic burn index: A nationwide retrospective study

Intentional injuries and patient survival of burns: A 10-year retrospective cohort in southern Brazil

Inclusion of coexisting morbidity in a TBSA% and age based model for the prediction of mortality after burns does not increase its predictive power

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