Prognostic scoring systems in burns: A review

Sheppard, NN; Hemington-Gorse, Sarah; Shelley, Odhran; Philp, Bruce; Dziewulski, Peter

doi:10.1016/j.burns.2011.07.017

Cited by 128 publications

(85 citation statements)

References 48 publications

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“…Severity scores are simple but useful tools for physicians when evaluating the state of the patient. Scoring systems aim to use the most predictive pre-morbid and injury factors to yield an expected likelihood of death for a given patient [14]. Baux and prognostic burn index (PBI) scores provide a mortality rate by summing age and percentage of total burn surface area (%TBSA) [2], while the abbreviated burns severity index (ABSI) also considers the gender and presence of inhalation injury [15].…”

Section: Introductionmentioning

confidence: 99%

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Casanova

Campos

Fernández-Fernández-Arroyo

et al. 2015

Artificial Intelligence in Medicine

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Abstract. Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient's evolution. Experiments conducted on 480 patients show the improvement of survival prediction.

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Section: Introductionmentioning

confidence: 99%

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Casanova

Campos

Fernández-Fernández-Arroyo

et al. 2015

Artificial Intelligence in Medicine

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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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“…[6] Osler et al [7] evaluated Baux score in 39 888 patients from the National Burn Registry of the USA and added inhalation injury as scoring component, creating revised Baux score. The Belgian Outcome in Burn Injury score [8] uses revised Baux score variables in different statistical model, which was derived from study of 5246 Belgian patients.…”

Section: Discussionmentioning

confidence: 99%

Can we make an early “do not resuscitate order” decision in severe burn patients?

Yüce

Acar²,

Erkal³

et al. 2016

Ulus Travma Acil Cerrahi Derg

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BACKGROUND: The present study was conducted to examine topic of issuing early do-not-resuscitate (DNR) order at first diagnosis of patients with severe burn injuries in light of current law in Turkey and the medical literature. DNR requires withholding cardiopulmonary resuscitation in event of respiratory or cardiac arrest and allowing natural death to occur. It is frequently enacted for terminal cancer patients and elderly patients with irreversible neurological disorders.

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Prognostic scoring systems in burns: A review

Cited by 128 publications

References 48 publications

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

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

Can we make an early “do not resuscitate order” decision in severe burn patients?

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