Date Published: February 6, 2019
Publisher: Public Library of Science
Author(s): Youngmin Kim, Dohern Kym, Jun Hur, Jinwoo Jeon, Jaechul Yoon, Haejun Yim, Yong Suk Cho, Wook Chun, Wisit Cheungpasitporn.
The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients.
This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis.
The Hangang model showed good calibration with the HL test (χ2 = 8.785, p = 0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value <0.001) when compared with FLAMES, respectively. This model reflects the current risk factors of mortality among adult burn patients. Furthermore, it was a highly discriminatory and well-calibrated model for the prediction of mortality in this cohort.
Prediction of critically ill patients in a systemic manner based on clear, objective data is an essential part of care in an Intensive Care Unit (ICU). The development of severity scoring systems has been transformed to predict outcomes in a more objective and reliable way and has sequentially influenced management decisions, including do-not-resuscitate status and the withdrawal of life support . Severity scoring systems have continued to be developed and various scoring systems have been used for the critical ill patient. Severity scoring systems should have validity, calibration, and discrimination to predict the severity of disease and mortality, as well as repeatability and reliability in different populations and diseases [2, 3]. There are generally two kinds of prediction models due to the different characteristics of individual diseases. One is used for the general intensive care patients and is focused on the acute physiological status and associated comorbidities assessed by the Acute Physiologic and Chronic Health Evaluation (APACHE) II , Simplified Acute Physiologic Score (SAPS) II , Logistic Organ Dysfunction Score (LODS) , and Sequential Organ Failure Assessment (SOFA) . The other is specific to each individual disease and consists of the disease-related features. Among burn patients, the abbreviated burn severity index (ABSI) , FLAMES (Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex) , the revised Baux index (rBaux) , the models which were developed by Ryan et al , and the Belgian outcome in burn injury (BOBI) group are known and used widely. These burn-specific prediction models, with the exception of FLAMES, consist of patient-related factors; no laboratory variables are included and even in FLAMES there is not burn specific laboratory factors. Therefore, these models are only able to determine some of the risk factors for mortality rather than a continuous range of risk factors . It is necessary to develop a prediction model that includes a wider range of treatment-related biological variables as well as patient-related variables to accurately reflect the rapid progress in burn treatment. Additionally, the existing scoring systems for the general critically ill patients do not accurately predict the severity and the risk of mortality in the burn patients because they were developed from the general ICU, and did not specifically take into consideration burn populations .
Despite the existence of several prediction models, there are not many realistic models to accurately predict the outcomes of burn patients . Various prediction models suggest that there is no ideal model to predict outcomes accurately in every population . The ideal prediction model generally is simple, reliable, and objective (observer independent) . However, in most burn-specific prediction models, it might be difficult to accurately reflect the risk of mortality, which has been changed as a result of the advancement of burn treatment. This is due to the fact that these models consist of patient-related variables (such as age and % TBSA) and do not contain objective laboratory values . The % TBSA burned was the most powerful predictor in this study, however, it is measured differently based on the experience of the treating physician; the estimation error can be up to 20% among inexperienced physicians . Therefore, in hospitals that are not specialized in treating burn patients, such errors can affect the model and make it difficult to accurately predict mortality. To compensate for these errors, prediction models should include the addition of objective laboratory results.
There are many severity scoring systems widely used in the ICU to predict outcomes and characterize the severity of the disease. All of these scoring systems have been developed for the mixed population in the ICU. Their accuracy among subgroups, such as burn patients, is questionable and therefore, burn-specific scoring systems are required for accurate prediction. This model reflects the burn specific risk factors such as serum myoglobin and LD as well as current risk factors for mortality; it is a highly discriminatory and well-calibrated model for the prediction of mortality in adult burn patients.