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Comparative Analysis of Composite Mortality Prediction Scores in Intensive Care Burn Patients.

ABSTRACT: Multiple outcome scoring models have been used in predicting mortality in burn patients. In this study, we compared the accuracy of five established models in predicting outcomes in burn patients admitted to the intensive care unit and assessed risk factors associated with mortality. Intensive care burn patients admitted between March 2007 and December 2020 with total body surface area (TBSA) affected ≥ 10% were analyzed. Multivariate analysis was conducted to examine variables associated with mortality. The ABSI, Ryan, BOBI, revised Baux and BUMP scores were analyzed by receiver operating characteristics. A total of 617 patients were included. Morality was 14.4%, with non-survivors being significantly older, male, and having experienced domestic burns. Multivariate analysis identified age, TBSA, full-thickness burns and renal insufficiency as independent mortality predictors. The BUMP score presented the highest mortality prognostication rate, followed by ABSI, revised Baux, BOBI and Ryan scores. BUMP, ABSI and revised Baux scores displayed AUC values exceeding 90%, indicating excellent prognostic capabilities. The BUMP score showed the highest accuracy of predicting mortality in intensive care burn patients and outperformed the most commonly used ABSI score in our cohort. The older models displayed adequate predictive performance and accuracy compared with the newest model.

SUBMITTER: Obed D

PROVIDER: S-EPMC9564531 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Comparative Analysis of Composite Mortality Prediction Scores in Intensive Care Burn Patients.

Obed Doha D Salim Mustafa M Dastagir Nadjib N Knoedler Samuel S Dastagir Khaled K Panayi Adriana C AC Vogt Peter M PM

International journal of environmental research and public health 20220928 19

Multiple outcome scoring models have been used in predicting mortality in burn patients. In this study, we compared the accuracy of five established models in predicting outcomes in burn patients admitted to the intensive care unit and assessed risk factors associated with mortality. Intensive care burn patients admitted between March 2007 and December 2020 with total body surface area (TBSA) affected ≥ 10% were analyzed. Multivariate analysis was conducted to examine variables associated with m ...[more]

PMID: 36231617

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Project description:BACKGROUND:Majority burn mortality prognostic scores were developed and validated in western populations. The primary objective of this study was to evaluate and identify possible risk factors which may be used to predict burns mortality in a local Malaysian burns intensive care unit. The secondary objective was to validate the five well known burn prognostic scores (Baux score, Abbreviated Burn Severity Index (ABSI) score, Ryan score, Belgium Outcome Burn Injury (BOBI) score and revised Baux score) to predict burn mortality prediction. METHODS:Patients that were treated at the Hospital Sultan Ismail's Burns Intensive Care (BICU) unit for acute burn injuries between 1 January 2010 to 31 December 2017 were included. Risk factors to predict in-patient burn mortality were gender, age, mechanism of injury, total body surface area burn (TBSA), inhalational injury, mechanical ventilation, presence of tracheotomy, time from of burn injury to BICU admission and initial centre of first emergency treatment was administered. These variables were analysed using univariate and multivariate analysis for the outcomes of death. All patients were scored retrospectively using the five-burn mortality prognostic scores. Predictive ability for burn mortality was analysed using the area under receiver operating curve (AUROC). RESULTS:A total of 525 patients (372 males and 153 females) with mean age of 34.5 ± 14.6 years were included. There were 463 survivors and 62 deaths (11.8% mortality rate). The outcome of the primary objective showed that amongst the burn mortality risk factors that remained after multivariate analysis were older age (p = 0.004), wider TBSA burn (p < 0.001) and presence of mechanical ventilation (p < 0.001). Outcome of secondary objective showed good AUROC value for the prediction of burn death for all five burn prediction scores (Baux score; AUROC:0.9, ABSI score; AUROC:0.92, Ryan score; AUROC:0.87, BOBI score; AUROC:0.91 and revised Baux score; AUROC:0.94). The revised Baux score had the best AUROC value of 0.94 to predict burns mortality. CONCLUSION:Current study evaluated and identified older age, total body surface area burns, and mechanical ventilation as significant predictors of burn mortality. In addition, the revised Baux score was the most accurate burn mortality risk score to predict mortality in a Malaysian burn's population.

Project description:IntroductionBurn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score.MethodsAdmission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test.ResultsA total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decision tree model, an extreme boosting model, a random forest model, a support-vector machine (SVM) model and a generalised linear regression model (GLM) were 0.83 (95% confidence interval [CI] = 0.72-0.94), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1) and 0.84 (95% CI = 0.74-0.94), respectively. AUCs for the Baux score and revised Baux score were 0.85 (95% CI = 0.75-0.95) and 0.84 (95% CI = 0.74-0.94). No significant differences were observed when comparing ML algorithms with Baux score and revised Baux score. Secondary variable selection was made to analyse model performance.ConclusionThis proof-of-concept study showed initial credibility in using ML algorithms to predict mortality in burn patients. The sample size was small and future studies are needed with larger sample sizes, further variable selections and prospective testing of the algorithms.Lay summaryBurn injuries are one of the most common traumatic injuries especially in countries with limited prevention and healthcare resources. To treat a patient with large burns who has been admitted to an intensive care unit, it is often necessary to assess the risk of a fatal outcome. Physicians traditionally use simplified scores to calculate risks. One commonly used score, the Baux score, uses age of the patient and the size of the burn to predict the risk of death. Adding the factor of inhalation injury, the score is then called the revised Baux score. However, there are a number of additional causes that can influence the risk of fatal outcomes that Baux scores do not take into account. Machine learning is a method of data modelling where the system learns to predict outcomes based on previous cases and is a branch of artificial intelligence. In this study we evaluated several machine learning methods for outcome prediction in patients admitted for burn injury. We gathered data on 93 patients at admission to the intensive care unit and our experiments show that machine learning methods can reach an accuracy comparable with Baux scores in calculating the risk of fatal outcomes. This study represents a proof of principle and future studies on larger patient series are required to verify our results as well as to evaluate the methods on patients in real-life situations.

Project description:ImportanceMortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies.ObjectiveCompare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection.Design setting and participantsProspective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018-June 2019 were included.Main outcomes and measuresThe primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33-62) and 61% (n = 207) were male. Fifty-nine percent (n = 199) required mechanical ventilation and 35% (n = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% (n = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) (p < 0.001). Predictive capacity was similar among patients with suspected infection.Conclusion and relevanceAll tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.

Project description:Background: The mortality prediction scores were widely used in pediatric intensive care units. However, their performances were unclear in Chinese patients and there were also no reports based on large sample sizes in China. This study aims to evaluate the performances of three existing severity assessment scores in predicting PICU mortality and to identify important determinants. Methods: This prospective observational cohort study was carried out in eight multidisciplinary, tertiary-care PICUs of teaching hospitals in China. All eligible patients admitted to the PICUs between Aug 1, 2016, and Jul 31, 2017, were consecutively enrolled, among whom 3,957 were included for analysis. We calculated PCIS, PRISM IV, and PELOD-2 scores based on patient data collected in the first 24 h after PICU admission. The in-hospital mortality was defined as all-cause death within 3 months after admission. The discrimination of mortality was assessed using the area under the receiver-operating characteristics curve (AUC) and calibrated using the Hosmer-Lemeshow goodness-of-fit test. Results: A total of 4,770 eligible patients were recruited (median age 18.2 months, overall mortality rate 4.7%, median length of PICU stay 6 days), and 3,957 participants were included in the analysis. The AUC (95% confidence intervals, CI) were 0.74 (0.71-0.78), 0.76 (0.73-0.80), and 0.80 (0.77-0.83) for PCIS, PRISM IV, and PELOD-2, respectively. The Hosmer-Lemeshow test gave a chi-square of 3.16 for PCIS, 2.16 for PRISM IV and 4.81 for PELOD-2 (p ≥ 0.19). Cox regression identified five predictors from the items of scores better associated with higher death risk, with a C-index of 0.83 (95%CI 0.79-0.86), including higher platelet (HR = 1.85, 95% CI 1.59-2.16), invasive ventilation (HR = 1.40, 1.26-1.55), pupillary light reflex (HR = 1.31, 95% CI 1.22-1.42) scores, lower pH (HR 0.89, 0.84-0.94), and extreme PaO2 (HR 2.60, 95% CI 1.61-4.19 for the 1st quantile vs. 4th quantile) scores. Conclusions: Performances of the three scores in predicting PICU mortality are comparable, and five predictors were identified with better prediction to PICU mortality in Chinese patients.

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Comparative Analysis of Composite Mortality Prediction Scores in Intensive Care Burn Patients.

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Comparative Analysis of Composite Mortality Prediction Scores in Intensive Care Burn Patients.

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