American Burn Association Strategic Quality Summit 2022: Setting the Direction for the Future.

The American Burn Association (ABA) hosted a Burn Care Strategic Quality Summit (SQS) in an ongoing effort to advance the quality of burn care. The goals of the SQS were to discuss and describe characteristics of quality burn care, identify goals for advancing burn care, and develop a roadmap to guide future endeavors while integrating current ABA quality programs. Forty multidisciplinary members attended the two-day event. Prior to the event, they participated in a pre-meeting webinar, reviewed relevant literature, and contemplated statements regarding their vision for improving burn care. At the in-person, professionally facilitated Summit in Chicago, Illinois, in June 2022, participants discussed various elements of quality burn care and shared ideas on future initiatives to advance burn care through small and large group interactive activities. Key outcomes of the SQS included burn-related definitions of quality care, avenues for integration of current ABA quality programs, goals for advancing quality efforts in burn care, and work streams with tasks for a roadmap to guide future burn care quality-related endeavors. Work streams included roadmap development, data strategy, quality program integration, and partners and stakeholders. This paper summarizes the goals and outcomes of the SQS and describes the status of established ABA quality programs as a launching point for futurework.

Setting the Standard: Using the ABA Burn Registry to Benchmark Risk Adjusted Mortality.

Reports of single center experience and studies of larger databases have identified several predictors of burn center mortality, including age, burn size, and inhalation injury. None of these analyses has been broad enough to allow benchmarking across burn centers. The purpose of this study was to derive a reliable, risk-adjusted, statistical model of mortality based on real-life experience at many burn centers in the U.S. We used the American Burn Association 2020 Full Burn Research Dataset, from the Burn Center Quality Platform (BCQP) to identify 130,729 subjects from July 2015 through June 2020 across 103 unique burn centers. We selected 22 predictor variables, from over 50 recorded in the dataset, based on completeness (at least 75% complete required) and clinical significance. We used gradient-boosted regression, a form of machine learning, to predict mortality and compared this to traditional logistic regression. Model performance was evaluated with AUC and PR curves. The CatBoost model achieved a test AUC of 0.980 with an average precision of 0.800. The logistic regression produced an AUC of 0.951 with an average precision of 0.664. While AUC, the measure most reported in the literature, is high for both models, the CatBoost model is markedly more sensitive, leading to a substantial improvement in precision. Using BCQP data, we can predict burn mortality allowing comparison across burn centers participating in BCQP.

Using a National Burn Registry to Develop a Model for Risk-Adjusted Length of Stay Benchmarking.

Length of stay (LOS) is a frequently reported outcome after a burn injury. LOS benchmarking will benefit individual burn centers as a way to measure their performance and set expectations for patients. We sought to create a nationwide, risk-adjusted model to allow for LOS benchmarking based on the data from a national burn registry. Using data from the American Burn Association's Burn Care Quality Platform, we queried admissions from 7/2015 to 6/2020 and identified 130,729 records reported by 103 centers. Using 22 predictor variables, comparisons of unpenalized linear regression and Gradient boosted (CatBoost) regressor models were performed by measuring the R2 and concordance correlation coefficient on the application of the model to the test dataset. The CatBoost model applied to the bootstrapped versions of the entire dataset was used to calculate O/E ratios for individual burn centers. Analyses were run on 3 cohorts: all patients, 10-20% TBSA, >20% TBSA. The CatBoost model outperformed the linear regression model with a test R2 of 0.67 and CCC of 0.81 compared with the linear model with R2=0.50, CCC=0.68. The CatBoost was also less biased for higher and lower LOS durations. Gradient-boosted regression models provided greater model performance than traditional regression analysis. Using national burn data, we can predict LOS across contributing burn centers while accounting for patient and center characteristics, producing more meaningful O/E ratios. These models provide a risk-adjusted LOS benchmarking using a robust data source, the first of its kind, for burn centers.

American Burn Association Burn Prevention Framework.

In February 2020, burn prevention experts from a variety of professional backgrounds gathered for a national Burn Prevention Summit. Through lively discussion and debate, this group came to a consensus on several core burn prevention concepts in order to create a framework for burn prevention program planning. The resulting document includes components of a successful program, a five-step process for program planning, best practices in messaging, and general advice from the summit attendees. This framework is designed for both novice professionals who are new to burn prevention programming development and experienced professionals who would like to strengthen existing programming.