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

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This study developed a nationwide, risk-adjusted model for benchmarking burn patient length of stay (LOS). A Gradient Boosted (CatBoost) model accurately predicted LOS, outperforming traditional regression for burn care quality assessment.

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Area of Science:

  • Medical Informatics
  • Health Services Research
  • Burn Surgery Outcomes

Background:

  • Length of Stay (LOS) is a critical outcome measure in burn injury care.
  • Effective benchmarking requires risk adjustment to account for patient and center variations.
  • Current benchmarking methods may lack precision in predicting burn patient LOS.

Purpose of the Study:

  • To develop a nationwide, risk-adjusted model for benchmarking burn patient LOS.
  • To compare the performance of Gradient Boosted (CatBoost) models against traditional linear regression for LOS prediction.
  • To enable more accurate performance measurement and expectation setting for burn centers.

Main Methods:

  • Utilized data from the American Burn Association's Burn Care Quality Platform (7/2015-6/2020).
  • Developed and compared unpenalized linear regression and CatBoost regressor models using 22 predictor variables.
  • Validated models on a test dataset and calculated Observed/Expected (O/E) ratios for individual centers using bootstrapped CatBoost models.

Main Results:

  • The CatBoost model demonstrated superior performance (R²=0.67, CCC=0.81) compared to linear regression (R²=0.50, CCC=0.68).
  • CatBoost models exhibited less bias across varying LOS durations.
  • Risk-adjusted O/E ratios were generated for burn centers using the CatBoost model.

Conclusions:

  • Gradient-boosted regression models offer enhanced performance for LOS prediction in burn care.
  • A robust, risk-adjusted model using national data can effectively benchmark burn center performance.
  • This represents a novel approach to LOS benchmarking in burn centers, accounting for critical variables.