Not all patients meet the 1day per percent burn rule: A simple method for predicting hospital length of stay in patients with burn

Sandra L Taylor; Soman Sen; David G Greenhalgh; MaryBeth Lawless; Terese Curri; Tina L Palmieri

doi:10.1016/j.burns.2016.10.021

Not all patients meet the 1day per percent burn rule: A simple method for predicting hospital length of stay in patients with burn

Burns. 2017 Mar;43(2):282-289. doi: 10.1016/j.burns.2016.10.021. Epub 2016 Dec 29.

Authors

Sandra L Taylor¹, Soman Sen², David G Greenhalgh², MaryBeth Lawless³, Terese Curri³, Tina L Palmieri⁴

Affiliations

¹ University of California, Davis Medical Center, Department of Public Health Sciences, Sacramento, CA, United States.
² University of California, Davis Medical Center, Department of Surgery, Sacramento, CA, United States; Burn Department, Shriners Hospitals for Children Northern California, Sacramento, CA, United States.
³ University of California, Davis Medical Center, Department of Surgery, Sacramento, CA, United States.
⁴ University of California, Davis Medical Center, Department of Surgery, Sacramento, CA, United States; Burn Department, Shriners Hospitals for Children Northern California, Sacramento, CA, United States. Electronic address: tina.palmieri@ucdmc.ucdavis.edu.

PMID: 28041754
DOI: 10.1016/j.burns.2016.10.021

Abstract

Introduction: Hospital length of stay (LOS) is utilized to estimate resource utilization and quality of care. In burns the LOS estimation is 1day per percent total body surface area burn (1day/%TBSA). Our purpose was to evaluate the 1day/%TBSA burn rule and develop simple accurate formulas to predict LOS.

Methods: The American Burn Association National Burn Repository (NBR) from 2000 to 2013 was utilized to collate data on patients >18 years. We divided 106,543 records in half, utilizing one set to develop a model (training set) and the other to test the model (test set). We calculated the difference between observed and predicted LOS for all patients, and then examined the effect of inhalation injury and age using a linear regression model containing TBSA, age, inhalation injury and all two-way interactions. We compared predictive performance of the linear regression model to the 1day/%TBSA rule. Finally, we developed and validated three simple formulas to more accurately predict LOS than the 1day/% TBSA rule.

Results: LOS was significantly associated with patient age, TBSA, inhalation injury, and all two-way interactions. For patients <40 years without inhalation injury the main effect of TBSA was 0.71. For each decade increase in age, LOS increased by 0.74days/TBSA burn; inhalation injury added 1.70days. LOS was highly variable among patients with similar burn size, age and inhalation injury due to concomitant trauma, complications, and comorbidities. We developed 3 formulas to estimate patient LOS: (1) inhalation injury present, regardless of age (2) no inhalation injury and ≥40 years old (3) no inhalation injury and <40 years old.

Conclusions: Traditional LOS estimates of 1day/%TBSA burn rule is biased, underestimating LOS, particularly for patients >40 years with inhalation injury. The following formulas applied at admission can accurately estimate hospital LOS, improve prediction over 1day/%TBSA, and provide results comparable to complicated models.

Keywords: Burn; Hospital length of stay; Outcomes.

MeSH terms

Adult
Age Factors
Body Surface Area*
Burns / epidemiology*
Burns / pathology
Humans
Length of Stay / statistics & numerical data*
Linear Models
Smoke Inhalation Injury / epidemiology*
Trauma Severity Indices
United States / epidemiology