Back

Frailty-based prognostication of clinical outcomes in geriatric burn patients: a retrospective study

Lee, J. H.; Jinsi, M.; Feldman, M. J.; Hobgood, S.

2026-05-15 geriatric medicine
10.64898/2026.05.12.26353034 medRxiv
Show abstract

Burn trauma disproportionately impacts older adults, yet existing burn severity models emphasize age, total body surface area (TBSA), and inhalation injury without accounting for geriatric-specific vulnerabilities such as frailty. We conducted a retrospective cohort study of 326 geriatric patients admitted with burn injuries between 2020 and 2024 to evaluate how TBSA, burn location, inhalation injury, renal insufficiency, comorbidities, and functional dependence in activities of daily living (ADLs) and instrumental activities of daily living (IADLs) affect in- hospital mortality and discharge disposition. Based on multivariable logistic regression and chi- square analyses, TBSA, as expected, emerged as the strongest predictor across models. Each 1% increase in TBSA was associated with a 7% increase in the odds of in-hospital mortality (p=0.006) and a 12 to 19% increase in odds of post-acute care placement (p<0.001). Inhalation injury and renal insufficiency were also independently associated with increased odds of both mortality and post-acute care disposition, whereas respiratory comorbidity predicted mortality alone. Functional status demonstrated outcome-specific prognostic value: ADL dependence predicted mortality, while IADL dependence predicted discharge disposition. Patients with some ADL dependence had five-fold higher odds of in-hospital mortality (p=0.011), while some (OR=2.48, p= 0.039) and full IADL dependence (OR=2.61, p=0.025) were associated with higher odds of post-acute care placement. Integrating structured functional assessments that distinguish basic from instrumental limitations alongside established burn severity metrics may enhance prognostication and guide individualized care planning for older adults with burn injuries.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.