Objectives: To develop a fine-tuned version of the generative pretrained transformer (GPT)-4o artificial intelligence (AI) model able to estimate Functional Status Scale (FSS) scores among critically ill children. Design: Secondary analysis of a prospective, observational cohort of critically ill children 1 month to 18 years old who required invasive mechanical ventilation for greater than or equal to 3 days. Four patient notes from each of three hospitalization timepoints—baseline (history 95% CI, 0.49–0.70) and hospital discharge (0.51; 95% CI, 0.43–0.58) timepoints, with slightly lower agreement at PICU transfer (0.45; 95% CI, 0.37–0.54). For discrimination of normal total FSS scores (6–7) from abnormal scores (≥ 8), FSS-AI accuracy and positive predictive value were highest at the pre-illness baseline (0.90 and 0.95, respectively) and hospital discharge (0.81–0.75) timepoints. FSS-AI identified children with a new morbidity at hospital discharge (total FSS increase ≥ 3 or domain FSS increase ≥ 2) with accuracy and sensitivity of 0.75 and 0.56. Conclusions: A custom version of GPT-4o was able to estimate FSS scores at multiple hospitalization timepoints. The tool demonstrated moderate agreement with manually determined scores, could discriminate children with normal vs. abnormal FSS (best performance at baseline and hospital discharge timepoints), and had fair accuracy for detecting new morbidities present at hospital discharge.
Martin et al. (Wed,) studied this question.