Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection

Abstract BACKGROUND Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as non-infectious causes of respiratory failure can present with similar clinical features. METHODS We developed a LRTI diagnostic method combining the pulmonary transcriptomic biomarker FABP4 with electronic medical record (EMR) text assessment using the large language model Generative Pre-trained Transformer 4 (GPT-4). We evaluated this approach in a prospective cohort of critically ill adults with acute respiratory failure from whom tracheal aspirate FABP4 expression was measured by RNA sequencing. Patients with LRTI or non-infectious conditions were identified using retrospective, multi-physician clinical adjudication. We then confirmed our findings by applying this method to an independent validation cohort of 115 adults with acute respiratory failure. RESULTS In the derivation cohort, a combined classifier incorporating FABP4 expression and GPT-4– assisted EMR analysis achieved an AUC of 0.93 (±0.08) and an accuracy of 84%, outperforming FABP4 expression alone (AUC 0.84 ± 0.11) and GPT-4–based analysis alone (AUC 0.83 ± 0.07). By comparison, the primary medical team’s admission diagnosis had an accuracy of 72%. In the validation cohort, the combined classifier yielded an AUC of 0.98 (±0.04) and an accuracy of 96%. CONCLUSIONS Integrating a host transcriptional biomarker with EMR text analysis using a large language model may offer a promising new approach to improving the diagnosis of LRTIs in critically ill adults. Description We present the novel use of a host transcriptional biomarker combined with artificial intelligence analysis of electronic medical record data to diagnose lower respiratory tract infections in a derivation cohort of critically ill adults, then the validation of this approach in a second, fully independent, cohort. This approach demonstrated high diagnostic accuracy compared to a gold standard of post-hoc multi-physician adjudication.