Large language model detects previously undiagnosed heart failure with preserved ejection fraction in patients with metabolic-associated fatty liver disease: A multicenter cohort study
Year: 2026
Type: article
Study design: Observational Study
Abstract: Metabolic-associated fatty liver disease (MAFLD) and heart failure with preserved ejection fraction (HFpEF) share overlapping metabolic and inflammatory pathways, yet HFpEF is frequently underrecognized due to atypical symptoms and complex etiology. We aimed to evaluate a domain-tuned large language model (MedGuide-14B) for HFpEF detection from electronic health records (EHRs) among patients with MAFLD, and to compare outcomes between model-identified and clinically recognized cases. In this multicenter retrospective cohort, MedGuide-14B was fine-tuned on large-scale clinical encounters and utilized to analyze structured EHR data including demographics, comorbidities, and laboratory tests, together with free-text clinical notes. Patients were classified as clinically diagnosed HFpEF, MedGuide-identified HFpEF (defined as probability ≥0.70 based on ESC criteria), or non-HFpEF. Model performance was benchmarked against clinical diagnoses, and blinded validation was conducted for a prospectively sampled subset of MedGuide-identified cases. Outcomes included rehospitalization and mortality during follow-up. Among 24,011 patients with MAFLD, 3,049 (12.7%) had clinically diagnosed HFpEF, while MedGuide-14B additionally identified 4,226 (17.6%) previously undiagnosed cases, of which 90.4% were confirmed on blinded validation (κ = 0.85). For clinically diagnosed HFpEF, model performance achieved an AUC of 0.94, with a sensitivity of 95.0% and a specificity of 92.3%. Rehospitalization occurred in 67.2% of clinically diagnosed HFpEF, 55.6% of MedGuide-identified HFpEF, and 38.4% of non-HFpEF patients (P < 0.001). At 48 months, cumulative all-cause mortality was 18.9%, 12.3%, and 4.6%, respectively, and cardiovascular mortality was 10.8%, 5.9%, and 1.5% (log-rank P < 0.05). Applied to routine EHR data, a domain-tuned large language model substantially increased the detection of HFpEF among patients with MAFLD, identifying a sizeable and previously unrecognized subgroup at intermediate yet clinically meaningful risk. Embedding such a model into EHR workflows may enable earlier evaluation and targeted testing, although prospective validation across diverse settings is warranted.
Source: PLOS Digital Health
Authors: Xiaodan Lu, Xuliang Wang, Da Liu, Zhiyuan Zhang, Yuqiang Zhang, Tiancheng Zhang, Di Lu, Shengli Kuang, Chunjin Lai, Fushi Piao, Min Lin, Shengsong Zhu, Zhuchang Tian, Wei Fu, Yongbin Dai, Chengli Yao, Gang Li, Zhenzhong Zheng, Xiangbin Meng, Hongxing Luo
Institutions: Zhengzhou University, Henan Provincial People's Hospital, Zhengzhou People's Hospital, Peking University Third Hospital, Shenzhen Third People’s Hospital, Southwest Medical University, Affiliated Hospital of Southwest Medical University, Peng Cheng Laboratory, Peking University Shenzhen Hospital, University of Hong Kong - Shenzhen Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Fu Wai Hospital, State Key Laboratory of Cardiovascular Disease, Hebei Medical University, First Affiliated Hospital of Hebei Medical University, Tongji Hospital, Huazhong University of Science and Technology, Oslo University Hospital, Maastricht University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: G4437868111, G7841991069
OpenAlex ID: W7147720308