Clinical Features at Admission for Heart Failure and Their Prognostic Significance: AI-Enabled Phenotyping from DELIVER and PARAGON-HF

Abstract

MANUSCRIPT 1:Artificial Intelligence to Extract Structured Details from Unstructured Medical Records in a Global Heart Failure Trial Background: Global clinical trials collect extensive unstructured medical records that richly describe participants’ clinical presentation, but their narrative format precludes quantitative analysis. Converting these records into structured data could reveal insights into events like heart failure hospitalization (HFH) but is prohibitively labor intensive. Varied documentation styles, translated text, and scanned or handwritten documents pose challenges to data extraction in clinical trials. Large language models (LLMs) may be able to extract structured presenting features from unstructured trial records. Methods: We developed an LLM prompting workflow to extract 51 variables (symptoms, signs, laboratory and imaging results, and treatments) from HFH dossiers in the DELIVER trial. LLM outputs were validated against physician chart review in a random sample of 125 dossiers, a second physician reviewed 25 dossiers for inter-reviewer agreement. Accuracy and positive and negative predictive values were assessed against the physician gold-standard. The model was then applied across the full trial to quantify the frequency of HFH presenting features. Results: The LLM achieved an overall accuracy of 0.96 (95% CI, 0.95–0.97), PPV of 0.94 (95% CI, 0.92–0.95), and NPV of 0.97 (95% CI, 0.97–0.98). Accuracy exceeded 0.90 for 49 of 51 variables. The inter-reviewer concordance was 98%. In the full trial (1,237 HFHs), the most common features were dyspnea (92%), peripheral edema (68%), radiographic congestion (59%), elevated natriuretic peptides (65%), and administration of intravenous diuretics (84%). Conclusion: A prompt-engineered LLM accurately extracted structured data, including signs and symptoms, laboratory and imaging data from adjudication dossiers at scale in a global clinical trial to generate a structured, useable database. LLM-based data extraction could be extended to unlock quantitative insights from a wide range of narrative trial records. MANUSCRIPT 2:Prognostic Significance of Clinical Features at admission for Heart Failure: Insights from DELIVER and PARAGON-HF Background: Hospitalization for heart failure (HFH) is a sentinel event associated with high mortality. Whether specific symptoms, signs, and diagnostic findings during HFH are associated with subsequent mortality is unknown. Objective: To determine whether individual clinical features at HFH predict subsequent all-cause mortality in HFmrEF/HFpEF. Methods: We analyzed patients with HFH from two international randomized trials (DELIVER and PARAGON-HF). Sixteen clinical features spanning symptoms, signs, and diagnostic findings were extracted from HFH records using a validated artificial intelligence tool. Cox proportional hazards models were used to assess associations with all-cause mortality. Multiple testing was controlled using the false-discovery rate (FDR). Results: Among 1,534 patients, 507 post-HFH deaths occurred over a median follow-up of 1.2 years. At admission dyspnea (93%) and peripheral edema (70%) were commonly documented; in contrast, other signs of congestion including significant weight gain (19%), ascites (14%), and hepatojugular reflux (3%) were less frequently reported. Radiography showed pulmonary congestion in 59% and pleural effusion in 40%, and worsening kidney function relative to pre-hospitalization baseline occurred in 27%. Intravenous diuretics were administered in 83% of patients, and 10% required mechanical/procedural interventions. Five admission clinical features showed nominal associations with mortality, of which two remained FDR-significant: fatigue (HR 1.59, 95% CI 1.24–2.04) and worsening kidney function assessed as percent creatinine change from baseline (HR per 25% increase 1.10, 95% CI 1.04–1.17). Conclusions: Many classic signs of congestion were infrequently documented in adjudication records during HFH admissions. Fatigue and worsening kidney function were associated with greater subsequent mortality.