Identifying Reasons for ACEI/ARB Non-Use in CKD Using Scalable Clinical NLP with Schema-Guided LLM Augmentation

IMPORTANCEAlthough angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are recommended for people with chronic kidney disease (CKD), they remain underused. Barriers to adherence, such as adverse effects or patient refusal, are frequently embedded within unstructured clinical narratives and are therefore inaccessible to structured data analytics. Scalable natural language processing (NLP) approaches are needed to identify these barriers and support guideline-concordant care. OBJECTIVETo develop and evaluate an NLP model capable of identifying documented reasons for ACEI/ARB non-use within clinical notes of people with CKD in the Veterans Affairs (VA) healthcare system. DESIGN, SETTING, AND PARTICIPANTSThis retrospective study analyzed electronic health record data from 2005 to 2024 including people aged 18 to 80 years with CKD, defined by an estimated glomerular filtration rate (eGFR) of 20-60 mL/min/1.73 m2 and presence of albuminuria, across multiple VA medical centers. NLP models were trained on 1,025 manually annotated notes and further augmented with 4,600 synthetic examples generated through schema-guided large language model prompting. MAIN OUTCOMES AND MEASURESThe primary outcome was model performance in identifying notes containing at least one documented reason for ACEI/ARB non-use, evaluated using F1-score, precision, and recall. Secondary outcomes included model learning curve analyses and the effect of synthetic data augmentation on classification performance. RESULTSThe most common documented reasons for ACEI/ARB non-use were acute kidney injury (29.6%), increased creatinine (12.4%), cough (11.2%), and hypotension-related symptoms (11.1%). Across modeling approaches, training with synthetic data augmentation improved detection of notes containing reasons for non-use. Performance gains were statistically significant across all models (McNemar test, P < .05), with the random forest model using Nomic embeddings achieving the highest performance (F1 score, 0.79; 95% CI, 0.68-0.90). CONCLUSIONS AND RELEVANCEWe identified documented reasons for ACEI/ARB non-use (including both failures to initiate therapy and discontinuation after prior use) from unstructured text using an NLP method that does not require massive, expensive computing at inference time. By augmenting training data with schema-guided synthetic notes, we achieved robust, privacy-preserving performance within an NLP framework. This approach may support scalable clinical decision support systems to promote guideline-concordant prescribing.