Identifying cancer cachexia using clinical reasoning artificial intelligence (AI).

Abstract

1613 Background: Cancer cachexia, a severe wasting syndrome, affects up to 87% of cancer patients 1 and contributes to ≥20% of cancer deaths 2 . Despite this burden, ~85% of healthcare providers are unfamiliar with diagnostic criteria 3 , leaving patients often undiagnosed. This study evaluated whether a Clinical Reasoning AI model configured on four cachexia guidelines (Fearon 4 , Evans 5 , GLIM 6 and mGPS 7 criteria) and designed to emulate clinician chart review, could accurately identify cancer cachexia. Methods: Using structured (vitals, labs) and unstructured (clinical notes) electronic health record data the Clinical Reasoning AI employed retrieval-augmented generation 8 and agentic techniques with large language models (LLMs) to: (1) retrieve clinically relevant information based on the clinical guidelines via natural language processing (NLP) and embedding, and (2) apply guideline-based reasoning to classify patients into Group 1 (no cachexia features), Group 2 (meets ≥1 guideline regardless of documented diagnosis), or Group 3 (documented cancer cachexia). Accuracy was assessed against a gold standard of 50 patients classified by two independent clinicians, with conflicts resolved by a third. Performance was compared against baseline methods: ICD coding (ICD-9 799.4; ICD-10 C80.9), NLP using keywords like cancer or cachexia, and LLMs (Vanilla OpenAI GPT-o4-mini) prompted without explicit instruction to use guidelines. Results: The Clinical Reasoning AI demonstrated high precision, sensitivity, and specificity, outperforming baselines, particularly for Group 2 patients. ICD and NLP methods could not detect Group 2, due to code- and keyword-based constraints. Performance of LLMs without guidelines worsened with structured and unstructured data, indicating sensitivity to data overload and inability to prioritise clinically relevant information. Conclusions: These results show that embedding clinical guidelines within a structured retrieval-and-reasoning architecture enabled accurate identification of cancer cachexia, addressing a critical gap where standard coding and keyword methods fail. The guideline-driven approach maintained accurate clinical reasoning while processing complex EHR data. The Clinical Reasoning AI model is currently being deployed at the point of care to assess real-world implementation and impact on patient outcomes. Group 2 Group 3 AI Model Precision Sensitivity F1 Specificity Precision Sensitivity F1 Specificity ICD N/A N/A N/A N/A 0.759 0.815 0.786 0.696 Simple NLP N/A N/A N/A N/A 0.727 0.593 0.653 0.739 Vanilla OpenAI GPT-o4-mini (unstructured data only) 0.500 0.500 0.500 0.765 0.594 0.704 0.644 0.438 Vanilla OpenAI GPT-o4-mini (structured and unstructured data) 0.500 0.062 0.111 0.971 0.385 0.185 0.250 0.652 Clinical Reasoning AI with GPT-o4-mini 0.762 1.00 0.865 0.853 0.964 1.00 0.982 0.957