Scholarlink

<p>Assessment of preoperative frailty is essential for risk stratification and optimization of patients undergoing major abdominal surgery for hepatobiliary (HPB) and gastrointestinal (GI) malignancies. Large language model (LLM)-based agents may facilitate automated frailty scoring, thereby supporting targeted prehabilitation efforts to improve surgical outcomes. We compared the performance of an agentic LLM system with a single LLM system in calculating frailty scores and classifying frailty status. Using real-world preoperative notes documented within 60 days prior to surgery, we evaluated both approaches. The agentic LLM system outperformed the single LLM system in 4 out of 6 models for a binary frailty classification task and in 5 out of 6 models for generating patient Risk Assessment Index (RAI) scores. The greatest improvements were observed among lower-parameter models (Llama 3.1 8b, Qwen 2.5 7b). These findings suggest than an agentic workflow may enhance frailty assessment performance. Automating frailty score calculation from Electronic Health Record (EHR) data may enhance clinical efficiency and enable targeted prehabilitation strategies for patients with cancer undergoing surgery.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Use of AI agents to assess preoperative frailty in cancer patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

查看全文
  • Denise T. Lee,
  • Steven Yen,
  • Georgia Smits,
  • David S. Matteson,
  • Noah Cohen,
  • Eyal Klang,
  • Girish N. Nadkarni,
  • Michael L. Marin

摘要

Assessment of preoperative frailty is essential for risk stratification and optimization of patients undergoing major abdominal surgery for hepatobiliary (HPB) and gastrointestinal (GI) malignancies. Large language model (LLM)-based agents may facilitate automated frailty scoring, thereby supporting targeted prehabilitation efforts to improve surgical outcomes. We compared the performance of an agentic LLM system with a single LLM system in calculating frailty scores and classifying frailty status. Using real-world preoperative notes documented within 60 days prior to surgery, we evaluated both approaches. The agentic LLM system outperformed the single LLM system in 4 out of 6 models for a binary frailty classification task and in 5 out of 6 models for generating patient Risk Assessment Index (RAI) scores. The greatest improvements were observed among lower-parameter models (Llama 3.1 8b, Qwen 2.5 7b). These findings suggest than an agentic workflow may enhance frailty assessment performance. Automating frailty score calculation from Electronic Health Record (EHR) data may enhance clinical efficiency and enable targeted prehabilitation strategies for patients with cancer undergoing surgery.