Predicting Risk of Lung Cancer From Medical History.

IF 1.2 4区医学 Q4 HEALTH CARE SCIENCES & SERVICES

Quality Management in Health Care Pub Date : 2025-04-01 Epub Date: 2025-04-08 DOI:10.1097/QMH.0000000000000525

Amaljith Kuttamath

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引用次数: 0

Abstract

Background and objectives: Lung cancer causes 130 000 deaths annually in the United States, with treatment costs averaging $150 000 per patient and a 5-year survival rate of 20.5%. Current screening criteria rely on smoking history and age, missing other risk factors. This study aimed to identify clinical risk factors and social determinants of health (SDoH) for enhanced risk assessment using electronic health record (EHR) data.

Methods: We analyzed 410 298 patient records from the All of Us Research Program, including 9375 lung cancer cases identified through SNOMED coding. Using Logistic LASSO regression, we developed predictive models based on diagnoses grouped by body systems and their interactions.

Results: Respiratory, cardiovascular, and immune systems showed three-fold greater association with lung cancer than other systems. Brain metastasis showed the strongest association (odds ratio 5.0, 95% CI: 4.2-5.8). The final model achieved an AUC of 0.82 (95% CI: 0.80-0.84) and 78% sensitivity in validation. Patients with documented social determinants showed 2.5-fold higher risk (95% CI: 2.1-2.9).

Conclusions: EHR-based prediction models effectively identify lung cancer risk using readily available medical history data. These findings support expanding current screening criteria beyond traditional risk factors.

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从病史预测肺癌风险

背景和目的：在美国，肺癌每年导致13万人死亡，每位患者的平均治疗费用为15万美元，5年生存率为20.5%。目前的筛查标准依赖于吸烟史和年龄，而忽略了其他危险因素。本研究旨在利用电子健康记录（EHR）数据确定临床风险因素和健康的社会决定因素（SDoH），以加强风险评估。方法：我们分析了来自All of Us研究项目的410 298例患者记录，其中包括9375例通过SNOMED编码识别的肺癌病例。使用Logistic LASSO回归，我们建立了基于身体系统及其相互作用分组的诊断的预测模型。结果：呼吸系统、心血管系统和免疫系统与肺癌的相关性是其他系统的三倍。脑转移的相关性最强（优势比5.0,95% CI: 4.2-5.8）。最终模型的AUC为0.82 (95% CI: 0.80-0.84)，验证灵敏度为78%。记录在案的社会决定因素患者的风险高出2.5倍（95% CI: 2.1-2.9）。结论：基于ehr的预测模型利用现成的病史数据有效地识别肺癌风险。这些发现支持将目前的筛查标准扩展到传统的风险因素之外。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Quality Management in Health Care HEALTH CARE SCIENCES & SERVICES-

CiteScore

1.90

自引率

8.30%

发文量

108

期刊介绍： Quality Management in Health Care (QMHC) is a peer-reviewed journal that provides a forum for our readers to explore the theoretical, technical, and strategic elements of health care quality management. The journal''s primary focus is on organizational structure and processes as these affect the quality of care and patient outcomes. In particular, it: -Builds knowledge about the application of statistical tools, control charts, benchmarking, and other devices used in the ongoing monitoring and evaluation of care and of patient outcomes; -Encourages research in and evaluation of the results of various organizational strategies designed to bring about quantifiable improvements in patient outcomes; -Fosters the application of quality management science to patient care processes and clinical decision-making; -Fosters cooperation and communication among health care providers, payers and regulators in their efforts to improve the quality of patient outcomes; -Explores links among the various clinical, technical, administrative, and managerial disciplines involved in patient care, as well as the role and responsibilities of organizational governance in ongoing quality management.