社会经济因素和临床环境可通过机器学习模型预测偶然肺结节随访的依从性。

IF 4 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of the American College of Radiology Pub Date : 2024-10-01 DOI:10.1016/j.jacr.2024.02.031
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引用次数: 0

摘要

目的量化影响患者坚持偶发肺结节(IPNs)随访的人口、环境、社会经济和结节相关因素的相对重要性,并评估利用这些特征的机器学习模型的预测性能:我们从电子病历(EHR)中收集了 1610 个受试者的患者数据集,其中包括 13 个临床和社会经济预测因素,并将 IPN 随访依从性状态(及时/延迟/从不)作为结果。通过单变量分析和多变量逻辑回归来量化预测因素对随访依从性的影响。另外还拟合了三个机器学习模型(随机森林、神经网络和支持向量机)并进行了交叉验证,以检验不同模型架构的预测性能,并评估模型间的一致性:在单变量基础上,除合并症外,其他 13 个预测因素均与随访有显著关联。在多元逻辑回归中,住院病人或急诊病人的临床背景(与门诊病人相比,从不随访的OR值分别为7.28和8.56)和高结节风险(与低风险相比,OR值为0.25)是随访的最重要预测因素,而如果将临床背景从模型中剔除,性别、种族、婚姻状况则变得更加重要。临床背景本身与性别、种族、保险、就业、婚姻、收入、结节风险和吸烟状况相关,这表明临床背景在调解社会经济不平等方面发挥了作用。在交叉验证中,所有四个机器学习模型都表现出了相当好的预测性能,平均AUC为0.759-0.802,灵敏度为0.641-0.660,特异性为0.768-0.840:社会经济因素和临床环境可预测 IPN 随访的依从性,其中临床环境的作用最大,可能代表了未捕捉到的社会经济决定因素。
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Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models

Objective

To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features.

Methods

We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors’ contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance.

Results

On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840.

Conclusion

Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.
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来源期刊
Journal of the American College of Radiology
Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
6.30
自引率
8.90%
发文量
312
审稿时长
34 days
期刊介绍: The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.
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