Predicting who will fracture: Exploration of machine learning in the observational Women's Health Initiative Study dataset.

预测谁会骨折：观察性妇女健康倡议研究数据集中机器学习的探索。

• 批准号: 10370048
• 负责人: ALEX BUI
• 金额: $ 16.89万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10370048
• 关键词: Advisory Committees; Age; Area; Assessment tool; Bayesian Network; Belief; Bone Density; Bone Tissue; Cessation of life; Characteristics; Classification; Clinical; Data; Data Set; Deterioration; Discrimination; Disease; Early Intervention; Electronic Health Record; Fracture; Future; Goals; Guidelines; Individual; Inferior; Lead; Life; Logistic Regressions; Longitudinal cohort; Machine Learning; Menopause; Methodology; Methods; Modeling; Neck; Osteoporosis; Osteoporotic; Outcome; Performance; Pharmacology; Population; Postmenopause; Predictive Value; Preventive service; Prospective Studies; Public Health; ROC Curve; Risk; Risk Assessment; Risk Estimate; Risk Factors; Self Assessment; Sensitivity and Specificity; Site; Stratification; System; Techniques; Testing; United States; Validation; Woman; Women' s Health; age group; aged; base; bone fragility; bone loss; bone mass; chronic pain; design; disability; experience; follow-up; fracture risk; gradient boosting; human old age (65+); improved; indexing; insight; machine learning model; machine learning prediction; model building; osteoporosis with pathological fracture; performance tests; predictive modeling; predictive tools; random forest; repository; risk prediction; screening; screening guidelines; tool

PROJECT ABSTRACT Half of all postmenopausal women will experience an osteoporosis-related fracture in their remaining lifetimes. As these fractures can lead to disability, loss of independence, and death, it is important to identify who is at risk for early intervention and mitigation. While clinical guidelines support routine osteoporosis screening for women aged ≥65 years, only selective screening is recommended for younger postmenopausal women aged 50-64 based on the use of risk assessment tools (e.g., OST, FRAX, SCORE). However, we have shown that these tools – which were not specifically developed for women in this age group – do not differentiate well between women who do and do not have osteoporosis (based on bone mineral density, BMD) and/or subsequent fracture. The objective of this project is to explore machine learning (ML) to improve osteoporosis risk assessment in young postmenopausal women. Prior ML-based analyses for osteoporosis and related fractures exist but are on non-American populations and/or are of limited size. We will use the large Women's Health Initiative (WHI) Study (>160,000 individuals from the United States), to develop, validate, and compare different machine learning approaches (random forests; logistic regression; dynamic belief network, DBN) for younger postmenopausal women. ML models will be constructed and assessed for two tasks: 1) predicting fracture risk in women aged 50-64 (Aim 1); and 2) predicting osteoporosis (per BMD; Aim 2). In each case, we will build ML models using existing risk factors from current tools, as well as add additional variables collected from the WHI to identify new features that may improve predictive power. We will also assess the value of temporal model by building DBNs, using an individual's past observations to guide predictions. We will compute technical performance metrics (e.g., sensitivity, specificity, positive predictive value) and conduct error analyses to contrast what (sub)groups each model (in)correctly identifies. We will also perform sensitivity analyses to ascertain the impact of different variables on the robustness of the model's predictions. Lastly, we plan to externally validate (Aim 3) the models from Aims 1 & 2 using electronic health record (EHR) datasets from UCLA and UCSF, investigating the degree of transportability. Successful execution of this R21 will: 1) develop and test different ML models predicting major osteoporotic fracture and osteoporosis in US women; 2) identify potential additional variables that inform the risk of these conditions; and 3) provide insight into areas where such ML-models may be improved through stratifi- cation and/or future methodological approaches. The results from this R21 will serve as a baseline for a broader R01 to develop more effective predictive models for fracture and osteoporotic risk.

项目摘要 绝经后妇女中有一半会在剩余的一生中经历与骨质疏松相关的骨折。 由于这些断裂可能导致残疾，失去独立和死亡，因此必须确定谁处于危险之中很重要 进行早期干预和缓解。而临床指南支持女性常规骨质疏松症筛查 年龄≥65岁，仅建议对50-64岁的绝经后妇女进行选择性筛查 根据风险评估工具的使用（例如OST，FRAX，得分）。但是，我们已经证明了这些 工具 - 不是专门为这个年龄段的女性开发的 - 在之间没有很好的区分 做和没有骨质疏松症的妇女（基于骨矿物质密度，BMD）和/或随后的骨折。 该项目的目的是探索机器学习（ML），以改善骨质疏松症的风险评估 绝经后妇女。存在基于ML的骨质疏松症和相关骨折的分析，但仍在 非美国人口和/或规模有限。我们将使用大型妇女健康计划（WHI）研究 （来自美国的16万个人），开发，验证和比较不同的机器学习 年轻的后期后，方法（随机森林；逻辑回归；动态信念网络，DBN） 女性。 ML模型将被构建和评估两项任务：1）预测年龄妇女的骨折风险 50-64（AIM 1）; 2）预测骨质疏松症（每个BMD； AIM 2）。在每种情况下，我们都会使用 现有工具的现有风险因素，以及添加从WHI收集的其他变量以识别新的变量 可以提高预测能力的功能。我们还将通过构建DBN来评估临时模型的价值， 利用个人过去的观察来指导预测。我们将计算技术绩效指标 （例如，灵敏度，特异性，阳性预测价值）并进行误差分析以对比（子）组 每个模型（在）正确识别。我们还将执行灵敏度分析以确定不同的影响 关于模型预测的鲁棒性的变量。最后，我们计划外部验证（AIM 3）模型 使用来自UCLA和UCSF的电子健康记录（EHR）数据集的AIMS 1和2 运输能力。该R21的成功执行将：1）开发和测试预测主要的ML模型 美国妇女的骨质疏松骨折和骨质疏松症； 2）确定潜在的其他变量，以告知风险 这些条件； 3）提供有关通过Stratifi-可以改善此类ML模型的区域的洞察力 阳离子和/或未来的方法论方法。 R21的结果将成为更广泛的基线 R01开发出更有效的裂缝和骨质疏松风险的预测模型。