Dynamic scheduling of the upcoming screening exam based on screening history and other parameters

根据筛选历史和其他参数动态安排即将进行的筛选检查

• 批准号: 9810977
• 负责人: Dongfeng Wu
• 金额: $ 43.53万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9810977
• 关键词: Address; Age; Chronic Disease; Clinical; Computer software; Diagnosis; Disease; Early Diagnosis; Female; Foundations; Frequencies; Future; Gender; Goals; Incidence; Individual; Knowledge; Lead; Likelihood Functions; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Modality; Modeling; Motivation; Output; Patients; Persons; Physicians; Plug-in; Policy Maker; Probability; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Pulmonary Mass; Recording of previous events; Research; Risk; Risk Factors; Roentgen Rays; Schedule; Screening Result; Screening for cancer; Smoker; Smoking Status; Spiral Computed Tomography; Statistical Methods; Techniques; Testing; Thoracic Radiography; Time; Validation; X-Ray Computed Tomography; base; cohort; density; low-dose spiral CT; lung cancer screening; male; never smoker; pre-clinical; screening; simulation; software development; time interval; trial design; tv watching; user friendly software

Project Summary: Motivated by the need of physicians and patients to make informed decision, probability methods are proposed to address the problem of when to schedule the next exam for an asymptomatic individual with a history of negative screenings. The method provides a screening time interval to limit the risk of being a clinical incident case to a small value, such as 5% or 10%. The time interval is a function of the three key parameters (i.e., screening sensitivity, sojourn time in preclinical state and transition time distribution in disease-free state), a person's current age/gender, smoking status, and one's screening history. Distribution of Lead time (ie. diagnosis time advanced by screening) and probability of over-diagnosis are derived, if one would be diagnosed with cancer at the future scheduled time. A new likelihood function to estimate the three key parameters are proposed and will be validated to increase the accuracy of the estimation. The methods will be applied to four cohorts from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for lung cancer (PLCO-Lung) and four cohorts from the National Lung Screening Trials (NLST). The specific aims are: 1. To estimate the three key parameters using a new likelihood function in the eight cohorts using either chest X-ray or Low Dose Computed Tomography (CT). Eight cohorts (four from PLCO-Lung using X-ray: male/female smokers, male/female never-smokers; four from NLST: male/female smokers via X-ray, male/female smokers via CT) were identified. The three key parameters will be estimated when sensitivity is modeled as a function of the sojourn time and time spent in the preclinical state using our new likelihood method. This lays a foundation for predictions and estimations in Aim 2, since the future time interval is a function of the three key parameters; and we will plug in estimates of the three key parameters in Aim 2 to obtain the optimal screening time. 2. To schedule the next screening exam dynamically for an asymptomatic individual with any screening history. A new method using conditional probability of incidence before the next exam is proposed. The next screening time is found by limiting this probability to a small value, to guarantee early detection and control the risk of over-diagnosis. The lead-time distribution and the probability of over-diagnosis are derived, if one would be diagnosed with cancer at the next proposed screening time, so that predictive information can be provided to individuals on how early his/her disease could be detected, and the risk of over-diagnosis if one undergoes screening exams as suggested. 3. To develop a user-friendly software for Aims 1 and 2, and made available to the public. In summary policy makers can use information from this research to recommend future screening frequencies for different risk groups (e.g., age, gender, smoking status) under different screening techniques (X-ray or CT). Physicians may use results from Aim 3 (the developed software) to schedule the next exam for individuals.

项目概要： 基于医生和患者做出知情决策的需要，提出了概率方法 为了解决何时为具有以下病史的无症状个体安排下一次检查的问题， 消极的筛选该方法提供了筛选时间间隔，以限制成为临床事件的风险 case的值，例如5%或10%。时间间隔是三个关键参数的函数（即， 筛选灵敏度、在临床前状态的逗留时间和在无病状态的过渡时间分布）， 一个人的当前年龄/性别，吸烟状况和一个人的筛查史。交付周期（Lead Time） 诊断时间提前筛选）和过度诊断的概率，如果一个将是 在未来的预定时间被诊断出患有癌症。一种新的似然函数估计三个关键 参数的建议，并将进行验证，以提高估计的准确性。方法将是 应用于前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验的四个队列， （PLCO-Lung）和来自国家肺筛查试验（NLST）的四个队列。具体目标是： 1.使用新的似然函数估计8个队列中的3个关键参数， X射线或低剂量计算机断层扫描（CT）。8个队列（4个来自使用X线的PLCO-肺： 男性/女性吸烟者，男性/女性从不吸烟者; NLST中的4例：男性/女性吸烟者（通过X光检查）， 男性/女性吸烟者（通过CT）。当灵敏度为100%时，将估计三个关键参数。 使用我们新的可能性，将其建模为逗留时间和在临床前状态下花费的时间的函数 法这为目标2中的预测和估计奠定了基础，因为未来的时间间隔是一个 函数;我们将在目标2中插入三个关键参数的估计值， 获得最佳筛选时间。 2.为无症状个体动态安排下一次筛查， 历史提出了一种在下次考试前使用条件概率的新方法。的 通过将该概率限制为小值来找到下一个筛选时间，以保证早期检测， 控制过度诊断的风险。提前期分布和过度诊断的概率是 如果一个人在下一个建议的筛查时间被诊断出患有癌症， 可以向个人提供关于他/她的疾病可以多早被检测到以及 过度诊断，如果一个人按照建议进行筛查检查。 3.为目标1和2开发一个方便用户的软件，并向公众提供。 总之，政策制定者可以利用这项研究的信息来推荐未来的筛查频率。 对于不同的风险组（例如，年龄、性别、吸烟状况）在不同的筛查技术（X射线或CT）下进行。 医生可以使用Aim 3（开发的软件）的结果来安排个人的下一次检查。