Decreasing Unnecessary Invasive Lung Cancer Diagnostic Procedures

减少不必要的侵袭性肺癌诊断程序

• 批准号: 8201844
• 负责人: Eric L Grogan
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8201844
• 关键词: Affect; Alabama; Algorithms; American College of Surgeons; American College of Surgeons Oncology Group; Area Under Curve; Benign; Biological Markers; Biopsy; Cancer Diagnostics; Cancer Etiology; Cessation of life; Chest; Clinic; Clinical; Clinical Data; Cost Effectiveness Analysis; Data; Data Element; Data Set; Databases; Death Rate; Decision Analysis; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Dose; Epidemiology; Evaluation; Excision; Future; Image; Imaging Techniques; Individual; Judgment; K-Series Research Career Programs; Lesion; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Modeling; Morbidity - disease rate; Nodule; Operative Surgical Procedures; Outcome; Patient Care; Patients; Pilot Projects; Population; Provider; Research; Research Personnel; Resected; Safety; Sampling; Scanning; Surgeon; Techniques; Testing; Therapeutic; Thoracic Surgeon; Thoracic Surgical Procedures; Universities; Unnecessary Surgery; Validation; Veterans; Virginia; Work; X-Ray Computed Tomography; anticancer research; clinical practice; cohort; cost effective; cost effectiveness; design; evidence base; experience; high risk; improved; killings; mortality; operation; patient safety; predictive modeling; prevent; prospective; screening; skills; tool

DESCRIPTION (provided by applicant): Lung cancer is the number one cause of cancer death and Veterans are 25% to 76% more likely to develop this deadly disease. The main challenge in the field of lung cancer research is trying to prevent advanced lung cancers that kill patients and simultaneously minimize the potential harm caused by required invasive diagnostic techniques. Because lung cancer is so deadly, patients and providers must aggressively pursue a diagnosis to rule out cancer. The lung is not easily accessible and these biopsies often require an invasive and costly. Despite advanced imaging techniques and clinical judgment, up to 40% of the operations on patients with suspected lung cancer result in a benign diagnosis. The high rate of benign disease discovered by operative resection will continue until additional patient care. This career development award permits me to pursue research skills and investigator experience for 1) developing and validating evidence-based surgical algorithms for reducing unnecessary surgery, 2) improving patient safety by not missing cases of lung cancer, 3) implementing a safe and cost effective lung nodule clinical algorithm for patients with suspicious pulmonary nodules. Study One: To develop an evidence-based clinical algorithm for management of lung nodules referred for diagnostic surgical evaluation. We hypothesize that a new model predicting benign disease among patients presenting with suspicious pulmonary nodules will have a ROC area under the curve (AUC) of at least 0.85. Current models do not include all the epidemiological and imaging data used by surgeons to estimate the pre- surgical likelihood of cancer or benign disease and determine whether to operate on a suspicious nodule. This aim will combine the VA-TVHS patient database, Vanderbilt Lung Nodule Cohort, and the University of Virginia database into a 950 patient Lung Nodule Cohort. A regression model will be developed from this cohort and will also include an exploratory analysis of new lung cancer biomarkers. Study Two: To evaluate the generalizability of the lung nodule clinical algorithm for management of lung nodules referred for diagnostic surgical evaluation. We will externally validate the prediction tool developed in Study One with existing datasets from the University of Alabama, Birmingham (UAB) and the completed American College of Surgeons (ACOSOG) Z4031 cooperative trial. These datasets will be combined to form a 1500 patient validation cohort. Biomarkers will also be assessed in the ACOSOG dataset from stored clinical samples. Study Three: To evaluate the predicted impact of the lung nodule clinical algorithm on patient outcomes in a multi-institutional prospective cohort. The prospective 686-patient cohort will be from VA-TVHS, VA- Birmingham and VUMC thoracic surgery clinics. This study will NOT implement the diagnostic algorithm in clinical practice but provide a safe harbor to accomplish two aims. First, we will prospectively evaluate the number of patients potentially benefiting from such algorithm by not missing cases of lung cancer and avoiding unnecessary operations. Second, we will use decision analysis to perform an incremental cost-effectiveness analysis of our algorithm in this cohort. We hypothesize that use of the prediction tool will reduce the benign diagnosis rate in surgically resected pulmonary nodules from 40% to at least 30%, the overall accuracy will be over 85% and it will be cost effective. Future studies will design a prospective multi-institutional VA pilot study to evaluate the algorithm for patients referred for surgical evaluation of pulmonary nodules.

描述（由申请人提供）： 肺癌是癌症死亡的第一大原因，退伍军人患这种致命疾病的可能性高25％至76％。肺癌研究领域的主要挑战是试图防止杀死患者的晚期肺癌并同时最大程度地减少所需的侵入性诊断技术造成的潜在伤害。由于肺癌是如此致命，因此患者和提供者必须积极进行诊断以排除癌症。肺部不容易到达，这些活检通常需要侵入性和昂贵。尽管先进的成像技术和临床判断，但对怀疑肺癌患者的手术中多达40％导致良性诊断。手术切除发现的高良性疾病发生率将持续到额外的患者护理为止。该职业发展奖使我能够追求研究技能和研究人员的经验以1）开发和验证基于证据的手术算法，以减少不必要的手术，2）通过不缺失肺癌病例来改善患者的安全性，3）实施一种安全且具有可疑肺结核患者的安全且具有成本效益的肺结节临床算法。 研究一：开发一种基于证据的临床算法，用于用于诊断手术评估的肺结核管理。我们假设一个新的模型预测可疑肺结节的患者的良性疾病，将在曲线（AUC）下至少为0.85。当前的模型并未包括外科医生使用的所有流行病学和成像数据，以估计癌症或良性疾病的手术前可能性，并确定是否在可疑结节上进行操作。这个目标将将VA-TVHS患者数据库，Vanderbilt肺结核队列和弗吉尼亚大学数据库结合在一起，成为950名患者肺结核队列。该队列将开发回归模型，还将包括对新肺癌生物标志物的探索性分析。 研究第二：评估肺结节临床算法的普遍性用于用于诊断手术评估的肺结节的管理。我们将在外部验证研究中开发的预测工具，该工具与阿拉巴马大学，伯明翰大学（UAB）的现有数据集和完整的美国外科医生学院（ACOSOG）Z4031合作试验。这些数据集将组合成1500个患者验证队列。也将在储存的临床样品的AcoSOG数据集中评估生物标志物。 研究三：评估肺结节临床算法对多机构前瞻性队列中患者结局的预测影响。前瞻性686患者队列将来自VA-TVHS，VA-BIRMINGHAM和VUMC胸外手术诊所。这项研究将不会在临床实践中实施诊断算法，而是提供了实现两个目标的安全港。首先，我们将前瞻性评估潜在的算法中可能受益的患者数量，而不会缺少肺癌病例并避免不必要的手术。其次，我们将使用决策分析对我们的算法进行增量成本效益分析。我们假设使用预测工具将使手术切除的肺结核的良性诊断率从40％降低到至少30％，总体准确性将超过85％，并且具有成本效益。 未来的研究将设计一项前瞻性多机构VA初步研究，以评估转介手术评估肺结核的患者的算法。