Patient performance status: prediction model development and validation

患者表现状态：预测模型开发和验证

• 批准号: 7739405
• 负责人: AMY J DAVIDOFF
• 金额: $ 20.25万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7739405
• 关键词: Accounting; Activities of Daily Living; Address; Affect; Age; Algorithms; Applications Grants; Calibration; Cancer Patient; Caring; Characteristics; Clinical; Clinical Treatment; Clinical Trials; Code; Colon Carcinoma; Colorectal; Comorbidity; Complement; Cost Analysis; Data; Data Analyses; Data Element; Data Set; Data Sources; Diagnostic Procedure; Diagnostic Services; Disease; Elderly; Eligibility Determination; Enrollment; Face; Grant; Health; Health Status; Healthcare; Information Systems; Insurance; Knowledge; Lead; Link; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Measurement; Measures; Medical Surveillance; Medicare; Medicare Part A; Medicare claim; Methods; Modeling; Morbidity - disease rate; NIH Program Announcements; Non-Small-Cell Lung Carcinoma; Observational Study; Oncologist; Outcome; Output; Patients; Pattern; Performance; Performance Status; Policy Maker; Procedures; Process; Relative (related person); Research; Research Personnel; Role; Sampling; Selection for Treatments; Services; Source; Staging; Statistical Methods; Surveys; Survival Analysis; Techniques; Testing; United States; Validation; Work; age effect; base; beneficiary; cancer therapy; chemotherapy; comparative effectiveness; cost; data modeling; design; effectiveness research; fitness; functional status; improved; model development; mortality; neoplasm registry; older patient; programs; public health relevance; treatment effect

DESCRIPTION (provided by applicant): Patient Performance Status: Prediction Model Development and Validation (Davidoff, PI) Project Summary Cancer is a leading cause of morbidity and mortality, particularly among the elderly in the U.S., and treatment of cancer accounts for a substantial portion of health care spending. Clinical trials are the key mechanism for studying efficacy of new therapies, but retrospective data analyses using administrative (e.g. insurance enrollment and claims) data provide an important complement in examining treatment patterns, disparities in treatment, and effects on survival and costs of care for cancer patients. Although these latter studies commonly control for co-morbid conditions present in patients with cancer, one of the significant limitations is that they cannot control for patient performance status (PS). PS, a measure of functional status and physical performance, is used by clinicians to determine whether a cancer patient receives any treatment, and type and intensity of treatment. When the effects of cancer treatment on survival and costs are analyzed without controls for PS, the resulting estimates may be biased. The objective of this application is to develop a method to incorporate information on PS into analyses of cancer treatment that rely on administrative data. The approach will be to develop a multivariate regression model for PS using a dataset that has PS, enrollment and claims data. The model will be designed so that the independent variables are derived solely from data elements available in enrollment and claims files. The estimated coefficients from the model can be used to predict PS in a dataset that has enrollment and claims data but no direct measure of PS; the predicted PS can be incorporated into analyses using those data. The specific aims for this project are to: 1) develop a prediction model for PS based on demographic characteristics and measures generated from diagnostic and procedure codes in insurance claims data; and 2) examine predictive validity of the PS model. The source of data for model development will be the 1997-2006 Medicare Current Beneficiary Survey (MCBS), a nationally representative survey of Medicare beneficiaries linked to Medicare Part A and B claims data. The MCBS includes detailed information on functional status and physical performance that will be used to measure PS. Enrollment and claims data will be used to identify and test candidate predictor variables for the model. The second source of data, used in the validation step, will be Surveillance, Epidemiology, and End Results (SEER) cancer registry data from 1997-2002, linked to Medicare claims. The project team recently completed analyses of treatment patterns and survival for elderly Stage III colon cancer and locally advanced non-small-cell lung cancer. The PS model validation will incorporate predicted PS into those previously estimated models to test explanatory power, and to assess the impact of including PS on estimated effects of comorbidity and age on treatment, and effects of treatment on survival. Further model refinement and comparative effectiveness research using this model will be proposed as part of a subsequent R01 grant submission. PUBLIC HEALTH RELEVANCE: Retrospective data analyses using enrollment and claims data provide an important complement to clinical trials in examining use of chemotherapy, disparities in use, and effects on survival of cancer patients. A significant limitation to these analyses is that they cannot control for patient performance status (PS), a measure of functional status used by oncologists to determine treatment, and that has independent effects on survival. The objective of this application is to develop and validate a multivariate prediction model to incorporate information on PS into claims-based analyses. The results of this work will address a key methodologic limitation to many current studies, thus enhancing our understanding of factors that affect treatment for elderly cancer patients, and the effects of those treatments on survival.

描述（由申请人提供）： 患者表现状况：预测模型开发和验证（Davidoff，PI） 项目摘要 癌症是发病率和死亡率的主要原因，特别是在美国老年人中，癌症治疗占很大一部分的医疗保健支出。临床试验是研究新疗法疗效的关键机制，但使用行政数据（例如保险登记和理赔）进行回顾性数据分析为检查治疗模式、治疗差异以及对癌症生存和护理成本的影响提供了重要的补充患者。尽管后面这些研究通常控制癌症患者中存在的共病状况，但显着的局限性之一是它们无法控制患者的体力状态（PS）。 PS 是功能状态和身体表现的衡量标准，临床医生使用它来确定癌症患者是否接受任何治疗以及治疗的类型和强度。当在没有 PS 控制的情况下分析癌症治疗对生存和成本的影响时，所得的估计可能会有偏差。此应用程序的目的是开发一种方法，将 PS 信息纳入依赖管理数据的癌症治疗分析中。该方法将使用包含 PS、注册和索赔数据的数据集开发 PS 多元回归模型。该模型的设计使得自变量仅源自登记和索赔文件中可用的数据元素。模型的估计系数可用于预测数据集中的 PS，该数据集中有登记和索赔数据，但没有直接测量 PS；预测的 PS 可以纳入使用这些数据的分析中。该项目的具体目标是： 1）根据保险索赔数据中的诊断和程序代码生成的人口特征和测量值，开发 PS 预测模型； 2) 检查 PS 模型的预测有效性。模型开发的数据来源将是 1997-2006 年 Medicare 当前受益人调查 (MCBS)，这是一项与 Medicare A 部分和 B 部分索赔数据相关的全国代表性的 Medicare 受益人调查。 MCBS 包含有关功能状态和身体表现的详细信息，将用于测量 PS。登记和索赔数据将用于识别和测试模型的候选预测变量。验证步骤中使用的第二个数据源是 1997 年至 2002 年的监测、流行病学和最终结果 (SEER) 癌症登记数据，与医疗保险索赔相关。该项目团队最近完成了老年III期结肠癌和局部晚期非小细胞肺癌的治疗模式和生存率分析。 PS 模型验证将把预测的 PS 纳入之前估计的模型中，以测试解释力，并评估纳入 PS 对合并症和年龄对治疗的估计影响以及治疗对生存的影响的影响。使用该模型的进一步模型细化和比较有效性研究将作为后续 R01 拨款申请的一部分提出。公共卫生相关性：使用入组和索赔数据进行的回顾性数据分析为临床试验提供了重要的补充，以检查化疗的使用、使用差异以及对癌症患者生存的影响。这些分析的一个显着限制是它们无法控制患者的体力状态（PS），这是肿瘤学家用来确定治疗的功能状态的衡量标准，并且对生存具有独立影响。此应用程序的目标是开发和验证多元预测模型，将 PS 信息纳入基于索赔的分析中。这项工作的结果将解决许多当前研究的关键方法学限制，从而增强我们对影响老年癌症患者治疗的因素以及这些治疗对生存的影响的理解。