Individualized Risk Assessment in Patients with Multiple, Chronic Conditions

多种慢性病患者的个体化风险评估

• 批准号: 8725922
• 负责人: David R Flum
• 金额: $ 16.58万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8725922
• 关键词: Individualized; Risk; Assessment; Patients; Multiple

DESCRIPTION (provided by applicant): The proportions of patients aged 50 years or older with multiple chronic conditions having surgical procedures are estimated to double in the next 50 years. More than 80 million surgical procedures are performed each year in the United States; these procedures are also increasingly being performed in those with multiple chronic conditions. These patients have the greatest risk of serious complications and procedure-related deaths. Although complication rates from major surgery rise with age and multiple conditions, the effect of specific combinations or sequences of comorbid conditions on outcome is not well understood. Identifying patients at increased risk for complications and adverse outcomes is critical to direct healthcare teams to adjust techniques or interventions, improve decision-making and quality improvement. The most common technique used to quantify the burden of health conditions using secondary data are comorbidity indices, or numeric scores based on a summation of the number of conditions that apply pre-determined "weights" to give certain conditions more importance over others. Comorbidity indices, however, are subject to several limitations and have not been widely incorporated into the routine assessment of patients in clinical care. The availability of a reliable, easy to use and accessible risk prediction tool for adverse events in surgery is essential. We hypothesize that the current techniques for assessing and predicting the relationship of multiple health conditions and outcome may be improved by: 1) using condition specific diagnoses and condition specific outcomes 2) evaluating specific combinations of conditions to assess if their contribution to the risk of outcome is something other than additive and, 3) determining if the temporal sequence of conditions contributes to the prediction of risk beyond the conventional assessment of whether the conditions are present at all. The last decade has seen a rapid increase in the number of available techniques for building predictive models, especially targeting applications with much larger numbers of attributes. This project applies a novel risk prediction strategy to a nationally representative administrative claims database, including longitudinal records from millions of patients. The focus of the project is on the enhanced risk prediction ability of novel dynamic statistical models that will relate the timing, sequence, combination, and clustering of chronic conditions to effectiveness, safety, resource use and cost of the 10 most commonly performed major elective surgical procedures. These novel risk prediction models will utilize dynamic statistical modeling and machine learning techniques to create an easy to use, interactive risk prediction platform. Successfully improving upon a risk prediction tool for adverse events in surgery will better inform patient-centered decision-making, direct healthcare teams to adjust techniques and interventions, help target quality improvement interventions, allow more equitable reimbursement activities and even support accountable care organization activities that rely on accurate estimates of population risk and health.

描述（由申请人提供）：在未来50年内估计具有多种慢性疾病的50岁或多个慢性疾病患者的比例将翻一番。在美国，每年进行超过8000万手术程序；这些程序也越来越多地在具有多种慢性病的人中执行。这些患者有严重并发症和程序相关死亡的风险最大。尽管大手术的并发症发生率随着年龄和多种状况而升高，但尚不清楚特定组合或合并症条件序列对结局的影响。确定患者患有并发症和不良后果风险的患者对于指导医疗团队调整技术或干预，改善决策和质量改善至关重要。用于使用二级数据量化健康状况负担的最常见技术是合并症指数，或基于数字分数，基于对应用预定的“权重”的条件数量的总结，以使某些条件比其他条件更重要。但是，合并症指数受到了几个局限性，并未被广泛纳入临床护理中患者的常规评估中。对于手术中不良事件的可靠，易于使用和可访问的风险预测工具的可用性至关重要。我们假设我们可以通过以下方式来改进和预测多种健康状况和结果的关系的当前技术：1）使用条件特定的诊断和条件特定的特定结果2）评估条件的特定组合以评估其对结果风险的贡献是否在添加性的情况下进行，以及3）是否会在预测中确定条件的临时序列是否有助于预测的临时序列。在过去的十年中，用于构建预测模型的可用技术数量迅速增加，尤其是针对具有大量属性的应用程序。该项目将新颖的风险预测策略应用于全国 代表性行政索赔数据库，包括数百万患者的纵向记录。该项目的重点放在新型动态统计模型的增强风险预测能力上，该模型将将慢性病条件的时间，顺序，组合和聚类与有效性，安全性，资源使用以及10个最常见的大型选举手术程序的有效性，安全性，资源使用和成本相关联。这些新颖的风险预测模型将利用动态统计建模和机器学习技术来创建易于使用的交互式风险预测平台。成功改善手术不良事件的风险预测工具将更好地为以患者为中心的决策，直接医疗团队调整技术和干预措施，有助于目标质量改善，允许更公平的报销活动，甚至支持依靠准确估计人口风险和健康的负责任的护理组织活动。