Individualized Risk Assessment in Patients with Multiple, Chronic Conditions

描述（由申请人提供）：50岁或以上患有多种慢性疾病的患者接受外科手术的比例估计在未来50年将翻一番。美国每年进行的外科手术超过8000万例；这些手术也越来越多地用于患有多种慢性疾病的患者。这些患者发生严重并发症和手术相关死亡的风险最高。虽然大手术的并发症率随着年龄和多种情况而上升，但合并症的特定组合或顺序对结果的影响尚不清楚。识别并发症和不良后果风险增加的患者对于指导医疗团队调整技术或干预措施、改进决策和提高质量至关重要。利用二手数据量化健康状况负担的最常用技术是共病指数，即基于应用预先确定的“权重”使某些状况比其他状况更重要的状况数目总和的数字分数。然而，合并症指数受到一些限制，并没有被广泛纳入临床护理患者的常规评估。提供可靠、易于使用和可获得的手术不良事件风险预测工具是至关重要的。我们假设，目前用于评估和预测多种健康状况和结果之间关系的技术可以通过以下方式得到改进：1)使用特定条件的诊断和特定条件的结果2)评估特定条件的组合，以评估它们对结果风险的贡献是否不是附加的，3)确定条件的时间序列是否有助于预测风险，而不是传统的评估条件是否存在。在过去的十年中，用于构建预测模型的可用技术的数量迅速增加，特别是针对具有大量属性的应用程序。本项目将一种新颖的风险预测策略应用于一个国家

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.