Use of electronic data to improve risk adjustment for antibiotic utilization metrics

在美国，每年至少有200万人感染耐药细菌， 至少有23,000人因此直接死亡。过度使用抗生素是导致 耐药细菌。这使得联邦机构建议急性护理机构 抗菌药物管理计划，并使用国家质量论坛记录抗菌药物使用数据 美国疾病控制和预防中心（CDC）国家医疗安全网络（NQF） （NHSN）抗菌药物使用测量。许多人认为，抗菌药物利用数据将不可避免地 用于判断医院的绩效，并作为绩效工资的结果。知识上的差距在于 CDC目前使用的抗菌药物利用数据风险调整方法是次优的， 不存在针对患者共病状况进行调整的方法。我们的长期目标是提高质量， 公开报告的医疗质量指标的有效性，包括医疗相关感染， 抗菌药物利用数据。本提案的总体目标是确定哪些合并症 应用于抗菌药物利用数据的风险调整。我们的中心假设是， 通过ICD代码识别的疾病，这些代码很容易从美国各地的医院以电子方式获得 国家可用于改善抗菌药物利用指标的风险调整。这样做的理由 建议需要进一步改进抗菌药物使用的风险调整方法 指标.现在是为抗菌药物的使用制定适当的风险调整措施的理想时机， CMS尚未将抗生素使用纳入其基于价值的采购系统。合并症是一种 这是一个逻辑起点，因为它们已被证明是其他传染病结局的重要预测因素 并且容易获得。我们计划测试我们的中心假设，从而实现这一目标。 通过追求以下具体目标的建议：目标1：进行一项成年患者的队列研究， 美国多家医院，以确定哪些电子获得的合并症是风险 不同抗生素利用指标的因素。目的2：证明使用共病风险调整 条件影响医院抗菌药物使用的排名。这项研究的预期成果是， 使用ICD编码识别可用于调整抗菌药物使用风险的共病情况 数据疾病控制和预防中心和CMS实施这些措施将导致获得更有效的临床可用数据。的 我们研究的意义在于，它将识别容易获得的电子共病条件， 用于更好地调整这些抗生素利用指标的风险。这项研究的创新之处在于， 有人探索了使用ICD编码对抗菌药物使用数据进行风险调整。这项工作 挑战了现有的抗菌药物利用指标风险调整范式。此外，该项目 有可能显著影响抗菌药物利用指标和按效付费方法。

Each year in the United States at least 2 million people become infected with antibiotic-resistant bacteria and at least 23,000 people die as a direct result. Overuse of antibiotics is a key factor driving the emergence of antibiotic-resistant bacteria. This has led federal agencies to recommend acute care facilities have antimicrobial stewardship programs and record antimicrobial utilization data using the National Quality Forum (NQF)-endorsed Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) Antimicrobial Use Measure. Many believe that it is inevitable that antimicrobial utilization data will be used to judge performance of hospitals and as a pay-for-performance outcome. The gap in knowledge is that current risk adjustment methods used by the CDC for antimicrobial utilization data are sub-optimal because methods to adjust for patient comorbid conditions do not exist. Our long-term goal is to improve the quality and validity of publicly reported metrics for healthcare quality including healthcare-associated infection and antimicrobial utilization data. The overall objective of this proposal is to determine which comorbid conditions should be used for risk adjustment of antimicrobial utilization data. Our central hypothesis is that comorbid conditions identified by ICD codes that are easily obtained electronically from hospitals across the United States can be used to improve risk adjustment of antimicrobial utilization metrics. The rationale for this proposal is the need for further advancement in risk adjustment methodology for antimicrobial utilization metrics. Now is the ideal time to establish appropriate risk adjustment measures for antimicrobial use because CMS has not yet incorporated antibiotic use into its value-based purchasing system. Comorbid conditions are a logical starting point as they have been proven to be significant predictors of other infectious disease outcomes and are easy to obtain. We plan to test our central hypothesis and, thereby, accomplish the objective of this proposal by pursuing the following specific aims: Aim 1: Perform a cohort study of adult patients admitted to multiple hospitals across the United States to determine which electronically obtained comorbidities are risk factors for different antibiotic utilization metrics. Aim 2: Demonstrate that risk adjustment using comorbid conditions affect hospital rankings of antimicrobial utilization. The expected outcome of this research is the identification of comorbid conditions using ICD codes for that can be used to risk adjust antimicrobial utilization data. The implementation of these by the CDC and CMS will lead to more valid publically available data. The significance of our research is that it will identify easily available electronically comorbid conditions that could be used to better risk adjust these antibiotic utilization metrics. The proposed research is innovative in that no one has explored the use of ICD codes for risk adjustment of antimicrobial utilization data. This work challenges the existing paradigm of risk adjustment of antimicrobial utilization metrics. In addition, this project has the potential to significantly impact antimicrobial utilization metrics and pay-for-performance methods.