Electronically Available Patient Claims Data Improve Models for Comparing Antibiotic Use Across Hospitals: Results From 576 US Facilities.
Electronically Available Patient Claims Data Improve Models for Comparing Antibiotic Use Across Hospitals: Results From 576 US Facilities.
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DOI：
10.1093/cid/ciaa1127
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发表时间：
2021-12-06
期刊：
影响因子：
--
通讯作者：
Harris AD
中科院分区：
文献类型：
--
作者：
Goodman KE;Pineles L;Magder LS;Anderson DJ;Ashley ED;Polk RE;Quan H;Trick WE;Woeltje KF;Leekha S;Cosgrove SE;Harris AD
疾病控制和预防中心（CDC）使用标准化的抗菌药物施用比率（SAAR）-即预测与预测比率-来比较设施之间的抗生素使用。CDC模型在预测抗生素使用时调整了设施特征，但不包括可能影响使用的患者诊断和合并症。本研究旨在确定与适当抗生素使用有因果关系的合并症，并将包括这些合并症和其他患者水平索赔变量的模型与风险调整住院抗生素使用的设施模型进行比较。该研究包括2016-2017年从Premier Database医院出院的成年人。对于每次入院，我们提取设施，索赔和抗生素数据。我们评估了7个模型来预测入院的抗生素治疗天数（DOT）：CDC设施模型、以不同复杂程度添加患者临床结构的模型，以及对已发布的患者变量模型的外部验证。我们计算了医院特定的SAAR，以量化对医院排名的影响。另外，我们使用德尔菲共识方法来确定与适当抗生素使用相关的Elixhauser合并症。该研究包括576家医院的11701326名住院患者。与CDC设施模型相比，添加Delphi选择的合并症和细菌感染指标的模型对所有抗生素结果更准确。对于抗生素总使用量，其准确性提高了24%（各自的平均绝对误差：3.11 vs 2.35 DOT），导致31-33%的医院进入调整后的最低或最高使用量四分位数。将电子可用的患者索赔数据添加到设施模型中始终可以改善抗生素使用预测，并使医院的使用排名发生实质性变化。本研究旨在评估，在美国医院的大型和多样化队列中，将合并症和其他索赔数据衍生的患者变量添加到设施变量模型中是否可以改善住院抗生素使用的风险调整。
The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)—that is, observed-to-predicted ratios—to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. The study included adults discharged from Premier Database hospitals in 2016–2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission’s antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31–33% more hospitals moving into bottom or top usage quartiles postadjustment. Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals’ utilization rankings. This study aimed to evaluate, across a large and diverse cohort of US hospitals, whether adding comorbidities and other claims data-derived patient variables to facility-variable models improves risk-adjustment of inpatient antibiotic utilization.
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影响因子：
3
作者：
Elixhauser, A;Steiner, C;Coffey, RN
通讯作者：
Coffey, RN
影响因子：
2
作者：
Chan, K. H.;Moser, E. A.;Bell, T.
通讯作者：
Bell, T.
影响因子：
11.8
作者：
Kazakova, Sophia, V;Baggs, James;Jernigan, John A.
通讯作者：
Jernigan, John A.
DOI：
10.1093/cid/ciaa326
发表时间：
2020-12-17
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
O'Leary EN;Edwards JR;Srinivasan A;Neuhauser MM;Webb AK;Soe MM;Hicks LA;Wise W;Wu H;Pollock DA
通讯作者：
Pollock DA
影响因子：
11.8
作者：
Jackson, Sarah S.;Leekha, Surbhi;Harris, Anthony D.
通讯作者：
Harris, Anthony D.