Background Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use. Methods This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated negative binomial mixed-model with two random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts. Results 1,804,300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR 4-10). The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve=0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT. Conclusion A model using electronically-available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

中文翻译：

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一种新的风险调整指标，用于比较医院出院时抗生素处方情况

背景 出院时过度使用抗生素很常见，但没有指标可以评估医院在护理过渡期间的表现。我们建立了一个风险调整指标来比较医院出院后抗生素的总体使用情况。方法 这是一项回顾性研究，涵盖 2018 年至 2021 年退伍军人健康管理局内所有急诊入院情况。对于出院回家的患者，我们收集了抗生素和相关协变量的数据。我们为每家医院建立了一个零膨胀负二项式混合模型，其中有两个随机截距，以预测出院后抗生素暴露和治疗时间 (LOT)。数据被分为训练集和测试集，以使用绝对误差评估模型性能。医院绩效由预测的随机截距决定。结果 纳入了 129 家医院的 1,804,300 名入院患者。住院期间使用抗生素的比例为 41.5%，出院时使用抗生素的比例为 19.5%。出院后处方抗生素的中位 LOT 为 7 (IQR 4-10)。该预测模型准确地检测出院后抗生素的使用情况，包括准确识别任何暴露（精确回忆曲线下面积 = 0.97）和出院后 LOT 的可靠预测（平均绝对误差 = 1.48）。根据该模型，39 家 (30.2%) 医院在出院时开出抗生素的频率低于预期，并且使用的 LOT 时间也比预期短。 28 家 (21.7%) 医院在出院时更频繁地开出抗生素，并使用更长的 LOT。结论 使用电子数据的模型能够预测出院时处方的抗生素使用情况，并表明一些医院在护理过渡期间在减少抗生素过度使用方面更为成功。该指标可以帮助医院发现改善出院时抗生素管理的机会。