BACKGROUND This study aims at gathering evidence about the relation between 30-day mortality and 30-day unplanned readmission and patient and hospital factors. By definition, we refer to 30-day mortality and 30-day unplanned readmission as the number of deaths and non-programmed hospitalizations for any cause within 30 days after the incident heart failure (HF). In particular, the focus is on the role played by hospital-level factors. METHODS A multi-level logistic model that combines patient- and hospital-level covariates has been developed to better disentangle the role played by the two groups of covariates. Later on, hospital outliers in term of better-than-expected/worst-than-expected performers have been identified by comparing expected cases vs. observed cases. Hospitals performance in terms of 30-day mortality and 30-day unplanned readmission rates have been visualized through the creation of funnel plots. Covariates have been selected coherently to past literature. Data comes from the hospital discharge forms for Heart Failure patients in the Lombardy Region (Northern Italy). Considering incident cases for HF in the timespan 2010-2012, 78,907 records for adult patients from 117 hospitals have been collected after quality checks. RESULTS Our results show that 30-day mortality and 30-day unplanned readmissions are explained by hospital-level covariates, paving the way for the design and implementation of evidence-based improvement strategies. While the percentage of surgical DRG (OR = 1.001; CI (1.000-1.002)) and the hospital type of structure (Research hospitals vs. non-research public hospitals (OR = 0.62; CI (0.48-0.80)) and Non-research private hospitals vs. non-research hospitals OR = 0.75; CI (0.63-0.90)) are significant for mortality, the mean length of stay (OR = 0.96; CI (0.95-0.98)) is significant for unplanned readmission, showing that mortality and readmission rates might be improved through different strategies. CONCLUSION Our results confirm that hospital-level covariates do affect quality of care, and that 30-day mortality and 30-day unplanned readmission are affected by different managerial choices. This confirms that hospitals should be accountable for their "added value" to quality of care.

中文翻译：

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心力衰竭患者30天死亡率和再入院率的多层次模型：行政数据中患者与医院因素之间的关系。

背景技术本研究旨在收集有关30天死亡率和30天计划外再次入院与患者和医院因素之间关系的证据。根据定义，我们将30天死亡率和30天非计划再入院率称为事件性心力衰竭（HF）后30天内任何原因的死亡人数和未计划的住院治疗次数。特别是，重点是医院层面因素所起的作用。方法已经开发了一种结合患者和医院级别协变量的多级逻辑模型，以更好地区分两组协变量所扮演的角色。后来，通过比较预期病例与观察到的病例，鉴定出表现优于预期/最差的医院异常值。通过创建漏斗图可以直观地看到医院在30天死亡率和30天计划外再入院率方面的表现。协变量已与过去的文献连贯地选择。数据来自伦巴第大区（意大利北部）心力衰竭患者的出院表格。考虑到2010-2012年间发生的HF事件，经过质量检查后，已收集了117家医院的成年患者78,907条记录。结果我们的结果表明，医院级协变量可以解释30天死亡率和30天计划外再次入院，为设计和实施循证改善策略铺平了道路。而手术DRG的百分比（OR = 1.001; CI（1.000-1.002））和医院的结构类型（研究医院vs. 非研究型公立医院（OR = 0.62; CI（0.48-0.80））和非研究型私立医院与非研究型医院OR = 0.75；CI（0.63-0.90））对于死亡率很重要，平均住院时间（OR = 0.96; CI（0.95-0.98））对于计划外的再次入院很重要，表明死亡率和再入院率可以通过不同的策略来提高。结论我们的结果证实医院水平的协变量确实会影响护理质量，并且30天的死亡率和30天的计划外再次入院会受到不同管理选择的影响。这证实了医院应该对医疗质量的“附加值”负责。98））对于计划外的再入院非常重要，表明死亡率和再入院率可以通过不同的策略来提高。结论我们的结果证实医院水平的协变量确实会影响护理质量，并且30天的死亡率和30天的计划外再次入院会受到不同管理选择的影响。这证实了医院应该对医疗质量的“附加值”负责。98））对于计划外的再入院非常重要，表明死亡率和再入院率可以通过不同的策略来提高。结论我们的结果证实医院水平的协变量确实会影响护理质量，并且30天的死亡率和30天的计划外再次入院会受到不同管理选择的影响。这证实了医院应该对医疗质量的“附加值”负责。