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Abstract：
BACKGROUND: Standardized Mortality Ratios (SMRs) are case-mix adjusted mortality rates per hospital and are used to evaluate quality of care. However, acute care is increasingly organized on a regional level, with more severe patients admitted to specialized hospitals. We hypothesize that the current case-mix adjustment insufficiently captures differences in case-mix between non-specialized and specialized hospitals. We aim to improve the SMR by adding proxies of disease severity to the model and by calculating a regional SMR (RSMR) for acute cerebrovascular disease (CVD) and myocardial infarction (MI).
METHODS: We used data from the Dutch National Basic Registration of Hospital Care. We selected all admissions from 2016 to 2018. SMRs and RSMRs were calculated by dividing the observed in-hospital mortality by the expected in-hospital mortality. The expected in-hospital mortality was calculated using logistic regression with adjustment for age, sex, socioeconomic status, severity of main diagnosis, urgency of admission, Charlson comorbidity index, place of residence before admission, month/year of admission, and in-hospital mortality as outcome.
RESULTS: The IQR of hospital SMRs of CVD was 0.85-1.10, median 0.94, with higher SMRs for specialized hospitals (median 1.12, IQR 1.00-1.28, 71%-SMR > 1) than for non-specialized hospitals (median 0.92, IQR 0.82-1.07, 32%-SMR > 1). The IQR of RSMRs was 0.92-1.09, median 1.00. The IQR of hospital SMRs of MI was 0.76-1.14, median 0.98, with higher SMRs for specialized hospitals (median 1.00, IQR 0.89-1.25, 50%-SMR > 1 versus median 0.94, IQR 0.74-1.11, 44%-SMR > 1). The IQR of RSMRs was 0.90-1.08, median 1.00. Adjustment for proxies of disease severity mostly led to lower SMRs of specialized hospitals.
CONCLUSIONS: SMRs of acute regionally organized diseases do not only measure differences in quality of care between hospitals, but merely measure differences in case-mix between hospitals. Although the addition of proxies of disease severity improves the model to calculate SMRs, real disease severity scores would be preferred. However, such scores are not available in administrative data. As a consequence, the usefulness of the current SMR as quality indicator is very limited. RSMRs are potentially more useful, since they fit regional organization and might be a more valid representation of quality of care.
METHODS: We used data from the Dutch National Basic Registration of Hospital Care. We selected all admissions from 2016 to 2018. SMRs and RSMRs were calculated by dividing the observed in-hospital mortality by the expected in-hospital mortality. The expected in-hospital mortality was calculated using logistic regression with adjustment for age, sex, socioeconomic status, severity of main diagnosis, urgency of admission, Charlson comorbidity index, place of residence before admission, month/year of admission, and in-hospital mortality as outcome.
RESULTS: The IQR of hospital SMRs of CVD was 0.85-1.10, median 0.94, with higher SMRs for specialized hospitals (median 1.12, IQR 1.00-1.28, 71%-SMR > 1) than for non-specialized hospitals (median 0.92, IQR 0.82-1.07, 32%-SMR > 1). The IQR of RSMRs was 0.92-1.09, median 1.00. The IQR of hospital SMRs of MI was 0.76-1.14, median 0.98, with higher SMRs for specialized hospitals (median 1.00, IQR 0.89-1.25, 50%-SMR > 1 versus median 0.94, IQR 0.74-1.11, 44%-SMR > 1). The IQR of RSMRs was 0.90-1.08, median 1.00. Adjustment for proxies of disease severity mostly led to lower SMRs of specialized hospitals.
CONCLUSIONS: SMRs of acute regionally organized diseases do not only measure differences in quality of care between hospitals, but merely measure differences in case-mix between hospitals. Although the addition of proxies of disease severity improves the model to calculate SMRs, real disease severity scores would be preferred. However, such scores are not available in administrative data. As a consequence, the usefulness of the current SMR as quality indicator is very limited. RSMRs are potentially more useful, since they fit regional organization and might be a more valid representation of quality of care.
摘要：
背景：标准化死亡率（SMR）是每个医院的病例组合调整死亡率，用于评估护理质量。然而,急性护理越来越多地在区域一级组织起来，更严重的患者被送往专科医院。我们假设当前的病例组合调整不足以捕获非专科医院和专科医院之间病例组合的差异。我们的目标是通过在模型中添加疾病严重程度的代理并计算急性脑血管疾病（CVD）和心肌梗塞（MI）的区域SMR（RSMR）来改善SMR。
方法：我们使用了来自荷兰国家医院护理基本注册的数据。我们选择了2016年至2018年的所有招生。通过将观察到的院内死亡率除以预期的院内死亡率来计算SMR和RSMR。使用逻辑回归计算预期住院死亡率，并校正年龄，性别,社会经济地位,主要诊断的严重程度，入学的紧迫性，Charlson合并症指数,入境前的居住地，月/年入学，和住院死亡率作为结果。
结果：CVD的医院SMR的IQR为0.85-1.10，中位数为0.94，专科医院的SMR（中位数为1.12，IQR为1.00-1.28，71％-SMR>1）高于非专科医院（中位数为0.92，IQR为0.82-1.07，32％-SMR>1）。RSMR的IQR为0.92-1.09，中位数为1.00。MI的医院SMR的IQR为0.76-1.14，中位数为0.98，专科医院的SMR较高（中位数为1.00，IQR为0.89-1.25，50％-SMR>1，中位数为0.94，IQR为0.74-1.11，44％-SMR>1）。RSMR的IQR为0.90-1.08，中位数为1.00。调整疾病严重程度的代理主要导致专科医院的SMR降低。
结论：SMR的急性区域性组织疾病不仅可以衡量医院之间的护理质量差异，而只是衡量医院之间病例组合的差异。尽管增加了疾病严重程度的代理改进了计算SMR的模型，真实的疾病严重程度评分将是首选。然而,这些分数在行政数据中不可用。因此,当前SMR作为质量指标的有用性非常有限。RSMR可能更有用，因为它们适合区域组织，并且可能是更有效的医疗质量代表。
方法：我们使用了来自荷兰国家医院护理基本注册的数据。我们选择了2016年至2018年的所有招生。通过将观察到的院内死亡率除以预期的院内死亡率来计算SMR和RSMR。使用逻辑回归计算预期住院死亡率，并校正年龄，性别,社会经济地位,主要诊断的严重程度，入学的紧迫性，Charlson合并症指数,入境前的居住地，月/年入学，和住院死亡率作为结果。
结果：CVD的医院SMR的IQR为0.85-1.10，中位数为0.94，专科医院的SMR（中位数为1.12，IQR为1.00-1.28，71％-SMR>1）高于非专科医院（中位数为0.92，IQR为0.82-1.07，32％-SMR>1）。RSMR的IQR为0.92-1.09，中位数为1.00。MI的医院SMR的IQR为0.76-1.14，中位数为0.98，专科医院的SMR较高（中位数为1.00，IQR为0.89-1.25，50％-SMR>1，中位数为0.94，IQR为0.74-1.11，44％-SMR>1）。RSMR的IQR为0.90-1.08，中位数为1.00。调整疾病严重程度的代理主要导致专科医院的SMR降低。
结论：SMR的急性区域性组织疾病不仅可以衡量医院之间的护理质量差异，而只是衡量医院之间病例组合的差异。尽管增加了疾病严重程度的代理改进了计算SMR的模型，真实的疾病严重程度评分将是首选。然而,这些分数在行政数据中不可用。因此,当前SMR作为质量指标的有用性非常有限。RSMR可能更有用，因为它们适合区域组织，并且可能是更有效的医疗质量代表。
