König Volker, Kolzter Olaf, Albuszies Gerd, Thölen Frank
CLINOTEL Krankenhausverbund gGmbH, Köln, Deutschland.
CLINOTEL Krankenhausverbund gGmbH, Köln, Deutschland.
Z Evid Fortbild Qual Gesundhwes. 2018 May;133:30-39. doi: 10.1016/j.zefq.2018.03.001. Epub 2018 Mar 31.
Inpatient administrative data from hospitals is already used nationally and internationally in many areas of internal and public quality assurance in healthcare. For sepsis as the principal condition, only a few published approaches are available for Germany. The aim of this investigation is to identify factors influencing hospital mortality by employing appropriate analytical methods in order to improve the internal quality management of sepsis.
The analysis was based on data from 754,727 DRG cases of the CLINOTEL hospital network charged in 2015. The association then included 45 hospitals of all supply levels with the exception of university hospitals (range of beds: 100 to 1,172 per hospital). Cases of sepsis were identified via the ICD codes of their principal diagnosis. Multiple logistic regression analysis was used to determine the factors influencing in-hospital lethality for this population. The model was developed using sociodemographic and other potential variables that could be derived from the DRG data set, and taking into account current literature data. The model obtained was validated with inpatient administrative data of 2016 (51 hospitals, 850,776 DRG cases).
Following the definition of the inclusion criteria, 5,608 cases of sepsis (2016: 6,384 cases) were identified in 2015. A total of 12 significant and, over both years, stable factors were identified, including age, severity of sepsis, reason for hospital admission and various comorbidities. The AUC value of the model, as a measure of predictability, is above 0.8 (H-L test p>0.05, R value=0.27), which is an excellent result.
The CLINOTEL model of risk adjustment for in-hospital lethality can be used to determine the mortality probability of patients with sepsis as principal diagnosis with a very high degree of accuracy, taking into account the case mix. Further studies are needed to confirm whether the model presented here will prove its value in the internal quality assurance of hospitals.
医院的住院管理数据已在国内和国际上广泛应用于医疗保健内部和公共质量保证的许多领域。对于以脓毒症作为主要病症的情况，德国仅有少数已发表的方法。本研究的目的是通过采用适当的分析方法来确定影响医院死亡率的因素，以改善脓毒症的内部质量管理。
该分析基于2015年CLINOTEL医院网络中754,727例诊断相关分组（DRG）病例的数据。该关联研究随后纳入了除大学医院外所有供应层级的45家医院（每家医院床位范围：100至1172张）。通过主要诊断的国际疾病分类（ICD）编码来识别脓毒症病例。采用多元逻辑回归分析来确定该人群中影响院内死亡率的因素。该模型是利用社会人口统计学和其他可从DRG数据集中得出的潜在变量，并参考当前文献数据开发而成。所得到的模型用2016年的住院管理数据（51家医院，850,776例DRG病例）进行了验证。
按照纳入标准的定义，2015年识别出5608例脓毒症病例（2016年：6384例）。总共确定了12个显著且在两年间均稳定的因素，包括年龄、脓毒症严重程度、入院原因以及各种合并症。作为预测能力衡量指标的模型曲线下面积（AUC）值高于0.8（Hosmer-Lemeshow检验p>0.05，R值=0.27），这是一个优异的结果。
CLINOTEL医院内死亡率风险调整模型可用于非常准确地确定以脓毒症作为主要诊断的患者的死亡概率，同时考虑病例组合情况。需要进一步研究来确认此处呈现的模型是否会在医院内部质量保证中证明其价值。
