Schubert Ingrid, Hammer Antje, Köster Ingrid
PMV forschungsgruppe an der Klinik und Poliklinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters der Universität zu Köln, Köln, Deutschland.
Institut für Patientensicherheit, Universitätsklinikum Bonn, Bonn, Deutschland.
Z Evid Fortbild Qual Gesundhwes. 2017 Oct;126:66-75. doi: 10.1016/j.zefq.2017.06.008. Epub 2017 Aug 12.
Information on disease severity is relevant for many studies with claims data in health service research, but only limited information is available in routine data. Stroke serves as an example to analyse whether the combination of different information in claims data can provide insight into the severity of a disease.
As a first step, a literature search was conducted. Strategies to assess the severity of a disease by means of routine data were examined with regard to approval and applicability to German sickness fund data. In order to apply and extend the identified procedures, the statutory health insurance sample AOK Hessen/KV Hessen (VSH) served as data source. It is an 18.75 % random sample of persons insured by the AOK Hessen, with 2013 being the most recent year. Stroke patients were identified by the ICD-10 GM code I63 and I64. Patients with said diagnoses being coded as a hospital discharge diagnosis in 2012 were included due to an acute event in 2012 (n=944). The follow-up time was one year.
Ten studies covering seven different methods to assess stroke severity were identified. Codes for coma (4.2 % of stroke patients in the SHI sample) as well as coma and/or the application of a PEG tube (9.8 % of the stroke patients) were applied as a proxy for disease severity of acute cases. Taking age, sex and comorbidity into consideration, patients in a coma show a significantly increased risk of mortality compared to those without coma. Three operationalisations were chosen as possible proxies for disease severity of stroke in the further course of disease: i) sequelae (hemiplegia, neurological neglect), ii) duration of the index inpatient stay, and iii) nursing care/ care level 3 for the first time after stroke. The latter proxy has the highest explanatory value for SHI costs.
The studies identified use many variables mainly based on hospital information in order to describe disease severity. With the exception of coma, these proxies were neither validated nor did the authors provide more detailed grounds for their use. An identified score for stroke severity could not be applied to SHI data. To develop a comparable score requires a linkage of clinical and administrative data. Since routine data include information from all sectors of care, it should be explored whether these data (for example, the patients' care needs) are suitable to assess disease severity. For validation, separate databases and, optimally, primary patient data are necessary.
疾病严重程度信息在许多利用医保理赔数据开展的卫生服务研究中至关重要，但常规数据中此类信息有限。以中风为例，分析医保理赔数据中不同信息的组合能否深入了解疾病严重程度。
第一步进行文献检索。研究了通过常规数据评估疾病严重程度的策略在德国疾病基金数据中的认可度和适用性。为应用和扩展已确定的程序，选取法定医疗保险样本黑森州AOK/KV黑森州（VSH）作为数据源。这是黑森州AOK参保人员的18.75%随机样本，最近一年为2013年。通过ICD - 10 GM编码I63和I64识别中风患者。因2012年急性事件在2012年被编码为出院诊断的上述诊断患者被纳入（n = 944）。随访时间为一年。
共识别出10项研究，涵盖7种评估中风严重程度的不同方法。昏迷编码（在法定医疗保险样本中占中风患者的4.2%）以及昏迷和/或PEG管的使用（占中风患者的9.8%）被用作急性病例疾病严重程度的替代指标。考虑到年龄、性别和合并症，昏迷患者的死亡风险显著高于未昏迷患者。在疾病后续过程中，选择了三种操作化方法作为中风疾病严重程度的可能替代指标：i）后遗症（偏瘫、神经忽视），ii）首次住院时间，iii）中风后首次接受的护理/三级护理水平。后一种替代指标对法定医疗保险费用的解释力最高。
已识别出的研究使用了许多主要基于医院信息的变量来描述疾病严重程度。除昏迷外，这些替代指标既未经验证，作者也未提供更多使用依据。已识别出的中风严重程度评分无法应用于法定医疗保险数据。要制定可比评分，需要将临床数据和管理数据相链接。由于常规数据包含来自所有护理部门的信息，应探讨这些数据（例如患者的护理需求）是否适合评估疾病严重程度。为进行验证，需要单独的数据库，最好是原始患者数据。
