Helgeland Jon, Kristoffersen Doris Tove, Skyrud Katrine Damgaard, Lindman Anja Schou
Quality Measurement Unit, Norwegian Institute of Public Health, Oslo, Norway.
Department of Registration, Institute of Population-Based Cancer Research, Cancer Registry of Norway, Oslo, Norway.
PLoS One. 2016 May 20;11(5):e0156075. doi: 10.1371/journal.pone.0156075. eCollection 2016.
The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records.
We used PAS data from all Norwegian hospitals (2005-2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed.
The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes.
This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.
本研究旨在通过病历回顾性研究,评估患者管理数据(PAS)作为计算髋部骨折后30天死亡率质量指标的有效性。
我们使用了挪威所有医院(2005 - 2009年)的PAS数据,并与国家登记处的生命状态数据合并,以计算每家医院(n = 51)的30天病例组合调整死亡率。我们采用分层抽样来建立医院和病例的代表性样本。医院根据高、低和中等死亡率进行分层,分别抽取了4家、3家和5家医院。在医院内部,病例根据入院年份、年龄、住院时间和30天生命状态(存活/死亡)进行分层抽样。最终研究样本包括来自11家医院的1043例病例。临床信息从病历中提取。病历和PAS中的诊断及临床信息用于定义明确和可能的髋部骨折。我们使用逻辑回归分析来估计未测量混杂因素导致的医院间系统差异。最后,为研究未测量混杂因素对识别死亡率异常医院的影响,进行了敏感性分析。
明确髋部骨折的估计总体阳性预测值为95.9%,明确或可能髋部骨折的为99.7%,医院之间无统计学显著差异。死亡率估计中额外的系统性医院偏差在逻辑尺度上的标准差为0.044。未测量混杂因素对异常值检测的影响小到中等,仅在医院病例数较多时明显。
本研究表明PAS数据足以识别髋部骨折病例,未测量的病例组合差异影响较小。总之,在挪威,PAS数据足以计算髋部骨折后30天死亡率作为质量指标。