Department of Pharmacy Care Systems, Harrison School of Pharmacy, Auburn University, 020 Foy Hall, Auburn, AL, 36849, USA,
Drug Saf. 2013 Oct;36 Suppl 1:S27-32. doi: 10.1007/s40264-013-0104-0.
Observational data can be useful for drug safety research, but accurate measurement of adverse health outcomes is paramount. Best practices for identifying important health outcomes of interest (HOI) are needed.
To evaluate the extent to which health outcome definitions commonly used in observational database studies identify cases that are consistent with expert panel assessment of the underlying data.
Competing HOI definitions were used to identify potential cases of acute liver injury (ALI; n = 208), acute kidney injury (AKI; n = 200), and myocardial infarction (MI; n = 204) in the Truven MarketScan Lab Database (MSLR). Panelists reviewed patient-level data and answered questions about whether they believed the case actually reflected the HOI and their certainty of case classification on a 10-point scale (1 = unlikely to 10 = likely). Each patient was reviewed independently by two panelists. Case disagreements were resolved through consensus meetings. Positive predictive value (PPV) was calculated as the number of cases deemed to be true over the total number of sampled cases. Kappa statistics assessed inter-rater agreement.
PPV ranged from 0 to 52 % across ALI definitions, 12 to 82 % across AKI definitions, and 1 to 56 % across MI definitions. Certainty scores on the 10-point scale paralleled the PPV, with a range of mean values from 1.7 to 4.8 across ALI definitions, 3.1 to 6.0 across AKI definitions, and 2.8 to 5.7 across MI definitions. Inter-rater agreement was low to moderate (Kappa range 0.0-0.6).
IMPLICATIONS/CONCLUSIONS: Existing HOI definitions had relatively low PPV based on expert panel review. Experts commonly disagreed on case classification. Additional work is needed to refine HOI case definitions if observational data are to be reliably used for health outcome assessment.
观察性数据可用于药物安全性研究,但准确测量不良健康结局至关重要。需要确定识别感兴趣的重要健康结局(HOI)的最佳实践。
评估在多大程度上,观察性数据库研究中常用的健康结局定义可以识别与专家小组对基础数据的评估一致的病例。
在 Truven MarketScan Lab 数据库(MSLR)中,使用竞争性 HOI 定义来识别潜在的急性肝损伤(ALI;n=208)、急性肾损伤(AKI;n=200)和心肌梗死(MI;n=204)病例。专家组审查患者水平数据,并回答有关他们是否认为该病例实际上反映了 HOI 以及他们对病例分类的确定性的问题,评分范围为 1-10(1=不太可能,10=很可能)。每位患者由两位专家组独立审查。通过共识会议解决病例分歧。阳性预测值(PPV)定义为被认为是真实病例的数量与抽样病例总数的比值。Kappa 统计量评估了组内一致性。
在 ALI 定义中,PPV 从 0 到 52%不等,在 AKI 定义中,PPV 从 12%到 82%不等,在 MI 定义中,PPV 从 1%到 56%不等。10 分制评分的确定性得分与 PPV 相似,在 ALI 定义中,平均值范围从 1.7 到 4.8,在 AKI 定义中,平均值范围从 3.1 到 6.0,在 MI 定义中,平均值范围从 2.8 到 5.7。组内一致性较低到中等(Kappa 范围 0.0-0.6)。
意义/结论:根据专家组审查,现有的 HOI 定义的 PPV 相对较低。专家对病例分类的意见通常不一致。如果要可靠地使用观察性数据进行健康结局评估,则需要进一步改进 HOI 病例定义。