Scalfaro Erik, Streefkerk Henk Johan, Merz Michael, Meier Christoph, Lewis David
Patient Safety, Novartis Pharma AG, Basel, Switzerland.
Preclinical Safety, Novartis Institutes for BioMedical Research, Basel, Switzerland.
Drug Saf. 2017 Aug;40(8):715-727. doi: 10.1007/s40264-017-0541-2.
Data incompleteness in pharmacovigilance (PV) health records limits the use of current causality assessment methods for drug-induced liver injury (DILI). In addition to the inherent complexity of this adverse event, identifying cases of high causal probability is difficult.
The aim was to evaluate the performance of an improved, algorithmic and standardised method called the Pharmacovigilance-Roussel Uclaf Causality Assessment Method (PV-RUCAM), to support assessment of suspected DILI. Performance was compared in different settings with regard to applicability and differentiation capacity.
A PV-RUCAM score was developed based on the seven sections contained in the original RUCAM. The score provides cut-off values for or against DILI causality, and was applied on two datasets of bona fide individual case safety reports (ICSRs) extracted randomly from clinical trial reports and a third dataset of electronic health records from a global PV database. The performance of PV-RUCAM adjudication was compared against two standards: a validated causality assessment method (original RUCAM) and global introspection.
The findings showed moderate agreement against standards. The overall error margin of no false negatives was satisfactory, with 100% sensitivity, 91% specificity, a 25% positive predictive value and a 100% negative predictive value. The Spearman's rank correlation coefficient illustrated a statistically significant monotonic association between expert adjudication and PV-RUCAM outputs (R = 0.93). Finally, there was high inter-rater agreement (K = 0.79) between two PV-RUCAM assessors.
Within the PV setting of a pharmaceutical company, the PV-RUCAM has the potential to facilitate and improve the assessment done by non-expert PV professionals compared with other methods when incomplete reports must be evaluated for suspected DILI. Prospective validation of the algorithmic tool is necessary prior to implementation for routine use.
药物警戒（PV）健康记录中的数据不完整性限制了当前药物性肝损伤（DILI）因果关系评估方法的应用。除了这种不良事件本身固有的复杂性外，识别高因果概率的病例也很困难。
旨在评估一种改进的、算法化且标准化的方法——药物警戒-罗素·优克福因果关系评估方法（PV-RUCAM）的性能，以支持对疑似DILI的评估。在不同场景下比较了该方法在适用性和区分能力方面的性能。
基于原始RUCAM中的七个部分制定了PV-RUCAM评分。该评分提供了支持或反对DILI因果关系的截断值，并应用于从临床试验报告中随机提取的两个真实个体病例安全报告（ICSR）数据集以及来自全球PV数据库的电子健康记录的第三个数据集。将PV-RUCAM判定的性能与两个标准进行了比较：一种经过验证的因果关系评估方法（原始RUCAM）和整体反思。
研究结果显示与标准的一致性中等。无假阴性的总体误差幅度令人满意，灵敏度为100%，特异性为91%，阳性预测值为25%，阴性预测值为100%。斯皮尔曼等级相关系数表明专家判定与PV-RUCAM输出之间存在统计学上显著的单调关联（R = 0.93）。最后，两位PV-RUCAM评估者之间的评分者间一致性较高（K = 0.79）。
在制药公司的PV环境中，与其他方法相比，当必须评估不完整报告中的疑似DILI时，PV-RUCAM有潜力促进和改进非专家PV专业人员的评估。在将该算法工具用于常规使用之前，有必要进行前瞻性验证。
