Ralph Edwards I
Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
Drug Saf. 2017 May;40(5):365-372. doi: 10.1007/s40264-017-0509-2.
Causality in pharmacovigilance is a difficult and time consuming exercise. This paper presents the challenges in determining causation by drug therapy. The first is that causation is complex and needs to be viewed from the context of the patient treated, rather than the drug product. Multiple causal vectors should be considered if we are to tackle the many issues involved in, for example, medication error and the many other factors that lead to bad outcomes from therapy, including failure to recognise known risk factors. The aim of pharmacovigilance is not only a bureaucratic exercise in public health norms, but is mainly concerned with small minorities of statistical outliers-and even individuals-whose experiences from harms may together form messages about causation that will prevent further at-risk patients from exposure, or at least assist with earlier recognition of drug-related harm and better management of such harm. This requires more time, more data, more analysis and more patient and clinical involvement in reporting useful clinical detail. The paradigm shift back towards gathering more case data relating to possible causation can be selective and would not be just retrogressive, nor necessarily too costly. Greater transparency of hypotheses and availability of anonymised case data will enrol more expertise into evaluations and hypothesis testing, and the provision of more complete and useful information should reduce clinical burdens from bad patient outcomes as well as their overall costs to society.
药物警戒中的因果关系判定是一项困难且耗时的工作。本文阐述了药物治疗因果关系判定中的挑战。首先,因果关系很复杂,需要从接受治疗的患者背景而非药品本身来考量。如果我们要解决例如用药错误以及导致治疗不良后果的许多其他因素(包括未能识别已知风险因素)所涉及的诸多问题,就应考虑多个因果向量。药物警戒的目的不仅是遵循公共卫生规范进行的官僚式工作,主要还涉及统计学上的少数异常值甚至个体,他们的伤害经历可能共同形成有关因果关系的信息,从而防止更多有风险的患者暴露于风险中,或者至少有助于更早地识别药物相关伤害并更好地管理此类伤害。这需要更多时间、更多数据、更多分析,以及患者和临床人员更多地参与报告有用的临床细节。回归到收集更多与可能的因果关系相关的病例数据的模式转变可以是有选择性的,不会只是倒退,也不一定成本过高。提高假设的透明度和提供匿名病例数据将吸引更多专业知识用于评估和假设检验,并且提供更完整、有用的信息应能减轻不良患者结局带来的临床负担以及其对社会造成的总体成本。