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评估心血管药物的安全性以辅助临床决策。

Assessing cardiovascular drug safety for clinical decision-making.

机构信息

AZCERT Inc., 1822 East Innovation Park Drive, Oro Valley, AZ 85755, USA.

出版信息

Nat Rev Cardiol. 2013 Jun;10(6):330-7. doi: 10.1038/nrcardio.2013.57. Epub 2013 Apr 16.

Abstract

Optimal therapeutic decision-making requires integration of patient-specific and therapy-specific information at the point of care, particularly when treating patients with complex cardiovascular conditions. The formidable task for the prescriber is to synthesize information about all therapeutic options and match the best treatment with the characteristics of the individual patient. Computerized decision support systems have been developed with the goal of integrating such information and presenting the acceptable therapeutic options on the basis of their effectiveness, often with limited consideration of their safety for a specific patient. Assessing the safety of therapies relative to each patient is difficult, and sometimes impossible, because the evidence required to make such an assessment is either imperfect or does not exist. In addition, many of the alerts sent to prescribers by decision-support systems are not perceived as credible, and 'alert fatigue' causes warnings to be ignored putting patients at risk of harm. The CredibleMeds.org and BrugadaDrugs.org websites are prototypes for evidence-based sources of safety information that rank drugs for their risk of a specific form of drug toxicity-in these cases, drug-induced arrhythmias. Broad incorporation of this type of information in electronic prescribing algorithms and clinical decision support could speed the evolution of safe personalized medicine.

摘要

最佳治疗决策需要在治疗点整合患者特定和治疗特定的信息,特别是在治疗患有复杂心血管疾病的患者时。对于处方者来说,艰巨的任务是综合所有治疗选择的信息,并根据个体患者的特点匹配最佳治疗。已经开发了计算机化决策支持系统,旨在整合这些信息,并根据其有效性提供可接受的治疗选择,通常很少考虑其对特定患者的安全性。评估相对于每个患者的治疗安全性是困难的,有时甚至是不可能的,因为进行此类评估所需的证据要么不完美,要么不存在。此外,决策支持系统向处方者发送的许多警报都被认为不可信,而“警报疲劳”导致警告被忽视,使患者面临伤害风险。CredibleMeds.org 和 BrugadaDrugs.org 网站是基于证据的安全信息来源的原型,这些来源根据药物引起特定类型药物毒性(在这些情况下为药物诱导的心律失常)的风险对药物进行排名。在电子处方算法和临床决策支持中广泛纳入此类信息,可以加速安全个体化药物的发展。

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