Optimizing therapeutics for hospitalized patients with impaired renal function

优化肾功能受损住院患者的治疗

• 批准号: 7132855
• 负责人: Joseph F. Peterson
• 金额: $ 7.65万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-08-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7132855
• 关键词: clinical research; kidney

DESCRIPTION (provided by applicant): A third of patients admitted to the hospital have underlying renal impairment or develop acute renal failure during their hospital course. The majority of these patients are prescribed a drug that is sensitive to renal function, and should be dose adjusted to avoid drug accumulation and toxicity. Over three decades of research has produced a variety of techniques to estimate renal clearance and other pharmacokinetic parameters which can instruct prescribers on how to adjust doses or frequencies of administration. However, few of these techniques are reliably applied by physicians or other providers, and inaccurate dosing in the setting of renal insufficiency has been identified as one of the major causes of preventable adverse drug events. Computerized prescriptions with automated guidance for prescribers will ultimately provide the safeguards and real-time assistance that is needed to prevent dosing errors. However, the existing implementations may not be sophisticated enough to offer accurate dosing recommendations. For example, the widely used Cockcroft-Gault estimate for renal clearance is inaccurate at the extremes of weight and age and with rapid changes in kidney function. All of these caveats are frequently present in hospitalized patients. It is unknown how many of the drug recommendations generated by a 'single-model1 approach would be altered if a more tailored, individualized approach was applied. We hypothesize that 3 major changes to a renal dose decision support system will substantially alter the guidance: 1) estimate drug clearance with a model tailored to patient characteristics 2) notification of changes to drug clearance which should alter existing drug therapy 3) apply medication-specific pharmacokinetic models to dose therapies with a narrow therapeutic range. We will test our hypothesis by simulating the performance of each redesigned component in a large dataset of patient and prescription data. Relevance to public health: Hospitalized patients with impaired kidney function need special care and monitoring to avoid drug accumulation and direct kidney toxicity. Computerized systems of care are needed to optimally assist with drug dosing and to assist physicians with maintaining vigilance when sudden changes in health status make prior drug dosing obsolete.

描述（由申请人提供）： 进入医院的患者中有三分之一在医院课程期间患有肾脏障碍或急性肾衰竭。这些患者中的大多数是对肾功能敏感的药物，应调整剂量以避免药物积累和毒性。超过三十年的研究产生了多种技术来估计肾脏清除率和其他药代动力学参数，这些技术可以指导处方者如何调整给药剂量或频率。但是，这些技术中很少有医生或其他提供者可靠地应用，并且在肾功能不全的情况下，剂量不准确地被确定为可预防不良药物事件的主要原因之一。 带有处方者自动指南的计算机处方最终将提供防止给药错误所需的保障和实时帮助。但是，现有的实现可能不够复杂，无法提供准确的给药建议。例如，使用广泛使用的Cockcroft-Gault估计肾脏清除率在体重和年龄的极端情况下不准确，并且随着肾功能的快速变化。所有这些警告经常出现在住院的患者中。如果采用更量身定制的个性化方法，则尚不清楚通过'单模型方法产生多少药物建议。我们假设3肾脏剂量决策支持系统的3个重大变化将大大改变指导：1）估算使用针对患者特征的模型估算药物清除率2）通知药物清除率的变化，该通知应改变现有药物治疗3）特定的药代动力学模型，可用于狭窄治疗范围的剂量疗法。我们将通过模拟大量患者和处方数据数据集中每个重新设计的组件的性能来检验我们的假设。 与公共卫生有关：肾功能受损的住院患者需要特殊护理和监测，以避免药物积累和直接肾脏毒性。需要计算机化的护理系统来最佳协助药物给药，并在健康状况突然变化使先前的药物给药过时时保持警惕。