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Optimizing therapeutics for hospitalized patients with impaired renal function

优化肾功能受损住院患者的治疗

基本信息

批准号：
7282350
负责人：
Joseph F. Peterson
金额：
$ 7.45万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-01 至 2009-08-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7282350
关键词：
Academic Medical Centers Accounting Acute Kidney Failure Age Caring Characteristics Chronic Kidney Failure Class Clinical Clinical Decision Support Systems Clinical Pharmacists Consult Consultations Creatinine clearance measurement Data Data Set Databases Decision Support Systems Diet Modification Dose Drug Kinetics Elderly Electronics Environment Equation Event Failure Foundations Frequencies Funding Glomerular Filtration Rate Health Status Hospitals Impaired Renal Function Impairment Inpatients Investigation Kidney Kidney Diseases Kidney Failure Laboratories Link Literature Medical Methods Modeling Monitor Nephrology Notification Numbers Obesity Operative Surgical Procedures Outcome Patients Performance Personal Satisfaction Pharmaceutical Preparations Pharmacotherapy Pharmacy facility Physicians Population Heterogeneity Provider Public Health Purpose Range Recommendation Recovery Renal clearance function Renal function Reporting Research Research Project Grants Resources Safety Serum Services Simulate Source Standards of Weights and Measures Subgroup System Techniques Testing Therapeutic Therapeutic Index Time Toxic effect Weight biomedical informatics care systems computerized computerized physician order entry design drug clearance improved next generation pharmacokinetic model prescription document prescription procedure prevent tool vigilance

项目摘要

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）：三分之一的入院患者有潜在的肾功能损害或在住院期间出现急性肾功能衰竭。大多数患者所服用的药物对肾功能敏感，应调整剂量以避免药物蓄积和毒性。三十多年的研究已经产生了多种估计肾脏清除率和其他药代动力学参数的技术，这些技术可以指导处方者如何调整给药剂量或频率。然而，这些技术很少被医生或其他提供者可靠地应用，并且肾功能不全情况下的不准确剂量已被确定为可预防的药物不良事件的主要原因之一。为处方者提供自动指导的计算机化处方最终将提供防止剂量错误所需的保障措施和实时帮助。然而，现有的实施可能不够复杂，无法提供准确的剂量建议。例如，广泛使用的 Cockcroft-Gault 肾清除率估计在极端体重和年龄以及肾功能快速变化的情况下是不准确的。所有这些警告都经常出现在住院患者身上。目前尚不清楚，如果采用更加量身定制、个性化的方法，“单一模型”方法生成的药物建议中有多少会发生变化。我们假设肾脏剂量决策支持系统的 3 个主要变化将极大地改变指导：1) 使用适合患者特征的模型估计药物清除率 2) 药物清除率变化的通知，这将改变现有的药物治疗 3) 将药物特异性药代动力学模型应用于治疗范围较窄的剂量治疗。我们将通过在患者和处方数据的大型数据集中模拟每个重新设计的组件的性能来检验我们的假设。与公共卫生的相关性：肾功能受损的住院患者需要特殊护理和监测，以避免药物蓄积和直接肾脏毒性。需要计算机化的护理系统来最佳地协助药物剂量，并在健康状况的突然变化使先前的药物剂量变得过时时帮助医生保持警惕。