基于模型预测患者对肾上腺素的特异性反应。

The open medical informatics journal Pub Date : 2010-01-01 Epub Date: 2010-07-29 DOI:10.2174/1874431101004010149
J Geoffrey Chase, Christina Starfinger, Christopher E Hann, James A Revie, Dave Stevenson, Geoffrey M Shaw, Thomas Desaive
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引用次数: 0

摘要

心血管和循环系统模型已在模拟心脏和循环系统疾病状态中得到验证。该模型还被证明能准确捕捉猪肺栓塞模型和不同血压水平下 PEEP(呼气末正压)滴定的主要血流动力学趋势。在这项研究中,现有模型和参数识别过程被用于研究不同肾上腺素剂量对健康和危重病人群体的影响,并开发出一种预测肾上腺素血流动力学反应的方法。该模型模拟了血液动力学对动脉血压和每搏容量(心脏指数)的影响,并确定了肾上腺素特异性参数。然后,利用文献中发表的研究数据,将这些参数的剂量依赖性变化与肾上腺素剂量联系起来。然后利用这些关系来预测未来患者对剂量变化或 1-12 小时内的特定反应。研究结果与 3 项已发表的肾上腺素剂量研究数据进行了比较,共包括 37 组数据。在对所有病例进行重新模拟并与临床数据进行比较时,所确定模型的绝对百分比误差均在 10%以内。所有确定的参数趋势都符合临床预期变化。在重新模拟并与临床数据比较时,预测血流动力学反应(N=15)的绝对百分比误差也在 10%以内。在临床上准确预测肾上腺素等肌力循环支持药物的效果,为这类基于模型的应用提供了巨大潜力。总之,这项工作是对基本数学模型、方法和途径的进一步临床概念验证,同时也为在重症监护决策支持中使用该模型进行肾上腺素临床滴定提供了模板。因此,它们进一步证明了即将开展的人体临床试验对该模型进行验证的合理性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Model-based prediction of the patient-specific response to adrenaline.

Model-based prediction of the patient-specific response to adrenaline.

Model-based prediction of the patient-specific response to adrenaline.

Model-based prediction of the patient-specific response to adrenaline.

A model for the cardiovascular and circulatory systems has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture the main hemodynamic trends in porcine models of pulmonary embolism and PEEP (positive end-expiratory pressure) titrations at different volemic levels. In this research, the existing model and parameter identification process are used to study the effect of different adrenaline doses in healthy and critically ill patient populations, and to develop a means of predicting the hemodynamic response to adrenaline. The hemodynamic effects on arterial blood pressures and stroke volume (cardiac index) are simulated in the model and adrenaline-specific parameters are identified. The dose dependent changes in these parameters are then related to adrenaline dose using data from studies published in the literature. These relationships are then used to predict the future, patient-specific response to a change in dose or over time periods from 1-12 hours. The results are compared to data from 3 published adrenaline dosing studies comprising a total of 37 data sets. Absolute percentage errors for the identified model are within 10% when re-simulated and compared to clinical data for all cases. All identified parameter trends match clinically expected changes. Absolute percentage errors for the predicted hemodynamic responses (N=15) are also within 10% when re-simulated and compared to clinical data. Clinically accurate prediction of the effect of inotropic circulatory support drugs, such as adrenaline, offers significant potential for this type of model-based application. Overall, this work represents a further clinical, proof of concept, of the underlying fundamental mathematical model, methods and approach, as well as providing a template for using the model in clinical titration of adrenaline in a decision support role in critical care. They are thus a further justification in support of upcoming human clinical trials to validate this model.

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