个性化脉搏波传播模型改善严重创伤性脑损伤患者血管加压药剂量管理。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-15 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013501
Kamil Wolos, Leszek Pstras, Urszula Bialonczyk, Malgorzata Debowska, Wojciech Dabrowski, Dorota Siwicka-Gieroba, Jan Poleszczuk
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引用次数: 0

摘要

本研究探讨了检查动脉脉搏波的形状并拟合脉搏波传播的基于生理学的数学模型是否可以为严重创伤性脑损伤(sTBI)患者的心血管系统状态提供额外的见解,从而有可能增强血管加压药的剂量策略。我们对重症监护室的25例sTBI患者进行了纵向研究。采用振荡测量法无创地记录手腕和脚踝的动脉脉搏波,并用于建立动脉血流动力学的0-1D模型。模型估计后,患者特定的心血管参数被用于统计模型,以预测在接下来的24小时内血管加压素(去甲肾上腺素)给药剂量的变化。模型与记录的脉搏波拟合令人满意,决定系数([公式:见文])约为0.9,测量值与模型估计的平均动脉压之差为0.1±1.0 mmHg([公式:见文]=0.99)。除少数患者外,我们未发现模型估计参数与记录脉搏波时去甲肾上腺素剂量之间存在明显关联。然而,我们的预测模型在整个数据集上进行训练和测试时达到了0.85的平衡精度,在使用留一交叉验证时达到了0.76,在总共77个观察值中有8个错误分类。因此,尽管已知患者对血管加压药物的血流动力学反应存在差异,但本文提出的方法能够以令人满意的准确性预测未来24小时内去甲肾上腺素剂量变化的方向。经过进一步的研究和广泛的验证,我们的方法可以为优化每位患者的血管加压剂剂量提供决策支持工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized pulse wave propagation modeling to improve vasopressor dosing management in patients with severe traumatic brain injury.

This study investigates whether examining the shape of arterial pulse waves and fitting to them a physiology-based mathematical model of pulse wave propagation can provide additional insights into the state of the cardiovascular system in patients with severe traumatic brain injury (sTBI), potentially enhancing vasopressor dosing strategies. We conducted a longitudinal study on 25 sTBI patients in an intensive care unit. Arterial pulse waves were recorded non-invasively from wrists and ankles using an oscillometric method and were used to inform a 0-1D model of the arterial blood flow dynamics. Model-estimated, patient-specific cardiovascular parameters were then used in a statistical model to predict changes in the administered dose of vasopressor (norepinephrine) in the next 24 hours. The model fits to the recorded pulse waves were satisfactory, with the coefficients of determination ([Formula: see text]) of approximately 0.9 and the differences between the measured and model-estimated mean arterial pressure of 0.1 ± 1.0 mmHg ([Formula: see text]=0.99). Except for a few patients, we found no clear association between the model-estimated parameters and norepinephrine dose at the time of pulse wave recording. Nevertheless, our predictive model achieved a balanced accuracy of 0.85 when trained and tested on the entire dataset and 0.76 when using the leave-one-out cross-validation, with 8 misclassifications among the total of 77 observations. Thus, despite the known inter-patient variability of hemodynamic response to vasopressors, the proposed method allowed predicting the direction of norepinephrine dose changes in the next 24 hours with satisfactory accuracy. Subject to further studies and extensive validation, our approach could inform a decision-support tool for optimizing vasopressor dosing on a per-patient basis.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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