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利用人体测量学指标和机器学习预测腹内高压

Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.

作者信息

Tayebi Salar, Wise Rob, Van Regenmortel Niels, Dits Hilde, Schoonheydt Karen, De Laet Inneke, Malbrain Luca, Stiens Johan, Dabrowski Wojciech, Malbrain Manu L N G

机构信息

Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, 1050, Belgium.

Adult Intensive Care, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK.

出版信息

Sci Rep. 2025 Mar 19;15(1):9532. doi: 10.1038/s41598-025-93823-7.

DOI:10.1038/s41598-025-93823-7
PMID:40108362
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11923215/
Abstract

Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently, the gold standard for measuring intra-abdominal pressure (IAP) is via the bladder. Measurement of IAP is important to identify IAH early and thus implement appropriate management in order to avoid complications. It may be possible to use anthropometric parameters to predict IAP and thus identify IAH non-invasively. This retrospective observational study investigated how the most relevant body parameters evolve in relation to IAP, and whether IAP can be predicted based on anthropometric parameters. The IAP and 28 body parameters of 96 critically ill patients were recorded. Following statistical analyses such as Pearson's and mutual information correlation, the collected data were used to train a simulation model to examine reliable relationships between IAP, predict IAP values, and detect IAH. Three metrics were shown to sufficiently predict intra-bladder pressure (IBP) with a Pearson's correlation of 0.75 (R = 0.56). These parameters are the difference between the convex and horizontal xiphoid-to-pubis distance, sagittal abdominal diameter, and abdominal compliance. Subsequently, we found 1 metric that is able to predict the presence of IAH with Pearson correlation of 0.89 (R = 0.79). This metric is the difference between the convex and horizontal xiphoid to pubis distance. Three measured body parameters showed a correlation of more than 50% with IBP and they are sufficient for a reliable prediction of IBP, however, IAH can be most reliably predicted based on the difference between the convex and horizontal xiphoid-pubis distance and sagittal abdominal diameter. Future studies with larger patient populations and diverse body shapes are warranted to confirm these findings.

摘要

近四分之一的重症患者患有腹腔内高压(IAH)。目前,测量腹腔内压力(IAP)的金标准是通过膀胱测量。IAP的测量对于早期识别IAH并因此实施适当的管理以避免并发症很重要。利用人体测量参数预测IAP并因此无创识别IAH是有可能的。这项回顾性观察研究调查了最相关的身体参数如何随IAP变化,以及是否可以基于人体测量参数预测IAP。记录了96例重症患者的IAP和28项身体参数。经过Pearson相关性分析和互信息相关性分析等统计分析后,收集的数据被用于训练一个模拟模型,以检验IAP之间的可靠关系、预测IAP值并检测IAH。结果显示,三个指标能充分预测膀胱内压(IBP),Pearson相关性为0.75(R = 0.56)。这些参数是剑突至耻骨凸面与水平面距离之差、腹部矢状径和腹部顺应性。随后,我们发现一个指标能够预测IAH的存在,Pearson相关性为0.89(R = 0.79)。这个指标是剑突至耻骨凸面与水平面距离之差。三个测量的身体参数与IBP的相关性超过50%,它们足以可靠地预测IBP,然而,基于剑突耻骨凸面与水平面距离之差和腹部矢状径最能可靠地预测IAH。有必要开展针对更大患者群体和不同体型的未来研究来证实这些发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/dc06ac3b13a1/41598_2025_93823_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/af5321324ddd/41598_2025_93823_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/b01b17bfc7d1/41598_2025_93823_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/03a28cb4a98d/41598_2025_93823_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/b259ccf36382/41598_2025_93823_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/5429dc62dbbd/41598_2025_93823_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/dc06ac3b13a1/41598_2025_93823_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/af5321324ddd/41598_2025_93823_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/b01b17bfc7d1/41598_2025_93823_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/03a28cb4a98d/41598_2025_93823_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/b259ccf36382/41598_2025_93823_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/5429dc62dbbd/41598_2025_93823_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c738/11923215/dc06ac3b13a1/41598_2025_93823_Fig6_HTML.jpg

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