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Background <p>Accurate, real-time blood pressure (BP) monitoring is critical in emergency and critical care, but current methods are limited. Invasive arterial catheterization, the gold standard, is often delayed in hypotensive patients, whereas noninvasive cuffs can be unreliable in a state of low perfusion. We hypothesized that carotid artery compressibility measured by point-of-care ultrasound (POCUS) and analyzed via artificial intelligence (AI) could be used to estimate arterial BP noninvasively.</p> Methods <p>We conducted a prospective observational study enrolling critically ill patients and post-return of spontaneous circulation (ROSC) patients in the emergency department. Standardized POCUS-guided carotid artery compression (POCUS-CAC) was performed. Video clips of POCUS-CAC were analyzed with RealCAC-Net, a deep learning model for quantifying carotid artery compressibility (CAC). The AI-derived maximum CAC value and concurrently measured BP were recorded. Diagnostic and regression analyses were conducted to evaluate the performance of CAC in classifying and predicting BP.</p> Results <p>A total of 372 ultrasound clips from 56 patients (30 critically ill, 26 post-ROSC) were analyzed. CAC demonstrated strong inverse correlations with systolic (<i>r</i> = −0.697), mean (<i>r</i> = −0.656), and diastolic (<i>r</i> = −0.576) arterial pressures. The model achieved excellent diagnostic performance for identifying hypotension: area under the curve of 0.90 (95% confidence interval (CI), 0.87–0.93) for systolic BP &lt; 60&#xa0;mmHg and 0.91 (95% CI, 0.88–0.94) for mean BP &lt; 40&#xa0;mmHg. Regression models enabled continuous BP estimation with root mean squared errors as low as 8.3&#xa0;mmHg. The model performed best in hypotensive ranges.</p> Conclusions <p>This proof-of-concept study demonstrated potential diagnostic utility of carotid artery compressibility for blood pressure estimation in critically ill and post-ROSC patients with hypotensive conditions. Our findings suggest that CAC may provide a noninvasive tool for hemodynamic monitoring, particularly in settings where invasive monitoring is not immediately available. Additional validation studies are warranted.</p> Graphical abstract <p></p>

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AI-driven carotid artery compressibility assessment via point-of-care ultrasound for blood pressure estimation in critically ill and post-resuscitation patients: a prospective observational study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Seung Jin Maeng,
  • Subin Park,
  • Ik Joon Jo,
  • Guntak Lee,
  • Sung Yeon Hwang,
  • Myung Jin Chung,
  • Jihyeon Kim,
  • Hakje Yoo,
  • Hee Yoon

摘要

Background

Accurate, real-time blood pressure (BP) monitoring is critical in emergency and critical care, but current methods are limited. Invasive arterial catheterization, the gold standard, is often delayed in hypotensive patients, whereas noninvasive cuffs can be unreliable in a state of low perfusion. We hypothesized that carotid artery compressibility measured by point-of-care ultrasound (POCUS) and analyzed via artificial intelligence (AI) could be used to estimate arterial BP noninvasively.

Methods

We conducted a prospective observational study enrolling critically ill patients and post-return of spontaneous circulation (ROSC) patients in the emergency department. Standardized POCUS-guided carotid artery compression (POCUS-CAC) was performed. Video clips of POCUS-CAC were analyzed with RealCAC-Net, a deep learning model for quantifying carotid artery compressibility (CAC). The AI-derived maximum CAC value and concurrently measured BP were recorded. Diagnostic and regression analyses were conducted to evaluate the performance of CAC in classifying and predicting BP.

Results

A total of 372 ultrasound clips from 56 patients (30 critically ill, 26 post-ROSC) were analyzed. CAC demonstrated strong inverse correlations with systolic (r = −0.697), mean (r = −0.656), and diastolic (r = −0.576) arterial pressures. The model achieved excellent diagnostic performance for identifying hypotension: area under the curve of 0.90 (95% confidence interval (CI), 0.87–0.93) for systolic BP < 60 mmHg and 0.91 (95% CI, 0.88–0.94) for mean BP < 40 mmHg. Regression models enabled continuous BP estimation with root mean squared errors as low as 8.3 mmHg. The model performed best in hypotensive ranges.

Conclusions

This proof-of-concept study demonstrated potential diagnostic utility of carotid artery compressibility for blood pressure estimation in critically ill and post-ROSC patients with hypotensive conditions. Our findings suggest that CAC may provide a noninvasive tool for hemodynamic monitoring, particularly in settings where invasive monitoring is not immediately available. Additional validation studies are warranted.

Graphical abstract