Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study.

IF 2 Q3 HEALTH CARE SCIENCES & SERVICES
Selene Y L Tan, Jia Xin Chai, Minwoo Choi, Umair Javaid, Brenda Pei Yi Tan, Belinda Si Ying Chow, Hairil Rizal Abdullah
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引用次数: 0

Abstract

Background: Blood pressure (BP) and hemoglobin concentration measurements are essential components of preoperative anesthetic evaluation. Remote photoplethysmography (rPPG) is an emerging technology that may be used to measure BP and hemoglobin concentration noninvasively with just a consumer-grade smartphone, replacing traditional in-person measurements. However, there is limited data regarding the use of this technology in patients with diverse skin tones and medical comorbidities. Hence, widespread applicability is yet to be achieved. The potential benefits of achieving this would be immense, allowing for greater convenience, accessibility, and reduction in labor and resources.

Objective: Our study aims to be the first to develop an algorithm for noninvasive rPPG-based BP and hemoglobin concentration measurement that can be used for preoperative evaluation of patients in real-world clinical practice settings.

Methods: We conducted the study at Singapore General Hospital from March 1, 2023, to June 28, 2024. A total of 200 patients were recruited. Our primary analysis compared the accuracy of rPPG-based systolic and diastolic BP measurements against measurements taken with automated BP measuring devices. Our secondary analysis compared the accuracy of rPPG-based hemoglobin concentration measurement against traditional blood sampling.

Results: Our model performed best with diastolic BP predictions, with a mean absolute percentage error of 7.52% and a mean difference of 0.16 mm Hg (SD 3.22 mm Hg) between reference and measured readings. The 95% CI for the mean difference between predicted and measured diastolic BP was ±0.57 (-0.41 to 0.73) mm Hg. Systolic BP predictions yielded a mean absolute percentage error of 9.52% and a mean difference of 2.69 mm Hg (SD 7.86 mm Hg). The 95% CI for the mean difference between predicted and measured systolic BP was ±1.14 (-1.54 to -3.83) mm Hg. Hemoglobin concentration predictions had a mean absolute percentage error of 8.52%, with a mean difference of 0.23 g/dL (SD 0.67 g/dL). The 95% CI for the mean difference between predicted and reference measured hemoglobin concentration was ±0.10 (95% CI 0.13-0.33) g/dL.

Conclusions: Noninvasive rPPG-based measurement of BP and hemoglobin concentration at the preoperative evaluation setting has great potential for improving convenience, improving efficiency, and conserving resources for patients and health care providers. Our model was able to accurately predict diastolic BP in patients with diverse skin tones and medical comorbidities. The findings of this study serve as a basis for further studies to develop and validate the model for noninvasive rPPG-based BP and hemoglobin concentration measurement.

在术前评估设置中用于血压和血红蛋白水平评估的远程光容积描记技术:算法开发研究。
背景:血压(BP)和血红蛋白浓度测量是术前麻醉评估的重要组成部分。远程光电脉搏波描记术(rPPG)是一项新兴技术,可用于通过消费级智能手机无创测量血压和血红蛋白浓度,取代传统的面对面测量。然而,关于该技术在不同肤色和医疗合并症患者中的应用的数据有限。因此,广泛的适用性还有待实现。实现这一目标的潜在好处将是巨大的,允许更大的便利性、可访问性和减少劳动力和资源。目的:我们的研究旨在首次开发一种基于rppg的无创血压和血红蛋白浓度测量算法,可用于现实世界临床实践环境中患者的术前评估。方法:研究于2023年3月1日至2024年6月28日在新加坡总医院进行。总共招募了200名患者。我们的初步分析比较了基于rppg的收缩压和舒张压测量与自动血压测量装置测量的准确性。我们的二次分析比较了基于rppg的血红蛋白浓度测量与传统血液采样的准确性。结果:我们的模型在舒张压预测方面表现最好,平均绝对百分比误差为7.52%,参考和测量读数之间的平均差值为0.16 mm Hg (SD 3.22 mm Hg)。预测舒张压与测量舒张压平均差值的95% CI为±0.57 (-0.41 ~ 0.73)mm Hg。舒张压预测的平均绝对百分比误差为9.52%,平均差值为2.69 mm Hg (SD为7.86 mm Hg)。预测收缩压与测量收缩压平均差值的95% CI为±1.14(-1.54至-3.83)mm Hg。血红蛋白浓度预测的平均绝对百分比误差为8.52%,平均差值为0.23 g/dL (SD为0.67 g/dL)。预测血红蛋白浓度与参考测量血红蛋白浓度的平均差异的95% CI为±0.10 (95% CI 0.13-0.33) g/dL。结论:术前评估时基于无创rppg的血压和血红蛋白浓度测量对患者和医护人员方便、高效、节约资源具有很大的潜力。我们的模型能够准确预测不同肤色和医疗合并症患者的舒张压。本研究结果为进一步研究建立和验证基于rppg的无创血压和血红蛋白浓度测量模型奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Formative Research
JMIR Formative Research Medicine-Medicine (miscellaneous)
CiteScore
2.70
自引率
9.10%
发文量
579
审稿时长
12 weeks
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