Xue Feng, Romero-Ortuno Roman
Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland.
The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland.
Sensors (Basel). 2025 Jun 4;25(11):3548. doi: 10.3390/s25113548.
This study aimed to investigate the neurocardiovascular responses during an Active Stand (AS) test, utilizing both pre-processed and raw signals, to predict adverse health outcomes including orthostatic intolerance (OI) during the AS, and future falls and mortality.
A total of 2794 participants from The Irish Longitudinal Study on Ageing (TILDA) were included. Continuous cardiovascular (heart rate (HR), systolic (sBP), and diastolic (dBP) blood pressure) and near infra-red spectroscopy-based neurovascular (tissue saturation index (TSI), oxygenated hemoglobin (OHb), and deoxygenated hemoglobin (HHb)) signals were analyzed using Statistical Parametric Mapping (SPM) to identify significant group differences across health outcomes.
The results demonstrated that raw (unprocessed) signals, particularly OHb and sBP/dBP, were more effective in capturing significant physiological differences associated with mortality and OI compared to pre-processed signals. Specifically, for OI, raw sBP and dBP captured significant changes across the entire test, whereas pre-processed signals showed intermittent significance. TSI captured OI only in its pre-processed form, at approximately 10 s post-stand. For mortality, raw OHb was effective throughout the AS test. No significant differences were observed in either pre-processed or raw signals related to falls, suggesting that fall risk may require a multifactorial assessment beyond neurocardiovascular responses.
These findings highlight the potential utility of raw signal analysis in improving risk stratification for OI and mortality, with further studies needed to validate these findings and refine predictive models for clinical applications. This study underscores the importance of retaining raw data for certain physiological assessments and provides a foundation for future work in developing machine-learning models for early health outcome detection.
本研究旨在利用预处理信号和原始信号，调查主动站立（AS）测试期间的神经心血管反应，以预测不良健康结局，包括AS期间的体位性不耐受（OI）以及未来的跌倒和死亡率。
纳入了来自爱尔兰老龄化纵向研究（TILDA）的2794名参与者。使用统计参数映射（SPM）分析连续的心血管（心率（HR）、收缩压（sBP）和舒张压（dBP））以及基于近红外光谱的神经血管（组织饱和度指数（TSI）、氧合血红蛋白（OHb）和脱氧血红蛋白（HHb））信号，以确定不同健康结局之间的显著组间差异。
结果表明，与预处理信号相比，原始（未处理）信号，特别是OHb和sBP/dBP，在捕捉与死亡率和OI相关的显著生理差异方面更有效。具体而言，对于OI，原始sBP和dBP在整个测试过程中捕捉到了显著变化，而预处理信号则显示出间歇性的显著性。TSI仅在其预处理形式下，在站立后约10秒时捕捉到OI。对于死亡率，原始OHb在整个AS测试过程中都有效。在与跌倒相关的预处理信号或原始信号中均未观察到显著差异，这表明跌倒风险可能需要除神经心血管反应之外的多因素评估。
这些发现凸显了原始信号分析在改善OI和死亡率风险分层方面的潜在效用，需要进一步研究来验证这些发现并完善临床应用的预测模型。本研究强调了保留原始数据用于某些生理评估的重要性，并为未来开发用于早期健康结局检测的机器学习模型的工作奠定了基础。
