Search Results (260)

Background: Falls are a major public health issue among older adults, often related to postural or orthostatic hypotension (OH). The optimal timing and methods for measuring blood pressure (BP) to assess OH and its relationship with falls are uncertain. Methods: We analyzed data from two older cohorts: the PROHEALTH study (n = 30, aged ≥ 65 years) and the NILVAD-CBF trial (n = 58, aged ≥ 50 years). Continuous beat-to-beat BP was measured during active stand tests. We assessed orthostatic BP responses during sit-to-stand and supine-to-stand maneuvers and calculated the associations between orthostatic BP response variables and falls. Results: In the PROHEALTH cohort, participants with a history of falls exhibited a significantly lower baseline BP (115 ± 13/68 ± 10 vs. 142 ± 21/79 ± 11 mmHg; p = 0.004/0.018) and lower systolic BP (SBP) nadir (90 ± 22 vs. 112 ± 25 mmHg; p = 0.043) than non-fallers. SBP recovery within three minutes post-stand was delayed in fallers but rapid in non-fallers. A lower resting BP was associated with fall risk, and a lower BP nadir within 10 s after standing showed a trend toward a higher fall risk. No significant associations were found in the NILVAD-CBF cohort (prospective falls). Conclusions: Our findings demonstrate that a lower resting SBP and diastolic BP (DBP) are associated with an increased fall risk in older adults, with a lower SBP and DBP nadir after standing also showing a potential association. Persistent OH or delayed BP recovery is identified as a potentially relevant fall risk factor. The supine-to-stand test was more sensitive in detecting OH than the sit-to-stand test. Continuous BP monitoring provides the advantage of detecting pathophysiologic orthostatic BP responses for fall risk assessment in older adults. Further research with larger cohorts is warranted to validate our findings. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Background: Hypotension following epidural labor analgesia (ELA) is its most common complication, affecting approximately 20% of patients and posing risks to both maternal and fetal health. As digital tools and predictive analytics increasingly shape perioperative and obstetric anesthesia practices, real-world implementation data are needed to guide their integration into clinical care. Current monitoring practices rely on intermittent non-invasive blood pressure (NIBP) measurements, which may delay recognition and treatment of hypotension. The Hypotension Prediction Index (HPI) algorithm uses continuous arterial waveform monitoring to predict hypotension for potentially earlier intervention. This clinical trial evaluated the feasibility, acceptability, and efficacy of continuous HPI-guided treatment in reducing time-to-treatment for ELA-associated hypotension and improving maternal hemodynamics. Methods: This was a prospective randomized controlled trial design involving healthy pregnant individuals receiving ELA. Participants were randomized into two groups: Group CM (conventional monitoring with NIBP) and Group HPI (continuous noninvasive blood pressure monitoring). In Group HPI, hypotension treatment was guided by HPI output; in Group CM, treatment was based on NIBP readings. Feasibility, appropriateness, and acceptability outcomes were assessed among subjects and their bedside nurse using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) instruments. The primary efficacy outcome was time-to-treatment of hypotension, defined as the duration between onset of hypotension and administration of a vasopressor or fluid therapy. This outcome was chosen to evaluate the clinical responsiveness enabled by HPI monitoring. Hypotension is defined as a mean arterial pressure (MAP) < 65 mmHg for more than 1 min in Group CM and an HPI threshold < 75 for more than 1 min in Group HPI. Secondary outcomes included total time in hypotension, vasopressor doses, and hemodynamic parameters. Results: There were 30 patients (Group HPI, n = 16; Group CM, n = 14) included in the final analysis. Subjects and clinicians alike rated the acceptability, appropriateness, and feasibility of the continuous monitoring device highly, with median scores ≥ 4 across all domains, indicating favorable perceptions of the intervention. The cumulative probability of time-to-treatment of hypotension was lower by 75 min after ELA initiation in Group HPI (65%) than Group CM (71%), although this difference was not statistically significant (log-rank p = 0.66). Mixed models indicated trends that Group HPI had higher cardiac output (β = 0.58, 95% confidence interval −0.18 to 1.34, p = 0.13) and lower systemic vascular resistance (β = −97.22, 95% confidence interval −200.84 to 6.40, p = 0.07) throughout the monitoring period. No differences were found in total vasopressor use or intravenous fluid administration. Conclusions: Continuous monitoring and precision hypotension treatment is feasible, appropriate, and acceptable to both patients and clinicians in a labor and delivery setting. These hypothesis-generating results support that HPI-guided treatment may be associated with hemodynamic trends that warrant further investigation to determine definitive efficacy in labor analgesia contexts. Full article

Traditional cuff-based blood pressure (BP) monitoring methods provide only intermittent readings, while invasive alternatives pose clinical risks. Recent studies have demonstrated feasibility of estimating continuous non-invasive cuff-less BP using photoplethysmogram (PPG) signals alone. However, existing approaches rely on complex manual feature engineering and/or multiple model architectures, resulting in inefficient epoch training numbers and limited performance. This research proposes cBP-Tnet, an efficient single-channel and model multi-task Transformer network designed for PPG signal automatic feature extraction. cBP-Tnet employed specialized hyperparameters—integrating adaptive Kalman filtering, outlier elimination, signal synchronization, and data augmentation—leveraging multi-head self-attention and multi-task learning strategies to identify subtle and shared waveform patterns associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP). We used the MIMIC-II public dataset (500 patients with 202,956 samples) for experimentation. Results showed mean absolute errors of 4.32 mmHg for SBP and 2.18 mmHg for DBP. For the first time, both SBP and DBP meet the Association for the Advancement of Medical Instrumentation’s international standard (<5 mmHg, >85 subjects). Furthermore, the network efficiently reduces the epoch training number by 13.67% when compared to other deep learning methods. Thus, this establishes cBP-Tnet’s potential for integration into wearable and home-based healthcare devices with continuous non-invasive cuff-less blood pressure monitoring. Full article

In the context of the relentless pursuit of precision, intelligence, and personalization within the realm of medical technology, the real-time monitoring of human physiological signals has assumed heightened significance. Implantable wireless sensor devices have exhibited extraordinary capabilities in tracking internal physiological parameters, including intraocular pressure, blood glucose levels, electrocardiographic activity, and arterial blood pressure. These devices are characterized by elevated temporal continuity and exceptional measurement accuracy. This paper undertakes an in-depth investigation into the key technologies underlying biodegradable implantable sensing devices. Initially, it expounds on diverse sensing mechanisms employed in implantable devices. Additionally, it presents common data transmission and power supply strategies for wireless sensing systems. Finally, it introduces biodegradable materials suitable for human implantation and their respective application domains and enumerates several implantable devices that are either under development or have already been commercialized. Through an in-depth and comprehensive discourse on the current state of development and extant challenges in this domain, the development trajectory of biodegradable devices is put forward. Moreover, this paper also serves as a valuable reference for the design and selection of implantable medical devices. Full article

This study evaluates the accuracy of a low-cost wearable system for the continuous monitoring of vital signs, including heart rate, blood oxygen saturation (Sp${\mathrm{O}}_2$), blood pressure trend (BPT), and body temperature. The prototype was built using the nRF52840 microcontroller, which integrates photoplethysmography and infrared sensors. The heart rate and Sp${\mathrm{O}}_2$ data were collected under three body positions (Rest, Sitting, and Standing), while all measurements were performed using both anatomical configurations: BPT-Finger and BPT-Earlobe. Results were compared against validated commercial devices: UT-100 for heart rate and Sp${\mathrm{O}}_2$, G-TECH LA800 for blood pressure, and G-TECH THGTSC3 for body temperature. Ten participants were monitored over a ten-day period. Bland–Altman analysis revealed clinically acceptable agreement thresholds of ±5 mmHg for blood pressure, ±5–10 bpm for heart rate, ±4% for Sp${\mathrm{O}}_2$, and ±0.5 °C for temperature. Both wearable configurations demonstrated clinically acceptable agreement across all vital signs. The BPT-Earlobe configuration exhibited superior stability and lower variability in the Rest and Sitting positions, likely due to reduced motion artifacts. Conversely, the BPT-Finger configuration showed higher Sp${\mathrm{O}}_2$ accuracy in the Standing position, with narrower limits of agreement. These findings highlight the importance of sensor placement in maintaining measurement consistency across physiological conditions. With an estimated cost of only ~USD 130—compared to ~USD 590 for the commercial alternatives—the proposed system presents a cost-effective, scalable, and accessible solution for decentralized health monitoring, particularly in underserved or remote environments. Full article

Cardiovascular disease (CVD) is the leading cause of global mortality, with hypertension affecting over one billion people. Current noninvasive blood pressure (BP) systems, like cuffs, suffer from discomfort and placement errors and lack continuous monitoring. Wearable solutions promise improvements, but technologies like photoplethysmography (PPG) and bioimpedance (BIOZ) face usability and clinical accuracy limitations. PPG is sensitive to skin tone and body mass index (BMI) variability, while BIOZ struggles with electrode contact and reusability. We present a novel, strain gauge-based wearable BP device that directly quantifies pressure via a dual transducer system, compensating for tissue deformation and external forces to enable continuous, accurate BP measurement. The reusable, energy-efficient, and compact design suits long-term daily use. A novel leg press protocol across 10 subjects (systolic: 71.04–241.42 mmHg, diastolic: 53.46–123.84 mmHg) validated its performance under dynamic conditions, achieving mean absolute errors of 2.45 ± 3.99 mmHg (systolic) and 1.59 ± 2.08 mmHg (diastolic). The device showed enhanced robustness compared to the Finapres, with less motion-induced noise. This technology significantly advances current methods by delivering continuous, real-time BP monitoring without reliance on electrodes, independent of skin tone, while maintaining a high accuracy and user comfort. Full article

Cardiovascular disease is a major health threat closely associated with blood pressure levels. While continuous monitoring is essential, traditional cuff-based devices are inconvenient for long-term use. Current methods often fail to balance deep learning capabilities with interpretability, limiting further accuracy improvements. To address this problem, we propose a novel two-branch deep learning framework combining Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) for photoplethysmography (PPG)-based cuffless blood pressure estimation. The ResNet branch processes 60 features selected by Support Vector Machine-Recursive Feature Elimination (SVM-RFE) from manually extracted features, including our newly proposed trend features, while the BiLSTM branch processes complete PPG waveforms. Testing on 220 waveform segments from 218 patients in the MIMIC-IV dataset, our method achieves mean absolute errors of 3.47 mmHg and 2.81 mmHg, with standard deviations of 5.06 mmHg and 4.11 mmHg for systolic and diastolic blood pressure. This performance meets the Association for the Advancement of Medical Instrumentation (AAMI) standards and achieves an A rating according to British Hypertension Society (BHS) standards. Full article

The monitoring of peripheral electrical bioimpedance (EBI) variations is a promising method that has the potential to replace invasive or burdensome techniques for cardiovascular measurements. Segmental or continuous recording of peripheral pulse waves can serve as a basis for calculating prognostic markers like pulse wave velocity (PWV) or include parameters such as pulse transit time (PTT) or pulse arrival time (PAT) for noninvasive blood pressure (BP) estimation, as well as potentially novel cardiovascular risk indicators. However, several technical, analytical, and interpretative aspects need to be resolved before the EBI method can be adopted in clinical practice. Our goal was to investigate and improve the application of EBI, executing its comparison with other cardiovascular assessment methods in patients hospitalized for coronary catheterization procedures. Methods: We analyzed data from 44 non-acute patients aged 45–74 years who were hospitalized for coronary catheterization at East Tallinn Central Hospital between 2020 and 2021. The radial EBI and electrocardiogram (ECG) were measured simultaneously with central and contralateral pressure curves. The Savitzky–Golay filter was used for signal smoothing. The Hankel matrix decomposer was applied for the extraction of cardiac waveforms from multi-component signals. After extracting the cardiac component, a period detection algorithm was applied to EBI and blood pressure curves. Results: Seven points of interest were detected on the pressure and EBI curves, and four with good representativeness were selected for further analysis. The Spearman correlation coefficient was low for all but the central and distal pressure curve systolic upstroke time points. A high positive correlation was found between PWV measured both invasively and with EBI. The median value of complimentary pulse wave velocity (CPWV), a parameter proposed in the paper, was significantly lower in patients with normal coronaries compared to patients with any stage of coronary disease. Conclusions: With regard to wearable devices, the EBI-derived PAT can serve as a substrate for PWV calculations and cardiovascular risk assessment, although these data require further confirmation. Full article

Background: In systemic sclerosis (SSc), endothelial dysfunction, inflammation, and reduced nitric oxide levels may disrupt circadian blood pressure (BP) regulation. There are studies showing that inflammatory and certain other cells in diseases like SSc exhibit diurnal rhythms. In our study, we examined the effect of SSc on BP. In particular, the frequency of the non-dipper pattern (lack of nighttime BP reduction) in SSc patients has not been adequately investigated. The aim of this study was to evaluate the 24 h BP profile in SSc patients and to compare the frequency of the non-dipper pattern with that of the non-scleroderma group. Additionally, the identification of disrupted circadian BP patterns in SSc patients aims to contribute to the development of personalized, time-sensitive BP monitoring strategies in the future and to support the applicability of personalized medicine in this context. Methods: A total of 31 SSc patients diagnosed according to the 2013 ACR/EULAR classification criteria and 30 age- and sex-matched individuals without SSc were included in this prospective study. BP changes between day and night were evaluated by measuring BP every 30 min with a 24 h ambulatory blood pressure monitoring (ABPM) device. The non-dipper pattern was defined as a decrease in BP of less than 10% during the night compared to the day. To better assess BP fluctuations during the night, nighttime measurements were divided into two time periods: first, 24:00–04:00, and then 04:00–08:00. Additionally, laboratory and clinical parameters and SSc subtypes were compared between the groups. Results: The ABPM findings were compared between the groups with and without SSc. The non-dipper pattern was significantly more common in the SSc group at all time intervals. The non-dipper pattern was observed in 25.8% of the non-SSc group and 83.9% of SSc patients (p < 0.001). In the period between 24:00 and 04:00, the prevalence was 25.8% in the control group and 71.0% in SSc patients (p < 0.001), and between 04:00 and 08:00, it was 35.5% in the control group and 80.6% in SSc patients (p < 0.001). No significant difference was found in non-dipper patterns between individuals with diffuse and limited cutaneous forms of systemic sclerosis. Conclusions: The non-dipper BP pattern is significantly more common in patients with SSc, indicating the disruption of the circadian rhythm affecting BP. Analysis performed by dividing the night into specific time periods revealed that this deterioration continued throughout the night. The findings highlight the importance of circadian BP monitoring in SSc patients and may contribute to future risk stratification and treatment strategies. Circadian BP analysis in SSc may help to develop strategies that are personalized for these patients and tailored to their physiological rhythm. Full article

Sex differences in response to hypovolemia are still an open issue, which is readdressed here by exploiting the potential of near-infrared spectroscopy (NIRS) to monitor the response of lower body negative pressure (LBNP) in upper and lower limbs. In 28 subjects in a supine position, non-invasive arterial blood pressure was continuously monitored along with NIRS parameters from the forearm and thigh during randomized 90 s exposure to LBNP at −10, −20, −30, and −40 mmHg, followed by one 5 min exposure to −30 mmHg. LBNP did not affect arterial blood pressure, slightly increased the heart rate, and affected blood volume in both limbs (p < 0.005). Slopes of tissue oxygenation and deoxy-hemoglobin revealed pressure (p < 0.001) and sex (p < 0.05) dependences for the vasoconstrictive response to LBNP in both arms and legs, with some evidence of larger vasoconstriction in legs. Most variables reached a stable value within 90 s in the arm, while longer time courses were observed in the leg. NIRS is a valuable methodology to detect early LBNP-induced hemodynamic changes, providing that blood volume and blood flow contribution are discriminated. A comparative analysis of time courses proved useful in revealing stronger vasoconstrictive responses in males than in females and in lower limbs than in upper limbs. The same approach could be applied to other experimental contexts. Full article

Hypertension and blood pressure variability (BPV) are major risk factors for cardiovascular disease (CVD). Single-channel photoplethysmography (PPG) has emerged as a promising daily blood pressure (BP) monitoring tool. However, estimating BP trends presents challenges due to complex temporal dependencies and continuous fluctuations. Traditional methods often address BP prediction as isolated tasks and focus solely on temporal dependencies within a limited time window, which may fall short of capturing the intricate BP fluctuation patterns implied in varying time spans, particularly amidst constant BP variations. To address this, we propose a novel deep learning model featuring a two-stage architecture and a new input structure called contextual cycles. This model estimates beat-to-beat systolic blood pressure (SBP) trends as a sequence prediction task, transforming the output from a single SBP value into a sequence. In the first stage, parallel ResU Blocks are utilized to extract fine-grained features from each cycle. The generated feature vectors are then processed by Transformer layers with relative position encoding (RPE) to capture inter-cycle interactions and temporal dependencies in the second stage. Our proposed model demonstrates robust performance in beat-to-beat SBP trend estimation, achieving a mean absolute error (MAE) of 3.186 mmHg, a Pearson correlation coefficient applied to sequences (${\mathrm{R}}_{\mathrm{seq}}$) of 0.743, and a variability error (VE) of 1.199 mmHg. It excels in steady and abrupt substantial fluctuation states, outperforming baseline models. The results reveal that our method meets the requirements of the AAMI standard and achieves grade A according to the BHS standard. Overall, our proposed method shows significant potential for reliable daily health monitoring. Full article

This observational pilot study investigated neurocardiovascular responses to an active stand test using continuous physiological monitoring and functional data analysis (FDA) in older women. A sample of 25 community-dwelling female adults aged 59–78 years (mean age: 70.3 years) participated. Participants were dichotomized into comparison groups based on five factors: age (<70 vs. ≥70 years); the presence of initial orthostatic hypotension (IOH, yes/no); body mass index (BMI < 25 vs. ≥25 kg/m²); antihypertensive medication use (yes/no); and physical frailty status assessed by the Survey of Health, Ageing and Retirement in Europe—Frailty Instrument (SHARE-FI score < −0.5 vs. ≥−0.5). Each participant completed an active stand test during which six physiological signals were continuously recorded: systolic (sBP) and diastolic (dBP) blood pressure and heart rate (HR) via digital artery photoplethysmography and left frontal oxygenated hemoglobin (O₂Hb), deoxygenated hemoglobin (HHb), and tissue saturation index (TSI) via near-infrared spectroscopy (NIRS). The signal analysis focused on a standardized 200 s window spanning 50 s before to 150 s after the stand, with all signals resampled and synchronized at 5 Hz. FDA was used to statistically compare the full time series between groups for each signal. Group-level differences revealed that younger participants (<70 years) exhibited significantly higher HR in multiple periods following the stand (~10 s, ~30 s, ~90 s, and ~140 s post-stand) compared to their older counterparts. Participants with IOH demonstrated significantly lower sBP at ~10 s, ~80 s, and ~130 s post-stand and lower dBP at ~10 s post-stand. Among participants classified as overweight/obese (BMI ≥ 25 kg/m²), significantly lower levels of HHb were observed at ~10 s, ~30–50 s, and ~60 s post-stand, while O₂Hb levels were reduced at ~50 s, ~60 s, ~70–110 s, ~130 s, and ~140 s post-stand. No statistically significant group-level differences were observed based on antihypertensive medication use or frailty status. These findings demonstrate the utility of FDA in detecting subtle, time-dependent physiological variations during orthostatic challenge and underscore the value of continuous neurocardiovascular monitoring in assessing orthostatic tolerance in aging populations. Full article

Background: 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. Methods: 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 (O₂Hb), and deoxygenated hemoglobin (HHb)) signals were analyzed using Statistical Parametric Mapping (SPM) to identify significant group differences across health outcomes. Results: The results demonstrated that raw (unprocessed) signals, particularly O₂Hb 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 O₂Hb 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. Conclusions: 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. Full article