Intraoperative Hypotension Prediction: Current Methods, Controversies, and Research Outlook.

Intraoperative hypotension prediction has been increasingly emphasized due to its potential clinical value in reducing organ injury and the broad availability of large-scale patient datasets and powerful machine learning tools. Hypotension prediction methods can mitigate low blood pressure exposure time. However, they have yet to be convincingly demonstrated to improve objective outcomes; furthermore, they have recently become controversial. This review presents the current state of intraoperative hypotension prediction and makes recommendations on future research. We begin by overviewing the current hypotension prediction methods, which generally rely on the prevailing mean arterial pressure as one of the important input variables and typically show good sensitivity and specificity but low positive predictive value in forecasting near-term acute hypotensive events. We make specific suggestions on improving the definition of acute hypotensive events and evaluating hypotension prediction methods, along with general proposals on extending the methods to predict reduced blood flow and treatment effects. We present a start of a risk-benefit analysis of hypotension prediction methods in clinical practice. We conclude by coalescing this analysis with the current evidence to offer an outlook on prediction methods for intraoperative hypotension. A shift in research toward tailoring hypotension prediction methods to individual patients and pursuing methods to predict appropriate treatment in response to hypotension appear most promising to improve outcomes.

Cuffless Blood Pressure Measurement.

Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions.

Current evidence suggests that estimating blood pressure from convenient ECG waveforms alone is not viable.

Cuffless blood pressure (BP) measurement could improve hypertension awareness and control and is being widely pursued. Some have proposed to estimate BP from the electrocardiogram (ECG) alone despite little physiological basis. In this minireview, we extracted the most relevant articles related to ECG-based BP estimation. Our findings suggest that, as expected, estimating BP from ECG does not appear to be viable. Most notably, we have not found any evidence that ECG features can track BP changes. At best, certain ECG features may indicate heart disease and thus correlate with high BP, but this may not be clinically useful.

Comparison of accuracy of two uncalibrated pulse contour cardiac output monitors in off-pump coronary artery bypass surgery patients using pulmonary artery catheter-thermodilution as a reference.

BACKGROUND: Cardiac output (CO) is a key measure of adequacy of organ and tissue perfusion, especially in critically ill or complex surgical patients. CO monitoring technology continues to evolve. Recently developed CO monitors rely on unique algorithms based on pulse contour analysis of an arterial blood pressure (ABP) waveform. The objective of this investigation was to compare the accuracy of two monitors using different methods of pulse contour analysis - the Retia Argos device and the Edwards Vigileo-FloTrac device - with pulmonary artery catheter (PAC)-thermodilution as a reference. METHODS: Fifty-eight patients undergoing off-pump coronary artery bypass surgery formed the study cohort. A total of 572 triplets of CO measurements from each device - Argos, Vigileo-FloTrac (third generation), and thermodilution - were available before and after interventions (e.g., vasopressors, fluids, and inotropes). Bland-Altman analysis accounting for repeated measurements per subject and concordance analysis were applied to assess the accuracy of the CO values and intervention-induced CO changes of each pulse contour device against thermodilution. Cluster bootstrapping was employed to statistically compare the root-mean-squared-errors (RMSE = √(µ2 + σ2), where µ and σ are the Bland-Altman bias and precision errors) and concordance rates of the two devices. RESULTS: The RMSE (mean (95% confidence intervals)) for CO values was 1.16 (1.00-1.32) L/min for the Argos device and 1.54 (1.33-1.77) L/min for the Vigileo-FloTrac device; the concordance rate for intervention-induced CO changes was 87 (82-92)% for the Argos device and 72 (65-78)% for the Vigileo-FloTrac device; and the RMSE for the CO changes was 17 (15-19)% for the Argos device and 21 (19-23)% for the Vigileo-FloTrac device (p < 0.0167 for all comparisons). CONCLUSIONS: In comparison with CO measured by the PAC, the Argos device proved to be more accurate than the Vigileo-FloTrac device in CO trending and absolute CO measurement in patients undergoing off-pump coronary artery bypass surgery.

Wearable and Nearable Biosensors and Systems for Healthcare.

Biosensors and systems in the form of wearables and "nearables" (i [...].

Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography.

This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

Nonlinear identification of the total baroreflex arc: higher-order nonlinearity.

The total baroreflex arc is the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP). The nonlinear dynamics of this system were recently characterized. First, Gaussian white noise CSP stimulation was employed in open-loop conditions in normotensive and hypertensive rats with sectioned vagal and aortic depressor nerves. Nonparametric system identification was then applied to measured CSP and AP to establish a second-order nonlinear Uryson model. The aim in this study was to assess the importance of higher-order nonlinear dynamics via development and evaluation of a third-order nonlinear model of the total arc using the same experimental data. Third-order Volterra and Uryson models were developed by employing nonparametric and parametric identification methods. The R2 values between the AP predicted by the best third-order Volterra model and measured AP in response to Gaussian white noise CSP not utilized in developing the model were 0.69 ± 0.03 and 0.70 ± 0.03 for normotensive and hypertensive rats, respectively. The analogous R2 values for the best third-order Uryson model were 0.71 ± 0.03 and 0.73 ± 0.03. These R2 values were not statistically different from the corresponding values for the previously established second-order Uryson model, which were both 0.71 ± 0.03 (P > 0.1). Furthermore, none of the third-order models predicted well-known nonlinear behaviors including thresholding and saturation better than the second-order Uryson model. Additional experiments suggested that the unexplained AP variance was partly due to higher brain center activity. In conclusion, the second-order Uryson model sufficed to represent the sympathetically mediated total arc under the employed experimental conditions.

Nonlinear identification of the total baroreflex arc: chronic hypertension model.

The total baroreflex arc is the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP). Its linear dynamic functioning has been shown to be preserved in spontaneously hypertensive rats (SHR). However, the system is known to exhibit nonlinear dynamic behaviors. The aim of this study was to establish nonlinear dynamic models of the total arc (and its subsystems) in hypertensive rats and to compare these models with previously published models for normotensive rats. Hypertensive rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned. The carotid sinus regions were isolated and attached to a servo-controlled piston pump. AP and sympathetic nerve activity were measured while CSP was controlled via the pump using Gaussian white noise stimulation. Second-order, nonlinear dynamics models were developed by application of nonparametric system identification to a portion of the measurements. The models of the total arc predicted AP 21-43% better (P < 0.005) than conventional linear dynamic models in response to a new portion of the CSP measurement. The linear and nonlinear terms of these validated models were compared with the corresponding terms of an analogous model for normotensive rats. The nonlinear gains for the hypertensive rats were significantly larger than those for the normotensive rats [-0.38 ± 0.04 (unitless) vs. -0.22 ± 0.03, P < 0.01], whereas the linear gains were similar. Hence, nonlinear dynamic functioning of the sympathetically mediated total arc may enhance baroreflex buffering of AP increases more in SHR than normotensive rats.

Nonlinear identification of the total baroreflex arc.

The total baroreflex arc [the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP)] is known to exhibit nonlinear behaviors. However, few studies have quantitatively characterized its nonlinear dynamics. The aim of this study was to develop a nonlinear model of the sympathetically mediated total arc without assuming any model form. Normal rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned, the carotid sinus regions were isolated and attached to a servo-controlled piston pump, and the AP and sympathetic nerve activity (SNA) were measured. CSP was perturbed using a Gaussian white noise signal. A second-order Volterra model was developed by applying nonparametric identification to the measurements. The second-order kernel was mainly diagonal, but the diagonal differed in shape from the first-order kernel. Hence, a reduced second-order model was similarly developed comprising a linear dynamic system in parallel with a squaring system in cascade with a slower linear dynamic system. This "Uryson" model predicted AP changes 12% better (P < 0.01) than a linear model in response to new Gaussian white noise CSP. The model also predicted nonlinear behaviors, including thresholding and mean responses to CSP changes about the mean. Models of the neural arc (the system relating CSP to SNA) and peripheral arc (the system relating SNA to AP) were likewise developed and tested. However, these models of subsystems of the total arc showed approximately linear behaviors. In conclusion, the validated nonlinear model of the total arc revealed that the system takes on an Uryson structure.

Stimulation of the cardiopulmonary baroreflex enhances ventricular contractility in awake dogs: a mathematical analysis study.

The cardiopulmonary baroreflex responds to an increase in central venous pressure (CVP) by decreasing total peripheral resistance and increasing heart rate (HR) in dogs. However, the direction of ventricular contractility change is not well understood. The aim was to elucidate the cardiopulmonary baroreflex control of ventricular contractility during normal physiological conditions via a mathematical analysis. Spontaneous beat-to-beat fluctuations in maximal ventricular elastance (Emax), which is perhaps the best available index of ventricular contractility, CVP, arterial blood pressure (ABP), and HR were measured from awake dogs at rest before and after ß-adrenergic receptor blockade. An autoregressive exogenous input model was employed to jointly identify the three causal transfer functions relating beat-to-beat fluctuations in CVP to Emax (CVP → Emax), which characterizes the cardiopulmonary baroreflex control of ventricular contractility, ABP to Emax, which characterizes the arterial baroreflex control of ventricular contractility, and HR to Emax, which characterizes the force-frequency relation. The CVP → Emax transfer function showed a static gain of 0.037 ± 0.010 ml(-1) (different from zero; P < 0.05) and an overall time constant of 3.2 ± 1.2 s. Hence, Emax would increase and reach steady state in ∼16 s in response to a step increase in CVP, without any change to ABP or HR, due to the cardiopulmonary baroreflex. Following ß-adrenergic receptor blockade, the CVP → Emax transfer function showed a static gain of 0.0007 ± 0.0113 ml(-1) (different from control; P < 0.10). Hence, Emax would change little in steady state in response to a step increase in CVP. Stimulation of the cardiopulmonary baroreflex increases ventricular contractility through ß-adrenergic receptor system mediation.

A Physical Model-Based Approach to One-Point Calibration of Pulse Transit Time to Blood Pressure.

OBJECTIVE: To develop a novel physical model-based approach to enable 1-point calibration of pulse transit time (PTT) to blood pressure (BP). METHODS: The proposed PTT-BP calibration model is derived by combining the Bramwell-Hill equation and a phenomenological model of the arterial compliance (AC) curve. By imposing a physiologically plausible constraint on the skewness of AC at positive and negative transmural pressures, the number of tunable parameters in the PTT-BP calibration model reduces to 1. Hence, as opposed to most existing PTT-BP calibration models requiring multiple (≥2) PTT-BP measurements to personalize, the PTT-BP calibration model can be personalized to an individual subject using a single PTT-BP measurement pair. Equipped with the physically relevant PTT-AC and AC-BP relationships, the proposed approach may serve as a universal means to calibrate PTT to BP over a wide BP range. The validity and proof-of-concept of the proposed approach were evaluated using PTT and BP measurements collected from 22 healthy young volunteers undergoing large BP changes. RESULTS: The proposed approach modestly yet significantly outperformed an empiric linear PTT-BP calibration with a group-average slope and subject-specific intercept in terms of bias (5.5 mmHg vs 6.4 mmHg), precision (8.4 mmHg vs 9.4 mmHg), mean absolute error (7.8 mmHg vs 8.8 mmHg), and root-mean-squared error (8.7 mmHg vs 10.3 mmHg, all in the case of diastolic BP). CONCLUSION: We demonstrated the preliminary proof-of-concept of an innovative physical model-based approach to one-point PTT-BP calibration. SIGNIFICANCE: The proposed physical model-based approach has the potential to enable more accurate and convenient calibration of PTT to BP.

Nonlinear Viscoelastic Modeling of Finger Arteries: Toward Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method.

OBJECTIVE: Oscillometric finger pressing is a smartphone-based blood pressure (BP) monitoring method. Finger photoplethysmography (PPG) oscillations and pressure are measured during a steady increase in finger pressure, and an algorithm computes systolic BP (SP) and diastolic BP (DP) from the measurements. The objective was to assess the impact of finger artery viscoelasticity on the BP computation. METHODS: Nonlinear viscoelastic models relating transmural pressure (finger BP - applied pressure) to PPG oscillations during finger pressing were developed. The output of each model to a measured transmural pressure input was fitted to measured PPG oscillations from 15 participants. A parametric sensitivity analysis was performed via model simulations to elucidate the viscoelastic effect on the derivative-based BP computation algorithm. RESULTS: A Wiener viscoelastic model comprising a first-order transfer function followed by a static sigmoidal function fitted the measured PPG oscillations better than an elastic model containing only the static function (median (IQR) error of 30.5% (25.6%-34.0%) vs 50.9% (46.7%-53.7%); p<0.01). In Wiener model simulations, the derivative algorithm underestimated SP, especially with high pulse pressure and low transfer function cutoff frequency (i.e., greater viscoelasticity). The mean of the normalized PPG waveform at the maximum oscillation beat was found to correlate with the cutoff frequency (r = -0.8) and could thus possibly be used to compensate for viscoelasticity. CONCLUSION: Finger artery viscoelasticity negatively impacts oscillometric BP computation algorithms but can potentially be compensated for using available measurements. SIGNIFICANCE: These findings may help in converting smartphones into truly cuffless BP monitors for improving hypertension awareness and control.

Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms.

This paper intends to investigate the feasibility of peripheral artery disease (PAD) diagnosis based on the analysis of non-invasive arterial pulse waveforms. We generated realistic synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals pertaining to PAD present at the abdominal aorta with a wide range of severity levels using a mathematical model that simulates arterial blood circulation and arterial BP-PVR relationships. We developed a deep learning (DL)-enabled algorithm that can diagnose PAD by analyzing brachial and tibial PVR waveforms, and evaluated its efficacy in comparison with the same DL-enabled algorithm based on brachial and tibial arterial BP waveforms as well as the ankle-brachial index (ABI). The results suggested that it is possible to detect PAD based on DL-enabled PVR waveform analysis with adequate accuracy, and its detection efficacy is close to when arterial BP is used (positive and negative predictive values at 40 % abdominal aorta occlusion: 0.78 vs 0.89 and 0.85 vs 0.94; area under the ROC curve (AUC): 0.90 vs 0.97). On the other hand, its efficacy in estimating PAD severity level is not as good as when arterial BP is used (r value: 0.77 vs 0.93; Bland-Altman limits of agreement: -32%-+32 % vs -20%-+19 %). In addition, DL-enabled PVR waveform analysis significantly outperformed ABI in both detection and severity estimation. In sum, the findings from this paper suggest the potential of DL-enabled non-invasive arterial pulse waveform analysis as an affordable and non-invasive means for PAD diagnosis.

Transmission line model as a digital twin for abdominal aortic aneurysm patients.

We investigated the potential of the transmission line model as a digital twin of aneurysmal aorta by comparatively analyzing how a uniform lossless tube-load model were fitted to the carotid and femoral artery tonometry waveforms pertaining to (i) 79 abdominal aortic aneurysm (AAA) patients vs their matched controls (CON) and (ii) 35 AAA patients before vs after endovascular aneurysm repair (EVAR). The uniform lossless tube-load model fitted the tonometry waveforms pertaining to AAA as well as CON and EVAR. In addition, the parameters in the tube-load model exhibited physiologically explainable changes: when normalized, both pulse transit time and reflection coefficient increased with AAA and decreased after EVAR, which can be explained by the increase in arterial compliance and the decrease in arterial inertance due to the aortic expansion associated with AAA. In sum, the tube-load model may have the potential as a digital twin to enable personalized AAA monitoring.

A smartphone application toward detection of systolic hypertension in underserved populations.

High systolic blood pressure (BP) is the most important modifiable risk factor for cardiovascular disease. Managing systolic hypertension is especially difficult in underserved populations wherein access to cuff BP devices is limited. We showed that ubiquitous smartphones without force sensing can be converted into absolute pulse pressure (PP) monitors. The concept is for the user to perform guided thumb and hand maneuvers with the phone to induce cuff-like actuation and allow built-in sensors to make cuff-like measurements for computing PP. We developed an Android smartphone PP application. The 'app' could be learned by volunteers and yielded PP with total error < 8 mmHg against cuff PP (N = 24). We also analyzed a large population-level database comprising adults less than 65 years old to show that PP plus other basic information can detect systolic hypertension with ROC AUC of 0.9. The smartphone PP app could ultimately help reduce the burden of systolic hypertension in underserved populations and thus health disparities.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

The Microsoft Research Aurora Project: Important Findings on Cuffless Blood Pressure Measurement.

Conventional blood pressure (BP) measurement devices based on an inflatable cuff only provide a narrow view of the continuous BP profile. Cuffless BP measuring technologies could permit numerous BP readings throughout daily life and thereby considerably improve the assessment and management of hypertension. Several wearable cuffless BP devices based on pulse wave analysis (applied to a photoplethysmography or tonometry waveform) with or without use of pulse arrival time are now available on the market. The key question is: Can these devices provide accurate measurement of BP? Microsoft Research recently published a complex article describing perhaps the most important and highest resource project to date (Aurora Project) on assessing the accuracy of several pulse wave analysis and pulse wave analysis-pulse arrival time devices. The overall results from 1125 participants were clear-cut negative. The present article motivates and describes emerging cuffless BP devices and then summarizes the Aurora Project. The study methodology and findings are next discussed in the context of regulatory-cleared devices, physiology, and related studies, and the study strengths and limitations are pinpointed thereafter. Finally, the implications of the Aurora Project are briefly stated and recommendations for future work are offered to finally realize the considerable potential of cuffless BP measurement in health care.

Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method: Analysis of Oscillation Width Variations Can Improve Diastolic Pressure Computation.

OBJECTIVE: Oscillometric finger pressing is a potential method for absolute blood pressure (BP) monitoring via a smartphone. The user presses their fingertip against a photoplethysmography-force sensor unit on a smartphone to steadily increase the external pressure on the underlying artery. Meanwhile, the phone guides the finger pressing and computes systolic BP (SP) and diastolic BP (DP) from the measured blood volume oscillations and finger pressure. The objective was to develop and evaluate reliable finger oscillometric BP computation algorithms. METHODS: The collapsibility of thin finger arteries was exploited in an oscillometric model to develop simple algorithms for computing BP from the finger pressing measurements. These algorithms extract features from "width" oscillograms (oscillation width versus finger pressure functions) and the conventional "height" oscillogram for markers of DP and SP. Finger pressing measurements were obtained using a custom system along with reference arm cuff BP measurements from 22 subjects. Measurements were also obtained during BP interventions in some subjects for 34 total measurements. RESULTS: An algorithm employing the average of width and height oscillogram features predicted DP with correlation of 0.86 and precision error of 8.6 mmHg with respect to the reference measurements. Analysis of arm oscillometric cuff pressure waveforms from an existing patient database provided evidence that the width oscillogram features are better suited to finger oscillometry. CONCLUSION: Analysis of oscillation width variations during finger pressing can improve DP computation. SIGNIFICANCE: The study findings may help in converting widely available devices into truly cuffless BP monitors for improving hypertension awareness and control.

Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study.

Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed as the standard deviation of multiple clinic visit-to-visit (VVV-BP) measures or from 24-h ambulatory BP recordings (ABPV). BP also varies on a beat-to-beat basis, quantified by methods that parse variation into discrete bins, e.g., low frequency (0.04-0.15 Hz, LF). However, beat-to-beat BPV requires continuous recordings that are not easily acquired. As a result, we know little about the relationship between LF-BPV and basic sociodemographic characteristics such as age, sex, and race and clinical conditions. Methods: We computed LF-BPV during an 11-min resting period in 2,118 participants in the Midlife in the US (MIDUS) study. Results: LF-BPV was negatively associated with age, greater in men than women, and unrelated to race or socioeconomic status. It was greater in participants with hypertension but unrelated to hyperlipidemia, hypertriglyceridemia, diabetes, elevated CRP, or obesity. LF-diastolic BPV (DBPV), but not-systolic BPV (SBPV), was negatively correlated with IL-6 and s-ICAM and positively correlated with urinary epinephrine and cortisol. Finally, LF-DBPV was negatively associated with mortality, an effect was rendered nonsignificant by adjustment by age but not other sociodemographic characteristics. Discussion: These findings, the first from a large, national sample, suggest that LF-BPV differs significantly from VVV-BP and ABPV. Confirming its relationship to sociodemographic risk factors and clinical outcomes requires further study with large and representative samples.

Toward Real-Time Blood Pressure Monitoring via High-Fidelity Iontronic Tonometric Sensors with High Sensitivity and Large Dynamic Ranges.

Continuous, noninvasive blood pressure (CNIBP) monitoring provides valuable hemodynamic information that renders detection of the early onset of cardiovascular diseases. Wearable mechano-electric pressure sensors that mount on the skin are promising candidates for monitoring continuous blood pressure (BP) pulse waveforms due to their excellent conformability, simple sensing mechanisms, and convenient signal acquisition. However, it is challenging to acquire high-fidelity BP pulse waveforms since it requires highly sensitive sensors (sensitivity larger than 4 × 10-5 kPa-1 ) that respond linearly with pressure change over a large dynamic range, covering the typical BP range (5-25 kPa). Herein, this work introduces a high-fidelity, iontronic-based tonometric sensor (ITS) with high sensitivity (4.82 kPa-1 ), good linearity (R2 > 0.995), and a large dynamic range (up to 180% output change) over a broad working range (0 to 38 kPa). Additionally, the ITS demonstrates a low limit of detection at 40 Pa, a fast load response time (35 ms) and release time (35 ms), as well as a stable response over 5000 load per release cycles, paving ways for potential applications in human-interface interaction, electronic skins, and robotic haptics. This work further explores the application of the ITS in monitoring real-time, beat-to-beat BP by measuring the brachial and radial pulse waveforms. This work provides a rational design of a wearable pressure sensor with high sensitivity, good linearity, and a large dynamic range for real-time CNIBP monitoring.