Continuous Non-invasive Blood Pressure Research Articles

Non-invasive methods of cardiac output measurement and their importance in everyday clinical practice: the current state of knowledge

Cardiac output is an important parameter in the evaluation of the patients’ haemodynamic status which can be measured using a number of methods with varying degrees of invasiveness. These include invasive (such as pulmonary artery catheterization, transpulmonary thermodilution, lithium dilution), minimally invasive (catheter-based arterial pressure curve contour analysis, transoesophageal Doppler) and non-invasive (electrical bioimpedance, transthoracic echocardiography, measurement of exhaled CO2, continuous non-invasive blood pressure measurement) techniques. With advances in medicine, the role of non-invasive methods as an alternative to pulmonary artery catheterization continues to grow. The undoubted advantage of non-invasive methods is their safety for the patients and their wide availability. In contrast, invasive methods are characterized by greater measurement precision. It is up to the physician to choose the most appropriate haemodynamic monitoring method depending on the clinical situation. The aim of this article is to discuss non-invasive cardiac output measurement techniques and their application in everyday clinical practice.

Noninvasive continuous intradialytic blood pressure monitoring: the key to improving haemodynamic stability.

Intradialytic hypotension (IDH) occurs in 20% of haemodialysis treatments, leading to end-organ ischaemia, increased morbidity and mortality; and contributing to poor quality of life for patients. Treatment of IDH is reactive since brachial blood pressure (BP) is recorded only intermittently during haemodialysis, making early detection and prediction of hypotension impossible. Noninvasive continuous BP monitoring would allow earlier detection of IDH and thus support the development of methods for its prediction and consequently prevention. Noninvasive continuous BP monitoring is not yet part of routine practice in renal dialysis units, with a small number of devices (e.g. finger cuffs) having occasionally been used in research settings. In use, patients frequently report pain or discomfort at measurement sites. Additionally, these devices can be unreliable in patients with reduced blood flow to the digits, often manifest in dialysis patients. All existing methods are sensitive to patient movement.A new method for continuously estimating BP has been developed by monitoring arterial pressure near the arteriovenous fistula which can be achieved without any extraneous monitoring equipment attached to the patient. Additionally, artificial intelligence-based methods for real-time prediction of IDH are currently emerging. Key monitoring technologies and computational methods are emerging to support the development of real-time IDH prediction.

NB-SQI: A novel non-binary signal quality index for continuous blood pressure waveforms

Continuous blood pressure monitoring has utmost importance in healthcare, but is usually realised by invasive arterial cannulation. Non-invasive solutions are starting to become a viable option, but these solutions are more sensitive to sensor placement and patient movement. To support these non-invasive monitoring devices, a continuous signal quality feedback would be advantageous. The aim of this study is to define a non-binary signal quality index (NB-SQI) for continuous blood pressure waveforms that can be used for monitoring devices. The definition of this NB-SQI is based on features derived from the continuous waveform of the cardiac cycle and it tests the abnormality of the signal and measures the normalised deviation of its characteristics from an expected value. The method was validated with a dataset of 20 signals, each 10 s long, and labelled by senior specialists in the field of vascular surgery and anesthesiology. The NB-SQI showed a 0.95 correlation with the labels of the validation set. The tendency of the NB-SQI was tested on generated and measured signals corrupted by generated noises on different levels. The results showed that the NB-SQI gives a quantitative estimation in the higher quality range, and recognises the abnormal and noise corrupted segments. The defined NB-SQI can have a significant contribution to the development of non-invasive continuous blood pressure monitors, but it can also be used in signal processing or signal classification for diagnostic purposes.

Combined deep CNN\u2013LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

ClearSight™ finger cuff versus invasive arterial pressure measurement in patients with body mass index above 45 kg/m2

BackgroundMeasuring blood pressure in patients with obesity is challenging. The ClearSight™ finger cuff (FC) uses the vascular unloading technique to provide continuous non-invasive blood pressure measurements. We aimed to test the agreement of the FC with invasive radial arterial monitoring (INV) in patients with obesity.MethodsParticipants had a body mass index (BMI) ≥45 kg/m2 and underwent laparoscopic bariatric surgery. FC and INV measurements were obtained simultaneously every 5 min on each patient, following induction of anesthesia. Agreement over time was assessed using modified Bland-Altman plots and error grid analysis permitted clinical interpretation of the results. Four-quadrant plots allowed assessment of concordance in blood pressure changes.ResultsThe 30 participants had a median (IQR) BMI of 50.2 kg/m2 (IQR 48.3–55.3). The observed bias (SD, 95% limits of agreement) for systolic blood pressure (SBP) was 14.3 mmHg (14.1, -13.4 – 42.0), 5.2 mmHg (10.9, -16.0 – 26.5) for mean arterial pressure (MAP) and 2.6 mmHg (10.8, -18.6 – 23.8) for diastolic blood pressure (DBP). Error grid analysis showed that the proportion of readings in risk zones A-E were 90.8, 6.5, 2.7, 0 and 0% for SBP and 91.4, 4.3, 4.3, 0 and 0% for MAP, respectively. Discordance occurred in ≤8% of pairs for consecutive change in SBP, MAP and DBP.ConclusionsThe vascular unloading technique was not adequately in agreement with radial arterial monitoring. Evaluation in a larger sample is required before recommending this technique for intraoperative monitoring of patients with BMI ≥45 kg/m2.

Maternal haemodynamics during labour epidural analgesia with and without adrenaline.

Labour is one of the most painful experiences in a woman's life. Epidural analgesia using low-concentration local anaesthetics and lipophilic opioids is the gold standard for pain relief during labour. Pregnancy in general, particularly labour, is associated with changes in maternal haemodynamic variables, such as cardiac output and heart rate, which increase and peak during uterine contractions. Adrenaline is added to labour epidural solutions to enhance efficacy by stimulating the α2-adrenoreceptor. The minimal effective concentration of adrenaline was found to be 2μgmL-1 for postoperative analgesia. The addition of adrenaline may also produce vasoconstriction, limiting the absorption of fentanyl into the systemic circulation, thereby reducing foetal exposure. However, adrenaline may influence the haemodynamic fluctuations, possibly adding to the strain on the circulatory system. The aim of this study was to compare the haemodynamic changes after application of labour epidural analgesia with or without adrenaline 2μgmL-1. This was a secondary analysis of a single-centre, randomised double-blind trial. Forty-one nulliparous women in labour requesting epidural analgesia were randomised to receive epidural solution of bupivacaine 1mgmL-1, fentanyl 2μgmL-1 with or without adrenaline 2μgmL-1. The participants were monitored using a Nexfin CC continuous non-invasive blood pressure and cardiac output monitor. The primary outcomes were changes in peak systolic blood pressure and cardiac output during uterine contraction within 30min after epidural activation. The effect of adrenaline was tested statistically using a linear mixed-effects model of the outcome variables' dependency on time, adrenaline, and their interaction. After excluding three patients due to poor data quality and two due to a malfunctioning epidural catheter, 36 patients (18 in each group) were analysed. The addition of adrenaline to the solution had no significant effect on the temporal changes in peak systolic blood pressure (p=0.26), peak cardiac output (0.84), or heart rate (p=0.91). Furthermore, no significant temporal changes in maternal haemodynamics (peak systolic blood pressure, p=0.54, peak cardiac output, p=0.59, or heart rate p=0.55) were associated with epidural analgesia during 30min after epidural activation in both groups despite good analgesia. The addition of 2μgmL-1 adrenaline to the epidural solution is not likely to change maternal haemodynamics during labour.

Evaluation of Cardiovascular Functions during the Practice of Different Types of Yogic Breathing Techniques.

Introduction: Yoga is the science of right living practice to promote health. Many studies have documented the cardiovascular effects of various yogic breathing techniques (YBTs), comparing the cardiovascular changes before and after the practice. However, there is a lack of study reporting the cardiovascular changes during the practice of YBT.Materials and Methods: Twenty healthy individuals performed four different YBTs (Bhastrika, Bhramari, Kapalbhati, and Kumbhaka) in four different orders. Cardiovascular variables such as systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), heart rate (HR), stroke volume (SV), cardiac output (CO), pulse interval (PI), and total peripheral resistant (TPR) were assessed using a continuous noninvasive blood pressure monitoring system, before, during, and immediately after each YBT. Data were analyzed using repeated measures analysis of variance followed by post hoc analysis with Bonferroni adjustment for multiple comparisons using Statistical Package for the Social Sciences, Version 16.0.Results: Results of this study showed a significant increase in DBP, MAP, HR, and CO along with a reduction in PI during Bhastrika; a significant increase in DBP, MAP, HR, and TPR with a reduction in SV, CO, and PI during Bhramari pranayama; a significant increase in SBP, DBP, MAP, HR, and CO with a reduction in PI during Kapalbhati; and a significant increase in SBP, DBP, MAP, and TPR with a reduction in SV and CO during Kumbhaka practice.Conclusion: In healthy individuals, cardiovascular changes during the practice of Bhastrika and Kapalbhati are more or less similar to each other and are different from those of Bhramari and Kumbhaka in most of the variables.

Cuff-less continuous blood pressure measurement based on multiple types of information fusion

Non-invasive and cuff-less real-time continuous blood pressure monitoring plays an important role in both intensive care unit (ICU) and the management of chronic diseases such as hypertension and cardiovascular disease. Although pulse wave transit time (PTT) and pulse waveform parameters (PWPs) can be extracted from photoplethysmography (PPG) and electrocardiographic (ECG) to estimate blood pressure, the value of blood pressure is also constrained by personal characteristics parameters (PCPs). For example, the blood pressure may vary with height, weight and age. In this paper, we present a cuff-less continuous blood pressure measurement method based on multiple types of information fusion. First, we recruited 186 volunteers and measured their personal data (PPG signal, ECG signal, height, age, weight, gender). Second, extract the PTT and PWPs from the PPG signal and ECG signal. Then, according to the extracted parameters, the blood pressure models of control group and experimental group were established respectively (the experimental group uses the multiple types of information fusion method to model, the control group only considers PTT and PWPs). Experiments show that, comparing with the control group, the experimental group systolic blood pressure (SBP) correlation was increased from 0.9355 to 0.9948, root mean square error (RMSE) was reduced from 5.2 mmHg to 1.5 mmHg, while the diastolic blood pressure (DBP) correlation was improved from 0.9331 to 0.9931, RMSE was reduced from 2.9 mmHg to 0.9 mmHg. Therefore, the modeling method proposed in this paper can get more accurate blood pressure values. Further promotion, to achieve the measurement with the "modeling" method, all "restriction" elements must be taken into account in order to obtain a more accurate and stable model.

Cerebral perfusion pressure and autoregulation in eclampsia—a case control study

Dynamic cerebral autoregulation and cerebral perfusion pressure are altered in pregnancies complicated by preeclampsia compared with normotensive pregnancies, but the connections of dynamic cerebral autoregulation, cerebral perfusion pressure, and cerebral complications in preeclampsia remain unclear. This study aimed to assess dynamic cerebral autoregulation and cerebral perfusion pressure after delivery in women with eclampsia, in women with preeclampsia both with and without severe features, and in normotensive women. This was a prospective case control study at a large referral hospital in Cape Town, South Africa. The recruitment of participants was done at diagnosis (cases) or at admission for delivery (controls). Transcranial Doppler examinations with continuous noninvasive blood pressure measurements and end-tidal CO2 monitoring were conducted for cases and controls after delivery. Cerebral perfusion pressure and dynamic cerebral autoregulation index were calculated, and values were compared among groups. We included 16 women with eclampsia, 18 women with preeclampsia with severe features, 32 women with preeclampsia without severe features, and 21 normotensive women with uncomplicated pregnancies. Dynamic cerebral autoregulation was depressed in pregnant women with eclampsia; (autoregulation index, 3.9; interquartile range, 3.1-5.2) compared with all other groups (those with preeclampsia with severe features, autoregulation index, 5.6 [interquartile range, 4.4-6.8]; those with preeclampsia without severe features, autoregulation index, 6.8 [interquartile range, 5.1-7.4]; and normotensive controls, autoregulation index, 7.1 [interquartile range, 6.1-7.9]). Pregnant women with eclampsia had increased cerebral perfusion pressure (109.5 mm Hg; interquartile range, 91.2-130.9) compared with those with preeclampsia without severe features and those with normal blood pressure (84 mm Hg [interquartile range, 73.0-122.0] and 80.0 mm Hg [interquartile range, 67.5-92.0], respectively); furthermore, there was no difference in cerebral perfusion pressure between pregnant women with eclampsia and pregnant women with preeclampsia with severe features (109.5 mm Hg [interquartile range, 91.2-130.9] vs 96.5 mm Hg [interquartile range, 75.8-110.5]). Cerebral perfusion pressure and dynamic cerebral autoregulation are altered in eclampsia and may be important in the pathophysiological pathway and constitute a therapeutic target in the prevention of cerebral complications in preeclampsia.

A novel art of continuous noninvasive blood pressure measurement

Wearable sensors to continuously measure blood pressure and derived cardiovascular variables have the potential to revolutionize patient monitoring. Current wearable methods analyzing time components (e.g., pulse transit time) still lack clinical accuracy, whereas existing technologies for direct blood pressure measurement are too bulky. Here we present an innovative art of continuous noninvasive hemodynamic monitoring (CNAP2GO). It directly measures blood pressure by using a volume control technique and could be used for small wearable sensors integrated in a finger-ring. As a software prototype, CNAP2GO showed excellent blood pressure measurement performance in comparison with invasive reference measurements in 46 patients having surgery. The resulting pulsatile blood pressure signal carries information to derive cardiac output and other hemodynamic variables. We show that CNAP2GO can self-calibrate and be miniaturized for wearable approaches. CNAP2GO potentially constitutes the breakthrough for wearable sensors for blood pressure and flow monitoring in both ambulatory and in-hospital clinical settings.

Body Mass Index and Arterial Stiffness Are Associated With Greater Beat-to-Beat Blood Pressure Variability After Transient Ischemic Attack or Minor Stroke.

[Figure: see text].

Few-shot pulse wave contour classification based on multi-scale feature extraction

The annotation procedure of pulse wave contour (PWC) is expensive and time-consuming, thereby hindering the formation of large-scale datasets to match the requirements of deep learning. To obtain better results under the condition of few-shot PWC, a small-parameter unit structure and a multi-scale feature-extraction model are proposed. In the small-parameter unit structure, information of adjacent cells is transmitted through state variables. Simultaneously, a forgetting gate is used to update the information and retain long-term dependence of PWC in the form of unit series. The multi-scale feature-extraction model is an integrated model containing three parts. Convolution neural networks are used to extract spatial features of single-period PWC and rhythm features of multi-period PWC. Recursive neural networks are used to retain the long-term dependence features of PWC. Finally, an inference layer is used for classification through extracted features. Classification experiments of cardiovascular diseases are performed on photoplethysmography dataset and continuous non-invasive blood pressure dataset. Results show that the classification accuracy of the multi-scale feature-extraction model on the two datasets respectively can reach 80% and 96%, respectively.

Surrogate based continuous noninvasive blood pressure measurement.

Arterial blood pressure is one of the most often measured vital parameters in clinical practice. State-of-the-art noninvasive ABP measurement technologies have noticeable limitations and are mainly based on uncomfortable techniques of complete or partial arterial occlusion by cuffs. Most commonplace devices provide only intermittent measurements, and continuous systems are bulky and difficult to apply correctly for nonprofessionals. Continuous cuffless ABP measurements are still an unmet clinical need and a topic of ongoing research, with only few commercially available devices. This paper discusses surrogate-based noninvasive blood pressure measurement techniques. It covers measurement methods of continuously and noninvasively inferring BP from surrogate signals without applying external pressures, except for reference or initialization purposes. The BP is estimated by processing signal features, so called surrogates, which are modulated by variations of BP. Discussed techniques include well-known approaches such as pulse transit time and pulse arrival time techniques, pulse wave analysis or combinations thereof. Despite a long research history, these methods have not found widespread use in clinical and ambulatory practice, in part due to technical limitations and the lack of a standardized regulatory framework. This work summarizes findings from an invited workshop of experts in the fields covering clinical expertise, engineering aspects, commercialization and standardization issues. The goal is to provide an application driven outlook, starting with clinical needs, and extending to technical actuality. It provides an outline of recommended research directions and includes a detailed overview of clinical use case scenarios for these technologies, opportunities, and limitations.

This Month in Anesthesiology

This Month in Anesthesiology

Detection of hypotension during spinal anesthesia for caesarean section with continuous non-invasive arterial pressure monitoring and intermittent oscillometric blood pressure monitoring in patients treated with ephedrine or phenylephrine

Introduction/Objective. Despite frequent side effects such as hypotension, spinal anesthesia (SA) is still one of the best anesthetic methods for elective cesarean section (CS). Intermittent, oscillometric, noninvasive blood pressure monitoring (NIBP) frequently leads to missed hypotensive episodes. The objective was to compare continuous non-invasive arterial pressure (CNAP) monitoring with NIBP in the terms of efficiency to detect hypotension. Methods. In this study, we compared CNAP and NIBP monitoring for hypotension detection in 76 patients divided into two groups of 38 patients treated with ephedrine (E) or phenylephrine (P), during threeminute intervals, starting from SA, by the end of the surgery. Results. In E group, significantly lower mean systolic blood pressure (SBP) values with CNAP compared with NIBP (p = 0.008) was detected. By monitoring CNAP, we detected 31 (81.6%) hypotensive patients in E group and significantly lower number, 20 (52.6%), with NIBP (p = 0.001), while in P group CNAP detected 34 patients (89.5%) and NIBP only 18 (47.3%), p = 0.001. By monitoring CNAP, we detected significantly higher number of hypotensive intervals in E and P groups (p < 0.001). Umbilical vein pH was lower within hypotensive compared with normotensive patients in E and P groups, with CNAP and NIBP, respectively (p < 0.001, p = 0.027 in E, and p = 0.009, p < 0.001, in P group). Conclusion. CNAP is more efficient in hypotension detection for CS during SA, which allows faster treatment of hypotension, thus improving fetal and maternal outcome.

Machine Learning Method for Continuous Noninvasive Blood Pressure Detection Based on Random Forest

In order to reduce the influence of differences in human characteristics on the blood pressure prediction model and further improve the accuracy of blood pressure prediction, this paper establishes support vector machine regression model and random forest regression model for accurate blood pressure measurement. First, the photoelectric method is used to obtain the photoelectric plethysmography signal (PPG) and ECG signals from people of different ages, and the blood pressure value is roughly estimated based on the high-quality physiological signals and the vascular elastic cavity model; then the human body characteristics are used as the input parameters of the blood pressure prediction model, and the model parameters are used to find the best parameter combination to improve the prediction performance of the model; finally, through a lot of training and learning, the best blood pressure prediction model is selected to achieve accurate measurement of blood pressure values. It has been verified by experiments that the average absolute error of diastolic and systolic blood pressure based on the random forest optimization model meets the standard of less than 5mmHg formulated by AAMI (American Medical Instrument Promotion Association), which is better consistent with the method of mercury sphygmomanometer, and has more excellent performance than support vector machine regression model under the same conditions.

Elimination of Multicollinearity in Continuous Non-Invasive Blood Pressure Approximation by Information Criterion

Continuous and unobtrusive method of measuring blood pressure has been gaining more attention in the healthcare community. With the application of data analysis techniques in biosignals like Electrocardiogram (ECG) and Photoplethysmogram (PPG), several predictors are obtained that correlates well with the blood pressure. But the BP approximation regression models formed using these predictors suffers from multicollinearity (higher correlation between predictors). The article proposes the use of information criterion-based model ensemble approach to reduce the effect of multicollinearity in the continuous BP estimation. The study focuses on forming pool of candidate models from feature subsets. The best performing models are selected based on information criterion and combined to form the ensemble model. Experiments with performed with MIMIC-II dataset that consists of 104 records with simultaneously recorded PPG and arterial BP. The results suggest that the technique achieves Mean Absolute Error (MAE) of 5.81 mm Hg and 3.35 mm Hg for systolic and diastolic BP and Root Mean Square Error (RMSE) of 6.08 mm Hg and 4.12 mm Hg for systolic and diastolic BP respectively. The error measures conform to the standards set by American Association of Medical Instrumentation (AAMI). The method reveals that the ensemble model based on information criterion outperforms well compared to the usage of single model.

Progression of Beat-to-Beat Blood Pressure Variability Despite Best Medical Management.

Beat-to-beat variability in blood pressure (BP) is associated with recurrent stroke despite good control of hypertension. However, no study has identified rates of progression of beat-to-beat BP variability (BPV), its determinants, or which patient groups are particularly affected, limiting understanding of its potential as a treatment target. In consecutive patients one month after a transient ischaemic attack or nondisabling stroke (Oxford Vascular Study), continuous noninvasive BP was measured beat-to-beat over 5 minutes (Finometer). Arterial stiffness was measured by carotid-femoral pulse wave velocity (Sphygmocor). Repeat assessments were performed at the 5-year follow-up visit and agreement determined by intraclass correlation coefficient. Rates of progression of systolic BPV (SBPV) and diastolic BPV (DBPV) and their determinants were estimated by mixed-effect linear models, adjusted for age, sex, and cardiovascular risk factors. One hundred eighty-eight of 310 surviving, eligible patients had repeat assessments after a median of 5.8 years. Pulse wave velocity was highly reproducible but SBPV and DBPV were not (intraclass correlation coefficient: 0.71, 0.10, and 0.16, respectively), however, all 3 progressed significantly (pulse wave velocity, 2.39%, P<0.0001; SBPV, 8.36%, P<0.0001; DBPV, 9.7, P<0.0001). Rate of progression of pulse wave velocity, SBPV, and DBPV all increased significantly with age (P<0.0001), with an increasingly positive skew and were particularly associated with female sex (pulse wave velocity P=0.00035; SBPV P<0.0001; DBPV P<0.0001) and aortic mean SBP (SBPV P=0.037, DBPV P<0.0001). Beat-to-beat BP variability progresses significantly in high-risk patients, particularly in older individuals with elevated aortic systolic pressure. Beat-to-beat BPV and its progression represent potential new therapeutic targets to reduce cardiovascular risk.

An iterative run-to-run learning model to derive continuous brachial pressure estimates from arterial and venous lines during dialysis treatment

Objective:Non-invasive continuous blood pressure monitoring is not yet part of routine practice in renal dialysis units but could be a valuable tool in the detection and prevention of significant variations in patient blood pressure during treatment. Feasibility studies have delivered an initial validation of a method which utilises pressure sensors in the extra-corporeal dialysis circuit, without any direct contact with the person receiving treatment. Our main objective is to further develop this novel methodology from its current early development status to a continuous-time brachial artery pressure estimator. Methods:During an in vivo patient feasibility study with concurrent measurement validation by Finapres Nova experimental physiological measurement device, real-time continuous dialysis line pressures, and intermittent occluding arm cuff pressure data were collected over the entire period of (typically 4-hour) dialysis treatments. There was found to be an underlying quasi-linear relationship between arterial line and brachial pressure measurements which supported the development of a mathematical function to describe the relationship between arterial dialysis line pressure and brachial artery BP. However, unmodelled non-linearities, dynamics and time-varying parameters present challenges to the development of an accurate BP estimation system. In this paper, we start to address the problem of physiological parameter time variance by novel application of an iterative learning run-to-run modelling methodology originally developed for process control engineering applications to a parameterised BP model. Results:The iterative run-to-run learning methodology was applied to the real-time data measured during an observational study in 9 patients, supporting subsequent development of an adaptive real-time BP estimator. Tracking of patient BP is analysed for all the subjects in our patient study, supported only by intermittent updates from BP cuff measurements. Conclusion:The methodology and associated technology is shown to be capable of tracking patient BP non-invasively via arterial line pressure measurement during complete 4-hour treatment sessions. A robust and tractable method is demonstrated, and future refinements to the approach are defined.

Abstract 17298: Validation of Beat-to-Beat Blood Pressure and Left Ventricular Ejection Time by the Caretaker Physiological Monitor Against Invasive Central Aortic Measurement

Objective: There is an unmet need for noninvasive continuous blood pressure (BP) monitoring technologies in various clinical settings. We examined the accuracy of noninvasive Caretaker device against invasively measured central aortic BP. Methods: Beat-to-beat BP by Caretaker was recorded simultaneously with central aortic BP measured in patients undergoing cardiac catheterization. We derived correlations and Bland-Altman comparisons, after calibrating the Caretaker with 20 seconds of the initial catheter readings, as well as trend analyses for both systolic (SBP) and diastolic BP (DBP). We also measured left ventricular ejection time (LVET) from both aortic pressure tracing and Caretaker and compared the two. Results: A total of 47 patients were included in the study. A total of 31,369 beats obtained during the diagnostic portion of coronary angiogram were used for analysis. The correlations for SBP and DBP were 0.89 and 0.78, respectively (p &lt; 0.001 for both). The Bland-Altman comparison yielded overall mean differences of 2.11 mmHg (SD 7.40) for SBP and 1.46 mmHg (SD 6.12) for DBP respectively (p &lt;0.001 for all comparisons). The trend analysis yielded concordances of 86% and 85% for SBP and DBP, respectively. The correlation and Bland-Altman analyses for the LVET comparison yielded 0.89 (p&lt; 0.001) with a mean difference of 13.9 ms (SD 14.4 ms). Conclusion: Beat-to-beat BP by Caretaker showed excellent agreement and high concordance in the direction and the degree of BP change with central aortic BP. This study supports the satisfactory performance of the Caretaker device in continuous tracking of beat-to-beat BP and LVET measurements.