Ramakrishna Mukkamala’s research while affiliated with University of Pittsburgh and other places

Cuffless blood pressure (BP) measurement offers considerable potential for clinical practice but is a challenging technological field. Many are investigating pulse wave analysis with or without pulse arrival time in which machine learning is applied to pulsatile waveforms obtained with mobile devices (eg, wristbands, smartphones) to estimate BP. These methods generally require individual user calibration with cuff BP measurements or demographics (eg, age, sex). This calibration makes it difficult to evaluate the method’s accuracy, and many studies claiming accuracy used inadequate testing procedures. Yet, publications and regulatory-cleared devices continue to rise, seemingly implying technological advancements. An update is provided on the flurry of activity in cuffless BP technologies over the last 2 to 3 years, covering the clinical need, the latest devices, recent publications based on pulse wave analysis and pulse arrival time, progress in developing validation standards for cuffless BP devices, and recent publications on other cuffless BP measurement principles. Despite the high volume of research and development, to date, there is no compelling evidence that pulse wave analysis and pulse arrival time can provide significant added value in BP measurement accuracy beyond the cuff BP or demographic data for calibration. Thus, it is reasonable to at least be skeptical of published and future studies on pulse wave analysis and pulse arrival time for cuffless BP measurement with uncertain testing procedures. It is important to focus on establishing robust validation standards for cuffless BP devices requiring individual user calibration and also pursuing cuffless and calibration-free BP measurement methodologies going forward.

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.

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.

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.

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.

Objective According to the recent European Society of Hypertension (ESH) validation protocol for cuffless blood pressure (BP) devices, a novel isometric handgrip exercise test can be used as an alternative procedure to the bicycle test for assessing BP increase accuracy. This study evaluated the BP and heart rate (HR) response during the ESH handgrip exercise test. Design and method Four baseline BP/HR measurements (R1-R2-R3-R4; mercury sphygmomanometer; 1-min intervals) were performed in resting sitting posture. The maximum grip strength of the dominant hand was determined using a grip strength dynamometer. Handgrip exercise was then performed using resistance set to 30% of the maximum grip strength (using a device with adjustable grip strength). Participants performed an ‘initial exercise session’ to induce BP increase via 12 sets of 8 repetitive handgrips alternating between hands (6 sets per hand, starting with the dominant one). Then, the first post-exercise reference BP measurement (R5) was obtained. Afterwards, a maintenance phase followed with 6 sessions of 4 sets of 8 repetitive handgrips with BP/HR measurements after each session (R6-R7-R8-R9-R10-R11). Resting BP was determined as the average of R3 and R4, whereas the average of R5-R7-R9-R11 was used for determining post exercise reference BP/HR levels. Results 45 individuals were analyzed [age 47±12 years, body mass index (BMI) 27±4 kg/m2, 69% males, 33% on antihypertensive therapy, resting systolic/diastolic BP 124±16/81±11 mmHg, HR 75±13 beats/min]. In 22% of the participants resting systolic BP was >= 140 mmHg. The average exercise-induced systolic BP increase was 7±5 mmHg. A systolic BP increase >= 8 mmHg, >= 10 mmHg, <5 mmHg was observed in 38%, 27%, and 33% of the participants respectively. No significant changes were observed in diastolic BP (1±3 mmHg) and HR (1±4 beats/min). There was a trend towards an inverse association between BMI and systolic BP increase (r= -0.27, P=0.07). Conclusions The ESH handgrip exercise validation test for cuffless BP devices produces sustained increases in systolic BP in a considerable proportion of participants, similar to the bicycle test. Thus, this test is useful for validating the ability of cuffless devices to track BP increases.

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">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.

At Present, cameras are increasingly used to measure physiological signals from human face and body for contactless health monitoring, thereby eliminating mechanical contact with the skin that are common in wearable sensors. This is an emerging research direction developing rapidly in the last decade and which is now gradually maturing into products for patient monitoring. Advancements in biomedical optics, physiological measurement, computer vision and artificial intelligence (AI) enabled various camera-based measurements, including vital signs like heart rate (HR), respiration rate (RR), oxygen saturation (SpO2), blood pressure (BP), and physiological markers that have diagnostic capabilities, such as the detection of arrhythmia, atrial fibrillation, apnea, hypertension, etc. Image and video analysis also permit the measurement of human semantics, context and behaviours that provide new insights into health informatics (e.g., facial analysis and body actigraphy for the assessment of patient delirium), which is a unique advantage of camera sensors as compared to biomedical sensors, like e.g., photoplethysmography (PPG) and electrocardiogram (ECG). Camera-based health monitoring will bring a rich set of compelling healthcare applications that directly improve upon contact-based monitoring solutions in various scenarios like clinical units including e.g., the intensive care unit (ICU), the neonatal ICU (NICU) or sleep centers, and assisted-living homes (e.g., elderly homes or confinement centers), improving patient care experience and people's quality of life.