Remote Monitoring of Patients Undergoing Transcatheter Aortic Valve Replacement: A Framework for Postprocedural Telemonitoring

Digital health technologies in enhancing patient and caregiver engagement in heart failure management: Opportunities and challenges

The management of heart failure has undergone significant evolution, advancing from the initial utilization of digitalis and diuretics to the contemporary practice of personalized medicine and sophisticated device therapy. Despite these advancements, the persistent challenge of high hospitalization and readmission rates underscores an urgent need for innovative solutions. This manuscript explores how the integration of digital health technologies into interventional cardiology marks a paradigm shift in the management of heart failure. These technologies are no longer mere adjuncts but have become foundational to a modern approach, providing tools for continuous monitoring, patient education, and improved outcomes post-intervention. Through an examination of current trends, this perspective article highlights the transformative impact of wearable technologies, telehealth platforms, and advanced analytical tools in reshaping patient engagement and enabling proactive care strategies. Case studies illustrate the practical advantages, including enhanced medication adherence, early detection of heart failure signs, and a reduction in healthcare facility burdens. Central to this new digital health landscape is the Information Technology Management (ITM) system, a framework poised to revolutionize patient and caregiver engagement and pave the way for the future of interventional cardiology. This manuscript delineates the ITM system's innovative architecture and its consequential role in refining current and prospective cardiological interventions.

The utility and impact of outpatient telemetry monitoring in post-transcatheter aortic valve replacement patients

BACKGROUND

Conduction disturbances are a common complication of transcatheter aortic valve replacement (TAVR). Mobile Cardiac Telemetry (MCT) allows for continuous monitoring with near "real time" alerts and has allowed for timely detection of conduction abnormalities and pacemaker placement in small trials. A standardized, systematic approach utilizing MCT devices post TAVR has not been widely implemented, leading to variation in use across hospital systems.

OBJECTIVES

Our aim was to evaluate the utility of a standardized, systematic approach utilizing routine MCT to facilitate safe and earlier discharge by identifying conduction disturbances requiring permanent pacemaker (PPM) placement. We also sought to assess the occurrence of actionable arrhythmias in post-TAVR patients.

METHODS

Using guidance from the JACC Scientific Expert Panel, a protocol was implemented starting in December 2019 to guide PPM placement post-TAVR across our health system. All patients who underwent TAVR from December 2019 to June 2021 across four hospitals within Northwell Health, who did not receive or have a pre-existing PPM received an MCT device at discharge and were monitored for 30 days. Clinical and follow-up data were collected and compared to pre initiative patients.

RESULTS

During the initiative 693 patients were monitored with MCT upon discharge, 21 of whom required PPM placement. Eight of these patients had no conduction abnormality on initial or discharge ECG. 59 (8.6 %) patients were found to have new atrial fibrillation or flutter via MCT monitoring. There were no adverse events in the initiative group. Prior to the initiative, 1281 patients underwent TAVR over a one-year period. The initiative group had significantly shorter length of stay than pre-initiative patients (2.5 ± 4.5 vs 3.0 ± 3.8 days, p < 0.001) and lower overall PPM placement rate within 30 days post-TAVR (16 % vs 20.5 %, P = 0.0125).

CONCLUSIONS

In our study, implementation of a standardized, systematic approach utilizing MCT in post-TAVR patients was safe and allowed for timely detection of conduction abnormalities requiring pacemaker placement. This strategy also detected new atrial fibrillation and flutter. Reduction in post TAVR pacemaker rate and length of stay were also noted although this effect is multifactorial.

Towards Longitudinal Monitoring of Leaflet Mobility in Prosthetic Aortic Valves via In-Situ Pressure Sensors: In-Silico Modeling and Analysis

BACKGROUND

Transcatheter aortic valves (TAVs) are susceptible to leaflet thrombosis which may lead to thromboembolic events, and early detection and intervention are believed to be the key to avoiding such adverse outcomes. An embedded sensor system installed on the valve stent, coupled with an appropriate machine learning-based continuous monitoring algorithm can facilitate early detection to predict severity of reduced leaflet motion (RLM) and avoid adverse outcomes.

METHODS

We present a data-driven, in silico, proof-of-concept analysis of a pressure microsensor based system for quantifying RLM in TAVs. We generate a dataset of 21 high-fidelity transvalvular flow simulations with healthy and mildly stenotic TAVs to train a logistic regression model to correlate individual leaflet mobility in each simulation with principal components of corresponding hemodynamic pressure recorded at strategic locations of the TAV stent. A separate test dataset of 7 simulations is also generated for prospective assessment of model performance.

RESULTS

An array of 6 sensors embedded on the TAV stent, with two sensors tracking individual leaflet, successfully correlates leaflet mobility with recorded pressure. The sensors are placed along leaflet centerlines, one in the sinus, and the other at the sino-tubular junction. The regression model is tuned using cross-validation to achieve high accuracy on both training (R² = 0.93) and test (R² = 0.77) sets.

CONCLUSION

Discrete blood pressure recordings on TAV stents can be successfully correlated with individual leaflet mobility. Further development of this technology can enable longitudinal monitoring of TAVs and early detection of valve failure.

Wearable Smartwatch Facilitated Remote Health Management for Patients Undergoing Transcatheter Aortic Valve Replacement

Background

In the recent decades, the development of novel digital health technologies enables doctors to monitor ECG and vital signs remotely. But the data on applying the noninvasive wearable smartwatch on patients with transcatheter aortic valve replacement (TAVR) are unknown.

Methods and Results

We performed a prospective, observational cohort study to evaluate the feasibility of a novel, virtual, and remote health care strategy for patients with TAVR discharged to home with smart wearable devices. A total of 100 consecutive patients with severe aortic stenosis who underwent elective transfemoral TAVR were enrolled and received the Huawei smartwatch at least 1 day before TAVR. Vital signs, including heart rate, rhythm, oxygen saturation, and activity, were continuously recorded. Single‐lead ECG was recorded twice per day in the week following TAVR discharge and at least 2 days a week for the subsequent month after TAVR discharge. A designated heart team member provided remote health care with the data from the smartwatch when the patient had a need. Thirty‐eight cardiac events were reported in 34 patients after discharge, with most of the events (76.0%) detected and confirmed by the smartwatch. Six patients were advised and readmitted to the hospital for arrhythmia events detected by the smartwatch, of whom 4 patients received pacemaker implantation. The remaining 28 (82.4%) patients received telemedicine monitoring instead of face‐to‐face clinical visits, and 3 of them received new medication treatment under the online guidance of doctors. New‐onset left branch bundle block was found in 48 patients, with transient characteristics, and recovered spontaneously in 30 patients, and new‐onset atrial fibrillation was detected in 4 patients. There were no significant differences in the average weekly heart rates or the ratio of abnormal or low oxygen saturation when compared with the baseline. The average daily steps increased over time significantly (baseline, 870±1353 steps; first week, 1986±2406 steps; second week, 2707±2716 steps; third week, 3059±3036 steps; fourth week, 3678±3485 steps, P <0.001).

Conclusions

Smartwatches can facilitate remote health care for patients discharged to home after undergoing TAVR and enable a novel remote follow‐up strategy. The majority of cardiac clinical events that occurred within 30‐day follow‐up were detected by the smartwatch, mainly because of the record of conduction abnormality.

Registration

URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04454177.

Detecting Aortic Valve Anomaly From Induced Murmurs: Insights From Computational Hemodynamic Models

Patients who receive transcatheter aortic valve replacement are at risk for leaflet thrombosis-related complications, and can benefit from continuous, longitudinal monitoring of the prosthesis. Conventional angiography modalities are expensive, hospital-centric and either invasive or employ potentially nephrotoxic contrast agents, which preclude their routine use. Heart sounds have been long recognized to contain valuable information about individual valve function, but the skill of auscultation is in decline due to its heavy reliance on the physician's proficiency leading to poor diagnostic repeatability. This subjectivity in diagnosis can be alleviated using machine learning techniques for anomaly detection. We present a computational and data-driven proof-of-concept analysis of a novel, auscultation-based technique for monitoring aortic valve, which is practical, non-invasive, and non-toxic. However, the underlying mechanisms leading to physiological and pathological heart sounds are not well-understood, which hinders development of such a technique. We first address this by performing direct numerical simulations of the complex interactions between turbulent blood flow in a canonical ascending aorta model and dynamic valve motion in 29 cases with healthy and stenotic valves. Using the turbulent pressure fluctuations on the aorta lumen boundary, we model the propagation of heart sounds, as elastic waves, through the patient's thorax. The heart sound may be recorded on the epidermal surface using a stethoscope/phonocardiograph. This approach allows us to correlate instantaneous hemodynamic phenomena and valve motion with the acoustic response. From this dataset we extract "acoustic signatures" of healthy and stenotic valves based on principal components of the recorded sound. These signatures are used to train a linear discriminant classifier by maximizing correlation between recorded heart sounds and valve status. We demonstrate that this classifier is capable of accurate prospective detection of anomalous valve function and that the principal component-based signatures capture prominent audible features of heart sounds, which have been historically used by physicians for diagnosis. Further development of such technology can enable inexpensive, safe and patient-centric at-home monitoring, and can extend beyond transcatheter valves to surgical as well as native valves.

Evaluating treatment-specific post-discharge quality-of-life and cost-effectiveness of TAVR and SAVR: Current practice & future directions

•

Post-TAVR HRQOL shows more rapid short-term improvement than SAVR within trials.

•

Higher TAVR use requires better real-world TAVR/SAVR cost-effectiveness comparisons.

•

Wearable devices should be used in real-world settings to compare TAVR/SAVR HRQOL.

Background

Aortic stenosis is a prevalent valvular heart disease that is treated primarily by surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR), which are common treatments for addressing symptoms secondary to valvular heart disease. This narrative review article focuses on the existing literature comparing recovery and cost-effectiveness for SAVR and TAVR.

Methods

Major databases were searched for relevant literature discussing HRQOL and cost-effectiveness of TAVR and SAVR. We also searched for studies analyzing the use of wearable devices to monitor post-discharge recovery patterns.

Results

The literature focusing on quality-of-life following TAVR and SAVR has been limited primarily to single-center observational studies and randomized controlled trials. Studies focused on TAVR report consistent and rapid improvement relative to baseline status. Common HRQOL instruments (SF-36, EQ-5D, KCCQ, MLHFQ) have been used to document that TF-TAVR is advantageous over SAVR at 1-month follow-up, with the benefits leveling off following 1 year. TF-TAVR is economically favorable relative to SAVR, with estimated incremental cost-effectiveness ratio values ranging from $50,000 to $63,000/QALY gained. TA-TAVR has not been reported to be advantageous from an HRQOL or cost-effectiveness perspective.

Conclusions

While real-world experiences are less described, large-scale trials have advanced our understanding of recovery and cost-effectiveness of aortic valve replacement treatment strategies. Future work should focus on scalable wearable device technology, such as smartwatches and heart-rate monitors, to facilitate real-world evaluation of TAVR and SAVR to support clinical decision-making and outcomes ascertainment.

Prosthetic Valve Monitoring via In Situ Pressure Sensors: In Silico Concept Evaluation using Supervised Learning

PURPOSE

Patients receiving transcatheter aortic valve replacement (TAVR) can benefit from continuous, longitudinal monitoring of valve prosthesis to prevent leaflet thrombosis-related complications. We present a computational proof-of-concept study of a novel, non-invasive and non-toxic valve monitoring technique for TAVs which uses pressure measurements from microsensors embedded on the valve stent. We perform a data-driven analysis to determine the signal processing and machine learning required to detect reduced mobility in individual leaflets.

METHODS

We use direct numerical simulations to describe hemodynamic differences in transvalvular flow in ascending aorta models with healthy and stenotic valves. A Cartesian-grid flow solver and a reduced-order valve model simulate the complex dynamics of blood flow and leaflet motion, respectively. The two-way fluid-structure interaction coupling is achieved using a sharp interface immersed boundary method.

RESULTS

From a dataset of 21 simulations, we show leaflets with reduced mobility result in large, asymmetric pressure fluctuations in their vicinity, particularly in the region extending from the aortic sinus to the sino-tubular junction (STJ). We train a linear classifier algorithm by correlating sinus and STJ pressure measurements on the stent surface to individual leaflet status. The algorithm was shown to have >90% accuracy for prospective detection of individual leaflet dysfunction.

CONCLUSIONS

We demonstrate that using only two discrete pressure measurements, per leaflet, on the TAV stent, individual leaflet status can be accurately predicted. Such a sensorized TAV system could enable safe and inexpensive detection of prosthetic valve dysfunction.