Pulse arrival time variation as a non-invasive marker of acute response to cardiac resynchronization therapy.

AIMS: Successful cardiac resynchronization therapy (CRT) shortens the pre-ejection period (PEP) which is prolonged in the left bundle branch block (LBBB). In a combined animal and patient study, we investigated if changes in the pulse arrival time (PAT) could be used to measure acute changes in PEP during CRT implantation and hence be used to evaluate acute CRT response non-invasively and in real time. METHODS AND RESULTS: In six canines, a pulse transducer was attached to a lower limb and PAT was measured together with left ventricular (LV) pressure by micromanometer at baseline, after induction of LBBB and during biventricular pacing. Time-to-peak LV dP/dt (Td) was used as a surrogate for PEP. In twelve LBBB patients during implantation of CRT, LV and femoral pressures were measured at baseline and during five different pacing configurations. PAT increased from baseline (277 ± 9 ms) to LBBB (313 ± 16 ms, P < 0.05) and shortened with biventricular pacing (290 ± 16 ms, P < 0.05) in animals. There was a strong relationship between changes in PAT and Td in patients (r2 = 0.91). Two patients were classified as non-responders at 6 months follow-up. CRT decreased PAT from 320 ± 41 to 298 ± 39 ms (P < 0.05) in the responders, while PAT increased by 5 and 8 ms in the two non-responders. CONCLUSION: This proof-of-concept study indicates that PAT can be used as a simple, non-invasive method to assess the acute effects of CRT in real time with the potential to identify long-term response in patients.

The 'Digital Twin' to enable the vision of precision cardiology.

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

Tracking Early Systolic Motion for Assessing Acute Response to Cardiac Resynchronization Therapy in Real Time.

An abnormal systolic motion is frequently observed in patients with left bundle branch block (LBBB), and it has been proposed as a predictor of response to cardiac resynchronization therapy (CRT). Our goal was to investigate if this motion can be monitored with miniaturized sensors feasible for clinical use to identify response to CRT in real time. Motion sensors were attached to the septum and the left ventricular (LV) lateral wall of eighteen anesthetized dogs. Recordings were performed during baseline, after induction of LBBB, and during biventricular pacing. The abnormal contraction pattern in LBBB was quantified by the septal flash index (SFI) equal to the early systolic shortening of the LV septal-to-lateral wall diameter divided by the maximum shortening achieved during ejection. In baseline, with normal electrical activation, there was limited early-systolic shortening and SFI was low (9 ± 8%). After induction of LBBB, this shortening and the SFI significantly increased (88 ± 34%, p < 0.001). Subsequently, CRT reduced it approximately back to baseline values (13 ± 13%, p < 0.001 vs. LBBB). The study showed the feasibility of using miniaturized sensors for continuous monitoring of the abnormal systolic motion of the LV in LBBB and how such sensors can be used to assess response to pacing in real time to guide CRT implantation.

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer.

BACKGROUND: Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects the time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. METHOD: A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. RESULT: The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines. CONCLUSION: Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure-displacement loop area.

Measurements of the left ventricular (LV) pressure trace are rarely performed despite high clinical interest. We estimated the LV pressure trace for an individual heart by scaling the isovolumic, ejection and filling phases of a normalized, averaged LV pressure trace to the time-points of opening and closing of the aortic and mitral valves detected in the individual heart. We developed a signal processing algorithm that automatically detected the time-points of these valve events from the motion signal of a miniaturized accelerometer attached to the heart surface. Furthermore, the pressure trace was used in combination with measured displacement from the accelerometer to calculate the pressure-displacement loop area. The method was tested on data from 34 animals during different interventions. The accuracy of the accelerometer-detected valve events was very good with a median difference of 2 ms compared to valve events defined from hemodynamic reference recordings acquired simultaneously with the accelerometer. The average correlation coefficient between the estimated and measured LV pressure traces was r = 0.98. Finally, the LV pressure-displacement loop areas calculated using the estimated and measured pressure traces showed very good correlation (r = 0.98). Hence, the pressure-displacement loop area can be assessed solely from accelerometer recordings with very good accuracy.

Monitoring cardiac function by accelerometer - detecting start systole from the acceleration signal makes additional ECG recordings for R-peak detection redundant.

A miniaturized accelerometer attached to the heart has been used for monitoring functional parameters such as early systolic velocity and displacement. Currently, processing of the accelerometer signal for derival of these functional parameters depends on determining start systole by detecting the ECG R-peaks. This study proposes an alternative method using only the accelerometer signal to detect start systole, making additional ECG recordings for this purpose redundant. A signal processing method for automatic detection of start systole by accelerometer alone was developed and compared with detected R-peaks in 15 pigs during 5 different interventions showing a difference of 30 ± 17 ms. Furthermore, the derived early systolic velocity and displacement using only accelerometer measurements correlated well (r2=0.91 and 0.82, respectively) with minor differences compared to the current method using ECG R-peaks as time reference. The results show that an accelerometer can be used to monitor cardiac function without the need to measure ECG which can simplify the monitoring system.

Low-Cost Wearable for Fatigue and Back-Stress Measurement in Nursing.

In recent years the need for informal home care in European countries is growing quickly due to increased life expectancy and demographic change. Informal caregivers have to overcome many obstacles ranging from a lack of adequate training to misjudging their physical and psychological abilities. The aim of this project is to create a low cost wearable device, which unobtrusively measures the physical stress load on caregivers. Two parameters with the most impact on performance and well-being of the caregiver have been identified: (i) fatigue and (ii) back-stress. Based on the measurements, an alert is issued if caregivers are not performing a task correctly, or if they are overexerting themselves. This paper discusses the design of such device and description of an initial prototype, its advantages and possible further development and applications.