Continuous Estimation of Acute Changes in Preload Using Epicardially Attached Accelerometers

Association between phonocardiography and echocardiography in heart failure patients with preserved ejection fraction

Aims

Heart failure with preserved ejection fraction (HFpEF) is associated with stiffened myocardium and elevated filling pressure that may be captured by heart sound (HS). We investigated the relationship between phonocardiography (PCG) and echocardiography in symptomatic patients suspected of HFpEF.

Methods and results

Consecutive symptomatic patients with sinus rhythm and left ventricular ejection fraction >45% were enrolled. Echocardiography was performed to evaluate the patients' diastolic function, accompanied by PCG measurements. Phonocardiography features including HS amplitude, frequency, and timing intervals were calculated, and their abilities to differentiate the ratio between early mitral inflow velocity and early diastolic mitral annular velocity (E/e') were investigated. Of 45 patients, variable ratio matching was applied to obtain two groups of patients with similar characteristics but different E/e'. Patients with a higher E/e' showed higher first and second HS frequencies and more fourth HS and longer systolic time intervals. The interval from QRS onset to first HS was the best feature for the prediction of E/e' > 9 [area under the curve (AUC): 0.72 (0.51-0.88)] in the matched patients. In comparison, N-terminal pro-brain natriuretic peptide (NT-proBNP) showed an AUC of 0.67 (0.46-0.85), a value not better than any PCG feature (P > 0.05).

Conclusion

Phonocardiography features stratify E/e' in symptomatic patients suspected of HFpEF with a diagnostic performance similar to NT-proBNP. Heart sound may serve as a simple non-invasive tool for evaluating HFpEF patients.

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 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.

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Objective

A method to estimate absolute left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) from epicardial accelerometer data and machine learning is proposed.

Methods

Five acute experiments were performed on pigs. Custom-made accelerometers were sutured epicardially onto the right ventricle, LV, and right atrium. Different pacing configurations and contractility modulations, using isoflurane and dobutamine infusions, were performed to create a wide variety of hemodynamic conditions. Automated beat-by-beat analysis was performed on the acceleration signals to evaluate amplitude, time, and energy-based features. For each sensing location, bootstrap aggregated classification tree ensembles were trained to estimate absolute maximum LV pressure (LVPmax) and LV dP/dtmax using amplitude, time, and energy-based features. After extraction of acceleration and pressure-based features, location specific, bootstrap aggregated classification ensembles were trained to estimate absolute values of LVPmax and its maximum rate of rise (LV dP/dtmax) from acceleration data.

Results

With a dataset of over 6,000 beats, the algorithm narrowed the selection of 17 predefined features to the most suitable 3 for each sensor location. Validation tests showed the minimal estimation accuracies to be 93% and 86% for LVPmax at estimation intervals of 20 and 10 mmHg, respectively. Models estimating LV dP/dtmax achieved an accuracy of minimal 93 and 87% at estimation intervals of 100 and 200 mmHg/s, respectively. Accuracies were similar for all sensor locations used.

Conclusion

Under pre-clinical conditions, the developed estimation method, employing epicardial accelerometers in conjunction with machine learning, can reliably estimate absolute LV pressure and its first derivative.

Effect of Pulse Indicator Continuous Cardiac Output Monitoring on Septic Shock Patients: A Meta-Analysis

Background

Septic shock (SS) is the most common severe syndrome in the Intensive Care Unit (ICU). Enhancing the monitoring of hemodynamic indexes in SS patients carries huge clinical implications for reducing patient mortality. Recently, pulse indicator continuous cardiac output (PICCO) has been widely used in clinical practice, but its advantages than central venous pressure (CVP) in guiding the treatment of SS patients remains to be refined. Therefore, this study is aimed at assessing the clinical effects of PICCO in the treatment of patients with SS.

Methods

The authors systematically searched several databases (PubMed, EMBASE, Cochrane Library, and China National Knowledge) between January 2001 and February 2021. When searching for relevant articles, the authors combined the following phrases describing the monitoring group (“pulse indicator continuous cardiac output,” “central venous pressure”) with the disease of interest as well as management (“SS,” “sepsis”). The outcomes were independently assessed by two reviewers who scored the articles for methodological quality using the Cochrane Collaboration's “risk of bias” tool. Forest plots, as well as sensitivity and bias analyses, were carried out for the included articles. The primary outcome measures were length of ICU stay, duration of mechanical ventilation, 28-day mortality, and fluid resuscitation volume.

Results

Ten studies comprising 350 cases monitored with PICCO and 373 cases monitored with traditional CVP were eventually identified. PICCO-monitored patients were observed to be significantly associated with shorter ICU stay than CVP-monitored patients (MD: −3.04, 95% CI: −4.74 to −1.34, P = 0.0005), shorter time of mechanical ventilation (MD: −1.84, 95% CI: −2.80 to −0.87, P = 0.0002), and lower 28-day mortality (RR: 0.67, 95% CI: 0.48 to 0.94, P = 0.02). The two groups showed no significant difference in subgroup analysis for fluid resuscitation volumes (P > 0.05).

Conclusion

PICCO monitoring technique can significantly improve the prognosis of SS patients, shorten the time of mechanical ventilation and ICU stay, and reduce the 28-day mortality, which has positive guiding significance for patients with SS. Given the limitations of the quantity and quality of included studies, further research is warranted to verify the conclusions.

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.