Tricuspid annuloplasty concomitant with mitral valve surgery: Effects on right ventricular remodeling

ObjectivesTricuspid valve annuloplasty (TVP) has been advocated concomitantly with left-sided cardiac surgery in case of more than moderate tricuspid regurgitation (TR) or tricuspid annular dilation (TAD) (diameter >40 mm or 21 mm/m²) even in the absence of significant TR. Data on postoperative right ventricular (RV) remodeling are lacking in such patients.MethodsPreoperative and postoperative echocardiography data from 45 consecutive TVP procedures, performed in mitral valve surgery in a single tertiary center, were retrospectively analyzed and compared with a propensity-matched control group of 33 procedures without concomitant TVP. RV function and geometry was analyzed by measuring RV size, fractional area change, and end-diastolic sphericity index (RVSI = long-axis length/short-axis width) and compared at baseline versus follow-up.ResultsAt a mean follow-up of 5 months, a favorable change in RV geometry was observed in TVP patients (RVSI increased from 1.99 ± 0.33 to 2.21 ± 0.42; P = .001), whereas the opposite was observed in the control group (RVSI decreased from 2.34 ± 0.52 to 2.17 ± 0.13; P = .05). Only in control patients, indexed RV end-diastolic area increased significantly (P = .003). In TVP patients, when comparing patients with baseline more than moderate TR (n = 13) to patients with isolated TAD (n = 32), there was a significant decrease in RV end-diastolic area only in the group with more than moderate TR (from 12.9 ± 3.5 cm2/m2 to 10.3 ± 1.9 cm2/m2; P = .009).ConclusionsAdding TVP to mitral valve surgery in patients with more than moderate TR or TAD leads to favorable changes in RV geometry and prevents postoperative RV dilation. This is most pronounced in patients with more than moderate TR at baseline

Automated flow rate calculation based on digital analysis of flow convergence proximal to regurgitant orifice

AbstractObjectives. The purpose of the study was to develop and validate an automated method for calculating regurgitant flow rate using color Doppler echocardiography.Background. The proximal flow convergence method is a promising approach to quantitate valvular regurgitation noninvasively because it allows one to calculate regurgitant flow rate and regurgitant orifice area; however, defining the location of the regurgitant orifice is often difficult and can lead to significant error in the calculated flow rates. To overcome this problem we developed an automated algorithm to locate the orifice and calculate flow rate based on the digital Doppler velocity map.Methods. This algorithm compares the observed velocities with the anticipated relative velocities, cos ϑ/μt2. The orifice is localized as the point with maximal correlation between predicted and observed velocity, whereas flow rate is specified as the slope of the regression line. We validated this algorithm in an in vitro model for flow through circular orifices with planar surroundings and a porcine bioprosthesis.Results. For flow through circular orifices, flow rates calculated on individual Doppler maps and on an average of eight velocity maps showed excellent agreement with true flow, with r = 0.977 and ΔQ = −3.7 ± 15.8 cm3/s and r = 0.991 and ΔQ = −4.3 ± 8.5 cm3/s, respectively. Calculated flow rates through the bioprosthesis correlated well but underestimated true flow, with r = 0.97, ΔQ = −10.9 ± 12.5 cm3/s, suggesting flow convergence over an >2π. This systematic underestimation was corrected by assuming an effective convergence angle of 212 δ.Conclusions. This algorithm accurately locates the regurgitant orifice and calculates regurgitant flow rate for circular orifices with planar surroundings. Automated analysis of the proximal flow field is also applicable to more physiologic surfaces surrounding the regurgitant orifice; however, the convergence angle should be adjusted. This automated algorithm should make quantification of regurgitant flow rate and regurgitant orifice area more reproducible and readily available in clinical cardiology practice

Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56%, sensitivity of 90.59%, precision of 86.52% and F-1-score of 88.50%. The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs

Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

eEduHeart I: A Multicenter, Randomized, Controlled Trial Investigating the Effectiveness of a Cardiac Web-Based eLearning Platform - Rationale and Study Design

&lt;b&gt;&lt;i&gt;Objectives:&lt;/i&gt;&lt;/b&gt; Cardiac telerehabilitation includes, in its most comprehensive format, telemonitoring, telecoaching, social interaction, and eLearning. The specific role of eLearning, however, was seldom assessed. The aim of eEduHeart I is to investigate the medium-term effectiveness of the addition of a cardiac web-based eLearing platform to conventional cardiac care. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; In this prospective, multicenter randomized, controlled trial, 1,000 patients with coronary artery disease will be randomized 1:1 to an intervention group (receiving 1-month unrestricted access to the cardiac eLearning platform in addition to conventional cardiac care) or to conventional cardiac care alone. The primary endpoint is health-related quality of life, assessed by the HeartQoL questionnaire at the 1- and 3-month follow-ups. Secondary endpoints include pathology-specific knowledge and self-reported eLearning platform user experience. Data on the eLearning platform usage will be gathered through web logging during the study period. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; eEduHeart I will be one of the first studies to report on the added value of eLearning. &lt;b&gt;&lt;i&gt;Conclusions:&lt;/i&gt;&lt;/b&gt; If the intervention is proven effective, current cardiac telerehabilitation programs can be augmented by including eLearning, too. The platform can then be used as a model for other chronic diseases in which patient education plays a key role.</jats:p

Mobile Phone-Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

BACKGROUND: Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered. OBJECTIVE: This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals. METHODS: A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF. RESULTS: A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects. CONCLUSIONS: The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.status: publishe