Martin Kropf

Normal range and usefulness of right ventricular systolic strain to detect subtle right ventricular systolic abnormalities in patients with heart failure: a multicentre study

Aims The aim of the present multicentre study was to analyse a large cohort of healthy subjects and patients with a common condition such as heart failure (HF) with the purpose of determining the normal range and the usefulness of right ventricular (RV) systolic strain to detect subtle RV systolic abnormalities using 2D speckle-tracking echocardiography. Methods and results We analysed 238 healthy subjects and a cohort of 642 patients characterized by asymptomatic patients (n = 216) and patients with HF with preserved (HFpEF) and reduced (HFrEF) ejection fraction (n = 218 and n = 208, respectively) prospectively included in 10 centres. The normal range of RV systolic strain analysing the healthy subjects was as follows: RV global strain −24.5 ± 3.8 and RV free wall strain −28.5 ± 4.8 (lowest expected value −17 and −19%, respectively). Concerning the ability of these myocardial parameters to detect subtle RV systolic abnormalities, RV global and free wall systolic strain were able to detect subtle RV longitudinal systolic abnormalities in a significant proportion of patients with HFrEF and to a lesser extent in HFpEF despite preserved tricuspid annular plane systolic excursion, tricuspid lateral annular peak systolic velocity by pulsed tissue Doppler imaging, and RV fractional area change. In addition, RV global and free wall systolic strain were significantly linked to the symptomatic status of the patients. Conclusions The findings from this study provide important data regarding the normal range of RV global and free wall systolic strain and highlight the clinical relevance of these RV myocardial parameters to detect subtle RV systolic abnormalities in patients with HF.

Clinical Relevance of Left Atrial Strain to Predict Recurrence of Atrial Fibrillation after Catheter Ablation: A Meta-Analysis.

The purpose of this meta‐analysis was to analyze the clinical relevance of left atrial (LA) strain to predict recurrence of atrial fibrillation (AF) after catheter ablation (CA).

Left ventricular longitudinal systolic function analysed by 2D speckle-tracking echocardiography in heart failure with preserved ejection fraction: a meta-analysis

Background The purpose of this meta-analysis was to confirm if the global longitudinal systolic function of the left ventricle (LV) is altered in patients with heart failure with preserved ejection fraction (HFpEF). Methods We searched in different databases (Medline, Embase and Cochrane) studies that analysed LV global longitudinal systolic strain (GLS) in patients with HFpEF and in controls (such as healthy subjects or asymptomatic patients with arterial hypertension, diabetes mellitus or coronary artery disease). Results Twenty-two studies (2284 patients with HFpEF and 2302 controls) were included in the final analysis. Patients with HFpEF had significantly lower GLS than healthy subjects (mean −15.7% (range −12% to −18.9%) vs mean −19.9% (range −17.1% to −21.5%), weighted mean difference −4.2% (95% CI −3.3% to −5.0%), p < 0.001, respectively). In addition, patients with HFpEF had also significantly lower GLS than asymptomatic patients (mean −15.5% (range −13.4% to −18.4%) vs mean −18.3% (range −15.1% to −20.4%), weighted mean difference −2.8%(95% CI −1.9% to −3.6%), p < 0.001, respectively). In line, 10 studies showed that the rate of abnormal GLS was significantly higher in patients with HFpEF (mean 65.4% (range 37%–95%)) than in asymptomatic subjects (mean 13% (range 0%–29.6%)). Regarding the prognostic relevance of abnormal GLS in HFpEF, two multicentre studies with large sample size (447 and 348) and high number of events (115 and 177) showed that patients with abnormal GLS had worse cardiovascular (CV) outcomes than those with normal GLS (HR for CV mortality and HF hospitalisation 2.14 (95% CI 1.26 to 3.66) and 1.94 (95% CI 1.22 to 3.07)), even adjusting these analyses for multiples clinical and echocardiographic variables. Conclusion The present meta-analysis analysing 2284 patients with HFpEF and 2302 controls confirms that the longitudinal systolic function of the LV is significantly altered in high proportion of patients with HFpEF. Further large multicentre studies with the aim to confirm the prognostic role of abnormal GLS in HFpEF are warranted.

Telemonitoring in heart failure patients with clinical decision support to optimize medication doses based on guidelines

The European Society of Cardiology guidelines for heart failure management are based on strong evidence that adherence to optimal medication is beneficial for heart failure patients. Telemonitoring with integrated clinical decision support enables physicians to adapt medication dose based on up to date vital parameters and reduces the number of hospital visits needed solely for up-titration of heart failure medication. Although keeping track of weight and blood pressure changes is recommended during unstable phases, e.g. post-discharge and during up-titration of medication, guidelines are rather vague regarding telehealth aspects. In this paper, we focus on the evaluation of a clinical decision support system for adaption of heart failure medication and for detecting early deteriorations through monitoring of blood pressure, heart rate and weight changes. This clinical decision support system is currently used in INTENSE-HF, a large scale telemonitoring trial with heart failure patients. The aim of this paper was to apply the decision support algorithm to an existing telemonitoring dataset, to assess the ability of the decision support concept to adhere to the guidelines and to discuss its limitations and potential improvements.

Cardiac anomaly detection based on time and frequency domain features using tree-based classifiers

OBJECTIVE Recent advantages in mHealth-enabled ECG recorders boosted the demand for algorithms, which are able to automatically detect cardiac anomalies with high accuracy. APPROACH We present a combined method of classical signal analysis and machine learning which has been developed during the Computing in Cardiology Challenge (CinC) 2017. Almost 400 hand-crafted features have been developed to reflect the complex physiology of cardiac arrhythmias and their appearance in single-channel ECG recordings. For the scope of this article, we performed several experiments on the publicly available challenge dataset to improve the classification accuracy. We compared the performance of two tree-based algorithms-gradient boosted trees and random forests-using different parameters for learning. We assessed the influence of five different sets of training annotations on the classifiers performance. Further, we present a new web-based ECG viewer to review and correct the training labels of a signal data set. Moreover, we analysed the feature importance and evaluated the model performance when using only a subset of the features. The primary data source used in the analysis was the dataset of the CinC 2017, consisting of 8528 signals from four classes. Our best results were achieved using a gradient boosted tree model which worked significantly better than random forests. MAIN RESULTS Official results of the challenge follow-up phase provided by the Challenge organizers on the full hidden test set are 90.8% (Normal), 84.1% (AF), 74.5% (Other), resulting in a mean F1-score of 83.2%, which was only 1.6% behind the challenge winner and 0.2% ahead of the next-best algorithm. Official results were rounded to two decimal places which lead to the equal-second best F1 F -score of 83% with five others. SIGNIFICANCE The algorithm achieved the second-best score among 80 algorithms of the Challenge follow-up phase equal with five others.

Predictive analytics for data driven decision support in health and care

Abstract Due to an ever-increasing amount of data generated in healthcare each day, healthcare professionals are more and more challenged with information. Predictive models based on machine learning algorithms can help to quickly identify patterns in clinical data. Requirements for data driven decision support systems for health and care (DS4H) are similar in many ways to applications in other domains. However, there are also various challenges which are specific to health and care settings. The present paper describes a) healthcare specific requirements for DS4H and b) how they were addressed in our Predictive Analytics Toolset for Health and care (PATH). PATH supports the following process: objective definition, data cleaning and pre-processing, feature engineering, evaluation, result visualization, interpretation and validation and deployment. The current state of the toolset already allows the user to switch between the various involved levels, i. e. raw data (ECG), pre-processed data (averaged heartbeat), extracted features (QT time), built models (to classify the ECG into a certain rhythm abnormality class) and outcome evaluation (e. g. a false positive case) and to assess the relevance of a given feature in the currently evaluated model as a whole and for the individual decision. This allows us to gain insights as a basis for improvements in the various steps from raw data to decisions.

Workflow-Supported Biosignal Integration in Multimodal Clinical Trials.

This work aims to describe the IT architecture of a multimodal clinical trial, in which workflow supported biosignal integration to several usually isolated IT systems had been implemented.