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BackgroundTelemonitoring of patients with chronic heart failure (CHF) is an emerging concept to detect early warning signs of impending acute decompensation in order to prevent hospitalization. ObjectiveThe goal of the MOBIle TELemonitoring in Heart Failure Patients Study (MOBITEL) was to evaluate the impact of home-based telemonitoring using Internet and mobile phone technology on the outcome of heart failure patients after an episode of acute decompensation. MethodsPatients were randomly allocated to pharmacological treatment (control group) or to pharmacological treatment with telemedical surveillance for 6 months (tele group). Patients randomized into the tele group were equipped with mobile phone–based patient terminals for data acquisition and data transmission to the monitoring center. Study physicians had continuous access to the data via a secure Web portal. If transmitted values went outside individually adjustable borders, study physicians were sent an email alert. Primary endpoint was hospitalization for worsening CHF or death from cardiovascular cause. ResultsThe study was stopped after randomization of 120 patients (85 male, 35 female); median age was 66 years (IQR 62-72). The control group comprised 54 patients (39 male, 15 female) with a median age of 67 years (IQR 61-72), and the tele group included 54 patients (40 male, 14 female) with a median age of 65 years (IQR 62-72). There was no significant difference between groups with regard to baseline characteristics. Twelve tele group patients were unable to begin data transmission due to the inability of these patients to properly operate the mobile phone (“never beginners”). Four patients did not finish the study due to personal reasons. Intention-to-treat analysis at study end indicated that 18 control group patients (33%) reached the primary endpoint (1 death, 17 hospitalizations), compared with 11 tele group patients (17%, 0 deaths, 11 hospitalizations; relative risk reduction 50%, 95% CI 3-74%, P = .06). Per-protocol analysis revealed that 15% of tele group patients (0 deaths, 8 hospitalizations) reached the primary endpoint (relative risk reduction 54%, 95% CI 7-79%, P= .04). NYHA class improved by one class in tele group patients only (P< .001). Tele group patients who were hospitalized for worsening heart failure during the study had a significantly shorter length of stay (median 6.5 days, IQR 5.5-8.3) compared with control group patients (median 10.0 days, IQR 7.0-13.0; P= .04). The event rate of never beginners was not higher than the event rate of control group patients. ConclusionsTelemonitoring using mobile phones as patient terminals has the potential to reduce frequency and duration of heart failure hospitalizations. Providing elderly patients with an adequate user interface for daily data acquisition remains a challenging component of such a concept.

BackgroundPatients with type 1 diabetes mellitus (DM1) have to be active participants in their treatment because they are inevitably responsible for their own day-to-day-care. Availability of mobile Internet access is advancing rapidly and mobile phones are now widely available at low cost. Thus, mobile phones have the potential to assist in daily diabetes management and to enable a telemedical interaction between patients and health care professionals. ObjectiveThe aim of the study was to evaluate the feasibility and user acceptance of a mobile phone–based data service to assist DM1 patients on intensive insulin treatment. MethodsA software application called Diab-Memory (based on Java 2 Mobile Edition) has been developed to support patients when entering diabetes-related data with synchronization to the remote database at the monitoring center. The data were then processed to generate statistics and trends, which were provided for the patient and his/her health care professional via a Web portal. The system has been evaluated in the course of a clinical before-after pilot trial. Outcome measures focused on patients’ adherence to the therapy, availability of the monitoring system, and the effects on metabolic status. General user acceptance of the system was evaluated using a questionnaire. ResultsTen patients (four female) with DM1 participated in the trial. Mean age was 36.6 years (± 11.0 years) and prestudy glycated hemoglobin (HbA1c) was 7.9% (± 1.1%). A total of 3850 log-ins were registered during the 3 months of the study. The total number of received datasets was 13003, which equates to an average of 14 transmitted parameters per patient per day. The service was well accepted by the patients (no dropouts), and data transmission via mobile phone was successful on the first attempt in 96.5% of cases. Upon completion of the study, a statistically significant improvement in metabolic control was observed (HbA1c: prestudy 7.9% ± 1.1% versus poststudy 7.5% ± 0.9%;P= .02). While there was a slight decrease in average blood glucose level (prestudy 141.8 mg/dL ± 22.5 mg/dL vs poststudy 141.2 mg/dL ± 23.1 mg/dL;P= .69), the difference was not statistically significant. ConclusionThe results of the clinical pilot trial indicate that this proposed diabetes management system was well accepted by the patients and practical for daily usage. Thus, using the mobile phone as patient terminal seems to provide a ubiquitous, easy-to-use, and cost efficient solution for patient-centered data acquisition in the management of DM1. To confirm the promising results of the pilot trial further research has to be done to study long-term effects on glycemic control and cost-effectiveness.

Background: CareNet is the IT-based tool for Case and Care Management (CCM) in Tyrol, which facilitates standardised documentation of CCM activities. Objectives: Analysing the pilot usage of CareNet Tyrol. Methods: Evaluation of the success and user experience of CareNet, expert interviews and a questionnaire-based assessment. Results: Feedback from users in both phases indicated that the CareNet platform provides general benefits, but falls short of fully supporting the daily work of CCM experts and avoiding the need for parallel use of different documentation tools. Conclusion: This paper provides an insight into the ongoing transition to digital documentation for CCM at LIV Tyrol. While user feedback highlights areas for improvement, digital documentation is proved to be beneficial for the CCM team. [ABSTRACT FROM AUTHOR]

Background Large-scale screening for atrial fibrillation (AF) requires reliable methods to identify at-risk populations. Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays. Methods and findings For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA.sub.2 DS.sub.2 -VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m.sup.2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p < 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p < 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation. Conclusions Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF. Trial registration Registry IRAS ID 97753 Health Research Authority (HRA), United Kingdom, Author(s): Winnie Chua 1, Jonathan P. Law 1,2, Victor R. Cardoso 1,3, Yanish Purmah 1,4,5, Georgiana Neculau 4,5, Muhammad Jawad-Ul-Qamar 1,4,5, Kalisha Russell 5, Ashley Turner 5, Samantha P. Tull [...]

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms. [ABSTRACT FROM AUTHOR]

Aims Atrial remodelling, defined as a change in atrial structure, promotes atrial fibrillation (AF). Bone morphogenetic protein 10 (BMP10) is an atrial-specific biomarker released to blood during atrial development and structural changes. We aimed to validate whether BMP10 is associated with AF recurrence after catheter ablation (CA) in a large cohort of patients. Methods and results We measured baseline BMP10 plasma concentrations in AF patients who underwent a first elective CA in the prospective Swiss-AF-PVI cohort study. The primary outcome was AF recurrence lasting longer than 30 s during a follow-up of 12 months. We constructed multivariable Cox proportional hazard models to determine the association of BMP10 and AF recurrence. A total of 1112 patients with AF (age 61 ± 10 years, 74% male, 60% paroxysmal AF) was included in our analysis. During 12 months of follow-up, 374 patients (34%) experienced AF recurrence. The probability for AF recurrence increased with increasing BMP10 concentration. In an unadjusted Cox proportional hazard model, a per-unit increase in log-transformed BMP10 was associated with a hazard ratio (HR) of 2.28 (95% CI 1.43; 3.62, P < 0.001) for AF recurrence. After multivariable adjustment, the HR of BMP10 for AF recurrence was 1.98 (95% CI 1.14; 3.42, P = 0.01), and there was a linear trend across BMP10 quartiles (P = 0.02 for linear trend). Conclusion The novel atrial-specific biomarker BMP10 was strongly associated with AF recurrence in patients undergoing CA for AF. ClinicalTrials.gov Identifier NCT03718364; https://clinicaltrials.gov/ct2/show/NCT03718364 [ABSTRACT FROM AUTHOR]

Aims Biomarkers specifically related to atrial tissue may increase the understanding of the pathophysiology of atrial fibrillation (AF) and further improve risk prediction in this setting. Bone morphogenetic protein 10 (BMP10) is a protein expressed in the atrial myocardium. We evaluated the association between BMP10 and the risk of ischaemic stroke and other cardiovascular events in large cohorts of patients with AF, treated with and without oral anticoagulation (OAC). Methods and results BMP10 was measured in plasma samples collected at randomisation in patients with AF without OAC in the ACTIVE A and AVERROES trials (n = 2974), and with OAC in the ARISTOTLE trial (n = 13 079). BMP10 was analysed with a prototype Elecsys immunoassay. Associations with outcomes were evaluated by Cox-regression models adjusted for clinical characteristics, kidney function, and N -terminal pro-B-type natriuretic peptide (NT-proBNP). Median concentrations of BMP10 were 2.47 and 2.44 ng/mL, in the non-OAC and OAC cohort, respectively. Increasing BMP10 was associated with lower body mass index, older age, female sex, kidney dysfunction, and AF rhythm. BMP10 was consistently associated with ischaemic stroke. In the non-OAC cohort, BMP10 increased the concordance index of the multivariable model from 0.713 to 0.733 (P = 0.004) and in the OAC cohort from 0.673 to 0.694 (P < 0.001). Additionally, BMP10 maintained a significant prognostic value after additionally adjusting for NT-proBNP. BMP10 was not independently associated with bleeding or with death. Conclusion The novel atrial biomarker BMP10 was independently associated with ischaemic stroke in patients with AF irrespective of OAC treatment. BMP10 seems to be more specifically related to the risk of ischaemic stroke in AF. One-sentence Summary In this study, BMP10 may be a novel specific biomarker of ischaemic stroke in patients with atrial fibrillation, irrespective of oral anticoagulation. [ABSTRACT FROM AUTHOR]