Use of sugar in coffee and tea and long-term risk of mortality in older adult Danish men: 32 years of follow-up from a prospective cohort study.

BACKGROUND: Tea and coffee are the most consumed beverages worldwide and very often sweetened with sugar. However, the association between the use of sugar in tea or coffee and adverse events is currently unclear. OBJECTIVES: To investigate the association between the addition of sugar to coffee or tea, and the risk of all-cause mortality, cardiovascular mortality, cancer mortality and incident diabetes mellitus. METHODS: Participants from the prospective Copenhagen Male Study, included from 1985 to 1986, without cardiovascular disease, cancer or diabetes mellitus at inclusion, who reported regular coffee or tea consumption were included. Self-reported number of cups of coffee and tea and use of sugar were derived from the study questionnaires. Quantity of sugar use was not reported. Primary outcome was all-cause mortality and secondary endpoints were cardiovascular mortality, cancer mortality and incident diabetes mellitus, all assessed through the Danish national registries. The association between adding sugar and all-cause mortality was analyzed by Cox regression analysis. Age, smoking status, daily alcohol intake, systolic blood pressure, body mass index, number of cups of coffee and/or tea consumed per day and socioeconomic status were included as covariates. Vital status of patients up and until 22.03.2017 was assessed. Sugar could be added to either coffee, tea or both. RESULTS: In total, 2923 men (mean age at inclusion: 63±5 years) were included, of which 1007 (34.5%) added sugar. In 32 years of follow-up, 2581 participants (88.3%) died, 1677 in the non-sugar group (87.5%) versus 904 in the sugar group (89.9%). Hazard ratio of the sugar group compared to the non-sugar group was 1.06 (95% CI 0.98;1.16) for all-cause mortality. An interaction term between number of cups of coffee and/or tea per day and adding sugar was 0.99 (0.96;1.01). A subgroup analysis of coffee-only drinkers showed a hazard ratio of 1.11 (0.99;1.26). The interaction term was 0.98 (0.94;1.02). Hazard ratios for the sugar group compared to the non-sugar group were 1.11 (95% CI 0.97;1.26) for cardiovascular disease mortality, 1.01 (95% CI 0.87;1.17) for cancer mortality and 1.04 (95% CI 0.79;1.36) for incident diabetes mellitus. CONCLUSION: In the present population of Danish men, use of sugar in tea and/or coffee was not significantly associated with increased risk of mortality or incident diabetes.

Fewer Worsening Heart Failure Events With HeartLogic on top of Standard Care: a Propensity-Matched Cohort Analysis.

BACKGROUND: The implantable cardiac defibrillator-based HeartLogic algorithm aims to detect impending fluid retention in patients with heart failure (HF). Studies show that HeartLogic is safe to integrate into clinical practice. The current study investigates whether HeartLogic provides clinical benefit on top of standard care and device telemonitoring in patients with HF. METHODS: A multicenter, retrospective, propensity-matched cohort analysis was performed in patients with HF and implantable cardiac defibrillators, and it compared HeartLogic to conventional telemonitoring. The primary endpoint was the number of worsening HF events. Hospitalizations and ambulatory visits due to HF were also evaluated. RESULTS: Propensity score matching yielded 127 pairs (median age 68 years, 80% male). Worsening HF events occurred more frequently in the control group (2; IQR 0-4) compared to the HeartLogic group (1; IQR 0-3; P = 0.004). The number of HF hospitalization days was higher in controls than in the HeartLogic group (8; IQR 5-12 vs 5; IQR 2-7; P = 0.023), and ambulatory visits for diuretic escalation were more frequent in the control group than in the HeartLogic group (2; IQR 0-3 vs 1; IQR 0-2; P = 0.0001). CONCLUSION: Integrating the HeartLogic algorithm in a well-equipped HF care path on top of standard care is associated with fewer worsening HF events and shorter duration of fluid retention-related hospitalizations.

Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study.

AIMS: Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery. METHODS AND RESULTS: We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27). CONCLUSION: Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research.

Performance of a HeartLogicTM Based Care Path in the Management of a Real-World Chronic Heart Failure Population.

Aim: Early detection of impending fluid retention and timely adjustment of (medical) therapy can prevent heart failure related hospitalizations. The multisensory cardiac implantable electronic device (CIED) based algorithm HeartLogicTM aims to alert in case of impending fluid retention. The aim of the current analysis is to evaluate the performance of the HeartLogicTM guided heart failure care path in a real-world heart failure population and to investigate whether the height of the index and the duration of the alert state are indicative of the degree of fluid retention. Methods: Consecutive adult heart failure patients with a CIED and an activated HeartLogicTM algorithm were eligible for inclusion. Patients were followed up according to the hospital's heart failure care path. The device technician reviewed alerts for a technical CIED checkup. Afterwards, the heart failure nurse contacted the patient to identify impending fluid retention. An alert was either true positive or false positive. Without an alert a patient was true negative or false negative. Results: Among 107 patients, [82 male, 70 (IQR 60-77) years, left ventricular ejection fraction 37 ± 11%] 130 HeartLogicTM alerts were available for analysis. Median follow up was 14 months [IQR 8-23]. The sensitivity to detect impending fluid retention was 79%, the specificity 88%. The positive predictive was value 71% and the negative predictive value 91%. The unexplained alert rate was 0.23 alerts/patient year and the false negative rate 0.17 alerts/patient year. True positive alerts [42 days (IQR 28-63)] lasted longer than false positive alerts [28 days (IQR 21-44)], p = 0.02. The maximal HeartLogicTM index was higher in true positive alerts [26 (IQR 21-34)] compared to false positive alerts [19 (IQR 17-24)], p < 0.01. Patients with higher HeartLogicTM indexes required more intense treatment (index height in outpatient setting 25 [IQR 20-32], day clinic treatment 28 [IQR 24-36] and hospitalized patients 45 [IQR 35-58], respectively), p < 0.01. Conclusion: The CIED-based HeartLogicTM algorithm facilitates early detection of impending fluid retention and thereby enables clinical action to prevent this at early stage. The current analysis illustrates that higher and persistent alerts are indicative for true positive alerts and higher index values are indicative for more severe fluid retention.

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate. OBJECTIVE: The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up-for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient's chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates. METHODS: Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size. RESULTS: A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I2 of 61.9, and therefore it was decided to not pool the results into a meta-analysis. CONCLUSIONS: Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.

Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM.

Heart failure (HF) hospitalisations due to decompensation are associated with shorter life expectancy and lower quality of life. These hospitalisations pose a significant burden on the patients, doctors and healthcare resources. Early detection of an upcoming episode of decompensation may facilitate timely optimisation of the ambulatory medical treatment and thereby prevent heart-failure-related hospitalisations. The HeartLogicTM algorithm combines data from five sensors of cardiac implantable electronic devices into a cumulative index value. It has been developed for early detection of fluid retention in heart failure patients. This review aims to provide an overview of the current literature and experience with the HeartLogicTM algorithm, illustrate how the index can be implemented in daily clinical practice and discuss ongoing studies and potential future developments of interest.

Clinical and economic impact of HeartLogic™ compared with standard care in heart failure patients.

AIMS: The implantable cardiac defibrillator/cardiac resynchronization therapy with defibrillator-based HeartLogic™ algorithm has recently been developed for early detection of impending decompensation in heart failure (HF) patients; but whether this novel algorithm can reduce HF hospitalizations has not been evaluated. We investigated if activation of the HeartLogic algorithm reduces the number of hospital admissions for decompensated HF in a 1 year post-activation period as compared with a 1 year pre-activation period. METHODS AND RESULTS: Heart failure patients with an implantable cardiac defibrillator/cardiac resynchronization therapy with defibrillator with the ability to activate HeartLogic and willingness to have remote device monitoring were included in this multicentre non-blinded single-arm trial with historical comparison. After a HeartLogic alert, the presence of HF symptoms and signs was evaluated. If there were two or more symptoms and signs apart from the HeartLogic alert, lifestyle advices were given and/or medication was adjusted. After activation of the algorithm, patients were followed for 1 year. HF events occurring in the 1 year prior to activation and in the 1 year after activation were compared. Of the 74 eligible patients (67.2 ± 10.3 years, 84% male), 68 patients completed the 1 year follow-up period. The total number of HF hospitalizations reduced from 27 in the pre-activation period to 7 in the post-activation period (P = 0.003). The number of patients hospitalized for HF declined from 21 to 7 (P = 0.005), and the hospitalization length of stay diminished from average 16 to 7 days (P = 0.079). Subgroup analysis showed similar results (P = 0.888) for patients receiving cardiac resynchronization therapy during the pre-activation period or not receiving cardiac resynchronization therapy, meaning that the effect of hospitalizations cannot solely be attributed to reverse remodelling. Subanalysis of a single-centre Belgian subpopulation showed important reductions in overall health economic costs (P = 0.025). CONCLUSION: Activation of the HeartLogic algorithm enables remote monitoring of HF patients, coincides with a significant reduction in hospitalizations for decompensated HF, and results in health economic benefits.

ESC working group on e-cardiology position paper: use of commercially available wearable technology for heart rate and activity tracking in primary and secondary cardiovascular prevention-in collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee.

Commercially available health technologies such as smartphones and smartwatches, activity trackers and eHealth applications, commonly referred to as wearables, are increasingly available and used both in the leisure and healthcare sector for pulse and fitness/activity tracking. The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate (HR) and activity tracking, in clinical cardiovascular healthcare to support their implementation into clinical care. The widespread use of HR and fitness tracking technologies provides unparalleled opportunities for capturing physiological information from large populations in the community, which has previously only been available in patient populations in the setting of healthcare provision. The availability of low-cost and high-volume physiological data from the community also provides unique challenges. While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary and secondary prevention. Technical aspects of HR tracking especially during activity need to be further validated. How to analyse, translate, and interpret large datasets of information into clinically applicable recommendations needs further consideration. While the current users of wearable technologies tend to be young, healthy and in the higher sociodemographic strata, wearables could potentially have a greater utility in the elderly and higher-risk population. Wearables may also provide a benefit through increased health awareness, democratization of health data and patient engagement. Use of continuous monitoring may provide opportunities for detection of risk factors and disease development earlier in the causal pathway, which may provide novel applications in both prevention and clinical research. However, wearables may also have potential adverse consequences due to unintended modification of behaviour, uncertain use and interpretation of large physiological data, a possible increase in social inequality due to differential access and technological literacy, challenges with regulatory bodies and privacy issues. In the present position paper, current applications as well as specific barriers and gaps in knowledge are identified and discussed in order to support the implementation of wearable technologies from gadget-ology into clinical cardiology.

Potential of eHealth smart technology in optimization and monitoring of heart failure treatment in adults with systemic right ventricular failure.

Aims: Patients with a systemic right ventricle (sRV) in the context of transposition of the great arteries (TGA) after atrial switch or congenitally corrected TGA are prone to heart failure and arrhythmias. This study evaluated feasibility, patient adherence, and satisfaction of a smart technology-based care pathway for heart failure treatment optimization in these patients. Methods and results: Patients with symptomatic sRV failure eligible for initiation of sacubitril/valsartan were provided with four smartphone compatible devices (blood pressure monitor, weight scale, step counter, and rhythm monitor) and were managed according to a smart technology-based care pathway. Biweekly sacubitril/valsartan titration visits were replaced by electronical visits, patients were advised to continue measurements at least weekly after titration. Data of 24 consecutive sRV patients (median age 47 years, 50% female) who participated in the smart technology-based care pathway were analysed. Median home-hospital distance was 65 km (maximum 227 km). Most patients (20, 83.3%) submitted weekly measurements; 100% submitted prior to electronical visits. Titration conventionally occurs during a hospital visit. By implementing eHealth smart technology, 68 such trips to hospital were replaced by virtual visits facilitated by remote monitoring. An eHealth questionnaire was completed by 22 patients (92%), and 96% expressed satisfaction. After titration, 30 instances of remote adjustment of heart failure medication in addition to scheduled outpatient clinic visits occurred, one (4%) heart failure admission followed, despite ambulant adjustments. Five patients (21%) sent in rhythm registrations (n = 17), of these 77% showed sinus rhythm, whereas supraventricular tachycardia was detected in the remaining four registrations. Conclusion: These data suggest that implementation of a smart technology-based care pathway for optimization of medical treatment sRV failure is feasible with high measurement adherence and patient satisfaction.

Continuum of Care: Positioning of the Virtual Hospital.

Introduction: Patients with multiple chronic diseases suffer from reduced life expectancy. Care for these patients is often divided over multiple healthcare professionals. eHealth might help to integrate care for these patients and create a continuum. It is the primary purpose of this paper to describe an intervention that integrates first, second, and third line care in patients with multiple chronic conditions using remote monitoring, remote therapy and data automatization, all integrated in a virtual care center (VCC). Methods: Patients diagnosed with three or more chronic conditions are included and given smartphone compatible devices for remote monitoring and a tablet for video consultations. Patients will be followed-up by the VCC, consisting of nurses who will coordinate care, supervised by general practitioners and medical specialists. Data is reviewed on a daily basis and patients are contacted on a weekly basis. Review of data is automated by computer algorithms. Patients are contacted in case of outcome abnormalities in the data. Patients can contact the VCC at any time. Follow-up of the study is 1 year. Results: The primary outcome of this study is the median number of nights admitted to the hospital per patient compared to the hospitalization data 12 months before enrolment. Secondary outcomes include all-cause mortality, event free survival, quality of life and satisfaction with technology and care. Conclusion: This study presents the concept of a VCC that integrates first, second, and third line care into a virtual ward using remote monitoring and video consultation.

Telemonitoring for Patients With COVID-19: Recommendations for Design and Implementation.

Despite significant efforts, the COVID-19 pandemic has put enormous pressure on health care systems around the world, threatening the quality of patient care. Telemonitoring offers the opportunity to carefully monitor patients with a confirmed or suspected case of COVID-19 from home and allows for the timely identification of worsening symptoms. Additionally, it may decrease the number of hospital visits and admissions, thereby reducing the use of scarce resources, optimizing health care capacity, and minimizing the risk of viral transmission. In this paper, we present a COVID-19 telemonitoring care pathway developed at a tertiary care hospital in the Netherlands, which combined the monitoring of vital parameters with video consultations for adequate clinical assessment. Additionally, we report a series of medical, scientific, organizational, and ethical recommendations that may be used as a guide for the design and implementation of telemonitoring pathways for COVID-19 and other diseases worldwide.

Use of Smart Technology for the Early Diagnosis of Complications After Cardiac Surgery: The Box 2.0 Study Protocol.

BACKGROUND: Atrial fibrillation (AF), sternal wound infection, and cardiac decompensation are complications that can occur after cardiac surgery. Early detection of these complications is clinically relevant, as early treatment is associated with better clinical outcomes. Remote monitoring with the use of a smartphone (mobile health [mHealth]) might improve the early detection of complications after cardiac surgery. OBJECTIVE: The primary aim of this study is to compare the detection rate of AF diagnosed with an mHealth solution to the detection rate of AF diagnosed with standard care. Secondary objectives include detection of sternal wound infection and cardiac decompensation, as well as assessment of quality of life, patient satisfaction, and cost-effectiveness. METHODS: The Box 2.0 is a study with a prospective intervention group and a historical control group for comparison. Patients undergoing cardiac surgery at Leiden University Medical Center are eligible for enrollment. In this study, 365 historical patients will be used as controls and 365 other participants will be asked to receive either The Box 2.0 intervention consisting of seven home measurement devices along with a video consultation 2 weeks after discharge or standard cardiac care for 3 months. Patient information will be analyzed according to the intention-to-treat principle. The Box 2.0 devices include a blood pressure monitor, thermometer, weight scale, step count watch, single-lead electrocardiogram (ECG) device, 12-lead ECG device, and pulse oximeter. RESULTS: The study started in November 2018. The primary outcome of this study is the detection rate of AF in both groups. Quality of life is measured with the five-level EuroQol five-dimension (EQ-5D-5L) questionnaire. Cost-effectiveness is calculated from a society perspective using prices from Dutch costing guidelines and quality of life data from the study. In the historical cohort, 93.9% (336/358) completed the EQ-5D-5L and patient satisfaction questionnaires 3 months after cardiac surgery. CONCLUSIONS: The rationale and design of a study to investigate mHealth devices in postoperative cardiac surgery patients are presented. The first results are expected in September 2020. TRIAL REGISTRATION: ClinicalTrials.gov NCT03690492; http://clinicaltrials.gov/show/NCT03690492. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16326.

Effect of Smartphone-Enabled Health Monitoring Devices vs Regular Follow-up on Blood Pressure Control Among Patients After Myocardial Infarction: A Randomized Clinical Trial.

Importance: Smart technology via smartphone-compatible devices might improve blood pressure (BP) regulation in patients after myocardial infarction. Objectives: To investigate whether smart technology in clinical practice can improve BP regulation and to evaluate the feasibility of such an intervention. Design, Setting, and Participants: This study was an investigator-initiated, single-center, nonblinded, feasibility, randomized clinical trial conducted at the Department of Cardiology of the Leiden University Medical Center between May 2016 and December 2018. Two hundred patients, who were admitted with either ST-segment elevation myocardial infarction or non-ST-segment acute coronary syndrome, were randomized in a 1:1 fashion between follow-up groups using smart technology and regular care. Statistical analysis was performed from January 2019 to March 2019. Interventions: For patients randomized to regular care, 4 physical outpatient clinic visits were scheduled in the year following the initial event. In the intervention group, patients were given 4 smartphone-compatible devices (weight scale, BP monitor, rhythm monitor, and step counter). In addition, 2 in-person outpatient clinic visits were replaced by electronic visits. Main Outcomes and Measures: The primary outcome was BP control. Secondary outcomes, as a parameter of feasibility, included patient satisfaction (general questionnaire and smart technology-specific questionnaire), measurement adherence, all-cause mortality, and hospitalizations for nonfatal adverse cardiac events. Results: In total, 200 patients (median age, 59.7 years [interquartile range, 52.9-65.6 years]; 156 men [78%]) were included, of whom 100 were randomized to the intervention group and 100 to the control group. After 1 year, 79% of patients in the intervention group had controlled BP vs 76% of patients in the control group (P = .64). General satisfaction with care was the same between groups (mean [SD] scores, 82.6 [14.1] vs 82.0 [15.1]; P = .88). The all-cause mortality rate was 2% in both groups (P > .99). A total of 20 hospitalizations for nonfatal adverse cardiac events occurred (8 in the intervention group and 12 in the control group). Of all patients, 32% sent in measurements each week, with 63% sending data for more than 80% of the weeks they participated in the trial. In the intervention group only, 90.3% of patients were satisfied with the smart technology intervention. Conclusions and Relevance: These findings suggest that smart technology yields similar percentages of patients with regulated BP compared with the standard of care. Such an intervention is feasible in clinical practice and is accepted by patients. More research is mandatory to improve patient selection of such an intervention. Trial Registration: ClinicalTrials.gov Identifier: NCT02976376.

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs. METHODS: We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization. RESULTS: Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively). CONCLUSIONS: Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change.

eHealth to improve patient outcome in rehabilitating myocardial infarction patients.

INTRODUCTION: Cardiac rehabilitation is aimed at risk factor modification and improving quality of life. eHealth has a couple of potential benefits to improve this aim. The primary purpose of this review is to summarize available literature for eHealth strategies that have been investigated in randomized controlled trials in post-myocardial infarction (MI) patients. The second purpose of this review is to investigate the clinical effectiveness in post-MI patients. Areas covered: The literature was searched using PubMed. Randomized controlled trials (RCTs) describing interventions in patients that had experienced an ST-elevation myocardial infarction or non-ST acute coronary syndrome were eligible for inclusion. Fifteen full-texts were included and their results are described in this review. These RCTs described interventions that used remote coaching or remote monitoring in post-MI patients. Most interventions resulted in an improved cardiovascular risk profile. Remote coaching had a positive effect on activity and dietary intake. Expert opinion: eHealth might be clinically beneficial in post-MI patients, particularly for risk estimation. Moreover, eHealth as a tool for remote coaching on activity is a good addition to traditional cardiac rehabilitation programs. Further research needs to corroborate these findings.

Implementation of smart technology to improve medication adherence in patients with cardiovascular disease: is it effective?

INTRODUCTION: Medication adherence is of key importance in the treatment of cardiovascular disease. Studies consistently show that a substantial proportion of patients is non-adherent. AREAS COVERED: For this review, telemedicine solutions that can potentially improve medication adherence in patients with cardiovascular disease were reviewed. A total of 475 PubMed papers were reviewed, of which 74 were assessed. EXPERT COMMENTARY: Papers showed that evidence regarding telemedicine solutions is mostly conflictive. Simple SMS reminders might work for patients who do not take their medication because of forgetfulness. Educational interventions and coaching interventions, primarily delivered by telephone or via a web-based platform can be effective tools to enhance medication adherence. Finally, it should be noted that current developments in software engineering may dramatically change the way non-adherence is addressed in the nearby future.

Comparison of the diagnostic accuracy of four smartphone-compatible blood pressure monitors in post-myocardial infarction patients.

Introduction Smartphone-compatible blood pressure devices may be a good alternative to enable self-measurement of blood pressure by patients. Furthermore, automatic transferral of data to the hospital allows for remote monitoring. To our knowledge, no study has compared four of these smartphone-compatible blood pressure devices. Methods Patients who were followed up for acute myocardial infarction were asked to participate during their outpatient clinic visit. After five minutes of rest, six blood pressure devices were applied. The order was randomised. Four devices were smartphone-compatible. One device was an automated oscillometric device. One device was a handheld aneroid sphygmomanometer (reference device). All measurements were compared using a linear mixed model. Results A total of 43 patients (62.7 ± 11.3 years, 79% male) were included. Compared to the reference device, four blood pressure monitors yielded a significant higher mean systolic blood pressure and four monitors yielded a significant higher diastolic BP. One device yielded a non-significant lower mean systolic blood pressure and one device yielded a non-significant higher mean diastolic blood pressure. Except for one blood pressure device, all mean differences were smaller than 5 mmHg. Conclusion In this study, average inter-device variability was shown to be statistically significant, however four devices remained within the predefined range of 5 mmHg for both systolic and diastolic blood pressures.

Mobile phones in cryptogenic strOke patients Bringing sIngle Lead ECGs for Atrial Fibrillation detection (MOBILE-AF): study protocol for a randomised controlled trial.

BACKGROUND: Recently published randomised clinical trials indicate that prolonged electrocardiom (ECG) monitoring might enhance the detection of paroxysmal atrial fibrillation (AF) in cryptogenic stroke or transient ischaemic attack (TIA) patients. A device that might be suitable for prolonged ECG monitoring is a smartphone-compatible ECG device (Kardia Mobile, Alivecor, San Francisco, CA, USA) that allows the patient to record a single-lead ECG without the presence of trained health care staff. The MOBILE-AF trial will investigate the effectiveness of the ECG device for AF detection in patients with cryptogenic stroke or TIA. In this paper, the rationale and design of the MOBILE-AF trial is presented. METHODS: For this international, multicentre trial, 200 patients with cryptogenic stroke or TIA will be randomised. One hundred patients will receive the ECG device and will be asked to record their ECG twice daily during a period of 1 year. One hundred patients will receive a 7-day Holter monitor. DISCUSSION: The primary outcome of this study is the percentage of patients in which AF is detected in the first year after the index ischaemic stroke or TIA. Secondary outcomes include markers for AF prediction, orally administered anticoagulation therapy changes, as well as the incidence of recurrent stroke and major bleeds. First results can be expected in mid-2019. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT02507986 . Registered on 15 July 2015.

Intra-individual ECG changes over 25 years: How long can elective ECGs be used as reference for acute ischemia detection?

BACKGROUND: The guidelines advocate, in patients with chest pain, comparison of the acute ECG with a previously made, non-ischemic ECG that serves as a reference, to corroborate the working diagnosis of acute coronary syndrome (ACS). Our approach of this serial comparison is to compute the differences between the ST vectors at the J point and 60 ms thereafter (∆ST(J+0), ∆ST(J+60)) and between the ventricular gradient (VG) vectors (∆VG). In the current study, we investigate if reference ECGs remain valid in time. METHODS: We studied 6 elective non-ischemic ECGs (ECG0, ECG1, …, ECG5), 5 years apart, in 88 patients. Within each patient, serial comparisons were done 1) between all successive ECGs, and 2) between each of ECG1, ECG2, …, ECG5 and ECG0, computing, in addition to ∆ST(J+0), ∆ST(J+60) and ∆VG, the difference in heart rates, ∆HR. Additionally, relevant clinical events and the diagnoses associated with each ECG were collected. Linear regression was used to assess trends in ∆ST(J+0), ∆ST(J+60) and ∆VG; multiple linear regression was used to assess the influence of the clinical events and diagnoses on ∆ST(J+0), ∆ST(J+60) and ∆VG. RESULTS: There were no trends in the differences between successive ECGs. Positive trends were seen with increasing time lapses between ECGs: ∆ST(J+0), ∆ST(J+60) and ∆VG increased per year by 0.65 µV, 1.45 µV and 3.69 mV∙ms, respectively. Extrapolation to a time lapse of 0 yielded 50.92 µV, 36.63 µV and 20.91 mV∙ms for the short-term reproducibility of ∆ST(J+0), ∆ST(J+60) and ∆VG, respectively. Multiple linear regression revealed that clinical variables could explain only 10%, 17% and 13% of the variability in ∆ST(J+0), ∆ST(J+60) and ∆VG, respectively. CONCLUSION: With a view on ischemia detection thresholds in the order of magnitude of 58 µV for ∆ST and 26 mV·ms for ∆VG, our study suggests that it is important to have a recent ECG available for the detection of myocardial ischemia, as an "aged" ECG may have lost its validity as a reference.

Performance of ST and ventricular gradient difference vectors in electrocardiographic detection of acute myocardial ischemia.

INTRODUCTION: Serial analysis could improve ECG diagnosis of myocardial ischemia caused by acute coronary occlusion. METHODS: We analyzed ECG pairs of 84 cases and 398 controls. In case-patients, who underwent elective percutaneous coronary intervention, ischemic ECGs during balloon occlusion were compared with preceding non-ischemic ECGs. In control-patients, two elective non-ischemic ECGs were compared. In each ECG the ST vector at the J point and the ventricular gradient (VG) vector was computed, after which difference vectors ΔST and ΔVG were computed within patients. Finally, receiver operating characteristic analysis was done. RESULTS: Areas under the curve were 0.906 (P<0.001; CI 0.862-0.949; SE 0.022) for ΔST and 0.880 (P<0.001; CI 0.833-0.926; SE 0.024) for ΔVG. Sensitivity and specificity of conventional ST-elevation myocardial infarction (STEMI) criteria were 70.2% and 89.1%, respectively. At matched serial analysis specificity and STEMI specificity, serial analysis sensitivity was 78.6% for ΔST and 71.4% for ΔVG (not significantly different from STEMI sensitivity). At matched serial analysis sensitivity and STEMI sensitivity, serial analysis specificity was 96.5% for ΔST and 89.3% for ΔVG; ΔST and STEMI specificities differed significantly (P<0.001). CONCLUSION: Detection of acute myocardial ischemia by serial ECG analysis of ST and VG vectors has equal or even superior performance than the STEMI criteria. This concept should be further evaluated in triage ECGs of patients suspected from having acute myocardial ischemia.