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

An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.

Mobile health for cardiovascular risk management after cardiac surgery: results of a sub-analysis of The Box 2.0 study

Aims

Lowering low-density lipoprotein (LDL-C) and blood pressure (BP) levels to guideline recommended values reduces the risk of major adverse cardiac events in patients who underwent coronary artery bypass grafting (CABG). To improve cardiovascular risk management, this study evaluated the effects of mobile health (mHealth) on BP and cholesterol levels in patients after standalone CABG.

Methods and results

This study is a post hoc analysis of an observational cohort study among 228 adult patients who underwent standalone CABG surgery at a tertiary care hospital in The Netherlands. A total of 117 patients received standard care, and 111 patients underwent an mHealth intervention. This consisted of frequent BP and weight monitoring with regimen adjustment in case of high BP. Primary outcome was difference in systolic BP and LDL-C between baseline and value after three months of follow-up. Mean age in the intervention group was 62.7 years, 98 (88.3%) patients were male. A total of 26 449 mHealth measurements were recorded. At three months, systolic BP decreased by 7.0 mmHg [standard deviation (SD): 15.1] in the intervention group vs. -0.3 mmHg (SD: 17.6; P < 0.00001) in controls; body weight decreased by 1.76 kg (SD: 3.23) in the intervention group vs. -0.31 kg (SD: 2.55; P = 0.002) in controls. Serum LDL-C was significantly lower in the intervention group vs. controls (median: 1.8 vs. 2.0 mmol/L; P = 0.0002).

Conclusion

This study showed an association between home monitoring after CABG and a reduction in systolic BP, body weight, and serum LDL-C. The causality of the association between the observed weight loss and decreased LDL-C in intervention group patients remains to be investigated.

Relationship between Complete Revascularization and Survival after Post-Infarction Ventricular Septal Rupture

INTRODUCTION

A well-known and fatal complication of myocardial infarction (MI) is post-infarction ventricular septal rupture (VSR). The benefits and risks associated with coronary angiography and subsequent coronary artery bypass grafting in these patients have sparked controversy. The aim of this study was to determine the outcome of revascularization following MI.

METHOD

Patients aged between 55 and 78 years were considered for the post-infarction ventricular septal rupture from 2011 to 2017. Factors such as age, sex, anthropometric measurements, systolic and diastolic blood pressure (SBP and DBP), and biochemical parameters like CPK-MB, cholesterol, low-density lipoprotein, high-density lipoprotein, and triglycerides were measured using standard methods.The estimated Glomerular Filtration Rate (eGFR), a measure of kidney function, was also determined. Additionally, coronary angiographic factors including ECG changes, left ventricular (LV) systolic function, right ventricular (RV) function, Pulmonary Artery Pressure (PAP), proximal coronary lesions in VSR, systolic PAP, Right Atrial Pressure (RAP), and mortality rate were determined.

RESULTS

The study enrolled a total of 81 patients who had been surgically treated for post-infarction VSR. These patients were divided into two groups: survivors (n=35) and non-survivors (N=41). The mean systolic and diastolic blood pressure was higher in the survivor group (115.3 ± 18.7 vs. 96.3 ± 25.3 and 74.6 ± 12.2 vs. 61.2 ± 19.0, P=0.001). PCI was performed in 2.9% of survivors and 9.8% of non-survivors. Angiographic data revealed that 17 (33%) and 33 (63%) patients had single and multiple coronary artery diseases, respectively. CPK-MB levels were significantly higher in the non-survivors group (P<0.05). Echocardiographic findings, including LV ejection fraction, RV ejection fraction, systolic PAP, and the anatomic location of VSR, did not significantly differ between survivors and non-survivors.

CONCLUSION

Based on these findings, it is recommended to avoid complete revascularization during surgical repair of post-infarction ventricular septal rupture, as it would not improve the outcome.

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.

Use of a mobile health application by adult non-congenital cardiac surgery patients: A feasibility study

Mobile Health (mHealth) technologies are becoming integral to our healthcare system. This study evaluated the feasibility (compliance, usability and user satisfaction) of a mHealth application (app) for delivering Enhanced Recovery Protocols (ERPs) information to Cardiac Surgery (CS) patients peri-operatively. This single centre, prospective cohort study involved patients undergoing CS. Patients received a mHealth app developed for the study at consent and for 6-8 weeks post-surgery. Patients completed system usability, patient satisfaction and quality of life surveys pre- and post-surgery. A total of 65 patients participated in the study (mean age of 64 years). The app achieved an overall utilization rate of 75% (68% vs 81% for <65 and ≥65 years respectively). Pre-surgery, the majority of patients found the app easy to use (94%), user-friendly (89%), and felt confident using the app (92%). The majority also found the app's educational information useful (90%) and easy to find (88%). 75% of patients reported that they would like to use the app frequently. This percentage decreased to 57% in the post-discharge survey. A lower percentage of patients ≥65 years indicated their preference for the app over printed information (51% vs 87%) and their recommendation for the app (84% vs 100% for >65 and <65 years respectively) in the post-surgery survey. MHealth technology is feasible for peri-operative CS patient education, including older adult patients. The majority of patients were satisfied with the app and would recommend using it over the use of printed materials.

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.

Telemedicine in Cardiology: Modern Technologies to Improve Cardiovascular Patients’ Outcomes—A Narrative Review

The registration of physical signals has long been an important part of cardiological diagnostics. Current technology makes it possible to send large amounts of data to remote locations. Solutions that enable diagnosis and treatment without direct contact with patients are of enormous value, especially during the COVID-19 outbreak, as the elderly require special protection. The most important examples of telemonitoring in cardiology include the use of implanted devices such as pacemakers and defibrillators, as well as wearable sensors and data processing units. The arrythmia detection and monitoring patients with heart failure are the best studied in the clinical setting, although in many instances we still lack clear evidence of benefits of remote approaches vs. standard care. Monitoring for ischemia is less well studied. It is clear however that the economic and organizational gains of telemonitoring for healthcare systems are substantial. Both patients and healthcare professionals have expressed an enormous demand for the further development of such technologies. In addition to these subjects, in this paper we also describe the safety concerns associated with transmitting and storing potentially sensitive personal data.

The Box: Methods and Results of a Real World Experience of mHealth Implementation in Clinical Practice

Background

Mobile health (mHealth) is an emerging field of scientific interest worldwide. Potential benefits include increased patient engagement, improved clinical outcomes, and reduced health care costs. However, mHealth is often studied in projects or trials, and structural implantation in clinical practice is less common.

Objective

The purpose of this paper is to outline the design of the Box and its implementation and use in an outpatient clinic setting. The impact on logistical outcomes and patient and provider satisfaction is discussed.

Methods

In 2016, an mHealth care track including smartphone-compatible devices, named the Box, was implemented in the cardiology department of a tertiary medical center in the Netherlands. Patients with myocardial infarction, rhythm disorders, cardiac surgery, heart failure, and congenital heart disease received devices to measure daily weight, blood pressure, heart rate, temperature, and oxygen saturation. In addition, professional and patient user comments on the experience with the care track were obtained via structured interviews.

Results

From 2016 to April 2020, a total of 1140 patients were connected to the mHealth care track. On average, a Box cost €350 (US $375), not including extra staff costs. The median patient age was 60.8 (IQR 52.9-69.3) years, and 73.59% (839/1140) were male. A median of 260 (IQR 105-641) measurements was taken on a median of 189 (IQR 98-372) days. Patients praised the ease of use of the devices and felt more involved with their illness and care. Professionals reported more productive outpatient consultations as well as improved insight into health parameters such as blood pressure and weight. A feedback loop from the hospital to patient to focus on measurements was commented as an important improvement by both patients and professionals.

Conclusions

In this study, the design and implementation of an mHealth care track for outpatient follow-up of patients with various cardiovascular diseases is described. Data from these 4 years indicate that mHealth is feasible to incorporate in outpatient management and is generally well-accepted by patients and providers. Limitations include the need for manual measurement data checks and the risk of data overload. Moreover, the tertiary care setting in which the Box was introduced may limit the external validity of logistical and financial end points to other medical centers. More evidence is needed to show the effects of mHealth on clinical outcomes and on cost-effectiveness.