Utilisation of Digital Monitoring System for Heart Failure Patients: Experience from a Tertiary Healthcare in Bangladesh

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Digital monitoring (DM) approaches aim to improve the early detection of HF decompensations and prevent readmissions. However, knowledge about the impact of telemonitoring on preventing readmissions and mortality. The study aimed to test the feasibility of implementing a digital care platform in a HF clinic and assess its potential benefits to our HF patients with various comorbidities. Methods We conducted a prospective study at United Hospital from April to October, 2024. The DM platform for patient-reported symptoms and weight and blood pressure measurements, phone calls with secondary care nurses, and rapid response to alerts by treating physicians. DM solution data were linked to patient register data onhospital registry. Eligible patients had at least 1 visit the clinic within the last 12 months and self-reported New York Heart Association class I-IV. We excluded patients with terminal renal failure, cancer with a prognosis of 1 year or more, severe cognitive impairment, terminal cirrhosis, and patients who were at high risk of losing follow-up. Results A total of 1536 patients with HF were included in the analysis. The number of patients with at least one hospitalisation (all-cause) during the follow-up was much higher in the PV-groups than in the DM group (40% vs 10%; p = 0.01). The number of inpatient days per patient due to HF decreased by 48% during the DM period (mean 1.2, 95% CI 0.1–2.3 days vs 2.3, 95% CI 1-3.6 days with PV; P = 0.17). The number of emergency care visits decreased significantly during the DM period by 44% (mean 0.7, 95% CI 0.4-1 vs mean 1.3, 95% CI 0.9–1.7 with PV; P = 0.001). Conclusions The results suggest that the telemonitoring solution can reduce hospital-related costs for patients with HF with a recent hospital admission. Cardiac & Cardiovascular Systems Epidemiology heart failure telemonitoring hospital Bangladesh Figures Figure 1 Figure 2 Figure 3 Background The prevalence of heart failure (HF) and related costs is increasing worldwide due to an aging population [1]. The estimated prevalence of HF in the adult population is 1–2%, increasing to 10% in older adults [2,3]. HF often leads to gradual or acute changes in HF symptoms (decompensation) that require repeated and prolonged hospitalization [4]. Hospital admission is a strong predictor of further hospital admission: 20–25% of patients with HF are rehospitalized within 1 month and approximately 50% within 5 months of discharge [5]. Decompensation requiring hospitalization is also linked to increased mortality. A European registry study following patients for 1 year after hospitalization reported mortality rates of 24% for acute HF and 6.4% for chronic HF [6]. Hospitalization accounts for around 80% of HF healthcare costs [1]. An early return to the hospital following discharge may be a result of incomplete inpatient treatment and poor coordination and planning of follow-up care. Even for patients with regular follow-up care, however, the signs of decompensation may not occur during cardiology visits. Patients often contact clinics when symptoms are at an advanced stage [7]. Self-monitoring of symptoms, such as increased blood pressure, weight gain, or other health status-related symptoms, is particularly important in HF management [4]. Self-monitoring requires patients to be motivated to measure symptoms associated with HF and to have access to clinical advice when symptoms appear [8]. Remote monitoring aims to improve monitoring of patients’ health status and is defined as a part of DM [9]. A basic level of remote monitoring involves regular and structured telephone support provided by healthcare professionals to discuss symptoms, self-monitoring measurements, lifestyle, and drug therapy. Structured telephone support can reduce HF-related hospitalization but does not seem to have an impact on the all-cause hospitalization of patients with HF [10]. Remote monitoring solutions are non-invasive stand-alone systems in which patient data on biometric measurements (such as body weight, blood pressure, and heart rate) and reported symptoms are frequently transmitted to healthcare professionals through a secure digital system. HCPs manually review the data on digital platforms, which may also include integrated automated alerts, and necessary action is taken to optimize treatment. The effect of non-invasive digital monitoring (DM)has been compared to the physical visit (PV) in several studies, primarily through randomized trials. Some studies found DM had a beneficial impact on reducing hospitalization [11], while others did not find any effect [12,13]. A more recent meta-analysis, encompassing 91 randomized trials and observational studies, revealed that non-invasive telemonitoring reduced all-cause mortality by 16%, first hospitalization by 19%, and total HF hospitalizations by 15%. When comparing DMstudies and developing optimal DM approaches, it is crucial to consider various determinants, including the DM intervention models, health care systems, and the characteristics of the population with HF in the studies [14]. A number of studies from across the world, especially in developed countries explored the usefulness of DM [15–18]. Knowing the utilisation of DM may enable policymakers to enact appropriate policies to curb the increase in HF-related mortality and morbidity, allocate resources appropriately, and build healthcare systems that can cope effectively with expected increases in HF prevalence. In Bangladesh, there is no evidence to inform policymaking regarding telehealth utilisation in HF patients, which can further disallow discussions on different coping strategies management of cardiovascular diseases there is no single study has been conducted in Bangladesh. Therefore, the study aimed to test the feasibility of implementing an innovative DM in a natural healthcare environment and assess its potential benefits to our HF patients with various comorbidities. Methods Study design and settings This prospective cross-sectional study was conducted at HF unit, United Hospital, Dhaka, Bangladesh. The study hospital is the country’s one of the largest specialised hospitals where people come from across the country due to availability and potential access to better treatment options. Participants and survey procedures Individuals visited HF unit, and were receiving active treatment for HF. Patients visited HF unit at least 1 time and self-reported New York Heart Association (NYHA) class I-IV were eligible for the study. In terms of patients’ eligibility, the inclusion criteria also stated that patients must be able to manage the DM devices and digital platform used in the study. We excluded patients with terminal renal failure, cancer with a prognosis of 1 year or more, severe cognitive impairment, terminal cirrhosis, receiving palliative care and patients who were at high risk of losing follow-up. The study participants were consisted of two groups: (i) patients who utilised HF centre physically and (ii) patients who utilised DM. The intervention consisted of the following 3 components conducted by registered nurses/others: predischarge HF education, regularly scheduled telephone coaching, and home telemonitoring of weight, blood pressure, heart rate, and symptoms. The predischarge health education was conducted by a study nurse or other health carers who guided patients through a booklet developed for patients with medication adherence, salt avoidance, fluid monitoring, exercising with HF, and daily checkup of weight and edema, as well as when to call the HF treatment team. We primarily selected a total of 1765 data items for analysis. After eliminating incomplete and insufficient quality information, a total of 1536 data were selected for analytical exploration ( Fig. 1 ). Study Procedures Patients maintained regular cardiology appointments and laboratory tests planned by a cardiologist for each patient with HF according to local care guidelines for HF treatment. Nurses followed up with patients through phone calls, depending on the state of HF. During the follow-up period, patients measured their weight and blood pressure at home, and nurses followed up with patients through phone calls to discuss their health status and measurement results. DM was added consisted of a digital platform, home measurement devices, and nurses monitoring patients through the digital platform. The digital platform used United Hospital’s remote patient monitoring platform, customized for the study. Patients used their smartphones, handheld devices, or personal computers to access the digital platform. Patients measured their weight with a digital scale and their blood pressure were measured and transferred the measurements into the digital platform ( Fig. 2 ) . Depending on the health status, patient to contact a nurse or a nurse to contact the patient to validate the health status. If needed, nurses referred a patient to a cardiologist to optimize HF care or medication. The treating cardiologist reacted to the nurses’ referrals within 24 hours. For urgent alerts, the nurses advised patients to go to emergency care. Nurses also provided technical support for patients as required. The digital platform collected blood pressure data and laboratory results from regular health care visits, not for the alert algorithm but to allow nurses to evaluate the patient’s health status. A unique personal identification number for each patient in connected the patient registers data. All-cause hospitalizations included hospitalizations with any diagnosis. Sociodemographic and clinical characteristics We considered a number of clinical characteristics based on the registry (supplementary file 1). Sociodemographic characteristics included gender (male or female), age in years, and monthly income. Statistical analysis The study analysis included only patients who completed the study. Patient demographics and NYHA class were summarized as n (%) of patients per category or median (IQR). During the care and DM periods, n (%) of patients with at least 1 hospitalization and the mean number of inpatient days per patient (95% CI) were reported. The mean number (95% CI) of visits per patient (primary, secondary, and emergency) and the mean number of calls (primary and secondary care) per patient were also reported for each period. The normal distribution of each variable was assessed through visual inspection and the Shapiro-Wilk test. For data found to be nonnormally distributed, differences between hospital care and telemonitoring periods were tested using the Wilcoxon signed rank test, and a value of P < .05 was considered statistically significant. The Pearson chi-square test with Yates correction was used for testing the difference between hospital care and DM periods (a binary variable). Results Participant’s basic characteristics A total of 1536 patients with HF were included in our study. Of those, most were male (84%, n = 1292) and older than 55 years (62%, n = 949). Approximately 39%(n = 596) of the patients’ monthly income was more than 60 thousand Bangladesh Taka. The basic characteristics of the participants are presented in Tabe 1. Table 1 Patients’ basic characteristics (N = 1536) Characteristics Frequency Percentage Gender Male 1292 84.11 Female 244 15.89 Age in years (mean ± SD 53.2 ± 6.5) 55 949 61.78 Monthly Income (BDT) 60000 596 38.80 Participant’s clinical characteristics According to the classification of the New York Heart Association (NYHA), most of patients (85%, n = 1309) with HF were classified as class II followed by class III (9%, n = 145). In terms of ECG, 1466 patients had rhythm reports where 98% (n = 1440) had normal heart rhythm (sinus). In total, 321 subjects had Bundle Brunch Block (BBB) reports, which included 71%who had LBBB, followed by 28% who had RBBB. A total of 35 patients had a history of heart block, and 94% of them had first-degree blocks. A total of 1449 patients had reports on ECG QRS duration (milli second) while 941 (65%) of them had an ECG QRS duration of less than 130 milliseconds followed by 318 (22%) had > 150 milliseconds. Echo reports showed 1529 patients with left ventricular internal diameter end diastole (LVIDD) measurements, with the majority of them (45%, n = 688) having an LVIDD of 51–60 millimetres. Nevertheless, 1526 patients had left ventricular internal diameter end-systole (LVIDs) measurements, with the majority (38%, n = 573) having 41–50 mm LVIDs. A total of 994 patients' pulmonary artery systolic pressures (PASPs) were measured, and the majority (53%, n = 527) had a PASP of less than 30mm. Nearly two-thirds of patients (73%, n = 1123) had a history of Heart failure with reduced ejection fraction (HFrEF). Table 2 Patients’ clinical characteristics Variables Frequency Percentage The New York Heart Association (NYHA) classification (n = 1536) Class I 70 4.56 Class II 1309 85.22 Class III 145 9.44 Class IV 12 0.78 Electrocardiogram (ECG) Rhythm (n = 1466) Sinus 1440 98.23 Atrial Fibrillation (AF) 26 1.77 Bundle Branch Block (n = 321) Right Bundle Branch Block (RBBB) 89 27.73 Left Bundle Branch Block (LBBB) 228 71.03 Left anterior fascicular block (LAHB) 4 1.24 Heart Block (n = 35) First degree 33 94.29 Third degree 2 5.71 ECG QRS duration (milli second) (n = 1449) 150 ms 318 21.95 Echocardiogram (Echo) Left ventricular internal diameter end diastole (LVIDD) (in milli meter) (n = 1529) 30–40 mm 49 3.21 41–50 mm 348 22.76 51–60 mm 688 45.00 > 60 mm 444 29.04 Left ventricular internal diameter end systole (LVIDs) (in milli meter) (n = 1526) 20–30 mm 118 7.73 31–40 mm 383 25.10 41–50 mm 573 37.55 Out of Range (< 20) 452 29.62 Pulmonary artery systolic pressure (PASP) (in milli meter) (n = 994) 50 mm 117 11.77 Category of Heart Failure (n = 1536) Heart failure with reduced ejection fraction (HFrEF) 1123 73.11 Heart Failure with mid-range ejection fraction (HFmEF) 138 8.98 Heart failure with preserved ejection fraction (HFpEF) 108 7.03 Heart failure with improved ejection fraction (HFimpEF) 161 10.48 Others 6 0.39 Health care resource use Out of 1536 patients, 43 (3%) patients utilised DM while 1490 (97%) patients physically visited the centre (Fig. 3 ). The study found that the number of patients with at least one hospitalisation (all-cause) during the follow-up was much higher in the PV-groups than in the DM group (40% vs 10%; p = 0.01). The number of inpatient days per patient due to HF decreased by 48% during the DM period (mean 1.2, 95% CI 0.1–2.3 days vs 2.3, 95% CI 1-3.6 days with PV; P = 0.17). The number of emergency care visits decreased significantly during the DM period by 44% (mean 0.7, 95% CI 0.4-1 vs mean 1.3, 95% CI 0.9–1.7 with PV; P = 0.001). Table 3 Use of health care per patient in physical visit or digital monitoring solution. Variables PV DM Absolute change (relative change) p-value for differences All-cause hospitalizations (n = 49) Patients with at least1 event, n (%) 34(70.0) 15(30.0) -19(-40.0) 0.08 Inpatient days, mean (95% CI) 2.9(1.6) 1.7(1.2) -1.2(-41.0) 0.20 Hospitalizations for cardiovascular cause other than HF* (n = 12) Patients with ≥ 1 event, n (%) 7(58.0) 5(42.0) -2(-16.0) 0.70 Inpatient days, mean (95% CI) 0.2(0.3) 0.02(0.05) -0.16(-88.0) 0.40 Hospitalizations for HF (n = 37) Patients with ≥ 1 event, n (%) 21(57.0) 16(43.0) 5(11.0) 0.02 Mean inpatient days, days (95% CI) 2.3(1.3) 1.2(1.1) 1.1(-48.0) 0.17 Mean number of emergency care visit for cardiovascular causes, n (95% CI) 1.3(0.4) 0.7(0.3) -0.6(-44.0) 0.001 Mean number of secondary care visits (cardiology), n (95% CI) 1.8(0.5) 1.9(0.6) 0.1(+ 8.0) 0.80 Note Statistics were calculated with the Wilcoxon signed rank test or the Pearson chi-square test with Yates correction for the binary variables. Discussion In this study of a novel DM solution for patients with HF in a HF clinic in Bangladesh. The number of hospitalized patients was significantly lower during DM, and the mean length of stay decreased from 2.3 days to 1.2 days. The number of emergency visits was also significantly lower during DM. A comprehensive meta-analysis of recently published studies on DM has provided evidence that DM reduces mortality and hospitalizations in patients with HF [19]. On the other hand, individual studies show both beneficial and neutral effects of DM compared to PV [20–22]. The main objective of our study was to investigate whether DM can improve the detection of early signs of decompensation and decrease hospitalization and mortality. The variability of the results may be due to differences in the health care system, DM model, population with HF, and follow-up durations [9,14]. This study found that 47% of patients were hospitalized due to HF during the SOC period, versus 14% during the telemonitoring period. Vuorinen and colleagues [21] conducted a telemonitoring study and found that only 28% of patients were hospitalized in the SOC group and 17% in the remote monitoring group during a 6-month follow-up period. The inclusion criteria for the population with HF included NYHA II-IV. Still, they did not require a recent hospitalization, which could explain the lower HF hospitalization risk compared with this study. Our data align with previous studies showing that nearly half of patients with HF are rehospitalized within 6 months after discharge [21,22]. However, further studies using a similar population with HF are needed to confirm these findings. Like this study, Vuorinen et al [21] also found a non-statistically significant decrease in inpatient days with telemonitoring (mean 0.7 vs 1.4 days with SOC). The significant reduction in hospitalization-related costs and the number of patients hospitalized due to HF in this study supports the idea that telemonitoring reduces hospitalizations. A Spanish telemonitoring trial (n = 117) had similar findings to this study. In this trial, 50% of the patients were hospitalized in the SOC group versus 28% in the telemonitoring group over a 6-month follow-up period. The patients were enrolled in the study upon hospitalization [19]. Thus, the results from this study support our findings on telemonitoring's benefits in reducing hospitalizations. However, a large Better Effectiveness After Transition–Heart Failure (BEAT-HF) trial (n = 1437) conducted in California could not see a reduction in readmissions in patients with HF in a telemonitoring group compared to SOC during a 6-month follow-up period [20]. The BEAT-HF trial’s limitations where patients were recruited from academic medical centres. This may restrict the generalizability of the results, as most patients with HF do not seek care in academic medical centres. Upon receiving alerts, nurses advised patients to contact physicians, or nurses called the physicians, but physicians were not directly involved with the interventions. Thus, the monitoring may not have affected care in practice. In this study and the Spanish study, nurses and treating physicians collaborated upon receiving alerts, which may have increased the benefits of telemonitoring. For example, in this study, physicians reacted to patient alerts within 24 hours. Limitations and strength The study had some limitations. The study was conducted in a single tertiary care hospital in Dhak, which may limit the generalizability of the results. However, the study population is representative of the country, as all patients are directed to the same central hospital where recruitment was done. A randomized controlled trial design was not feasible due to the lack of resources and expertise. Patients were not randomized, and patients needed to be able to use the digital platform, which may have resulted in a possible selection bias. As the pre-post design uses a historical control group (i.e., patients on SOC in the period before starting telemonitoring), the underlying assumption in the analysis, given the deteriorating nature of HF, is that health care use in the absence of telemonitoring would remain at least at the same level as during SOC. Follow-up with telemonitoring was limited to 6 months, and it is unclear how use of health care services would develop beyond this period. Finally, due to the lack of direct medical and indirect cost of treatment, we had to calculate the cost based on prediction model. Therefore, the cost of this study is not representing actual fact. Despite those limitations, a strength of this study was the big sample size, which included both HF-related health care utilization. Conclusion In conclusion, our results suggest that the novel telemonitoring solution can help reduce hospital admissions and hospitalization costs as well as total healthcare costs in a population with HF with a recent hospital admission in the past 12 months. Further research should be conducted to validate these results in a larger and more diverse population with HF. Additionally, longitudinal studies can be carried out to assess the long-term effectiveness and sustainability of the telemonitoring solution in reducing hospital admissions and healthcare costs. The following must be considered when generalizing our results and applying our telemonitoring solution to other health care systems: our telemonitoring solution was applied to a patient population with a high risk of readmission due to a recent hospital admission. Declarations Data availability Data can be shared with the corresponding author upon request and for a valid reason. Funding We did not receive any financial support to conduct this study. Conflicts of interest The authors have no conflicts of interest. Ethics approval To ensure compliance with ethical standards and participant confidentiality, we obtained ethical approval from the Bangladesh Medical Research Council (BMRC) (Ref-25003092019). The data were de-identified to maintain anonymity prior to analysis. Before data collection, the purpose of the study was fully clarified to the participants, and their informed written consent was taken. Each of the steps of this study was completed following the Helsinki Declaration (1964). Authors’ contributions All authors critically reviewed earlier versions of the draft and approved the final manuscript. xx and xx conceived the paper. xx and xx developed the analysis plan. Xxx did the analysis and wrote the initial draft. All author contributed to the write up and editing. Acknowledgments We thank every participant for their voluntarily participation. We also grateful to hospital authority for allowing us conducting the study. References Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. May 2013;6 (3):606-619. Huusko J, Kurki S, Toppila I, Purmonen T, Lassenius M, Gullberg E, et al. Heart failure in Finland: clinical characteristics, mortality, and healthcare resource use. ESC Heart Fail. Aug 2019;6 (4):603-612. Roger VL. Epidemiology of heart failure. Circ Res. Aug 30, 2013;113 (6):646-659. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. Sep 21, 2021;42 (36):3599-3726. Desai AS, Stevenson LW. 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Vuorinen AL, Leppänen J, Kaijanranta H, Kulju M, Heliö T, van Gils M, et al. Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial. J Med Internet Res. Dec 11, 2014;16 (12):e282. Butler J, Marti C, Pina I, DeFilippi C. Scope of heart failure hospitalization. Congest Heart Fail. 2012;18 Suppl 1 (Suppl 1):S1-S4. Frederix I, Vanderlinden L, Verboven AS, Welten M, Wouters D, De Keulenaer G, et al. Long-term impact of a six-month telemedical care programme on mortality, heart failure readmissions and healthcare costs in patients with chronic heart failure. J Telemed Telecare. Jun 2019;25 (5):286-293. Supplementary File Supplementary File 1 is not available with this version. Additional Declarations The authors declare no competing interests. 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Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Fatema","middleName":"","lastName":"Begum","suffix":""},{"id":477944467,"identity":"75bb75e7-60e5-4bfc-9a61-9f361a98cb07","order_by":8,"name":"A M Shafique","email":"","orcid":"","institution":"Department of Cardiology, United Hospital, Dhaka-1212, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"M","lastName":"Shafique","suffix":""},{"id":477946485,"identity":"9770977e-8749-46c6-9d0e-3073626d75b7","order_by":9,"name":"Fahim Us Sunny","email":"","orcid":"","institution":"International Medical College and Hospital, Dhaka-1711, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Fahim","middleName":"Us","lastName":"Sunny","suffix":""},{"id":477944468,"identity":"955e48b0-1a54-4e53-8c69-60d621b7f7d1","order_by":10,"name":"Sheikh Sharfuddin Rajib","email":"","orcid":"","institution":"Department of Pharmaceutical Technology, University of Asia Pacific, Bangladesh.","correspondingAuthor":false,"prefix":"","firstName":"Sheikh","middleName":"Sharfuddin","lastName":"Rajib","suffix":""},{"id":477944469,"identity":"788ca94e-5b28-4b04-a547-ef0b4b130e32","order_by":11,"name":"Shah Miran","email":"","orcid":"","institution":"Dhaka National Medical College \u0026 Hospital, Dhaka-1100, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Shah","middleName":"","lastName":"Miran","suffix":""},{"id":477944470,"identity":"bc857f7e-ab08-4292-8da7-1260241c5e9a","order_by":12,"name":"Faroque Md Mohsin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDCCA4wPQCQDGwPzASBDQoYILcwGUC1sCSAtPMRrYWDgATEYCGvhu32YddONijv2fNI9n1/dqLHgYWA/fHQDPi2S55LZbueceZbYJnN2m3XOMaDDeNLSbuDTYnCG/9jt3LbDCWwSuduMc9iAWiR4zAhoYWYDabFnk8h5ZpzzjwQtjG0SOcyPc9uI0CIJ0pJz5nBim0SaGXNunwQPGyG/8IG1VBy2l5+R/Phzzrc6OX72w8fwakEGbBJgkljlIMD8gRTVo2AUjIJRMHIAAL8iSUFMswaeAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3432-4559","institution":"School of Global Public Health, New York University, New York, USA","correspondingAuthor":true,"prefix":"","firstName":"Faroque","middleName":"Md","lastName":"Moh","suffix":"Md"},{"id":477944471,"identity":"14f981e3-3f00-4faf-8071-14077f810ee7","order_by":13,"name":"N A M Momenuzzaman","email":"","orcid":"","institution":"Department of Cardiology, United Hospital, Dhaka-1212, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"N","middleName":"A M","lastName":"Momenuzzaman","suffix":""}],"badges":[],"createdAt":"2025-06-29 08:39:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7001596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7001596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85924956,"identity":"cc9cfe2d-728c-46f9-8657-7bb30f1026bb","added_by":"auto","created_at":"2025-07-03 08:30:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart for patients’ selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7001596/v1/4d96255885d25aef840885fc.png"},{"id":85924960,"identity":"fedd02ca-315c-4f78-bb0f-cd5672b86851","added_by":"auto","created_at":"2025-07-03 08:30:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":398474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA systemic presentation of the remote patient management model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7001596/v1/cf4fc2c0a016d049cc3b1d61.png"},{"id":85924957,"identity":"8dda9f60-92d5-4d98-bf5f-28a2132596ca","added_by":"auto","created_at":"2025-07-03 08:30:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUtilisation of Digital Monitoring\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7001596/v1/145055984a59b2567856a20c.png"},{"id":85926943,"identity":"ad1cc681-9fea-4d95-b0ad-47d4c9565f3b","added_by":"auto","created_at":"2025-07-03 08:46:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7001596/v1/897142be-b73f-43b1-87d0-1e19b02f460b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUtilisation of Digital Monitoring System for Heart Failure Patients: Experience from a Tertiary Healthcare in Bangladesh\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe prevalence of heart failure (HF) and related costs is increasing worldwide due to an aging population [1]. The estimated prevalence of HF in the adult population is 1\u0026ndash;2%, increasing to 10% in older adults [2,3]. HF often leads to gradual or acute changes in HF symptoms (decompensation) that require repeated and prolonged hospitalization [4]. Hospital admission is a strong predictor of further hospital admission: 20\u0026ndash;25% of patients with HF are rehospitalized within 1 month and approximately 50% within 5 months of discharge [5]. Decompensation requiring hospitalization is also linked to increased mortality. A European registry study following patients for 1 year after hospitalization reported mortality rates of 24% for acute HF and 6.4% for chronic HF [6]. Hospitalization accounts for around 80% of HF healthcare costs [1].\u003c/p\u003e \u003cp\u003eAn early return to the hospital following discharge may be a result of incomplete inpatient treatment and poor coordination and planning of follow-up care. Even for patients with regular follow-up care, however, the signs of decompensation may not occur during cardiology visits. Patients often contact clinics when symptoms are at an advanced stage [7]. Self-monitoring of symptoms, such as increased blood pressure, weight gain, or other health status-related symptoms, is particularly important in HF management [4]. Self-monitoring requires patients to be motivated to measure symptoms associated with HF and to have access to clinical advice when symptoms appear [8].\u003c/p\u003e \u003cp\u003eRemote monitoring aims to improve monitoring of patients\u0026rsquo; health status and is defined as a part of DM [9]. A basic level of remote monitoring involves regular and structured telephone support provided by healthcare professionals to discuss symptoms, self-monitoring measurements, lifestyle, and drug therapy. Structured telephone support can reduce HF-related hospitalization but does not seem to have an impact on the all-cause hospitalization of patients with HF [10]. Remote monitoring solutions are non-invasive stand-alone systems in which patient data on biometric measurements (such as body weight, blood pressure, and heart rate) and reported symptoms are frequently transmitted to healthcare professionals through a secure digital system. HCPs manually review the data on digital platforms, which may also include integrated automated alerts, and necessary action is taken to optimize treatment.\u003c/p\u003e \u003cp\u003eThe effect of non-invasive digital monitoring (DM)has been compared to the physical visit (PV) in several studies, primarily through randomized trials. Some studies found DM had a beneficial impact on reducing hospitalization [11], while others did not find any effect [12,13]. A more recent meta-analysis, encompassing 91 randomized trials and observational studies, revealed that non-invasive telemonitoring reduced all-cause mortality by 16%, first hospitalization by 19%, and total HF hospitalizations by 15%. When comparing DMstudies and developing optimal DM approaches, it is crucial to consider various determinants, including the DM intervention models, health care systems, and the characteristics of the population with HF in the studies [14].\u003c/p\u003e \u003cp\u003eA number of studies from across the world, especially in developed countries explored the usefulness of DM [15\u0026ndash;18]. Knowing the utilisation of DM may enable policymakers to enact appropriate policies to curb the increase in HF-related mortality and morbidity, allocate resources appropriately, and build healthcare systems that can cope effectively with expected increases in HF prevalence. In Bangladesh, there is no evidence to inform policymaking regarding telehealth utilisation in HF patients, which can further disallow discussions on different coping strategies management of cardiovascular diseases there is no single study has been conducted in Bangladesh. Therefore, the study aimed to test the feasibility of implementing an innovative DM in a natural healthcare environment and assess its potential benefits to our HF patients with various comorbidities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy design and settings\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis prospective cross-sectional study was conducted at HF unit, United Hospital, Dhaka, Bangladesh. The study hospital is the country\u0026rsquo;s one of the largest specialised hospitals where people come from across the country due to availability and potential access to better treatment options.\u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipants and survey procedures\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIndividuals visited HF unit, and were receiving active treatment for HF. Patients visited HF unit at least 1 time and self-reported New York Heart Association (NYHA) class I-IV were eligible for the study. In terms of patients\u0026rsquo; eligibility, the inclusion criteria also stated that patients must be able to manage the DM devices and digital platform used in the study. We excluded patients with terminal renal failure, cancer with a prognosis of 1 year or more, severe cognitive impairment, terminal cirrhosis, receiving palliative care and patients who were at high risk of losing follow-up. The study participants were consisted of two groups: (i) patients who utilised HF centre physically and (ii) patients who utilised DM. The intervention consisted of the following 3 components conducted by registered nurses/others: predischarge HF education, regularly scheduled telephone coaching, and home telemonitoring of weight, blood pressure, heart rate, and symptoms. The predischarge health education was conducted by a study nurse or other health carers who guided patients through a booklet developed for patients with medication adherence, salt avoidance, fluid monitoring, exercising with HF, and daily checkup of weight and edema, as well as when to call the HF treatment team. We primarily selected a total of 1765 data items for analysis. After eliminating incomplete and insufficient quality information, a total of 1536 data were selected for analytical exploration \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Procedures\u003c/b\u003e \u003c/p\u003e \u003cp\u003e Patients maintained regular cardiology appointments and laboratory tests planned by a cardiologist for each patient with HF according to local care guidelines for HF treatment. Nurses followed up with patients through phone calls, depending on the state of HF. During the follow-up period, patients measured their weight and blood pressure at home, and nurses followed up with patients through phone calls to discuss their health status and measurement results.\u003c/p\u003e \u003cp\u003eDM was added consisted of a digital platform, home measurement devices, and nurses monitoring patients through the digital platform. The digital platform used United Hospital\u0026rsquo;s remote patient monitoring platform, customized for the study. Patients used their smartphones, handheld devices, or personal computers to access the digital platform. Patients measured their weight with a digital scale and their blood pressure were measured and transferred the measurements into the digital platform \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDepending on the health status, patient to contact a nurse or a nurse to contact the patient to validate the health status. If needed, nurses referred a patient to a cardiologist to optimize HF care or medication. The treating cardiologist reacted to the nurses\u0026rsquo; referrals within 24 hours. For urgent alerts, the nurses advised patients to go to emergency care. Nurses also provided technical support for patients as required. The digital platform collected blood pressure data and laboratory results from regular health care visits, not for the alert algorithm but to allow nurses to evaluate the patient\u0026rsquo;s health status.\u003c/p\u003e \u003cp\u003eA unique personal identification number for each patient in connected the patient registers data. All-cause hospitalizations included hospitalizations with any diagnosis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSociodemographic and clinical characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe considered a number of clinical characteristics based on the registry (supplementary file 1). Sociodemographic characteristics included gender (male or female), age in years, and monthly income.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe study analysis included only patients who completed the study.\u003c/p\u003e \u003cp\u003ePatient demographics and NYHA class were summarized as n (%) of patients per category or median (IQR). During the care and DM periods, n (%) of patients with at least 1 hospitalization and the mean number of inpatient days per patient (95% CI) were reported. The mean number (95% CI) of visits per patient (primary, secondary, and emergency) and the mean number of calls (primary and secondary care) per patient were also reported for each period. The normal distribution of each variable was assessed through visual inspection and the Shapiro-Wilk test. For data found to be nonnormally distributed, differences between hospital care and telemonitoring periods were tested using the Wilcoxon signed rank test, and a value of P\u0026thinsp;\u0026lt;\u0026thinsp;.05 was considered statistically significant. The Pearson chi-square test with Yates correction was used for testing the difference between hospital care and DM periods (a binary variable).\u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eParticipant\u0026rsquo;s basic characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 1536 patients with HF were included in our study. Of those, most were male (84%, n\u0026thinsp;=\u0026thinsp;1292) and older than 55 years (62%, n\u0026thinsp;=\u0026thinsp;949). Approximately 39%(n\u0026thinsp;=\u0026thinsp;596) of the patients\u0026rsquo; monthly income was more than 60 thousand Bangladesh Taka. The basic characteristics of the participants are presented in \u003cb\u003eTabe 1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; basic characteristics (N\u0026thinsp;=\u0026thinsp;1536)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge in years\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD 53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly Income (BDT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20000\u0026ndash;40000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40000\u0026ndash;60000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipant\u0026rsquo;s clinical characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to the classification of the New York Heart Association (NYHA), most of patients (85%, n\u0026thinsp;=\u0026thinsp;1309) with HF were classified as class II followed by class III (9%, n\u0026thinsp;=\u0026thinsp;145). In terms of ECG, 1466 patients had rhythm reports where 98% (n\u0026thinsp;=\u0026thinsp;1440) had normal heart rhythm (sinus). In total, 321 subjects had Bundle Brunch Block (BBB) reports, which included 71%who had LBBB, followed by 28% who had RBBB. A total of 35 patients had a history of heart block, and 94% of them had first-degree blocks. A total of 1449 patients had reports on ECG QRS duration (milli second) while 941 (65%) of them had an ECG QRS duration of less than 130 milliseconds followed by 318 (22%) had\u0026thinsp;\u0026gt;\u0026thinsp;150 milliseconds.\u003c/p\u003e \u003cp\u003eEcho reports showed 1529 patients with left ventricular internal diameter end diastole (LVIDD) measurements, with the majority of them (45%, n\u0026thinsp;=\u0026thinsp;688) having an LVIDD of 51\u0026ndash;60 millimetres. Nevertheless, 1526 patients had left ventricular internal diameter end-systole (LVIDs) measurements, with the majority (38%, n\u0026thinsp;=\u0026thinsp;573) having 41\u0026ndash;50 mm LVIDs. A total of 994 patients' pulmonary artery systolic pressures (PASPs) were measured, and the majority (53%, n\u0026thinsp;=\u0026thinsp;527) had a PASP of less than 30mm. Nearly two-thirds of patients (73%, n\u0026thinsp;=\u0026thinsp;1123) had a history of Heart failure with reduced ejection fraction (HFrEF).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eThe New York Heart Association (NYHA) classification (n\u0026thinsp;=\u0026thinsp;1536)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectrocardiogram (ECG)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhythm (n\u0026thinsp;=\u0026thinsp;1466)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial Fibrillation (AF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBundle Branch Block (n\u0026thinsp;=\u0026thinsp;321)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Bundle Branch Block (RBBB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Bundle Branch Block (LBBB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft anterior fascicular block (LAHB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHeart Block (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eECG QRS duration (milli second) (n\u0026thinsp;=\u0026thinsp;1449)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 130 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e130\u0026ndash;140 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 150 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiogram (Echo)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLeft ventricular internal diameter end diastole (LVIDD) (in milli meter) (n\u0026thinsp;=\u0026thinsp;1529)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;40 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 60 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLeft ventricular internal diameter end systole (LVIDs) (in milli meter) (n\u0026thinsp;=\u0026thinsp;1526)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;30 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut of Range (\u0026lt;\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePulmonary artery systolic pressure (PASP) (in milli meter) (n\u0026thinsp;=\u0026thinsp;994)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 30 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;40 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 50 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory of Heart Failure (n\u0026thinsp;=\u0026thinsp;1536)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure with reduced ejection fraction (HFrEF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure with mid-range ejection fraction (HFmEF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure with preserved ejection fraction (HFpEF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure with improved ejection fraction (HFimpEF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHealth care resource use\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOut of 1536 patients, 43 (3%) patients utilised DM while 1490 (97%) patients physically visited the centre (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study found that the number of patients with at least one hospitalisation (all-cause) during the follow-up was much higher in the PV-groups than in the DM group (40% vs 10%; p\u0026thinsp;=\u0026thinsp;0.01). The number of inpatient days per patient due to HF decreased by 48% during the DM period (mean 1.2, 95% CI 0.1\u0026ndash;2.3 days vs 2.3, 95% CI 1-3.6 days with PV; P\u0026thinsp;=\u0026thinsp;0.17). The number of emergency care visits decreased significantly during the DM period by 44% (mean 0.7, 95% CI 0.4-1 vs mean 1.3, 95% CI 0.9\u0026ndash;1.7 with PV; P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUse of health care per patient in physical visit or digital monitoring solution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsolute change (relative change)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value for differences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAll-cause hospitalizations (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients with at least1 event, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-19(-40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient days, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.2(-41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHospitalizations for cardiovascular cause other than HF* (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients with \u0026ge;\u0026thinsp;1 event, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2(-16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient days, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16(-88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHospitalizations for HF (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients with \u0026ge;\u0026thinsp;1 event, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean inpatient days, days (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1(-48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean number of emergency care visit for cardiovascular causes, n (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6(-44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean number of secondary care visits (cardiology), n (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1(+\u0026thinsp;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eStatistics were calculated with the Wilcoxon signed rank test or the Pearson chi-square test with Yates correction for the binary variables.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study of a novel DM solution for patients with HF in a HF clinic in Bangladesh. The number of hospitalized patients was significantly lower during DM, and the mean length of stay decreased from 2.3 days to 1.2 days. The number of emergency visits was also significantly lower during DM.\u003c/p\u003e \u003cp\u003eA comprehensive meta-analysis of recently published studies on DM has provided evidence that DM reduces mortality and hospitalizations in patients with HF [19]. On the other hand, individual studies show both beneficial and neutral effects of DM compared to PV [20\u0026ndash;22]. The main objective of our study was to investigate whether DM can improve the detection of early signs of decompensation and decrease hospitalization and mortality. The variability of the results may be due to differences in the health care system, DM model, population with HF, and follow-up durations [9,14].\u003c/p\u003e \u003cp\u003eThis study found that 47% of patients were hospitalized due to HF during the SOC period, versus 14% during the telemonitoring period. Vuorinen and colleagues [21] conducted a telemonitoring study and found that only 28% of patients were hospitalized in the SOC group and 17% in the remote monitoring group during a 6-month follow-up period. The inclusion criteria for the population with HF included NYHA II-IV. Still, they did not require a recent hospitalization, which could explain the lower HF hospitalization risk compared with this study. Our data align with previous studies showing that nearly half of patients with HF are rehospitalized within 6 months after discharge [21,22]. However, further studies using a similar population with HF are needed to confirm these findings. Like this study, Vuorinen et al [21] also found a non-statistically significant decrease in inpatient days with telemonitoring (mean 0.7 vs 1.4 days with SOC). The significant reduction in hospitalization-related costs and the number of patients hospitalized due to HF in this study supports the idea that telemonitoring reduces hospitalizations.\u003c/p\u003e \u003cp\u003eA Spanish telemonitoring trial (n\u0026thinsp;=\u0026thinsp;117) had similar findings to this study. In this trial, 50% of the patients were hospitalized in the SOC group versus 28% in the telemonitoring group over a 6-month follow-up period. The patients were enrolled in the study upon hospitalization [19]. Thus, the results from this study support our findings on telemonitoring's benefits in reducing hospitalizations. However, a large Better Effectiveness After Transition\u0026ndash;Heart Failure (BEAT-HF) trial (n\u0026thinsp;=\u0026thinsp;1437) conducted in California could not see a reduction in readmissions in patients with HF in a telemonitoring group compared to SOC during a 6-month follow-up period [20]. The BEAT-HF trial\u0026rsquo;s limitations where patients were recruited from academic medical centres. This may restrict the generalizability of the results, as most patients with HF do not seek care in academic medical centres. Upon receiving alerts, nurses advised patients to contact physicians, or nurses called the physicians, but physicians were not directly involved with the interventions. Thus, the monitoring may not have affected care in practice. In this study and the Spanish study, nurses and treating physicians collaborated upon receiving alerts, which may have increased the benefits of telemonitoring. For example, in this study, physicians reacted to patient alerts within 24 hours.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations and strength\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study had some limitations. The study was conducted in a single tertiary care hospital in Dhak, which may limit the generalizability of the results. However, the study population is representative of the country, as all patients are directed to the same central hospital where recruitment was done. A randomized controlled trial design was not feasible due to the lack of resources and expertise. Patients were not randomized, and patients needed to be able to use the digital platform, which may have resulted in a possible selection bias. As the pre-post design uses a historical control group (i.e., patients on SOC in the period before starting telemonitoring), the underlying assumption in the analysis, given the deteriorating nature of HF, is that health care use in the absence of telemonitoring would remain at least at the same level as during SOC. Follow-up with telemonitoring was limited to 6 months, and it is unclear how use of health care services would develop beyond this period. Finally, due to the lack of direct medical and indirect cost of treatment, we had to calculate the cost based on prediction model. Therefore, the cost of this study is not representing actual fact. Despite those limitations, a strength of this study was the big sample size, which included both HF-related health care utilization.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our results suggest that the novel telemonitoring solution can help reduce hospital admissions and hospitalization costs as well as total healthcare costs in a population with HF with a recent hospital admission in the past 12 months. Further research should be conducted to validate these results in a larger and more diverse population with HF. Additionally, longitudinal studies can be carried out to assess the long-term effectiveness and sustainability of the telemonitoring solution in reducing hospital admissions and healthcare costs. The following must be considered when generalizing our results and applying our telemonitoring solution to other health care systems: our telemonitoring solution was applied to a patient population with a high risk of readmission due to a recent hospital admission.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData can be shared with the corresponding author upon request and for a valid reason.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe did not receive any financial support to conduct this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure compliance with ethical standards and participant confidentiality, we obtained ethical approval from the Bangladesh Medical Research Council (BMRC) (Ref-25003092019). The data were de-identified to maintain anonymity prior to analysis. Before data collection, the purpose of the study was fully clarified to the participants, and their informed written consent was taken. Each of the steps of this study was completed following the Helsinki Declaration (1964).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed earlier versions of the draft and approved the final manuscript. xx and xx conceived the paper. xx and xx developed the analysis plan. Xxx did the analysis and wrote the initial draft. All author contributed to the write up and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank every participant for their voluntarily participation. We also grateful to hospital authority for allowing us conducting the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHeidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. May 2013;6 (3):606-619. \u003c/li\u003e\n\u003cli\u003eHuusko J, Kurki S, Toppila I, Purmonen T, Lassenius M, Gullberg E, et al. Heart failure in Finland: clinical characteristics, mortality, and healthcare resource use. ESC Heart Fail. Aug 2019;6 (4):603-612. \u003c/li\u003e\n\u003cli\u003eRoger VL. Epidemiology of heart failure. Circ Res. Aug 30, 2013;113 (6):646-659. \u003c/li\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, B\u0026ouml;hm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. Sep 21, 2021;42 (36):3599-3726. \u003c/li\u003e\n\u003cli\u003eDesai AS, Stevenson LW. Rehospitalization for heart failure: predict or prevent? Circulation. Jul 24, 2012;126 (4):501-506. \u003c/li\u003e\n\u003cli\u003eCrespo-Leiro MG, Anker SD, Maggioni AP, Coats AJ, Filippatos G, Ruschitzka F, et al. European Society of Cardiology Heart Failure Long-Term Registry (ESC-HF-LT): 1-year follow-up outcomes and differences across regions. Eur J Heart Fail. Jun 2016;18 (6):613-625. \u003c/li\u003e\n\u003cli\u003eSchiff GD, Fung S, Speroff T, McNutt RA. Decompensated heart failure: symptoms, patterns of onset, and contributing factors. Am J Med. Jun 01, 2003;114 (8):625-630. \u003c/li\u003e\n\u003cli\u003eClark AM, Freydberg CN, McAlister FA, Tsuyuki RT, Armstrong PW, Strain LA. Patient and informal caregivers\u0026apos; knowledge of heart failure: necessary but insufficient for effective self-care. Eur J Heart Fail. Jun 2009;11 (6):617-621. \u003c/li\u003e\n\u003cli\u003eSinghal A, Cowie MR. Digital health: implications for heart failure management. Card Fail Rev. Mar 2021;7:e08. \u003c/li\u003e\n\u003cli\u003eInglis SC, Clark RA, Dierckx R, Prieto-Merino D, Cleland JGF. Structured telephone support or non-invasive telemonitoring for patients with heart failure. Cochrane Database Syst Rev. Oct 31, 2015;2015 (10):CD007228. \u003c/li\u003e\n\u003cli\u003eKoehler F, Koehler K, Deckwart O, Prescher S, Wegscheider K, Kirwan BA, et al. Efficacy of telemedical interventional management in patients with heart failure (TIM-HF2): a randomised, controlled, parallel-group, unmasked trial. Lancet. Sep 22, 2018;392 (10152):1047-1057.\u003c/li\u003e\n\u003cli\u003eV\u0026ouml;ller H, Bindl D, Nagels K, Hofmann R, Vettorazzi E, Wegscheider K, et al. The first year of noninvasive remote telemonitoring in chronic heart failure is not cost saving but improves quality of life: The randomized controlled CardioBBEAT trial. Telemed J E Health. Mar 21, 2022;28 (11):1613-1622. \u003c/li\u003e\n\u003cli\u003eGalinier M, Roubille F, Berdague P, Brierre G, Cantie P, Dary P, et al. Telemonitoring versus standard care in heart failure: a randomised multicentre trial. Eur J Heart Fail. Jun 2020;22 (6):985-994. \u003c/li\u003e\n\u003cli\u003eScholte NTB, G\u0026uuml;rg\u0026ouml;ze MT, Aydin D, Theuns DAMJ, Manintveld OC, Ronner E, et al. Telemonitoring for heart failure: a meta-analysis. Eur Heart J. Aug 14, 2023;44 (31):2911-2926. \u003c/li\u003e\n\u003cli\u003eCom\u0026iacute;n-Colet J, Enjuanes C, Verd\u0026uacute;-Rotellar JM, Linas A, Ruiz-Rodriguez P, Gonz\u0026aacute;lez-Robledo G, et al. Impact on clinical events and healthcare costs of adding telemedicine to multidisciplinary disease management programmes for heart failure: Results of a randomized controlled trial. J Telemed Telecare. Jul 2016;22 (5):282-295. \u003c/li\u003e\n\u003cli\u003eVestergaard AS, Hansen L, S\u0026oslash;rensen SS, Jensen MB, Ehlers LH. Is telehealthcare for heart failure patients cost-effective? an economic evaluation alongside the Danish TeleCare North heart failure trial. BMJ Open. Jan 27, 2020;10 (1):e031670. \u003c/li\u003e\n\u003cli\u003eJiang X, Yao J, You JH. Telemonitoring versus usual care for elderly patients with heart failure discharged from the hospital in the united states: cost-effectiveness analysis. JMIR Mhealth Uhealth. Jul 06, 2020;8 (7):e17846. \u003c/li\u003e\n\u003cli\u003eSydow H, Prescher S, Koehler F, Koehler K, Dorenkamp M, Spethmann S, et al. Cost-effectiveness of noninvasive telemedical interventional management in patients with heart failure: health economic analysis of the TIM-HF2 trial. Clin Res Cardiol. Nov 2022;111 (11):1231-1244.\u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Marrero S, Yun S, Cainzos-Achirica M, Enjuanes C, Garay A, Farre N, et al. Impact of telemedicine on the clinical outcomes and healthcare costs of patients with chronic heart failure and mid-range or preserved ejection fraction managed in a multidisciplinary chronic heart failure programme: a sub-analysis of the iCOR randomized trial. J Telemed Telecare. 2020;26 (1-2):64-72. \u003c/li\u003e\n\u003cli\u003eOng MK, Romano PS, Edgington S, Aronow HU, Auerbach AD, Black JT, et al. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the Better Effectiveness After Transition -- Heart Failure (BEAT-HF) randomized clinical trial. JAMA Intern Med. Mar 2016;176 (3):310-318. \u003c/li\u003e\n\u003cli\u003eVuorinen AL, Lepp\u0026auml;nen J, Kaijanranta H, Kulju M, Heli\u0026ouml; T, van Gils M, et al. Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial. J Med Internet Res. Dec 11, 2014;16 (12):e282. \u003c/li\u003e\n\u003cli\u003eButler J, Marti C, Pina I, DeFilippi C. Scope of heart failure hospitalization. Congest Heart Fail. 2012;18 Suppl 1 (Suppl 1):S1-S4. \u003c/li\u003e\n\u003cli\u003eFrederix I, Vanderlinden L, Verboven AS, Welten M, Wouters D, De Keulenaer G, et al. Long-term impact of a six-month telemedical care programme on mortality, heart failure readmissions and healthcare costs in patients with chronic heart failure. J Telemed Telecare. Jun 2019;25 (5):286-293. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary File","content":"\u003cp\u003eSupplementary File 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"United Hospital, Dhaka, Bangladesh","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heart failure, telemonitoring, hospital, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-7001596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7001596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMany patients with chronic heart failure (HF) experience a reduced health status, leading to readmission after hospitalization despite receiving conventional care. Digital monitoring (DM) approaches aim to improve the early detection of HF decompensations and prevent readmissions. However, knowledge about the impact of telemonitoring on preventing readmissions and mortality. The study aimed to test the feasibility of implementing a digital care platform in a HF clinic and assess its potential benefits to our HF patients with various comorbidities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a prospective study at United Hospital from April to October, 2024. The DM platform for patient-reported symptoms and weight and blood pressure measurements, phone calls with secondary care nurses, and rapid response to alerts by treating physicians. DM solution data were linked to patient register data onhospital registry. Eligible patients had at least 1 visit the clinic within the last 12 months and self-reported New York Heart Association class I-IV. We excluded patients with terminal renal failure, cancer with a prognosis of 1 year or more, severe cognitive impairment, terminal cirrhosis, and patients who were at high risk of losing follow-up.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1536 patients with HF were included in the analysis. The number of patients with at least one hospitalisation (all-cause) during the follow-up was much higher in the PV-groups than in the DM group (40% vs 10%; p\u0026thinsp;=\u0026thinsp;0.01). The number of inpatient days per patient due to HF decreased by 48% during the DM period (mean 1.2, 95% CI 0.1\u0026ndash;2.3 days vs 2.3, 95% CI 1-3.6 days with PV; P\u0026thinsp;=\u0026thinsp;0.17). The number of emergency care visits decreased significantly during the DM period by 44% (mean 0.7, 95% CI 0.4-1 vs mean 1.3, 95% CI 0.9\u0026ndash;1.7 with PV; P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results suggest that the telemonitoring solution can reduce hospital-related costs for patients with HF with a recent hospital admission.\u003c/p\u003e","manuscriptTitle":"Utilisation of Digital Monitoring System for Heart Failure Patients: Experience from a Tertiary Healthcare in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 08:30:15","doi":"10.21203/rs.3.rs-7001596/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83940872-d34e-4002-a8a8-6232070ca1c8","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50745837,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":50745838,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-07-03T08:30:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 08:30:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7001596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7001596","identity":"rs-7001596","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}