Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability Study

Texting in the Fourth Trimester: mHealth for Postpartum Care

Rising maternal morbidity and mortality in the United States is a complex problem and is often tied to the postpartum period. Postpartum visits are poorly attended leading to gaps in contraception, mental health care, and care for chronic conditions. mHealth, health care supported by mobile technologies, has been shown to improve antenatal care adherence. Our study aims to determine whether an mHealth intervention of interactive, educational text messages can improve care for women postpartum.We conducted a randomized control trial of 191 women receiving OB/GYN care in our clinic from November 2019 to April 2020. Patients were randomized postpartum to receive either routine care or routine care with mHealth text messages and appointment reminders specific to delivery type. The primary endpoint was attendance at the 6-week postpartum visit with secondary endpoints of breastfeeding, contraception use, emergency visits, and postpartum depression scores (Edinburgh Postnatal Depression Scale). A subgroup analysis was additionally conducted to assess the impact of some visits shifting to telehealth due to the timing of the coronavirus disease 2019 (COVID-19) pandemic.Patient demographics were similar between the two groups. There was no significant difference in postpartum appointment attendance between text messages (n = 57, 59%) and control groups (n = 62, 66%; p = 0.31). In total, 117 patients were scheduled for in-person postpartum visits, and 74 for telehealth visits during the COVID-19 pandemic, with no significant difference in attendance rate between groups for either visit modality.Automated text messages alone did not increase our primary outcome of adherence to postpartum care, even when visits were conducted by telehealth. While mHealth has proven successful in other care areas, such as antepartum follow-up, further research is needed to determine whether it is an effective method to improve adherence to postpartum care, or whether other strategies must be developed, including augmentation with human navigators. · Interactive text messages did not improve postpartum visit adherence.. · COVID-19 disrupted routine postpartum care.. · More research is needed into mHealth interventions postpartum..

Platform-Based Patient-Clinician Digital Health Interventions for Care Transitions: Scoping Review

BACKGROUND

Care transitions are complex and can make patients vulnerable to adverse events. Poor communication among clinicians, patients, and their caregivers is a critical gap during these periods of transition. Technology solutions such as platform-based patient-clinician digital health interventions (DHIs) can provide support and education to patients.

OBJECTIVE

The aims of this scoping review were to explore the literature on platform-based patient-clinician DHIs specific to hospital-to-home care transitions and identify the barriers to and enablers of the uptake and implementation of these DHIs.

METHODS

A scoping review was conducted. A total of 4 databases (MEDLINE, CINAHL, Embase, and the Cochrane Central Register of Controlled Trials) were searched on July 13, 2022. Studies involving patients aged >18 years who used platform-based DHIs during their hospital-to-home transition were included. In total, 2 reviewers independently screened the articles for eligibility using a 2-stage process of title and abstract and full-text screening. Eligible studies underwent data extraction, and the results were analyzed using descriptive and narrative methods.

RESULTS

We screened 8322 articles, of which 97 (1.17%) met our inclusion criteria. DHIs were implemented using a mobile app (59/97, 61%), a web-based platform (28/97, 29%), or a combination of both (10/97, 10%). The 2 most common health conditions related to the DHIs were cardiac disease (22/97, 23%) and stroke (11/97, 11%). Outcomes varied greatly but were grouped by health care use, complications, and wellness outcomes. The top 2 barriers were lack of interest (13/97, 13%) and time constraints to use the DHIs (10/97, 10%), and the top 2 enablers were the ability to use the DHIs (17/97, 18%) and their ease of use (11/97, 11%). The main conflicting theme was access (enabler; 28/97, 29%) or limited access (barrier; 15/97, 15%) to technology or the internet.

CONCLUSIONS

Platform-based DHIs could help improve communication, coordination, and information sharing between clinicians and patients during transition periods. Further research is needed to assess the effectiveness of these platform-based DHIs on patient outcomes.

Benchmarking the clinical outcomes of Healthentia SaMD in chronic disease management: a systematic literature review comparison

Background

Software as a Medical Device (SaMD) and mobile health (mHealth) applications have revolutionized the healthcare landscape in the areas of remote patient monitoring (RPM) and digital therapeutics (DTx). These technological advancements offer a range of benefits, from improved patient engagement and real-time monitoring, to evidence-based personalized treatment plans, risk prediction, and enhanced clinical outcomes.

Objective

The systematic literature review aims to provide a comprehensive overview of the status of SaMD and mHealth apps, highlight the promising results, and discuss what is the potential of these technologies for improving health outcomes.

Methods

The research methodology was structured in two phases. In the first phase, a search was conducted in the EuropePMC (EPMC) database up to April 2024 for systematic reviews on studies using the PICO model. The study population comprised individuals afflicted by chronic diseases; the intervention involved the utilization of mHealth solutions in comparison to any alternative intervention; the desired outcome focused on the efficient monitoring of patients. Systematic reviews fulfilling these criteria were incorporated within the framework of this study. The second phase of the investigation involved identifying and assessing clinical studies referenced in the systematic reviews, followed by the synthesis of their risk profiles and clinical benefits.

Results

The results are rather positive, demonstrating how SaMDs can support the management of chronic diseases, satisfying patient safety and performance requirements. The principal findings, after the analysis of the extraction table referring to the 35 primary studies included, are: 24 studies (68.6%) analyzed clinical indications for type 2 diabetes mellitus (T2DM), six studies (17.1%) analyzed clinical indications for cardiovascular conditions, three studies (8.7%) analyzed clinical indications for cancer, one study (2.8%) analyzed clinical indications for chronic obstructive pulmonary disease (COPD), and one study (2.8%) analyzed clinical indications for hypertension. No severe adverse events related to the use of mHealth were reported in any of them. However, five studies (14.3%) reported mild adverse events (related to hypoglycemia, uncontrolled hypertension), and four studies (11.4%) reported technical issues with the devices (related to missing patient adherence requirements, Bluetooth unsuccessful pairing, and poor network connections). For what concerns variables of interest, out of the 35 studies, 14 reported positive results on the reduction of glycated hemoglobin (HbA1c) with the use of mHealth devices. Eight studies examined health-related quality of life (HRQoL); in three cases, there were no statistically significant differences, while the groups using mHealth devices in the other five studies experienced better HRQoL. Seven studies focused on physical activity and performance, all reflecting increased attention to physical activity levels. Six studies addressed depression and anxiety, with mostly self-reported benefits observed. Four studies each reported improvements in body fat and adherence to medications in the mHealth solutions arm. Three studies examined blood pressure (BP), reporting reduction in BP, and three studies addressed BMI, with one finding no statistically significant change and two instead BMI reduction. Two studies reported significant weight/waist reduction and reduced hospital readmissions. Finally, individual studies noted improvements in sleep quality/time, self-care/management, six-minute walk distance (6MWD), and exacerbation outcomes.

Conclusion

The systematic literature review demonstrates the significant potential of software as a medical device (SaMD) and mobile health (mHealth) applications in revolutionizing chronic disease management through remote patient monitoring (RPM) and digital therapeutics (DTx). The evidence synthesized from multiple systematic reviews and clinical studies indicates that these technologies, exemplified by solutions like Healthentia, can effectively support patient monitoring and improve health outcomes while meeting crucial safety and performance requirements. The positive results observed across various chronic conditions underscore the transformative role of digital health interventions in modern healthcare delivery. However, further research is needed to address long-term efficacy, cost-effectiveness, and integration into existing healthcare systems. As the field rapidly evolves, continued evaluation and refinement of these technologies will be essential to fully realize their potential in enhancing patient care and health management strategies.

Wearable Devices for Supporting Chronic Disease Self-Management: Scoping Review

BACKGROUND

People with chronic diseases can benefit from wearable devices in managing their health and encouraging healthy lifestyle habits. Wearables such as activity trackers or blood glucose monitoring devices can lead to positive health impacts, including improved physical activity adherence or better management of type 2 diabetes. Few literature reviews have focused on the intersection of various chronic diseases, the wearable devices used, and the outcomes evaluated in intervention studies, particularly in the context of primary health care.

OBJECTIVE

This study aims to identify and describe (1) the chronic diseases represented in intervention studies, (2) the types or combinations of wearables used, and (3) the health or health care outcomes assessed and measured.

METHODS

We conducted a scoping review following the Joanna Briggs Institute guidelines, searching the MEDLINE and Web of Science databases for studies published between 2012 and 2022. Pairs of reviewers independently screened titles and abstracts, applied the selection criteria, and performed full-text screening. We included interventions using wearables that automatically collected and transmitted data to adult populations with at least one chronic disease. We excluded studies with participants with only a predisposition to develop a chronic disease, hospitalized patients, patients with acute diseases, patients with active cancer, and cancer survivors. We included randomized controlled trials and cohort, pretest-posttest, observational, mixed methods, and qualitative studies.

RESULTS

After the removal of 1987 duplicates, we screened 4540 titles and abstracts. Of the remaining 304 articles after exclusions, we excluded 215 (70.7%) full texts and included 89 (29.3%). Of these 89 texts, 10 (11%) were related to the same interventions as those in the included studies, resulting in 79 studies being included. We structured the results according to chronic disease clusters: (1) diabetes, (2) heart failure, (3) other cardiovascular conditions, (4) hypertension, (5) multimorbidity and other combinations of chronic conditions, (6) chronic obstructive pulmonary disease, (7) chronic pain, (8) musculoskeletal conditions, and (9) asthma. Diabetes was the most frequent health condition (18/79, 23% of the studies), and wearable activity trackers were the most used (42/79, 53% of the studies). In the 79 included studies, 74 clinical, 73 behavioral, 36 patient technology experience, 28 health care system, and 25 holistic or biopsychosocial outcomes were reported.

CONCLUSIONS

This scoping review provides an overview of the wearable devices used in chronic disease self-management intervention studies, revealing disparities in both the range of chronic diseases studied and the variety of wearable devices used. These findings offer researchers valuable insights to further explore health care outcomes, validate the impact of concomitant device use, and expand their use to other chronic diseases.

TRIAL REGISTRATION

Open Science Framework Registries (OSF) s4wfm; https://osf.io/s4wfm.

Envisioning a path from the Internet of Medical Things to improved fertility care access: a mini-review

Devices that function within a network of interconnected systems and are equipped with sensors, software, and tools designed to collect and exchange data are widely known as the Internet of Things (IoT). In recent years, the rapid growth of IoT technology has sparked significant interest in leveraging these systems to enhance healthcare delivery across various medical fields, including fertility care and assisted reproductive technology. The subset of IoT devices applied within the healthcare sector is referred to as the Internet of Medical Things (IoMT). Despite this growing technological potential, there has been little exploration into using IoMT to address a critical challenge in fertility care: the ongoing lack of access for many individuals in need of these services. In this article, we examine various applications of IoMT in healthcare, as well as how these may be extrapolated to the fertility field, and more importantly, assess how they may aid in bridging the access gap. We also explore potential challenges and pitfalls associated with implementing the IoMT, which underscores the need for careful oversight in its adoption and utilization.

A Quality Improvement-based Approach to Implementing a Remote Monitoring-Based Bundle in Transitional Care Patients for Heart Failure

BACKGROUND

Congestive heart failure (HF) is a leading cause of hospitalization and readmission, leading to increased health care utilization and cost. This is complicated by high incidence, prevalence, and hospitalization rates among racial and ethnic minorities, with a widening in the mortality disparity gap. Remote patient monitoring (RPM) has the potential to proactively engage patients after discharge to optimize medication management and intervene to avoid rehospitalization. However, it also may widen the equity gap due to technological barriers and bias.

METHODS

A prospective, observational quality improvement (QI) initiative leveraging an amended tool from the Institute for Healthcare Improvement Model for Improvement was incorporated with an equity lens and five Plan-Do-Study-Act (PDSA) cycles at a single site. The intervention used an HF bundle that included RPM, clinical telepharmacy, remote therapeutic monitoring, and community paramedicine.

RESULTS

Between May 2022 and March 2023, five PDSA cycles were run involving 90 enrolled patients. In total, 38 (42.2%) patients received the complete HF bundle, 42 (46.7%) a partial bundle, and 10 (11.1%) only RPM. The patients with the complete bundle had a readmission rate of 2.6% compared to 14.3% in the partial bundle and 20.0% in RPM alone. The biggest impact of this program was the incorporation of community paramedicine. The program also noted an improvement in equitable enrollment after adjusting mid-program by avoiding cellular phone-enabled devices and transitioning to a hub-based model.

CONCLUSION

This single-site QI-based initiative implemented an HF-based RPM program that leveraged clinical telepharmacy and community paramedicine. This program identified a disparity of care gap regarding the equitable distribution of services and made mid-study adjustments to improve the disparity gap. The program found that use of the HF bundle resulted in a decreased hospital readmission rate.

The effect of telemedicine employing telemonitoring instruments on readmissions of patients with heart failure and/or COPD: a systematic review

Background

Hospital readmissions pose a challenge for modern healthcare systems. Our aim was to assess the efficacy of telemedicine incorporating telemonitoring of patients' vital signs in decreasing readmissions with a focus on a specific patient population particularly prone to rehospitalization: patients with heart failure (HF) and/or chronic obstructive pulmonary disease (COPD) through a comparative effectiveness systematic review.

Methods

Three major electronic databases, including PubMed, Scopus, and ProQuest's ABI/INFORM, were searched for English-language articles published between 2012 and 2023. The studies included in the review employed telemedicine incorporating telemonitoring technologies and quantified the effect on hospital readmissions in the HF and/or COPD populations.

Results

Thirty scientific articles referencing twenty-nine clinical studies were identified (total of 4,326 patients) and were assessed for risk of bias using the RoB2 (nine moderate risk, six serious risk) and ROBINS-I tools (two moderate risk, two serious risk), and the Newcastle-Ottawa Scale (three good-quality, four fair-quality, two poor-quality). Regarding the primary outcome of our study which was readmissions: the readmission-related outcome most studied was all-cause readmissions followed by HF and acute exacerbation of COPD readmissions. Fourteen studies suggested that telemedicine using telemonitoring decreases the readmission-related burden, while most of the remaining studies suggested that it had a neutral effect on hospital readmissions. Examination of prospective studies focusing on all-cause readmission resulted in the observation of a clearer association in the reduction of all-cause readmissions in patients with COPD compared to patients with HF (100% vs. 8%).

Conclusions

This systematic review suggests that current telemedicine interventions employing telemonitoring instruments can decrease the readmission rates of patients with COPD, but most likely do not impact the readmission-related burden of the HF population. Implementation of novel telemonitoring technologies and conduct of more high-quality studies as well as studies of populations with ≥2 chronic disease are necessary to draw definitive conclusions.

Systematic Review Registration

This study is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), identifier (INPLASY202460097).

Efficacy of a Telemonitoring System as a Complementary Strategy in the Treatment of Patients With Heart Failure: Randomized Clinical Trial

Episodes of decompensation are the main cause of hospital admissions in patients with heart failure. For this reason, the use of mobile apps emerges as an excellent strategy to improve coverage, real-time monitoring, and timeliness of care. ControlVit is an electronic application for early detection of complications studied within the context of a tertiary university hospital. Patients were randomized to the use of ControlVit versus placebo, during a 6-month follow-up. The primary outcome was the difference in numbers of readmissions and deaths for heart failure between both groups. One hundred forty patients were included (intervention = 71, placebo = 69), with an average age of 66 years old; 71% were men. The main etiology of heart failure was ischemic (60%), whereas the main comorbidities were arterial hypertension (44%), dyslipidemia (42%), hypothyroidism (38%), chronic kidney disease (38%), and diabetes mellitus (27%). The primary outcome occurred more frequently in the control group: readmission due to decompensation for heart failure (control group n = 14 vs intervention group n = 3; P = .0081), and death (control group n = 11 vs intervention group n = 3; P = .024). In heart failure patients, ControlVit is a useful and supplementary tool, which reduces hospital admissions due to episodes of decompensation.

Effect of using a mobile drug management application on medication adherence and hospital readmission among elderly patients with polypharmacy: a randomized controlled trial

BACKGROUND

Adherence to complex drug regimens and polypharmacy are among the challenges of old age, which may negatively affect their motivation to continue drug therapy or lead to incorrect drug consumption. The present study was conducted to evaluate the effect of using a mobile drug management application on medication adherence and hospital readmission among polypharmacy older adults.

METHODS

In this randomized controlled trial study conducted in 2022, with Trial Registration Number (IRCT20191231045966N1) (18/07/2021), 192 Iranian older adults with polypharmacy were selected according to the inclusion criteria and allocated to case and control groups using the block randomization method. The data collection tools included a demographic questionnaire, case report form, and Morisky Medication Adherence Scale. The intervention was done using a mobile drug management application. Drug adherence was measured at baseline and both with hospital readmission were measured after 8 weeks. The collected data were entered into the SPSS software version 22 and analyzed using descriptive (frequency, percentage, mean, standard deviation) and inferential (Chi-square, Fisher's exact test, independent t-test) statistics.

RESULTS

The case and control groups were homogeneous in terms of demographic variables and drug adherence level before the intervention. A significant difference was found in the drug adherence level after using the app (p < 0.001). Moreover, a significant difference was found in adverse events, including re-hospitalization due to disease aggravation, re-hospitalization due to error in medication consumption, falling, hypo or hypertension, and hypo or hyperglycemia, and medication use accuracy between the groups after the intervention (p < 0.05).

CONCLUSION

The results showed that using a mobile drug management application that meets the specifications of older adults can improve drug adherence, reduce the adverse events and pave the way for a better disease period management.

Economic Evaluations of Guideline-Directed Medical Therapies for Heart Failure With Reduced Ejection Fraction: A Systematic Review

OBJECTIVES

Decision-analytic models (DAMs) with varying structures and assumptions have been applied in economic evaluations (EEs) to assist decision making for heart failure with reduced ejection fraction (HFrEF) therapeutics. This systematic review aimed to summarize and critically appraise the EEs of guideline-directed medical therapies (GDMTs) for HFrEF.

METHODS

A systematic search of English articles and gray literature, published from January 2010, was performed on databases including MEDLINE, Embase, Scopus, NHSEED, health technology assessment, Cochrane Library, etc. The included studies were EEs with DAMs that compared the costs and outcomes of angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, angiotensin-receptor neprilysin inhibitors, beta-blockers, mineralocorticoid-receptor agonists, and sodium-glucose cotransporter-2 inhibitors. The study quality was evaluated using the Bias in Economic Evaluation (ECOBIAS) 2015 checklist and Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklists.

RESULTS

A total of 59 EEs were included. Markov model, with a lifetime horizon and a monthly cycle length, was most commonly used in evaluating GDMTs for HFrEF. Most EEs conducted in the high-income countries demonstrated that novel GDMTs for HFrEF were cost-effective compared with the standard of care, with the standardized median incremental cost-effectiveness ratio (ICER) of $21 361/quality-adjusted life-year. The key factors influencing ICERs and study conclusions included model structures, input parameters, clinical heterogeneity, and country-specific willingness-to-pay threshold.

CONCLUSIONS

Novel GDMTs were cost-effective compared with the standard of care. Given the heterogeneity of the DAMs and ICERs, alongside variations in willingness-to-pay thresholds across countries, there is a need to conduct country-specific EEs, particularly in low- and middle-income countries, using model structures that are coherent with the local decision context.

Smartphone-Based Remote Monitoring in Heart Failure With Reduced Ejection Fraction: Retrospective Cohort Study of Secondary Care Use and Costs

BACKGROUND

Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown.

OBJECTIVE

The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM.

METHODS

We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling.

RESULTS

A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustained by a univariate model controlling for hypertension. Over a 3-month period, secondary health care costs were approximately 4-fold lower in the RM group than the control group, despite the additional cost of RM itself (mean cost per patient GBP £465, US $581 vs GBP £1850, US $2313, respectively; P=.04).

CONCLUSIONS

This retrospective cohort study shows that smartphone-based RM of vital signs is feasible for HFrEF. This type of RM was associated with an approximately 2-fold reduction in ED attendance and a 4-fold reduction in emergency admissions over just 3 months after a new diagnosis with HFrEF. Costs were significantly lower in the RM group without increasing outpatient demand. This type of RM could be adjunctive to standard care to reduce admissions, enabling other resources to help patients unable to use RM.

Examining standardized tools used for the evaluation of mobile health applications for cardiovascular disease

Cardiovascular disease is one of the leading causes of death worldwide. Scarce resources and rising costs are pushing healthcare systems to their limits. There is an urgency to develop, optimize and evaluate technologies that provide more effective care for patients. Modern technologies, such as mobile health (mHealth) applications, can provide relief as a key strategy. To integrate digital interventions into care structures, a detailed impact assessment of all professional mHealth applications is needed. The aim of this study is to analyze the standardized tools used in the field of cardiovascular disease. The results show that questionnaires, usage logs, and key indicators are predominantly used. Although the identified mHealth interventions are specific to cardiovascular disease and thus require particular questions to evaluate apps, the user readiness, usability, or quality of life criteria are non-specific. Therefore, the results contribute to understanding how different mHealth interventions can be assessed, categorized, evaluated, and accepted.

An interpretable machine learning approach for predicting 30-day readmission after stroke

BACKGROUND

Stroke is the second leading cause of death worldwide and has a significantly high recurrence rate. We aimed to identify risk factors for stroke recurrence and develop an interpretable machine learning model to predict 30-day readmissions after stroke.

METHODS

Stroke patients deposited in electronic health records (EHRs) in Xuzhou Medical University Hospital between February 1, 2021, and November 30, 2021, were included in the study, and deceased patients were excluded. We extracted 74 features from EHRs, and the top 20 features (chi-2 value) were used to build machine learning models. 80% of the patients were used for pre-training. Subsequently, a 20% holdout dataset was used for verification. The Shapley Additive exPlanations (SHAP) method was used to explore the interpretability of the model.

RESULTS

The cohort included 6,558 patients, of whom the mean (SD) age was 65 (11) years, 3,926 were males (59.86 %), and 132 (2.01 %) were readmitted within 30 days. The area under the receiver operating characteristic curve (AUROC) for the optimized model was 0.80 (95 % CI 0.68-0.80). We used the SHAP method to identify the top 10 risk factors (i.e., severe carotid artery stenosis, weak, homocysteine, glycosylated hemoglobin, sex, lymphocyte percentage, neutrophilic granulocyte percentage, urine glucose, fresh cerebral infarction, and red blood cell count). The AUROC of a model with the 10 features was 0.80 (95 % CI 0.69-0.80) and was not significantly different from that of the model with 20 risk factors.

CONCLUSIONS

Our methods not only showed good performance in predicting 30-day readmissions after stroke but also revealed risk factors that provided valuable insights for treatments.

Vericiguat: A Novel Soluble Guanylate Cyclase Stimulator for Use in Patients With Heart Failure

Heart failure (HF) affects 6.2 million Americans and is increasing annually in its frequency. Treatment of HF has been at the forefront of medical advancements due to the financial burden on our health care system. As such, changes to the guidelines regarding standard of care have been evolving over the last decade with the recent additions of sacubitril-valsartan and sodium glucose co-transporter-2 inhibitors to standard of care in the treatment of HF. Despite the aforementioned expansions in treatment options, HF continues to have a significant impact on the American health care system. Most recently, a novel drug vericiguat that targets an unprecedented pathway for the treatment of HF was Food and Drug Administration approved for the management of patients with HF with a reduced ejection fraction with a recent hospitalization or need for outpatient intravenous diuretics. In clinical trials, vericiguat was associated with a reduction in death from cardiovascular causes and first hospitalization in comparison to placebo. The aim of this review is to provide a comprehensive literature analysis of the various trials surrounding the approval of vericiguat and to both inform and synthesize the data surrounding the clinical use of vericiguat. The introduction of Vericiguat should be considered as a treatment option in patients to decrease the mortality/morbidity of HF with reduced ejection fraction and to increase the quality of life.

Predictors of early heart failure rehospitalization among older adults with preserved and reduced ejection fraction: A review and derivation of a conceptual model

BACKGROUND

Heart failure (HF) is prevalent among older adults who suffer with either heart failure preserved ejection fraction (HFpEF) or heart failure reduced ejection fraction (HFrEF) and have a high rate of early HF rehospitalization. Preventing early rehospitalization is complex because of major differences between the two subtypes of HF as well as inadequate predictive models to identify key contributing factors.

OBJECTIVE

To present research addressing relationships between selected clinical, hemodynamic, social factors, and early (≤ 60-day) HF rehospitalization in older adults with HFpEF and HFrEF, derive a conceptual model of predictors of rehospitalization, and understand to what extent the literature addresses these predictors among older women.

METHODS

Four computerized databases were searched for research addressing clinical, hemodynamic, and social factors relevant to early HF rehospitalization and older adults post index hospitalization for HF.

RESULTS

21 full-text articles were included in the final review and organized thematically. Most studies focused on early (≤ 30-day) HF rehospitalizations, with limited attention given to the 31 to 60-day period. Specific clinical, hemodynamic, and social factors which influenced early HF rehospitalization were identified. The existing literature confirms that risk predictors or their combinations which influence early (≤ 60-day) HF rehospitalization after an index HF hospitalization remains inconsistent. Further, the literature fails to capture the influence of these predictors solely among older women. A conceptual model of risk predictors is proposed for clinical intervention.

CONCLUSION

Further evaluation to understand risk predictors of early (31 to 60-day) HF rehospitalizations among older women is needed.

Feasibility of a Reinforcement Learning-Enabled Digital Health Intervention to Promote Mammograms: Retrospective, Single-Arm, Observational Study

BACKGROUND

Preventive screenings such as mammograms promote health and detect disease. However, mammogram attendance lags clinical guidelines, with roughly one-quarter of women not completing their recommended mammograms. A scalable digital health intervention leveraging behavioral science and reinforcement learning and delivered via email was implemented in a US health system to promote uptake of recommended mammograms among patients who were 1 or more years overdue for the screening (ie, 2 or more years from last mammogram).

OBJECTIVE

The aim of this study was to establish the feasibility of a reinforcement learning-enabled mammography digital health intervention delivered via email. The research aims included understanding the intervention's reach and ability to elicit behavioral outcomes of scheduling and attending mammograms, as well as understanding reach and behavioral outcomes for women of different ages, races, educational attainment levels, and household incomes.

METHODS

The digital health intervention was implemented in a large Catholic health system in the Midwestern United States and targeted the system's existing patients who had not received a recommended mammogram in 2 or more years. From August 2020 to July 2022, 139,164 eligible women received behavioral science-based email messages assembled and delivered by a reinforcement learning model to encourage clinically recommended mammograms. Target outcome behaviors included scheduling and ultimately attending the mammogram appointment.

RESULTS

In total, 139,164 women received at least one intervention email during the study period, and 81.52% engaged with at least one email. Deliverability of emails exceeded 98%. Among message recipients, 24.99% scheduled mammograms and 22.02% attended mammograms (88.08% attendance rate among women who scheduled appointments). Results indicate no practical differences in the frequency at which people engage with the intervention or take action following a message based on their age, race, educational attainment, or household income, suggesting the intervention may equitably drive mammography across diverse populations.

CONCLUSIONS

The reinforcement learning-enabled email intervention is feasible to implement in a health system to engage patients who are overdue for their mammograms to schedule and attend a recommended screening. In this feasibility study, the intervention was associated with scheduling and attending mammograms for patients who were significantly overdue for recommended screening. Moreover, the intervention showed proportionate reach across demographic subpopulations. This suggests that the intervention may be effective at engaging patients of many different backgrounds who are overdue for screening. Future research will establish the effectiveness of this type of intervention compared to typical health system outreach to patients who have not had recommended screenings as well as identify ways to enhance its reach and impact.

Digital Health Literacy in Patients With Heart Failure in Times of Pandemic

This study sought to determine the effect of a digital health literacy program regarding knowledge and skills in the use of digital resources related to self-care and health empowerment for patients with heart failure. A cross-sectional pilot study was conducted before and after the program in patients (n = 28) with heart failure at a tertiary care center. Both a knowledge test and the Health Empowerment Scale were used with a Cronbach's α of 0.89. The information was processed using the statistical software Restudio, which allowed us to make a descriptive and inferential analysis. Seventy-five percent of the participants were men with an average age of 68 years, 60.7% had elementary schooling, 71.4% had preserved ejection fraction, and 57.6% had a family member as a caregiver. A statistically significant change ( P < .005) was found in the level of empowerment and the knowledge and skills in the use of digital resources applied in health. The results showed that the digital health literacy program is a cost-effective intervention that nursing professionals must integrate into continuity of care, not only in pandemic times but also in a permanent and standardized manner. An empowered patient with knowledge and skills in the use of digital resources is a patient with the ability to decide, satisfy needs, and solve problems, with critical thinking and control over their health.

Pragmatic randomized trial assessing the impact of digital health technology on quality of life in patients with heart failure: Design, rationale and implementation

BACKGROUND

Self-care and patient engagement are important elements of heart failure (HF) care, endorsed in the guidelines. Digital health tools may improve quality of life (QOL) in HF patients by promoting care, knowledge, and engagement. This manuscript describes the rationale and challenges of the design and implementation of a pragmatic randomized controlled trial to evaluate the efficacy of three digital health technologies in improving QOL for patients with HF.

HYPOTHESIS

We hypothesize that digital health interventions will improve QOL of HF patients through the early detection of warning signs of disease exacerbation, the opportunity of self-tracking symptoms, and the education provided, which enhances patient empowerment.

METHODS

Using a fully electronic enrollment and consent platform, the trial will randomize 200 patients across HF clinics in the Yale New Haven Health system to receive either usual care or one of three digital technologies designed to promote self-management and provide critical data to clinicians. The primary outcome is the change in QOL as assessed by the Kansas City Cardiomyopathy Questionnaire at 3 months.

RESULTS

First enrollment occurred in September 2021. Recruitment was anticipated to last 6-8 months and participants were followed for 6 months after randomization. Our recruitment efforts have highlighted the large digital divide in our population of interest.

CONCLUSION

Assessing clinical outcomes, patient usability, and ease of clinical integration of digital technologies will be beneficial in determining the feasibility of the integration of such technologies into the healthcare system.

Electronic Health Records and Heart Failure

Increasing the global adoption of electronic health records (EHRs) is transforming the delivery of clinical care. EHRs offer tools that are useful in the care of heart failure ranging from individualized risk stratification and decision support to population management. EHR tools can be combined to target specific areas of need such as the standardization of care, improved quality of care, and resource management. Leveraging EHR functionality has been shown to improve select outcomes including guideline-based therapies, reduction in adverse clinical outcomes, and improved cost-efficiency. Central to success is participation by clinicians and patients in the design and feedback of EHR tools.

A Framework for Evaluating Dashboards in Healthcare

In the era of 'information overload', effective information provision is essential for enabling rapid response and critical decision making. In making sense of diverse information sources, dashboards have become an indispensable tool, providing fast, effective, adaptable, and personalized access to information for professionals and the general public alike. However, these objectives place heavy requirements on dashboards as information systems in usability and effective design. Understanding these issues is challenging given the absence of consistent and comprehensive approaches to dashboard evaluation. In this article we systematically review literature on dashboard implementation in healthcare, where dashboards have been employed widely, and where there is widespread interest for improving the current state of the art, and subsequently analyse approaches taken towards evaluation. We draw upon consolidated dashboard literature and our own observations to introduce a general definition of dashboards which is more relevant to current trends, together with seven evaluation scenarios - task performance, behaviour change, interaction workflow, perceived engagement, potential utility, algorithm performance and system implementation. These scenarios distinguish different evaluation purposes which we illustrate through measurements, example studies, and common challenges in evaluation study design. We provide a breakdown of each evaluation scenario, and highlight some of the more subtle questions. We demonstrate the use of the proposed framework by a design study guided by this framework. We conclude by comparing this framework with existing literature, outlining a number of active discussion points and a set of dashboard evaluation best practices for the academic, clinical and software development communities alike.

Predictors of readmission in hospitalized heart failure patients

Introduction: Heart failure(HF) related hospitalization constitutes a significant proportion of healthcare cost. Unchanging rates of readmission during recent years, shows the importance of addressing this problem. Methods: Patients admitted with heart failure diagnosis in our institution during April 2018to August 2018 were selected. Clinical, para-clinical and imaging data were recorded. All included patients were followed up for 6 months. The primary endpoints of the study were prevalence of early readmission and the predictors of that. Secondary end points were in-hospital and 6-month post-discharge mortality rate and late readmission rate. Results: After excluding 94 patients due to missing data, 428 patients were selected. Mean age of patients was 58.5 years (±17.4) and 61% of patients were male. During follow-up, 99patients (24%) were readmitted. Early re-admission (30-day) occurred in 27 of the patients(6.6%). The predictors of readmission were older age ( P=0.006), lower LVEF (P <0.0001), higher body weight (P=0.01), ICD/CRT implantation ( P=0.001), Lower sodium ( P=0.01), higher Pro-BNP(P=0.01), Higher WBC count (P=0.01) and higher BUN level (P=0.02). Independent predictors of early readmission were history of device implantation (P=0.007), lower LVEF (P=0.016), QRS duration more than 120 ms (P=0.037), higher levels of BUN (P=0.008), higher levels of Pro-BNP(P=0.037) and higher levels of uric acid (P=0.035). Secondary end points including in-hospital and 6-month post-discharge mortality occurred in 11% and 14.4% of patients respectively. Conclusion: Lower age of our heart failure patients and high prevalence of ischemic cardiomyopathy, necessitate focusing on more preventable factors related to heart failure.

Continuous remote patient monitoring in heart failure patients (CASCADE study): mixed methods feasibility protocol (Preprint)

Background

Heart failure (HF) is a prevalent chronic disease and is associated with increases in mortality and morbidity. HF is a leading cause of hospitalizations and readmissions in the United States. A potentially promising area for preventing HF readmissions is continuous remote patient monitoring (CRPM).

Objective

The primary aim of this study is to determine the feasibility and preliminary efficacy of a CRPM solution in patients with HF at NorthShore University HealthSystem.

Methods

This study is a feasibility study and uses a wearable biosensor to continuously remotely monitor patients with HF for 30 days after discharge. Eligible patients admitted with an HF exacerbation at NorthShore University HealthSystem are being recruited, and the wearable biosensor is placed before discharge. The biosensor collects physiological ambulatory data, which are analyzed for signs of patient deterioration. Participants are also completing a daily survey through a dedicated study smartphone. If prespecified criteria from the physiological data and survey results are met, a notification is triggered, and a predetermined electronic health record–based pathway of telephonic management is completed. In phase 1, which has already been completed, 5 patients were enrolled and monitored for 30 days after discharge. The results of phase 1 were analyzed, and modifications to the program were made to optimize it. After analysis of the phase 1 results, 15 patients are being enrolled for phase 2, which is a calibration and testing period to enable further adjustments to be made. After phase 2, we will enroll 45 patients for phase 3. The combined results of phases 1, 2, and 3 will be analyzed to determine the feasibility of a CRPM program in patients with HF. Semistructured interviews are being conducted with key stakeholders, including patients, and these results will be analyzed using the affective adaptation of the technology acceptance model.

Results

During phase 1, of the 5 patients, 2 (40%) were readmitted during the study period. The study completion rate for phase 1 was 80% (4/5), and the study attrition rate was 20% (1/5). There were 57 protocol deviations out of 150 patient days in phase 1 of the study. The results of phase 1 were analyzed, and the study protocol was adjusted to optimize it for phases 2 and 3. Phase 2 and phase 3 results will be available by the end of 2022.

Conclusions

A CRPM program may offer a low-risk solution to improve care of patients with HF after hospital discharge and may help to decrease readmission of patients with HF to the hospital. This protocol may also lay the groundwork for the use of CRPM solutions in other groups of patients considered to be at high risk.

International Registered Report Identifier (IRRID)

DERR1-10.2196/36741

Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch

OBJECTIVE

We report on our experience of deploying a continuous remote patient monitoring (CRPM) study soft launch with structured cascading and escalation pathways on heart failure (HF) patients post-discharge. The lessons learned from the soft launch are used to modify and fine-tune the workflow process and study protocol.

METHODS

This soft launch was conducted at NorthShore University HealthSystem's Evanston Hospital from December 2020 to March 2021. Patients were provided with non-invasive wearable biosensors that continuously collect ambulatory physiological data, and a study phone that collects patient-reported outcomes. The physiological data are analyzed by machine learning algorithms, potentially identifying physiological perturbation in HF patients. Alerts from this algorithm may be cascaded with other patient status data to inform home health nurses' (HHNs') management via a structured protocol. HHNs review the monitoring platform daily. If the patient's status meets specific criteria, HHNs perform assessments and escalate patient cases to the HF team for further guidance on early intervention.

RESULTS

We enrolled five patients into the soft launch. Four participants adhered to study activities. Two out of five patients were readmitted, one due to HF, one due to infection. Observed miscommunication and protocol gaps were noted for protocol amendment. The study team adopted an organizational development method from change management theory to reconfigure the study protocol.

CONCLUSION

We sought to automate the monitoring aspects of post-discharge care by aligning a new technology that generates streaming data from a wearable device with a complex, multi-provider workflow into a novel protocol using iterative design, implementation, and evaluation methods to monitor post-discharge HF patients. CRPM with structured escalation and telemonitoring protocol shows potential to maintain patients in their home environment and reduce HF-related readmissions. Our results suggest that further education to engage and empower frontline workers using advanced technology is essential to scale up the approach.

Methods Used to Evaluate mHealth Applications for Cardiovascular Disease: A Quasi-Systematic Scoping Review

In the face of demographic change and constantly increasing health care costs, health care system decision-makers face ever greater challenges. Mobile health applications (mHealth apps) have the potential to combat this trend. However, in order to integrate mHealth apps into care structures, an evaluation of such apps is needed. In this paper, we focus on the criteria and methods of evaluating mHealth apps for cardiovascular disease and the implications for developing a widely applicable evaluation framework for mHealth interventions. Our aim is to derive substantiated patterns and starting points for future research by conducting a quasi-systematic scoping review of relevant peer-reviewed literature published in English or German between 2000 and 2021. We screened 4066 articles and identified n = 38 studies that met our inclusion criteria. The results of the data derived from these studies show that usability, motivation, and user experience were evaluated primarily using standardized questionnaires. Usage protocols and clinical outcomes were assessed primarily via laboratory diagnostics and quality-of-life questionnaires, and cost effectiveness was tested primarily based on economic measures. Based on these findings, we propose important considerations and elements for the development of a common evaluation framework for professional mHealth apps, including study designs, data collection tools, and perspectives.

The Effect of Noninvasive Telemonitoring for Chronic Heart Failure on Health Care Utilization: Systematic Review

Background

Chronic heart failure accounts for approximately 1%-2% of health care expenditures in most developed countries. These costs are primarily driven by hospitalizations and comorbidities. Telemonitoring has been proposed to reduce the number of hospitalizations and decrease the cost of treatment for patients with heart failure. However, the effects of telemonitoring on health care utilization remain unclear.

Objective

This systematic review aims to study the effect of telemonitoring programs on health care utilization and costs in patients with chronic heart failure. We assess the effect of telemonitoring on hospitalizations, emergency department visits, length of stay, hospital days, nonemergency department visits, and health care costs.

Methods

We searched PubMed, Embase, and Web of Science for randomized controlled trials and nonrandomized studies on noninvasive telemonitoring and health care utilization. We included studies published between January 2010 and August 2020. For each study, we extracted the reported data on the effect of telemonitoring on health care utilization. We used P<.05 and CIs not including 1.00 to determine whether the effect was statistically significant.

Results

We included 16 randomized controlled trials and 13 nonrandomized studies. Inclusion criteria, population characteristics, and outcome measures differed among the included studies. Most studies showed no effect of telemonitoring on health care utilization. The number of hospitalizations was significantly reduced in 38% (9/24) of studies, whereas emergency department visits were reduced in 13% (1/8) of studies. An increase in nonemergency department visits (6/9, 67% of studies) was reported. Health care costs showed ambiguous results, with 3 studies reporting an increase in health care costs, 3 studies reporting a reduction, and 4 studies reporting no significant differences. Health care cost reductions were realized through a reduction in hospitalizations, whereas increases were caused by the high costs of the telemonitoring program or increased health care utilization.

Conclusions

Most telemonitoring programs do not show clear effects on health care utilization measures, except for an increase in nonemergency outpatient department visits. This may be an unwarranted side effect rather than a prerequisite for effective telemonitoring. The consequences of telemonitoring on nonemergency outpatient visits should receive more attention from regulators, payers, and providers. This review further demonstrates the high clinical and methodological heterogeneity of telemonitoring programs. This should be taken into account in future meta-analyses aimed at identifying the effective components of telemonitoring programs.

Use of mobile applications for heart failure self-management: a systematic review of experimental and qualitative studies (Preprint)

Background

Heart failure self-management is essential to avoid decompensation and readmissions. Mobile apps seem promising in supporting heart failure self-management, and there has been a rapid growth in publications in this area. However, to date, systematic reviews have mostly focused on remote monitoring interventions using nonapp types of mobile technologies to transmit data to health care providers, rarely focusing on supporting patient self-management of heart failure.

Objective

This study aims to systematically review the evidence on the effect of heart failure self-management apps on health outcomes, patient-reported outcomes, and patient experience.

Methods

Four databases (PubMed, Embase, CINAHL, and PsycINFO) were searched for studies examining interventions that comprised a mobile app targeting heart failure self-management and reported any health-related outcomes or patient-reported outcomes or perspectives published from 2008 to December 2021. The studies were independently screened. The risk of bias was appraised using Cochrane tools. We performed a narrative synthesis of the results. The protocol was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42020158041).

Results

A total of 28 articles (randomized controlled trials [RCTs]: n=10, 36%), assessing 23 apps, and a total of 1397 participants were included. The most common app features were weight monitoring (19/23, 83%), symptom monitoring (18/23, 78%), and vital sign monitoring (15/23, 65%). Only 26% (6/23) of the apps provided all guideline-defined core components of heart failure self-management programs: education, symptom monitoring, medication support, and physical activity support. RCTs were small, involving altogether 717 participants, had ≤6 months of follow-up, and outcomes were predominantly self-reported. Approximately 20% (2/10) of RCTs reported a significant improvement in their primary outcomes: heart failure knowledge (P=.002) and self-care (P=.004). One of the RCTs found a significant reduction in readmissions (P=.02), and 20% (2/10) of RCTs reported higher unplanned clinic visits. Other experimental studies also found significant improvements in knowledge, self-care, and readmissions, among others. Less than half of the studies involved patients and clinicians in the design of apps. Engagement with the intervention was poorly reported, with only 11% (3/28) of studies quantifying app engagement metrics such as frequency of use over the study duration. The most desirable app features were automated self-monitoring and feedback, personalization, communication with clinicians, and data sharing and integration.

Conclusions

Mobile apps may improve heart failure self-management; however, more robust evaluation studies are needed to analyze key end points for heart failure. On the basis of the results of this review, we provide a road map for future studies in this area.

The course of readmission in frail older cardiac patients

AIM

The aim of this study is to explore patients' and (in)formal caregivers' perspectives on their role(s) and contributing factors in the course of unplanned hospital readmission of older cardiac patients in the Cardiac Care Bridge (CCB) program.

DESIGN

This study is a qualitative multiple case study alongside the CCB randomized trial, based on grounded theory principles.

METHODS

Five cases within the intervention group, with an unplanned hospital readmission within six months after randomization, were selected. In each case, semi-structured interviews were held with patients (n = 4), informal caregivers (n = 5), physical therapists (n = 4), and community nurses (n = 5) between April and June 2019. Patients' medical records were collected to reconstruct care processes before the readmission. Thematic analysis and the six-step analysis of Strauss & Corbin have been used.

RESULTS

Three main themes emerged. Patients experienced acute episodes of physical deterioration before unplanned hospital readmission. The involvement of (in)formal caregivers in adequate observation of patients' health status is vital to prevent rehospitalization (theme 1). Patients and (in)formal caregivers' perception of care needs did not always match, which resulted in hampering care support (theme 2). CCB caregivers experienced difficulties in providing care in some cases, resulting in limited care provision in addition to the existing care services (theme 3).

CONCLUSION

Early detection of deteriorating health status that leads to readmission was often lacking, due to the acuteness of the deterioration. Empowerment of patients and their informal caregivers in the recognition of early signs of deterioration and adequate collaboration between caregivers could support early detection. Patients' care needs and expectations should be prioritized to stimulate participation.

IMPACT

(In)formal caregivers may be able to prevent unplanned hospital readmission of older cardiac patients by ensuring: (1) early detection of health deterioration, (2) empowerment of patient and informal caregivers, and (3) clear understanding of patients' care needs and expectations.

Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research

With new advances in technology, remote monitoring of heart failure (HF) patients has become increasingly prevalent and has the potential to greatly enhance the outcome of care. Many studies have focused on implementing systems for the management of HF by analyzing physiological signals for the early detection of HF decompensation. This paper reviews recent literature exploring significant physiological variables, compares their reliability in predicting HF-related events, and examines the findings according to the monitored variables used such as body weight, bio-impedance, blood pressure, heart rate, and respiration rate. The reviewed studies identified correlations between the monitored variables and the number of alarms, HF-related events, and/or readmission rates. It was observed that the most promising results came from studies that used a combination of multiple parameters, compared to using an individual variable. The main challenges discussed include inaccurate data collection leading to contradictory outcomes from different studies, compliance with daily monitoring, and consideration of additional factors such as physical activity and diet. The findings demonstrate the need for a shared remote monitoring platform which can lead to a significant reduction of false alarms and help in collecting reliable data from the patients for clinical use especially for the prevention of cardiac events.

Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction

Increasing evidence suggests that digital health interventions (DHIs) are an effective tool to reduce hospital readmissions by improving adherence to guideline-directed therapy. We investigated whether sociodemographic characteristics influence use of a DHI targeting 30-day readmission reduction after acute myocardial infarction (AMI). Covariates included age, sex, race, native versus loaner iPhone, access to a Bluetooth-enabled blood pressure monitor, and disease severity as marked by treatment with CABG. Age, sex, and race were not significantly associated with DHI use before or after covariate adjustment (fully adjusted OR 0.98 (95%CI: 0.95-1.01), 0.6 (95%CI: 0.29-1.25), and 1.22 (95% CI: 0.60-2.48), respectively). Being married was associated with high DHI use (OR 2.12; 95% CI 1.02-4.39). Our findings suggest that DHIs may have a role in achieving equity in cardiovascular health given similar use by age, sex, and race. The presence of a spouse, perhaps a proxy for enhanced caregiver support, may encourage DHI use.

Effects of App-Based Transitional Care on the Self-Efficacy and Quality of Life of Patients With Spinal Cord Injury in China: Randomized Controlled Trial

BACKGROUND

Spinal cord injury (SCI) severely impairs the physical and mental health of patients, decreasing their self-efficacy in coping with daily life and quality of life (QOL). In China, a large gap remains between the complex long-term health needs of SCI patients and the current community care system. With the prevalence of mobile terminals, the usage of mobile health apps has the potential to fill this gap by extending qualified medical resources to the families of SCI patients. Our team developed the app Together for the transitional care of home-dwelling SCI patients in China.

OBJECTIVE

This study aimed to evaluate the effects of app-based transitional care on the self-efficacy and QOL of SCI patients.

METHODS

Through a three-round Delphi process, an Android app was designed. Both medical staff and patients could access the app. Medical staff used it for providing remote transitional care to SCI patients. Patients used it to view transitional care time and send messages to medical staff. Thereafter, a multicenter and assessor-blinded randomized controlled trial was conducted. Participants (n=98) who had SCI and lived at home following discharge were recruited and randomly assigned to a study group (n=49) and control group (n=49) using a randomized number list in four research centers. Patients in both groups received systematic discharge education before discharge. The study group received five follow-ups conducted by trained nurses through the app, which had four core functions, namely remote assessment, health education, interdisciplinary referral, and patient interaction, at weeks 2, 4, 6, 8, and 12 following discharge. The control group received a routine telephone follow-up conducted by nurses at week 12 following discharge. The outcome measures were the Moorong Self-Efficacy Scale (MSES) and 36-item Short-Form Health Survey (SF-36) scores. Data were collected before discharge (T0) and at weeks 12 (T1) and 24 following discharge (T2). Differences between the groups were tested by repeated measures analysis of variance and simple effect analysis.

RESULTS

After the follow-up, the total MSES scores in the study group improved over time (T0=67.80, T1=71.90, and T2=76.29) and were higher than those in the control group (T2=64.49) at 24 weeks following discharge (simple effect analysis: F1=8.506, P=.004). Regarding the total SF-36 score, although it was higher in patients from the study group (T2=65.36) than those from the control group (T2=58.77) at 24 weeks following discharge, only time effects were significant (F2,95=6.671, P=.002) and neither the group effects nor the interaction effects influenced the change in QOL (group effects: F1,96=0.082, P=.78; interaction effects: F2,95=3.059, P=.052).

CONCLUSIONS

This study confirmed that app-based transitional care improves the self-efficacy of SCI patients. Nevertheless, QOL improvement is not yet evident. Future investigations with larger sample sizes and longer observation periods are warranted to further verify the effects.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR-IPR-17012317; http://www.chictr.org.cn/showproj.aspx?proj=19828.

An assessment of race and gender-based biases among readmission predicting tools (HOSPITAL, LACE, and RAHF) in heart failure population

BACKGROUND

The objective of our study is to retrospectively investigate if the HOSPITAL score, LACE index, and RAHF scale exhibit any bias based on gender and race in heart failure readmissions.

METHODS

This is a retrospective cohort study with all adult medical patients discharged with congestive heart failure from 2016 to 2018 from Southern Illinois University School of Medicine Hospitalist service. The receiver operating characteristic (ROC) curve was constructed comparing prediction tools (HOSPITAL score, LACE index, and RAHF scale) performance based on gender and race by measuring the area under the curve (AUC). Absolute Between-ROC Area (ABROCA) values were calculated. All statistical analyses were performed using R version 3.6.2.

RESULTS

The performance of the HOSPITAL score in the majority and minority population showed a statistically significant difference between AUCs (0.714 and 0.633, p = 0.029) and an ABROCA of 0.081 indicating superior performance in predicting hospital readmissions in the majority group vs. the minority. The performance of RAHF score in females and males showed statistically significant differences between AUCs (0.567 and 0.527, p = 0.04) and an ABROCA of 0.04 indicating the superior performance of the RAHF score in females compared with males.

CONCLUSIONS

Our study demonstrated that the HOSPITAL score and the RAHF scale showed significant differences in predicting 30-day readmissions risk based on race and gender, respectively, in heart failure patients, whereas the LACE index did not show any significant difference.

Remote Monitoring in Chronic Heart Failure Patients: Is Non-Invasive Remote Monitoring the Way to Go?

Heart failure (HF) is a major health care issue, and the incidence of HF is only expected to grow further. Due to the frequent hospitalizations, HF places a major burden on the available hospital and healthcare resources. In the future, HF care should not only be organized solely at the clinical ward and outpatient clinics, but remote monitoring strategies are urgently needed to guide, monitor, and treat chronic HF patients remotely from their homes as well. The intuitiveness and relatively low costs of non-invasive remote monitoring tools make them an appealing and emerging concept for developing new medical apps and devices. The recent COVID-19 pandemic and the associated transition of patient care outside the hospital will boost the development of remote monitoring tools, and many strategies will be reinvented with modern tools. However, it is important to look carefully at the inconsistencies that have been reported in non-invasive remote monitoring effectiveness. With this review, we provide an up-to-date overview of the available evidence on non-invasive remote monitoring in chronic HF patients and provide future perspectives that may significantly benefit the broader group of HF patients.

Habits Heart App for Patient Engagement in Heart Failure Management: Pilot Feasibility Randomized Trial

Background

Due to the complexity and chronicity of heart failure, engaging yet simple patient self-management tools are needed.

Objective

This study aimed to assess the feasibility and patient engagement with a smartphone app designed for heart failure.

Methods

Patients with heart failure were randomized to intervention (smartphone with the Habits Heart App installed and Bluetooth-linked scale) or control (paper education material) groups. All intervention group patients were interviewed and monitored closely for app feasibility while receiving standard of care heart failure management by cardiologists. The Atlanta Heart Failure Knowledge Test, a quality of life survey (Kansas City Cardiomyopathy Questionnaire), and weight were assessed at baseline and final visits.

Results

Patients (N=28 patients; intervention: n=15; control: n=13) with heart failure (with reduced ejection fraction: 15/28, 54%; male: 20/28, 71%, female: 8/28, 29%; median age 63 years) were enrolled, and 82% of patients (N=23; intervention: 12/15, 80%; control: 11/13, 85%) completed both baseline and final visits (median follow up 60 days). In the intervention group, 2 out of the 12 patients who completed the study did not use the app after study onboarding due to illnesses and hospitalizations. Of the remaining 10 patients who used the app, 5 patients logged ≥1 interaction with the app per day on average, and 2 patients logged an interaction with the app every other day on average. The intervention group averaged 403 screen views (per patient) in 56 distinct sessions, 5-minute session durations, and 22 weight entries per patient. There was a direct correlation between duration of app use and improvement in heart failure knowledge (Atlanta Heart Failure Knowledge Test score; ρ=0.59, P=.04) and quality of life (Kansas City Cardiomyopathy Questionnaire score; ρ=0.63, P=.03). The correlation between app use and weight change was ρ=–0.40 (P=.19). Only 1 out of 11 patients in the control group retained education material by the follow-up visit.

Conclusions

The Habits Heart App with a Bluetooth-linked scale is a feasible way to engage patients in heart failure management, and barriers to app engagement were identified. A larger multicenter study may be warranted to evaluate the effectiveness of the app.

Trial Registration

ClinicalTrials.gov NCT03238729; http://clinicaltrials.gov/ct2/show/NCT03238729

The efficacy of telemedical care for heart failure: A meta-analysis of randomized controlled trials

INTRODUCTION

The efficacy of telemedical care for the treatment of heart failure remains controversial. We conduct a systematic review and meta-analysis to explore the impact of telemedical care on heart failure.

METHODS

We search PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through October 2020 for randomized controlled trials (RCTs) assessing the effect of telemedical care on heart failure. This meta-analysis is performed using the random-effect model.

RESULTS

Four RCTs involving 2516 patients are included in the meta-analysis. Overall, compared with control group for heart failure, telemedical care demonstrates no significant influence on cardiovascular death (OR = 0.74; 95% CI = 0.54 to 1.00; P = 0.05), mortality (OR = 0.86; 95% CI = 0.61 to 1.20; P = 0.38), hospital stay for heart failure (SMD = -1.57; 95% CI = -6.31 to 3.16; P = 0.52) or hospital stay for any readmission (SMD = -0.65; 95% CI = -8.98 to 7.68; P = 0.88), but can reduce the days lost due to death or heart failure readmissions (SMD = -6.50; 95% CI = -8.44 to -4.56; P < 0.00001).

CONCLUSIONS

Telemedical care may provide no additional benefits for heart failure.

Digital Health Applications in Heart Failure: a Critical Appraisal of Literature

Purpose of the review

Despite advancements in the diagnostic and therapeutic armamentarium, heart failure (HF) remains a major public health concern in the USA and worldwide. Digital health applications hold promise to bridge this gap and improve HF care. This review will provide the reader with a concise overview of the current digital health applications in HF, the main challenges to its use, and discuss the future of digital health for promoting care for HF patients.

Recent findings

Emerging evidence continues to support the potential role of digital health across the continuum of HF disease process including primary prevention, early detection, disease management, and reducing associated morbidity. There is also increasing emphasis on the need to pursue rigorous investigations to validate these promising claims, with some successful stories that have changed clinical practices.

Summary

Digital health technologies have emerged as potentially useful tools to complement HF care in both research and clinical realms. As digital technologies continue to play an increasing role in transforming healthcare delivery, creating the framework for its effective use would be necessary to ensure that digital health applications consistently improve outcomes and enhance care for HF patients.

Impact of Digital Health Monitoring in the Management of Inflammatory Bowel Disease

Technological advances now permit self-management strategies using mobile applications which could greatly benefit patient care. The purpose of this study was to investigate whether the use of the inflammatory bowel disease (IBD) digital health monitoring platform, HealthPROMISE, leads to better quality of care and improved health outcomes in IBD patients. IBD patients were recruited in gastroenterology clinics and asked to install the HealthPROMISE application onto their smartphones. Patient satisfaction, quality of care, quality of life, patient symptoms, and resource utilization metrics were collected throughout the study and sent directly to their healthcare teams. Patients with abnormal symptom/SIBDQ scores were flagged for their physicians to follow up. After one-year, patient outcome metrics were compared to baseline values. Overall, out of 59 patients enrolled in the study, 32 patients (54%) logged into the application at least once during the study period. The number of IBD-related ER visits/hospitalizations in the year of use compared to the prior year demonstrated a significant decrease from 25% of patients (8/32) to 3% (1/32) (p = 0.03). Patients also reported an increase in their understanding of the nature/causes of their condition after using the application (p = 0.026). No significant changes were observed in the number of quality indicators met (p = 0.67) or in SIBDQ scores (p = 0.48). Given the significant burden of IBD, there is a need to develop effective management strategies. This study demonstrated that digital health monitoring platforms may aid in reducing the number of ER visits and hospitalizations in IBD patients.

Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy

Despite clinical advances in its treatment, heart failure (HF) is associated with significant adverse clinical outcomes and is among the greatest drivers of healthcare utilization. Outpatient management of HF remains suboptimal, with gaps in the provision of evidence-based therapies, and difficulties in predicting and managing clinical decompensation. Remote patient monitoring (RPM) has the potential to address these issues, and thus has been of increasing interest to HF clinicians and health systems. Economic incentives, including increasing RPM reimbursement and HF readmission penalties, are also spurring increased interest in RPM. This review establishes a framework for evaluating RPM based on its various components: 1) patient data collection, 2) data transmission, analysis, and presentation, and 3) care team review and clinical action. The existing evidence regarding RPM in HF management is also reviewed. Based on the data, we identify RPM features associated with clinical efficacy and describe emerging digital tools that have the promise of addressing current needs.

Heart Failure with Preserved Ejection Fraction and 30-Day Readmission

OBJECTIVE

Several studies identify heart failure (HF) as a potential risk for hospital readmission; however, studies on predictability of heart failure readmission is limited. The objective of this work was to investigate whether a specific type of heart failure (HFpEF or HFrEF) has a higher association to the rate of 30-day hospital readmission and compare their predictability with the two risk scores: HOSPITAL score and LACE index.

DESIGN

Retrospective study from single academic center.

METHODS

Sample size included adult patients from an academic hospital in a two-year period (2015 - 2017). Exclusion criteria included death, transfer to another hospital, and unadvised leave from hospital. Baseline characteristics, diagnosis-related group, and ICD diagnosis codes were obtained. Variables affecting HOSPITAL score and LACE index and types of heart failure present were also extracted. Qualitative variables were compared using Pearson chi² or Fisher's exact test (reported as frequency) and quantitative variables using non-parametric Mann-Whitney U test (reported as mean ± standard deviation). Variables from univariate analysis with P values of 0.05 or less were further analyzed using multivariate logistic regression. Odds ratio was used to measure potential risk.

RESULTS

The sample size of adult patients in the study period was 1,916. All eligible cohort of patients who were readmitted were analyzed. Cumulative score indicators of HOSPITAL Score, LACE index (including the Charlson Comorbidity Index) predicted 30-day readmissions with P values of <0.001. The P value of HFpEF was found to be significant in the readmitted group (P < 0.001) compared to HFrEF (P = 0.141). Multivariate logistic regression further demonstrated the association of HFpEF with higher risk of readmission with odds ratio of 1.77 (95% CI: 1.25 - 2.50) and P value of 0.001.

CONCLUSIONS

Our data from an academic tertiary care center supports HFpEF as an independent risk factor for readmission. Multidisciplinary management of HFpEF may be an important target for interventions to reduce hospital readmissions.