Effectiveness of Clopidogrel vs Alternative P2Y12 Inhibitors Based on the ABCD-GENE Score.

BACKGROUND: An ABCD-GENE (age, body mass index, chronic kidney disease, diabetes, and CYP2C19 genetic variants) score ≥10 predicts reduced clopidogrel effectiveness, but its association with response to alternative therapy remains unclear. OBJECTIVES: The aim of this study was to evaluate the association between ABCD-GENE score and the effectiveness of clopidogrel vs alternative P2Y12 inhibitor (prasugrel or ticagrelor) therapy after percutaneous coronary intervention (PCI). METHODS: A total of 4,335 patients who underwent PCI, CYP2C19 genotyping, and P2Y12 inhibitor treatment were included. The primary outcome was major atherothrombotic events (MAE) within 1 year after PCI. Cox regression was performed to assess event risk in clopidogrel-treated (reference) vs alternatively treated patients, with stabilized inverse probability weights derived from exposure propensity scores after stratifying by ABCD-GENE score and further by CYP2C19 loss-of-function (LOF) genotype. RESULTS: Among patients with scores <10 (n = 3,200), MAE was not different with alternative therapy vs clopidogrel (weighted HR: 0.89; 95% CI: 0.65-1.22; P = 0.475). The risk for MAE also did not significantly differ by treatment among patients with scores ≥10 (n = 1,135; weighted HR: 0.75; 95% CI: 0.51-1.11; P = 0.155). Among CYP2C19 LOF allele carriers, MAE risk appeared lower with alternative therapy in both the group with scores <10 (weighted HR: 0.50; 95% CI: 0.25-1.01; P = 0.052) and the group with scores ≥10 (weighted HR: 0.48; 95% CI: 0.29-0.80; P = 0.004), while there was no difference in the group with scores <10 and no LOF alleles (weighted HR: 1.03; 95% CI: 0.70-1.51; P = 0.885). CONCLUSIONS: These data support the use of alternative therapy over clopidogrel in CYP2C19 LOF allele carriers after PCI, regardless of ABCD-GENE score, while clopidogrel is as effective as alternative therapy in non-LOF patients with scores <10.

Correction: Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

[This corrects the article DOI: 10.2196/14124.].

Correction: Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study.

[This corrects the article DOI: 10.2196/16391.].

Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery.

BACKGROUND: Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone. METHODS: A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty. RESULTS: The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone. CONCLUSIONS: Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.

Digital Health Intervention in Acute Myocardial Infarction.

BACKGROUND: Thirty-day readmissions among patients with acute myocardial infarction (AMI) contribute to the US health care burden of preventable complications and costs. Digital health interventions (DHIs) may improve patient health care self-management and outcomes. We aimed to determine if patients with AMI using a DHI have lower 30-day unplanned all-cause readmissions than a historical control. METHODS: This nonrandomized controlled trial with a historical control, conducted at 4 US hospitals from 2015 to 2019, included 1064 patients with AMI (DHI n=200, control n=864). The DHI integrated a smartphone application, smartwatch, and blood pressure monitor to support guideline-directed care during hospitalization and through 30-days post-discharge via (1) medication reminders, (2) vital sign and activity tracking, (3) education, and (4) outpatient care coordination. The Patient Activation Measure assessed patient knowledge, skills, and confidence for health care self-management. All-cause 30-day readmissions were measured through administrative databases. Propensity score-adjusted Cox proportional hazard models estimated hazard ratios of readmission for the DHI group relative to the control group. RESULTS: Following propensity score adjustment, baseline characteristics were well-balanced between the DHI versus control patients (standardized differences <0.07), including a mean age of 59.3 versus 60.1 years, 30% versus 29% Women, 70% versus 70% White, 54% versus 54% with private insurance, 61% versus 60% patients with a non ST-elevation myocardial infarction, and 15% versus 15% with high comorbidity burden. DHI patients were predominantly in the highest levels of patient activation for health care self-management (mean score 71.7±16.6 at 30 days). The DHI group had fewer all-cause 30-day readmissions than the control group (6.5% versus 16.8%, respectively). Adjusting for hospital site and a propensity score inclusive of age, sex, race, AMI type, comorbidities, and 6 additional confounding factors, the DHI group had a 52% lower risk for all-cause 30-day readmissions (hazard ratio, 0.48 [95% CI, 0.26-0.88]). Similar results were obtained in a sensitivity analysis employing propensity matching. CONCLUSIONS: Our results suggest that in patients with AMI, the DHI may be associated with high patient activation for health care self-management and lower risk of all-cause unplanned 30-day readmissions. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03760796.

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.

Digital Health Interventions for Cardiac Rehabilitation: Systematic Literature Review.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. Despite strong evidence supporting the benefits of cardiac rehabilitation (CR), over 80% of eligible patients do not participate in CR. Digital health technologies (ie, the delivery of care using the internet, wearable devices, and mobile apps) have the potential to address the challenges associated with traditional facility-based CR programs, but little is known about the comprehensiveness of these interventions to serve as digital approaches to CR. Overall, there is a lack of a systematic evaluation of the current literature on digital interventions for CR. OBJECTIVE: The objective of this systematic literature review is to provide an in-depth analysis of the potential of digital health technologies to address the challenges associated with traditional CR. Through this review, we aim to summarize the current literature on digital interventions for CR, identify the key components of CR that have been successfully addressed through digital interventions, and describe the gaps in research that need to be addressed for sustainable and scalable digital CR interventions. METHODS: Our strategy for identifying the primary literature pertaining to CR with digital solutions (defined as technology employed to deliver remote care beyond the use of the telephone) included a consultation with an expert in the field of digital CR and searches of the PubMed (MEDLINE), Embase, CINAHL, and Cochrane databases for original studies published from January 1990 to October 2018. RESULTS: Our search returned 31 eligible studies, of which 22 were randomized controlled trials. The reviewed CR interventions primarily targeted physical activity counseling (31/31, 100%), baseline assessment (30/31, 97%), and exercise training (27/31, 87%). The most commonly used modalities were smartphones or mobile devices (20/31, 65%), web-based portals (18/31, 58%), and email-SMS (11/31, 35%). Approximately one-third of the studies addressed the CR core components of nutrition counseling, psychological management, and weight management. In contrast, less than a third of the studies addressed other CR core components, including the management of lipids, diabetes, smoking cessation, and blood pressure. CONCLUSIONS: Digital technologies have the potential to increase access and participation in CR by mitigating the challenges associated with traditional, facility-based CR. However, previously evaluated interventions primarily focused on physical activity counseling and exercise training. Thus, further research is required with more comprehensive CR interventions and long-term follow-up to understand the clinical impact of digital interventions.

Mobile health application platform 'Corrie' personalises and empowers the heart attack recovery patient experience in the hospital and at home for an underserved heart attack survivor.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide with an estimated 17.5 million deaths annually, according to the World Health Organization (WHO). CVD prevention efforts have the potential to prevent the majority of these deaths by supporting guideline-directed medical therapy (GDMT) and lifestyle modification. Mobile health (mHealth) has the potential to address this gap, but has limited evaluation in clinical studies to date. We present the case of a middle-aged patient of low socioeconomic status, with multiple comorbidities, and no prior smartphone experience, who suffered an acute myocardial infarction (MI) and was given the Corrie intervention while hospitalised. The patient demonstrated improvement in lifestyle modification, adherence to GDMT and post-MI recovery through 2.4 years follow-up. This case supports (1) the potential of mHealth interventions to enhance patient experience and outcomes, (2) intuitive design for adoption and improvement in end user experience and (3) the capability of mHealth to reach and empower underserved patients.

Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study.

BACKGROUND: As smartphone ownership continues to rise, health care systems and technology companies are driven to develop mobile health (mHealth) interventions as both diagnostic and therapeutic tools. An important consideration during mHealth intervention development is how to achieve health equity despite demographic differences in smartphone ownership. One solution is through the recirculation of loaner smartphones; however, best practices for implementing such programs to optimize security, privacy, scalability, and convenience for participants are not well defined. OBJECTIVE: In this tutorial, we describe how we implemented our novel Corrie iShare program, a 30-day loaner iPhone and smartwatch recirculation program, as part of a multi-center mHealth intervention to improve recovery and access to guideline-directed therapy following acute myocardial infarction. METHODS: We conducted a prospective study utilizing a smartphone app and leveraged iOS enterprise features as well as cellular data service to automate recirculation. RESULTS: Our configuration protocol was shortened from 1 hour to 10 minutes. Of 200 participants, 92 (46.0%) did not own an iPhone and would have been excluded from the study without iShare. Among iShare participants, 72% (66/92) returned their loaned smartphones. CONCLUSIONS: The Corrie iShare program demonstrates the potential for a sustainable and scalable mHealth loaner program, enabling broader population reach while optimizing user experience. Implementation may face institutional constraints and software limitations. Consideration should be given to optimizing loaner returns.

The role of a clinician amid the rise of mobile health technology.

Mobile health (mHealth) interventions have demonstrated promise in improving outcomes by motivating patients to adopt and maintain healthy lifestyle changes as well as improve adherence to guideline-directed medical therapy. Early results combining behavioral economic strategies with mHealth delivery have demonstrated mixed results. In reviewing these studies, we propose that the success of a mHealth intervention links more strongly with how well it connects patients back to routine clinical care, rather than its behavior modification technique in isolation. This underscores the critical role of clinician-patient partnerships in the design and delivery of such interventions, while also raising important questions regarding long-term sustainability and scalability. Further exploration of our hypothesis may increase opportunities for multidisciplinary clinical teams to connect with and engage patients using mHealth technologies in unprecedented ways.

Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

The explosion of mobile health (mHealth) interventions has prompted significant investment and exploration that has extended past industry into academia. Although research in this space is emerging, it focuses on the clinical and population level impact across different populations. To realize the full potential of mHealth, an intimate understanding of how mHealth is being used by patients and potential differences in usage between various demographic groups must also be prioritized. In this viewpoint, we use our experiences in building an mHealth intervention that incorporates an iOS app, Bluetooth-enabled blood pressure cuff, and Apple Watch to share knowledge on (1) how user interaction data can be tracked in the context of health care privacy laws, (2) what is required for effective, nuanced communication between clinicians and engineers to design mHealth interventions that are patient-centered and have high clinical impact, and (3) how to handle and set up a process to handle user interaction data efficiently.

Corrie Health Digital Platform for Self-Management in Secondary Prevention After Acute Myocardial Infarction.

BACKGROUND: Unplanned readmissions after hospitalization for acute myocardial infarction are among the leading causes of preventable morbidity, mortality, and healthcare costs. Digital health interventions could be an effective tool in promoting self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction. A digital health intervention developed at Johns Hopkins-the Corrie Health Digital Platform (Corrie)-includes the first cardiology Apple CareKit smartphone application, which is paired with an Apple Watch and iHealth Bluetooth-enabled blood pressure cuff. Corrie targets: (1) self-management of cardiac medications, (2) self-tracking of vital signs, (3) education about cardiovascular disease through articles and animated videos, and (4) care coordination that includes outpatient follow-up appointments. METHODS AND RESULTS: The 3 phases of the MiCORE study (Myocardial infarction, Combined-device, Recovery Enhancement) include (1) the development of Corrie, (2) a pilot study to assess the usability and feasibility of Corrie, and (3) a prospective research study to primarily compare time to first readmission within 30 days postdischarge among patients with Corrie to patients in the historical standard of care comparison group. In Phase 2, the feasibility of deploying Corrie in an acute care setting was established among a sample of 60 patients with acute myocardial infarction. Phase 3 is ongoing and patients from 4 hospitals are being enrolled as early as possible during their hospital stay if they are 18 years or older, admitted with acute myocardial infarction (ST-segment-elevation myocardial infarction or type I non-ST-segment-elevation myocardial infarction), and own a smartphone. Patients are either being enrolled with their own personal devices or they are provided an iPhone and/or Apple Watch for the duration of the study. Phase 3 started in October 2017 and we aim to recruit 140 participants. CONCLUSIONS: This article will provide an in-depth understanding of the feasibility associated with implementing a digital health intervention in an acute care setting and the potential of Corrie as a self-management tool for acute myocardial infarction recovery.

Pediatric Antibacterial and Antifungal Trials From 2007 to 2017.

BACKGROUND AND OBJECTIVES: The impact of the Best Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA) on pediatric antibacterial or antifungal drug trials is unknown. Our objective was to identify and characterize trials conducted under the BPCA and/or the PREA. METHODS: Pediatric antibacterial and antifungal drug trials with industry or US federal funding registered in clinicaltrials.gov from 2007 to 2017 were identified. Those conducted under BPCA and/or PREA were identified through US Food and Drug Administration and National Institute of Child Health and Human Development databases. RESULTS: Of 17 495 pediatric trials registered on clinicaltrials.gov between October 2007 and September 2017, 122 systemic antibacterial or antifungal drug trials with industry or US federal funding were identified. Of these 122 trials, 98 (80%) involved antibacterials only, 23 (19%) antifungals only, and 1 (1%) both antibacterials and antifungals. These represented <1% (122 of 17 495) of pediatric trials. Neither pediatric antibacterial nor antifungal drug trials commonly enrolled neonates 0 to 30 days old (30% [30 of 99] vs 42% [10 of 24], respectively). Pediatric antibacterial and antifungal trials were commonly industry funded (79% [78 of 99] and 83% [20 of 24], respectively). In total, 65% (79 of 122) of pediatric antibacterial and/or antifungal drug trials were conducted under BPCA and/or PREA. Researchers in trials conducted under BPCA and/or PREA, relative to non-BPCA and/or PREA trials, more often collected pharmacokinetic data (70% [55 of 79] vs 26% [11 of 43]). CONCLUSIONS: Although the majority of pediatric antibacterial and/or antifungal drug trials were conducted under BPCA and/or PREA, the overall number was low. Greater effort is needed to stimulate such trials.

Transcriptomic Analysis of the Host Response and Innate Resilience to Enterotoxigenic Escherichia coli Infection in Humans.

BACKGROUND: Enterotoxigenic Escherichia coli (ETEC) is a globally prevalent cause of diarrhea. Though usually self-limited, it can be severe and debilitating. Little is known about the host transcriptional response to infection. We report the first gene expression analysis of the human host response to experimental challenge with ETEC. METHODS: We challenged 30 healthy adults with an unattenuated ETEC strain, and collected serial blood samples shortly after inoculation and daily for 8 days. We performed gene expression analysis on whole peripheral blood RNA samples from subjects in whom severe symptoms developed (n = 6) and a subset of those who remained asymptomatic (n = 6) despite shedding. RESULTS: Compared with baseline, symptomatic subjects demonstrated significantly different expression of 406 genes highlighting increased immune response and decreased protein synthesis. Compared with asymptomatic subjects, symptomatic subjects differentially expressed 254 genes primarily associated with immune response. This comparison also revealed 29 genes differentially expressed between groups at baseline, suggesting innate resilience to infection. Drug repositioning analysis identified several drug classes with potential utility in augmenting immune response or mitigating symptoms. CONCLUSIONS: There are statistically significant and biologically plausible differences in host gene expression induced by ETEC infection. Differential baseline expression of some genes may indicate resilience to infection.

What was old is new again: using the host response to diagnose infectious disease.

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, focusing on both pathogen discovery and the host response. Here, we review how 'omics' technologies improve sepsis diagnosis, early pathogen identification and personalize therapy. Such host response diagnostics are possible due to the confluence of advanced laboratory techniques (e.g., transcriptomics, metabolomics, proteomics) along with advanced mathematical modeling such as machine learning techniques. The road ahead is promising, but obstacles remain before the impact of such advanced diagnostic modalities is felt at the bedside.