Sze-Yuan Ooi

Introduction: Heart failure care requires coordination across hospital and community settings, yet information is often fragmented across electronic medical records and clinical systems. Clinical dashboards can bring together and display information to support care delivery and service management; however, existing dashboards have generally focused on specific measures, interventions and monitoring pathways. This study aimed to co-design, iteratively develop and user-test an integrated heart failure dashboard that links patient-level clinical decision-making with service-level management. Methods: A human-centred design approach comprising needs identification, collaborative ideation, iterative prototype development and end-user testing was applied across two complementary operational and clinical dashboard streams. Thirty-six clinicians, health service managers, data and implementation scientists, and consumers from metropolitan and regional services participated. Results: For operational decision-making, participants prioritised real-time visibility of patients across heart failure services, patient trajectories, service-performance information, and identification of variation in guideline-directed care and outcomes. Clinical priorities included rapid synthesis of longitudinal information, optimisation of guideline-directed medical therapy, continuity across care settings, and clinical workload prioritisation. These requirements informed the dashboard prototypes. Many prioritised information elements were incompletely represented in structured data and distributed across disconnected systems and structured and unstructured clinical data sources. Conclusion: Human-centred co-design identified complementary patient- and service-level information needs and translated them into linked dashboard prototypes. The proposed dashboard brings together current clinical status and longitudinal heart failure care history at the patient-level alongside service-level patterns. Implementation is required to evaluate whether these linked views improve care processes and patient outcomes.

Background: Mobile health (mHealth) interventions are increasing in popularity for the management of heart failure and coronary artery disease. The use of these interventions is dependent on rates of smartphone ownership. It is estimated that approximately 90% of the Australian adult population owns a smartphone; however, international studies suggest that smartphone ownership is significantly lower in patient populations, ranging from 34% to 91%. Smartphone ownership in patients with cardiovascular disease has not previously been examined. Objective: This study aimed to examine and compare pre–COVID-19 and post–COVID-19 pandemic smartphone ownership rates of inpatients admitted with coronary artery disease or heart failure. Methods: Data from prescreening logs of 2 multicenter randomized controlled trials, TeleClinical Care (TCC)-Pilot and TCC-Cardiac, were reviewed. TCC-Pilot recruited patients between February 2019 and March 2020. This formed the pre–COVID-19 cohort, with 377 patients screened who lived in Sydney, had a qualifying hospital admission, and had information regarding their phone ownership status. TCC-Cardiac recruited patients from July 2021 to February 2023, with 718 patients meeting the criteria and forming the post–COVID-19 cohort. Supplemental patient demographic and medical history data were collected from the electronic medical record. Results: In the pre–COVID-19 cohort (N=377), 194 (51.5%) patients owned smartphones, 79 (21%) owned phones that were incompatible with the mHealth intervention, and the remaining 104 (27.6%) did not own a mobile phone. Smartphone owners were predominantly male ( P <.001) and more often had private health insurance ( P =.002). In the post–COVID-19 cohort (N=718), 366 (51%) patients owned smartphones, 106 (14.8%) owned incompatible phones, and the remaining 246 (34.3%) did not own any mobile phone. In both cohorts, younger patients were more likely to own smartphones ( P <.001). Multiple comorbidities were associated with not owning a phone. Conclusions: Smartphone ownership accounted for just over 50% of the patients in this population. It was less common among older adults, patients with comorbidities, and those with markers of lower socioeconomic status. This needs to be considered when delivering mHealth interventions.

Objectives: This study aimed to develop and validate a machine learning model to predict deterioration using Australian hospital data, paying particular attention to the role of predictors not included in current scoring systems. Design: Retrospective cohort study using electronic health records from a large metropolitan health service. Setting: General hospital wards, excluding the Emergency Department, Intensive Care Unit, or Palliative Care. Participants: Inpatients over the age of 18. Main Outcome Measures: The primary outcomes of deterioration were mortality and ICU transfer within 24 hours of a newly available observation. A Gradient Boosted Tree model was estimated using patient demographics, vital signs, pathology results, and linear trends. Resulting feature importance was investigated using Shapley values. The model performance was validated against existing scoring systems, including Between the Flags (BTF) and the Modified / National Early Warning Score (MEWS/NEWS). Results: A Gradient Boosted Tree was developed from 121,608 patients and tested in 20,605 patients. The model, named aWARE, demonstrated higher discriminative ability (AUROC _mortality =0.93, AUROC _{ICU transfer} =0.84), and calibration when compared to baseline scores. Overall, the 10 most influential features unique between both outcomes were age, oxygen saturation to inspired oxygen ratio, respiratory rate, white cell count, venous lactate, heart rate to systolic blood pressure ratio, albumin, oxygen saturation, urea and heart rate. Of these, only 3 are included in BTF. Conclusion: The machine learning model proposed in this study identified more deteriorating patients and produced less false positive alerts than Between the Flags. Feature importance highlighted the deficit between strong predictors of deterioration and the parameters used in current scoring systems.

Aims: An infection following cardiac implantable electronic device (CIED) procedure is a serious complication, but its association with all-cause mortality is inconsistent across observational studies. To quantify the association between CIED infection and all-cause mortality in a large, contemporary cohort from New South Wales, Australia. Methods and results: This retrospective cohort study used linked hospital and mortality data and included all patients aged >18 years who underwent a CIED procedure between July 2017 and September 2022. Cardiac implantable electronic device infection was defined by the presence of relevant diagnosis codes. Cox regression to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) for the association of CIED infection with mortality, at 1-year, and at the end of follow-up, with CIED infection included as a time-dependent variable, and other potential risk factors for mortality included as fixed covariates. We followed 37,750 patients with CIED procedures {36% female, mean age [standard deviation (SD)] 75.8 [12.7] years}, and 487 (1.3%) CIED infections were identified. We observed 5771 (15.3%) deaths during an average follow-up of 25.2 (SD 16.8) months. Compared with no infection group, patients with CIED infection had a higher Kaplan-Meier mortality rate (19.4 vs. 6.8%) and adjusted hazard of mortality (aHR 2.73, 95% CI 2.10−3.54) at 12 months post-procedure. These differences were attenuated but still remained significant at the end of follow-up (aHR 1.83, 95% CI 1.52−2.19). Conclusion: In a complete, state–wide cohort of CIED patients, infection was associated with higher risks of both short–term and long–term mortality.

Background: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. Objective: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app–based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. Methods: In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients’ smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients’ usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months. Results: Overall, 164 inpatients were randomized (TCC: 81/164, 49.4%; control: 83/164, 50.6%; mean age 61.5, SD 12.3 years; 130/164, 79.3% men; 128/164, 78% admitted with ACS). There were 11 unplanned 30-day readmissions in both groups (P=.97). Over a mean follow-up of 193 days, the intervention was associated with a significant reduction in unplanned hospital readmissions (21 in TCC vs 41 in the control arm; P=.02), including cardiac readmissions (11 in TCC vs 25 in the control arm; P=.03), and higher rates of cardiac rehabilitation completion (20/51, 39% vs 9/49, 18%; P=.03) and medication adherence (57/76, 75% vs 37/74, 50%; P=.002). The average usability rating for the app was 4.5/5. The intervention cost Aus $6028 (US $4342.26) per cardiac readmission saved. When modeled in a mainstream clinical setting, enrollment of 237 patients was projected to have the same expenditure compared with usual care, and enrollment of 500 patients was projected to save approximately Aus $100,000 (approximately US $70,000) annually. Conclusions: TCC was feasible and safe for inpatients with either ACS or HF. The incidence of 30-day readmissions was similar; however, long-term benefits were demonstrated, including fewer readmissions over 6 months, improved medication adherence, and improved cardiac rehabilitation completion. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618001547235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375945

Background: Cardiovascular disease (CVD) remains the leading cause of death worldwide. Mobile phones have become ubiquitous in most developed societies. Smartphone apps, telemonitoring, and clinician-driven SMS allow for novel opportunities and methods in managing chronic CVD, such as ischemic heart disease, heart failure, and hypertension, and in the conduct and support of cardiac rehabilitation. Objective: A systematic review was conducted using seven electronic databases, identifying all relevant randomized control trials (RCTs) featuring a mobile phone intervention (MPI) used in the management of chronic CVD. Outcomes assessed included mortality, hospitalizations, blood pressure (BP), and BMI. Methods: Electronic data searches were performed using seven databases from January 2000 to June 2019. Relevant articles were reviewed and analyzed. Meta-analysis was performed using standard techniques. The odds ratio (OR) was used as a summary statistic for dichotomous variables. A random effect model was used. Results: A total of 26 RCTs including 6713 patients were identified and are described in this review, and 12 RCTs were included in the meta-analysis. In patients with heart failure, MPIs were associated with a significantly lower rate of hospitalizations (244/792, 30.8% vs 287/803, 35.7%; n=1595; OR 0.77, 95% CI 0.62 to 0.97; P=.03; I2=0%). In patients with hypertension, patients exposed to MPIs had a significantly lower systolic BP (mean difference 4.3 mm Hg; 95% CI −7.8 to −0.78 mm Hg; n=2023; P=.02). Conclusions: The available data suggest that MPIs may have a role as a valuable adjunct in the management of chronic CVD.

BACKGROUND Cardiovascular disease (CVD) remains the leading cause of death worldwide. Mobile phones have become ubiquitous in most developed societies. Smartphone apps, telemonitoring, and clinician-driven SMS allow for novel opportunities and methods in managing chronic CVD, such as ischemic heart disease, heart failure, and hypertension, and in the conduct and support of cardiac rehabilitation. OBJECTIVE A systematic review was conducted using seven electronic databases, identifying all relevant randomized control trials (RCTs) featuring a mobile phone intervention (MPI) used in the management of chronic CVD. Outcomes assessed included mortality, hospitalizations, blood pressure (BP), and BMI. METHODS Electronic data searches were performed using seven databases from January 2000 to June 2019. Relevant articles were reviewed and analyzed. Meta-analysis was performed using standard techniques. The odds ratio (OR) was used as a summary statistic for dichotomous variables. A random effect model was used. RESULTS A total of 26 RCTs including 6713 patients were identified and are described in this review, and 12 RCTs were included in the meta-analysis. In patients with heart failure, MPIs were associated with a significantly lower rate of hospitalizations (244/792, 30.8% vs 287/803, 35.7%; n=1595; OR 0.77, 95% CI 0.62 to 0.97; P=.03; I2=0%). In patients with hypertension, patients exposed to MPIs had a significantly lower systolic BP (mean difference 4.3 mm Hg; 95% CI −7.8 to −0.78 mm Hg; n=2023; P=.02). CONCLUSIONS The available data suggest that MPIs may have a role as a valuable adjunct in the management of chronic CVD.