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IntroductionProstate cancer (PCa) is the most common cancer in Canadian men. Current models of survivorship care are no longer adequate to address the chronic and complex survivorship needs of patients today. Virtual care models for cancer survivorship have recently been associated with comparable clinical outcomes and lower costs to traditional follow-up care, with patients favouring off-site and on-demand visits. Building on their viability, our research group conceived the Ned Clinic—a virtual PCa survivorship model that provides patients with access to lab results, collects patient-reported outcomes, alerts clinicians to emerging issues, and promotes patient self-care. Despite the promise of the Ned Clinic, the model remains limited by its dependence on oncology specialists, lack of an autonomous triage algorithm, and has only been implemented among PCa survivors living in Ontario.Methods and analysisOur programme of research comprises two main research objectives: (1) to evaluate the process and cost of implementing and sustaining five nurse-led virtual PCa survivorship clinics in three provinces across Canada and identify barriers and facilitators to implementation success and (2) to assess the impact of these virtual clinics on implementation and effectiveness outcomes of enrolled PCa survivors. The design phase will involve developing an autonomous triage algorithm and redesigning the Ned Clinic towards a nurse-led service model. Site-specific implementation plans will be developed to deploy a localised nurse-led virtual clinic at each centre. Effectiveness will be evaluated using a historical control study comparing the survivorship outcomes of 300 PCa survivors enrolled in the Ned Clinic with 300 PCa survivors receiving traditional follow-up care.Ethics and disseminationAppropriate site-specific ethics approval will be secured prior to each research phase. Knowledge translation efforts will include diffusion, dissemination, and application approaches to ensure that knowledge is translated to both academic and lay audiences.

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

Introduction: Testicular cancer is the most commonly diagnosed malignancy in young males. Testicular examination is a non-invasive and inexpensive means of detecting testicular cancer at an early stage. In this project, a set of 3D-printed models was developed to facilitate teaching testicular examination and improving understanding of testicular malignancies among patients and medical learners. Methods: Five scrotum models were designed: a control model with healthy testes, and four models containing a healthy testicle and a testicle with an endophytic mass of varying size. The anatomy, texture, and composition of the 3D-printed models were refined using an iterative process between the design team and urologists. The completed models were assessed by six urologists, two urology nurse practitioners, and 32 medical learners. Participants were asked to inspect and palpate each model, and to provide feedback using a five-point Likert scale. Results: Clinicians reported that the models enabled accurate simulation of a testicular examination involving both healthy and pathological testes (x̅=4.3±1.0). They agreed that the models would be useful teaching tools for both medical learners (x̅=4.8±0.5) and patients (x̅=4.8±0.7). Following an educational session with the models, medical learners reported improvements in confidence and skill in performing a testicular examination. Conclusions: 3D-printed models can effectively simulate palpation of both healthy and pathological testes. The developed models have the potential to be a useful adjunct in teaching testicular examination and in demonstrating abnormal findings that require further investigation.

Background: Digital health could serve as a low-cost means of enabling better self-care in patients living with heart failure (HF) in resource-limited settings such as Uganda. However, digital health interventions previously deployed in such settings have been unsuccessful due to a lack of local patient and clinician engagement in the design process. Objective: To engage Ugandan HF patients and clinicians regarding their experiences with HF management and technology, so as to inform the future design of a digital health intervention for HF patients in Uganda. Methods: The study employed a convergent parallel mixed-methods design. Data collection was completed at the Uganda Heart Institute in Kampala, Uganda. Data were ascertained through a patient survey and semi-structured interviews completed with HF patients, caregivers, physicians, and nurses. A conventional content analysis approach was used to qualitatively examine interview transcripts. Findings: Survey data were collected from 101 HF patients (62 female/39 male, aged 54.2 ± 17.5 years). Nearly half (48%) disagreed that they knew what to do in response to changes in their HF symptoms. Almost all patients (98%) had access to a mobile device. Many patients (63%) identified as comfortable in using mobile money – a local set of services that use Unstructured Supplementary Service Data (USSD). Interviews were completed with 19 HF patients, three caregivers, seven physicians, and three nurses. Qualitative analysis revealed four clusters of themes: overdependence of patients on the clinic, inconvenience associated with attending the clinic, inconsistent patient self-care behaviours at home, and technological abilities that favoured USSD-based services. Conclusions: Ugandan HF patients possess unmet information needs that leave them ill-equipped to care for themselves. Future digital health interventions for this population should empower patients with HF-specific information and reassurance in their self-care abilities. Based on patient preferences, such systems should harness USSD technology with which most patients are already comfortable.

Urothelial carcinoma in a crossed fused renal ectopia (CFRE) is an exceedingly rare clinical finding. We describe the surgical management used to treat upper tract urothelial carcinoma in a 64-year-old man with a right-to-left CFRE. Nephroureterectomy with bladder cuff excision was the treatment of choice. The fused kidney was carefully dissected until an area of demarcation emerged between the vasculature supplying the left and right moieties. Pressure was applied to the isthmus separating the two moieties, and a sharp incision was made to release the left moiety. The operation was completed with limited blood loss. Pathology revealed a high-grade T3 papillary urothelial carcinoma with negative margins. To our knowledge, the case is the first to report urothelial carcinoma in a right-to-left CFRE.

Background The prevalence of non-communicable diseases (NCDs) is rising in low- and middle-income countries (LMICs). Self-management, which enables patients to better manage their health, presents a potentially-scalable means of mitigating the growing burden of NCDs in LMICs. Though the effectiveness of self-management interventions in high-income countries is well-documented, the use of these strategies in LMICs has yet to be thoroughly summarized. Objective The purpose of this scoping review is to summarize the nature and effectiveness of past interventions that have enabled the self-management of NCDs in LMICs. Methods Using the scoping review methodology proposed by Arksey and O'Malley, PubMed was searched for relevant articles published between January 2007 and December 2018. The implemented search strategy comprised three major themes: self-management, NCDs and LMICs. Results Thirty-six original research articles were selected for inclusion. The selected studies largely focused on the self-management of diabetes (N = 21), hypertension (N = 7) and heart failure (N = 5). Most interventions involved the use of short message service (SMS, N = 17) or phone calls (N = 12), while others incorporated educational sessions (N = 10) or the deployment of medical devices (N = 4). The interventions were generally effective and often led to improvements in physiologic indicators, patient self-care and/or patient quality of life. However, the studies emphasized results in small populations, with little indication of future scaling of the intervention. Furthermore, the results indicate a need for further research into the self-management of cardiovascular diseases, as well as for the co-management of diabetes and cardiovascular disease. Conclusions Self-management appears to be an effective means of improving health outcomes in LMICs. Future strategies should include patients and clinicians in all stages of design and development, allowing for a focus on long-term sustainability, scalability and interoperability of the intervention in the target setting.

Background Prognostication of heart failure patients from cardiopulmonary exercise test (CPET) currently involves simplification of complex time-series data into summary indices. We hypothesized that prognostication could be improved by considering the totality of the data generated during a CPET, instead of using summary indices alone. Methods and Results Complete data from 1156 CPETs were used to predict clinical deterioration (characterized by initiation of mechanical circulatory support, listing for heart transplantation or mortality) 1 year post-CPET. We compared the prognostic value (area under the receiver operating characteristic curve) of (1) the most predictive summary indices, (2) staged data collected at discrete intervals using multivariable regression models, and (3) breath-by-breath data using a feedforward neural network. The top-performing models were compared with the commonly used CPET risk score, using absolute net reclassification index. All models were trained and assessed using a 100-iteration Monte Carlo cross-validation. A total of 190 (16.4%) patients experienced clinical deterioration. The summary indices demonstrated subpar discriminative value (area under the receiver operating characteristic curve ≤0.800). Each multivariable model outperformed the summary indices, with the neural network incorporating breath-by-breath data achieving the best performance (area under the receiver operating characteristic curve =0.842). When compared with the CPET risk score (area under the receiver operating characteristic curve =0.759), the top-performing model obtained a net reclassification index of 4.9%. Conclusions The current practice of considering summary indices in isolation fails to realize the full value of CPET data. This may lead to less accurate prognostication of patients and in consequence, inaccurate selection of patients for advanced therapy. Clinical practices, like CPET prognostication, must be continuously reevaluated to ensure optimal usage of valuable (and oft-underutilized) data sources.

Detailed epidemiologic examination of the distribution of Kawasaki disease (KD) cases could help elucidate the etiology and pathogenesis of this puzzling condition. Location of residence at KD admission was obtained for patients diagnosed in Canada (excluding Quebec) between March 2004 and March 2015. We identified 4,839 patients, 164 of whom (3.4%) developed a coronary artery aneurysm (CAA). A spatiotemporal clustering analysis was performed to determine whether non-random clusters emerged in the distributions of KD and CAA cases. A high-incidence KD cluster occurred in Toronto, ON, between October 2004 and May 2005 (116 cases; relative risk (RR) = 3.43; p