Changes in practice patterns in Japan from before to after JSDT 2013 guidelines on hemodialysis prescriptions: results from the JDOPPS

Background The Japanese Society for Dialysis Therapy (JSDT) published in 2013 inaugural hemodialysis (HD) guidelines. Specific targets include 1.4 for single-pool Kt/V (spKt/V) with a minimum dose of 1.2, minimum dialysis session length of 4 hours, minimum blood flow rate (BFR) of 200 mL/min, fluid removal rate no more than 15 mL/kg/hr, and hemodiafiltration (HDF) therapy for certain identified symptoms. We evaluated the effect of these guidelines on actual practice in the years spanning 2005 – 2018. Methods Analyses were carried out to describe trends in the above HD prescription practices from December 2005 to April 2013 (before guideline publication) to August 2018 based on prevalent patient cross-sections from approximately 60 randomly selected HD facilities participating in the Japan Dialysis Outcomes and Practice Patterns Study. Results From April 2006 to August 2017 continual rises occurred in mean spKt/V (from 1.35 to 1.49), and percent of patients having spKt/V>1.2 (71% to 85%). Mean BFR increased with time from 198.3 mL/min (April 2006) to 218.4 mL/min (August 2017) , along with percent of patients with BFR >200 ml/min (65% to 85%). HDF use increased slightly from 6% (April 2006 and August 2009) to 8% by April 2013, but increased greatly thereafter to 23% by August 2017. In contrast, mean HD treatment time showed little change from 2006-2017, whereas mean UFR declined from 11.3 in 2006 to 8.4 mL/Kg/hour in 2017. Conclusions From 2006 – 2018 Japanese HD patients experienced marked improvement in reaching the spKt/V target specified by the 2013 JSDT guidelines. This may have been due to moderate increase in mean BFR even though mean HD session length did not change much. In addition, HDF use increased dramatically in this time period. Other HD delivery changes during this time, such as increased use of super high flux dialyzers, also merit study. While we cannot definitively conclude a causal relationship between the publication of the guidelines and the subsequent practice changes in Japan, those changes moved practice closer to the recommendations of the guidelines.

COVID-19 Critical Care Simulations: An International Cross-Sectional Survey

Objective: To describe the utility and patterns of COVID-19 simulation scenarios across different international healthcare centers.Methods: This is a cross-sectional, international survey for multiple simulation centers team members, including team-leaders and healthcare workers (HCWs), based on each center's debriefing reports from 30 countries in all WHO regions. The main outcome measures were the COVID-19 simulations characteristics, facilitators, obstacles, and challenges encountered during the simulation sessions.Results: Invitation was sent to 343 simulation team leaders and multidisciplinary HCWs who responded; 121 completed the survey. The frequency of simulation sessions was monthly (27.1%), weekly (24.8%), twice weekly (19.8%), or daily (21.5%). Regarding the themes of the simulation sessions, they were COVID-19 patient arrival to ER (69.4%), COVID-19 patient intubation due to respiratory failure (66.1%), COVID-19 patient requiring CPR (53.7%), COVID-19 transport inside the hospital (53.7%), COVID-19 elective intubation in OR (37.2%), or Delivery of COVID-19 mother and neonatal care (19%). Among participants, 55.6% reported the team's full engagement in the simulation sessions. The average session length was 30–60 min. The debriefing process was conducted by the ICU facilitator in (51%) of the sessions followed by simulation staff in 41% of the sessions. A total of 80% reported significant improvement in clinical preparedness after simulation sessions, and 70% were satisfied with the COVID-19 sessions. Most perceived issues reported were related to infection control measures, followed by team dynamics, logistics, and patient transport issues.Conclusion: Simulation centers team leaders and HCWs reported positive feedback on COVID-19 simulation sessions with multidisciplinary personnel involvement. These drills are a valuable tool for rehearsing safe dynamics on the frontline of COVID-19. More research on COVID-19 simulation outcomes is warranted; to explore variable factors for each country and healthcare system.

A Clinical Feasibility Study of an Artificial Intelligence-Powered Clinical Decision Support System

Objective: We examine the feasibility of an Artificial Intelligence (AI)-powered clinical decision support system (CDSS), which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. Methods: Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with major depressive disorder (MDD) as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. Results: Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. Conclusions: Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment: A Longitudinal Feasibility Study (Preprint)

BACKGROUND Approximately two thirds of patients with major depressive disorder (MDD) do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence (AI)-powered clinical decision support systems (CDSS) to assist physicians in their treatment selection and management, improving personalization and use of best practices such as measurement-based care. Previous literature shows that in order for digital mental health tools to be successful, the tool must be easy to use for patients and physicians and feasible within existing clinical workflows. OBJECTIVE We examine the feasibility of an AI-powered clinical decision support system, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. METHODS Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with MDD as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. RESULTS Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. CONCLUSIONS Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies. CLINICALTRIAL NCT04061642

A Survey on Session-based Recommender Systems

Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.

Quantifying COVID-19’s Impact on Telemedicine Utilization: a Retrospective Observational Study (Preprint)

BACKGROUND While telemedicine has been expanding over the past decade, the COVID-19 pandemic era restrictions regarding in-person care have led to unprecedented levels of telemedicine utilization. To the authors’ knowledge, no studies to date have quantitatively analyzed both national and regional trends in telemedicine utilization during COVID-19, both of which have key implications for informing health policy. OBJECTIVE To investigate how trends in telemedicine utilization changed across the course of the COVID-19 pandemic. METHODS Using data from doxy.me, the largest free telemedicine platform, and the NIH Clinical Center, the largest U.S. clinical research hospital, we assessed changes in total telemedicine minutes, new provider registrations, monthly sessions, and average session length from March-November 2020. We also conducted state-level analysis of how telemedicine expansion differed by region. RESULTS National telemedicine utilization peaked in April 2020 at 291 million minutes and stabilized at 200-220 million monthly minutes from May to November 2020. Surges were strongest in New England and weakest in the South and West. Greater telemedicine expansion during COVID-19 was geographically associated with lower COVID-19 cases per capita. The nature of telemedicine visits also changed, as the average monthly visits per provider doubled and average visit length decreased by 60%. CONCLUSIONS The COVID-19 pandemic led to an abrupt and subsequently sustained uptick in telemedicine utilization. Regional and institute-level differences in telemedicine utilization should be further investigated to inform policy and procedures for sustaining meaningful telemedicine use in clinical practice.

Activation of VTA GABA Neurons Disrupts Reward Seeking by Altering Temporal Processing

The role of ventral tegmental area (VTA) dopamine in reward, cue processing, and interval timing is well characterized. Using a combinatorial viral approach to target activating DREADDs (Designer Receptors Exclusively Activated by Designer Drugs, hM3D) to GABAergic neurons in the VTA of male rats, we previously showed that activation disrupts responding to reward-predictive cues. Here we explored how VTA GABA neurons influence the perception of time in two fixed interval (FI) tasks, one where the reward or interval is not paired with predictive cues (Non-Cued FI), and another where the start of the FI is signaled by a constant tone that continues until the rewarded response is emitted (Cued FI). Under vehicle conditions in both tasks, responding was characterized by “scalloping” over the 30s FI, in which responding increased towards the end of the FI. However, when VTA GABA neurons were activated in the Non-Cued FI, the time between the end of the 30s interval and when the rats made a reinforced response increased. Additionally, post-reinforcement pauses and overall session length increased. In the Cued FI task, VTA GABA activation produced erratic responding, with a decrease in earned rewards. Thus, while both tasks were disrupted by VTA GABA activation, responding that is constrained by a cue was more sensitive to this manipulation, possibly due to convergent effects on timing and cue processing. Together these results demonstrate that VTA GABA activity disrupts the perception of interval timing, particularly when the timing is set by cues.