Use of a device-based remote management heart failure care pathway is associated with reduced hospitalization and improved patient outcomes: TriageHF Plus real-world evaluation

Background: Heart failure (HF) is a leading cause of hospital admission. However, prompt identification of worsening HF using implantable device data and proactive intervention may reduce hospitalizations. The validated TriageHF algorithm in enabled ICD/CRT devices uses sensor data to risk stratify patients for HF hospitalization in the next 30 days. TriageHF Plus is a novel device-based HF care pathway (DHFP) that uses high risk status as the trigger for remote intervention (see Figure 1 for pathway overview). Outcomes after DHFP implementation in a clinical setting have not been examined. Purpose(s): To evaluate the impact of TriageHF Plus clinical pathway on hospitalisation rates. Method(s): A prospective, multi-center evaluation comparing monthly hospitalization rates for patients enrolled in a DHFP with a concurrent standard of care (SoC) cohort and characterizing staffing resources necessary to implement the DHFP. The DHFP cohort received telephonic assessment and guideline-directed clinical care upon transition to high-risk status. Propensity scores (PS) were applied to DHFP and SoC cohorts to allow unbiased comparison. A negative binomial model was fitted to the monthly number of all-cause hospitalizations with treatment group (DHFP vs. SoC) as a covariate, using PS as weights. Result(s): Between 09/11/2019 and 06/24/2021, 758 patients were included in the study (443 DHFP, 315 SoC). Proportion CRT 76%/ 89% and LVEF <50% 78%/ 66% for DHFP/ SoC, respectively. 196 high risk transmissions prompted telephone assessment, with successful contact in 182;of which, 79 (43%) identified an explanatory acute medical issue. A secondary intervention was undertaken in 44/79 (56%). High risk transmissions took on average 19 minutes per clinical assessment (initial telephone triage and 30 day follow up). The rate of hospitalizations was 58% lower in the DHFP group, compared with SoC, after PS adjustment (IRR 0.42, 95% CI: 0.23, 0.76, p=0.004), see Figure 2. Sensitivity analyses showed Covid-19 had little effect on results. Conclusion(s): This is the first prospective, real-world evaluation of a device-based HF care pathway to report a reduction in hospitalizations and does so with minimal staffing time. Integrated into existing HF services, device-based remote monitoring of HF patients can improve outcomes. (Figure Presented) .

Use of a device-based remote management heart failure care pathway is associated with reduced hospitalization and improved patient outcomes: TriageHF Plus real-world evaluation

Background: Heart failure (HF) is a leading cause of hospital admission. However, prompt identification of worsening HF using implantable device data and proactive intervention may reduce hospitalizations.The validated TriageHF algorithm in enabled ICD/CRT devices uses sensor data to risk stratify patients for HF hospitalization in the next 30 days. TriageHF Plus is a novel device-based HF care pathway (DHFP) that uses "high” risk status as the trigger for remote intervention (see Figure 1 for pathway overview). Outcomes after DHFP implementation in a clinical setting have not been examined. Purpose: To evaluate the impact of TriageHF Plus clinical pathway on hospitalisation rates. Methods: A prospective, multi-center evaluation comparing monthly hospitalization rates for patients enrolled in a DHFP with a concurrent standard of care (SoC) cohort and characterizing staffing resources necessary to implement the DHFP. The DHFP cohort received telephonic assessment and guideline-directed clinical care upon transition to high-risk status. Propensity scores (PS) were applied to DHFP and SoC cohorts to allow unbiased comparison. A negative binomial model was fitted to the monthly number of all-cause hospitalizations with treatment group (DHFP vs. SoC) as a covariate, using PS as weights. Results: Between 09/11/2019 and 06/24/2021, 758 patients were included in the study (443 DHFP, 315 SoC). Proportion CRT 76%/ 89% and LVEF <50% 78%/ 66% for DHFP/ SoC, respectively.196 high risk transmissions prompted telephone assessment, with successful contact in 182;of which, 79 (43%) identified an explanatory acute medical issue. A secondary intervention was undertaken in 44/79 (56%). High risk transmissions took on average 19 minutes per clinical assessment (initial telephone triage and 30 day follow up). The rate of hospitalizations was 58% lower in the DHFP group, compared with SoC, after PS adjustment (IRR 0.42, 95% CI: 0.23, 0.76, p=0.004), see Figure 2. Sensitivity analyses showed Covid-19 had little effect on results. Conclusions: This is the first prospective, real-world evaluation of a device-based HF care pathway to report a reduction in hospitalizations and does so with minimal staffing time. Integrated into existing HF services, device-based remote monitoring of HF patients can improve outcomes. Funding Acknowledgement: Type of funding sources: Private company. Main funding source(s): MedtronicFigure 1. Pathway OverviewFigure 2. Outcomes

Family Dependency Drug Court Service Modifications During COVID-19

Introduction: The COVID-19 pandemic is colliding with the ongoing opioid crisis, potentially worsening outcomes for people with substance use disorder (SUD). In Florida, family dependency drug courts (FDDCs) are civil problem-solving courts that facilitate parent-child reunification after governmental removal of children due to parental drug use. FDDCs have been forced to modify existing services due to COVID-19, yet little is known about FDDC modifications in response to COVID-19 and its impacts. Therefore, we sought to identify 1) how courts adapted to COVID-19 and 2) the experiences of court staff regarding the switch to virtual technology due to COVID-19. Method(s): We recruited all court staff from five Florida FDDC courts from June 2020 to June 2021 for in-person and virtual focus groups and individual interviews as part of a larger study evaluating their implementation of evidence-based practices. Eight focus groups with a total of thirty individuals and five individual interviews occurred. Data collection from each court occurred at two time periods: March-June 2020 and February-March 2021. Focus groups and interviews were recorded and professionally transcribed. In Dedoose software, we analyzed data using the iterative categorization approach in which a coding template is created based on the research questions, which can be modified during the consensus coding process. These coded excerpts were then labeled with summaries in spreadsheets and used to identify themes within and across codes. Result(s): FDDC service modifications involved switching to virtual court services and allowing court clients to access treatment virtually (e.g., telehealth, peer support, 12-step groups). The experiences of court staff regarding the switch to virtual technology due to COVID-19 included: 1) client engagement in virtual services has decreased;2) client access and participation to virtual court services has increased;3) clients may lack the technology to use virtual court services;4) relationship between staff and clients has suffered due to virtual technology;and 5) pre-existing familiarity with technology and institutional support helped staff with adapting during COVID-19. Discussion(s): COVID-19 precipitated the justice system's rapid modification of operations by utilizing virtual technology. This is an opportunity to implement systemic changes that could increase accessibility and efficiency in the court, which has historically been slow to embrace innovations;feasibility and acceptability of virtual services offered must be examined for each court's need. Conclusion(s): There are still aspects of virtual services that need improvement. A client's inability to participate in virtual services due to a lack of technology may cause negative perceptions from staff. Considering the current technological divide in the US, the incorporation of virtual services into FDDCs may negatively impact those with limited access to technology. Future research should delve into potential models for hosting in-person, virtual, or a hybrid model of virtual court services meant to maximize the benefits of both court service models.

Triage-HF plus: 12-month study of remote monitoring pathway for triage of heart failure risk initiated during the Covid-19 pandemic

Background: The Covid-19 pandemic necessitated rapid adoption of remote monitoring across cardiovascular patient cohorts. Most patients with cardiac implantable electronic devices (CIEDs) are now able to be remotely monitored using either scheduled, patient- or threshold-triggered transmissions. The validated Triage Heart Failure Risk Score (Triage-HFRS) is a medical algorithm within company-specific CIEDs that can risk-stratify patients as low-, medium- or high-risk of worsening heart failure (WHF) in the next 30 days based on integrated monitoring of physiological parameters. Building on a previous proof-of-concept of the Triage-HF Plus pathway, we integrated remote data with simple 5-question telephone triage within a clinical pathway to identify WHF during the first year of the Covid-19 pandemic. Purpose: Prospective evaluation of clinical remote monitoring pathway integrating Triage-HFRS with protocolised telephone triage (Triage-HF Plus pathway). Methods: Prospective, real-world evaluation of clinical pathway serving a large urban region over a 12-month period, using data from April 2020 to April 2021 (initiated during the first wave of Covid-19 pandemic in the UK). From a population of 435 patients with CIEDs, 87 high Triage-HFRS alerts were received and patients contacted for telephone triage assessment. Screening questions were designed to identify episodes of WHF and non-HF events. Intervention was at discretion of the clinical practitioner and in line with guideline-directed practice. A consecutive sample of 115 medium risk scores received the same triage. Results: Successful contact was made with 72 (82.8%) high-risk patients. Classification for high scoring patients confirmed on triage included isolated heart failure (18.3%), heart failure concurrent to medical problem (5.7%), alternative medical problem (10.3%), and recent hospital admission (8.0%);triage reassured absence of acute cause of high score in 40.2%. The sensitivity and specificity for detection of WHF was 87.9% (0.77-0.99) and 59.4% (0.50-0.69) respectively. Positive and negative predictive values were 40.3% and 94.0%, respectively. Overall accuracy was 66.2%. Conclusions: The Triage-HF Plus pathway served as a useful remote monitoring tool for identifying patients with WHF whose care had been otherwise disrupted by the Covid-19 pandemic, allowing timely intervention and cementing the longer-term role for such models of care delivery. Crucially, in this multimorbid, high-cost population, relevant non-HF issues were also identified. The high negative predictive value further highlights the potential of proactive surveillance over conventional, periodic follow up.