Faculty Bibliography

OBJECTIVE:To increase the rate of low dose aspirin (LDA) counseling and treatment in patients with 2 or more moderate risk factors of preeclampsia(PMRF) from 9% to 50% within a four-month period after implementation of interventions. STUDY DESIGN/METHODS:A single-institution quality improvement initiative aimed at LDA screening and counseling of those with PMRF. Two groups were evaluated: pre-intervention (January - April 2022) and post-intervention (January - April 2023). This initiative focused on identifying PMRF and monitoring rates of LDA counseling and treatment. Rates were assessed at two-week intervals and presented on a run chart to visualize trends and measure progress over time. Providers underwent education utilizing preeclampsia (PEC) screening flowsheets and integrated a clinical decision-making (CDM) tool in initial prenatal visit documentation using a smart-tool. Patients were provided with educational flyers. RESULTS:In the pre-intervention group (n=126), 8.7% of patients received counseling on PMRF risk factors and LDA use, 7.9% were treated with LDA. In the post-intervention group (n=112) 52.7% of patients received counseling on PMRF risk factors and LDA use, and 35.7% were treated with LDA. There was an 83.5% increase in the percentage of patients counseled following intervention implementation. A progressive increase was noted in counseling rates within the 18 weeks post-intervention. CONCLUSION/CONCLUSIONS:Integrating PEC screening flowsheets, clinical decision-making tools, and patient education flyers effectively enhances LDA counseling for patients with ≥2 PMRF with additional benefits seen in high-risk patients. These interventions offer a replicable model to enhance guideline adherence and reduce preeclampsia risk in vulnerable populations.

BACKGROUND:Tools for predicting heart failure (HF) in CKD patients remain limited. We aimed to study whether standard ECG features or heart rate variability parameters predict de novo HF hospitalization in individuals with CKD. METHODS:Utilizing a large NYU ECG database linked with electronic health records (2012-2021), we analyzed a cohort of patients with pre-existing CKD. Besides standard ECG features, we extracted heart rate variability (measures the time between consecutive heart beats in milliseconds) features from the ECGs as predictors. The index ECG was the first ECG performed after the index eGFR date (baseline) and was required to be done prior to initiation of dialysis, end-stage kidney disease (ESKD), or transplant. The primary outcome was time to index HF hospitalization (≥30 days after the index ECG) based on discharge ICD-10 codes. LASSO-penalized Cox regression was used to identify predictors. Sensitivity analyses used Fine-Gray competing risk models for death and ESKD. RESULTS:Among 11,409 individuals (median age: 72; ∼50% male) with a median of 976 days, 880 individuals (8%) experienced an index HF hospitalization. Models incorporating ECG and clinical parameters had excellent discrimination (C-statistic 0.76 in the training set and 0.73 in the validation set). Among ECG features, the PR interval, corrected QT, and T axis were independently associated with higher risks of index HF hospitalization ≥30 days after the index ECG in both primary models (p<0.001 for all) and in models accounting for competing risks (p<0.01 for all). History of arrhythmia (hazard ratio (HR, 1.60, 95% CI: 1.36-1.88), valvular disease (HR1.51, 95% CI: 1.27-1.81), and diabetes (HR 1.41, 95% CI: 1.22-1.65) were the strongest clinical predictors. HRV parameters were not independently associated with HF. CONCLUSIONS:Although ECG-derived HRV indices were not independently associated with risk of HF, several standard ECG features are associated with HF hospitalization in CKD.

OBJECTIVE:We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS/METHODS:In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS:Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION/CONCLUSIONS:Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION/CONCLUSIONS:Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

PURPOSE/OBJECTIVE:A critical function of public health is to monitor diseases that impede quality of life and burden affected communities. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network aims to advance disease monitoring for diabetes using multi-site electronic health record (EHR) data. METHODS:This work involved validating and refining case definitions for accurate identification of type 1 and type 2 diabetes cases to estimate incidence and prevalence of diabetes in children, adolescents, and young adults through age 44 years. RESULTS:In this essay, we describe the challenges experienced by the Network and lessons learned. Challenges included accessing EHR data, harmonizing EHR data from heterogeneous health systems to a common data model, and developing methods to account for bias introduced by the non-representativeness of health care utilization data. Lessons learned included approaches for data quality assessment, bias correction, and scalability. CONCLUSIONS:As the US continues to evolve its public health data systems and its approach to chronic disease monitoring, the DiCAYA Network offers guidance on factors for success as well as pitfalls to avoid.

BACKGROUND:The COVID-19 pandemic had a significant impact on healthcare delivery. Older adults with multimorbidities were at risk of healthcare disruptions for the management of their chronic conditions. OBJECTIVE:To characterize healthcare disruptions during the COVID-19 healthcare shutdown and recovery period (March 7, 2020-October 6, 2020) and their effects on disease management among older adults with multimorbidities who were patients of NYC Health + Hospitals (H + H), the largest municipal safety-net system in the United States. DESIGN/METHODS:Observational. PATIENTS/METHODS:Patients aged 50 + with hypertension or diabetes and at least one other comorbidity, at least one H + H ambulatory visit in the six months before COVID-19 pandemic onset (March 6, 2020), and at least one visit in the post-acute shutdown period (October 7, 2020 to December 31, 2023). MAIN MEASURES/METHODS:We characterized disruption in care (defined as no ambulatory or telehealth visits during the acute shutdown) and estimated the effect of disruption on blood pressure control, hemoglobin A1c (HbA1c), and low-density lipoprotein (LDL) cholesterol using difference-in-differences models. KEY RESULTS/RESULTS:Out of 73,889 individuals in the study population, 12.5% (n = 9,202) received no ambulatory or telehealth care at H + H during the acute shutdown. Low pre-pandemic healthcare utilization, Medicaid insurance, and self-pay were independent predictors of care disruption. In adjusted analyses, the disruption group had a 3.0-percentage point (95% CI: 1.2-4.8) greater decrease in blood pressure control compared to those who received care. Disruption did not have a significant impact on mean HbA1c or LDL. CONCLUSIONS:Care disruption was associated with declines in blood pressure control, which while clinically modest, could impact risk of cardiovascular outcomes if sustained. Disruption did not affect HbA1c or LDL. Telehealth mitigated impacts of the pandemic on care disruption and subsequent disease management. Targeted outreach to those at risk of care disruption is needed during future crises.

INTRODUCTION/BACKGROUND:Process evaluation provides insight into how interventions are delivered across varying contexts and why interventions work in some contexts and not in others. This manuscript outlines the protocol for a process evaluation embedded in a hybrid type 1 effectiveness-implementation randomised clinical trial of incremental-start haemodialysis (HD) versus conventional HD delivered to patients starting chronic dialysis (the TwoPlus Study). The trial will simultaneously assess the effectiveness of incremental-start HD in real-world settings and the implementation strategies needed to successfully integrate this intervention into routine practice. This manuscript describes the rationale and methods used to capture how incremental-start HD is implemented across settings and the factors influencing its implementation success or failure within this trial. METHODS AND ANALYSIS/METHODS:We will use the Consolidated Framework for Implementation Research (CFIR) and the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) frameworks to inform process evaluation. Mixed methods include surveys conducted with treating providers (physicians) and dialysis personnel (nurses and dialysis administrators); semi-structured interviews with patient participants, caregivers of patient participants, treating providers (physicians and advanced practice practitioners), dialysis personnel (nurses, dieticians and social workers); and focus group meetings with study investigators and stakeholder partners. Data will be collected on the following implementation determinants: (a) organisational readiness to change, intervention acceptability and appropriateness; (b) inner setting characteristics underlying barriers and facilitators to the adoption of HD intervention at the enrollment centres; (c) external factors that mediate implementation; (d) adoption; (e) reach; (f) fidelity, to assess adherence to serial timed urine collection and HD treatment schedule; and (g) sustainability, to assess barriers and facilitators to maintaining intervention. Qualitative and quantitative data will be analysed iteratively and triangulated following a convergent parallel and pragmatic approach. Mixed methods analysis will use qualitative data to lend insight to quantitative findings. Process evaluation is important to understand factors influencing trial outcomes and identify potential contextual barriers and facilitators for the potential implementation of incremental-start HD into usual workflows in varied outpatient dialysis clinics and clinical practices. The process evaluation will help interpret and contextualise the trial clinical outcomes' findings. ETHICS AND DISSEMINATION/BACKGROUND:The study protocol was approved by the Wake Forest University School of Medicine Institutional Review Board (IRB). Findings from this study will be disseminated through peer-reviewed journals and scientific conferences. TRIAL REGISTRATION NUMBER/BACKGROUND:NCT05828823.

BACKGROUND:Studies of type 1 diabetes in sub-Saharan Africa have suggested that the clinical phenotype might differ from phenotypes reported elsewhere. We aimed to establish whether type 1 diabetes diagnosed in children and young adults in three countries across sub-Saharan Africa is of autoimmune origin. METHODS:In this observational, cross-sectional study, we identified participants without obesity from outpatient clinics in government and private hospitals in Cameroon, Uganda, and South Africa who were of self-reported Black African ethnicity with young-onset (age <30 years), insulin-treated, clinically diagnosed type 1 diabetes. We measured islet autoantibodies to GADA, IA-2A, and ZnT8A, and calculated a genetic risk score (GRS) for type 1 diabetes, which we compared with control populations without diabetes derived from the Uganda Genome Resource databank and other studies. Endogenous insulin secretion was assessed using plasma C-peptide. We compared findings with those for participants with self-reported Black (n=429) and White (n=2602) ancestry with type 1 diabetes from the SEARCH for Diabetes in Youth (SEARCH) study in the USA. FINDINGS/RESULTS:(19·5-24·1). Only 312 (34·9%) of 894 participants were positive for islet autoantibodies; these participants had classic features of type 1 diabetes, including 225 (82·7%) of 272 with plasma C-peptide <200 pmol/L, and high type 1 diabetes GRS. Those without islet autoantibodies (582 [65·1%] of 894) had significantly lower median type 1 diabetes GRS than those with autoantibodies (9·66 [IQR 7·77-11·33] vs 11·76 [10·49-12·91]; p<0·0001), suggesting a subgroup with a non-autoimmune diabetes subtype, with clinical features and C-peptide concentrations not consistent with type 2 diabetes. Among participants diagnosed younger than 20 years, autoantibody-negative diabetes was also observed in 65 (15·1%) of 429 participants with Black ancestry in SEARCH (although less frequently than in sub-Saharan Africa [59 (55·1%) of 107]), and these participants also had a low type 1 diabetes GRS (median 10·41 [IQR 8·65-12·22] in autoantibody-negative subgroup). No such pattern was observed in White participants in SEARCH: 241 (9·3%) of 2602 were autoantibody negative and median GRS for type 1 diabetes was similar in autoantibody-negative and autoantibody-positive participants (median 13·42 [IQR 11·80-14·61] vs 13·49 [12·29-14·58]). INTERPRETATION/CONCLUSIONS:In sub-Saharan Africa, clinically diagnosed type 1 diabetes is heterogeneous, comprising classic autoimmune type 1 diabetes and a novel, non-autoimmune, insulin-deficient diabetes subtype. There is evidence of this subtype in Black but not White individuals in the USA. Therefore, alternative causes must be considered in this group of individuals, and understanding the drivers of this subtype might offer new insights into prevention and treatment. FUNDING/BACKGROUND:UK National Institute of Health and Care Research. TRANSLATION/UNASSIGNED:For the French translation of the abstract see Supplementary Materials section.

OBJECTIVE:To examine the associations between patient out-of-pocket (OOP) costs and nonadherence to glucagon-like peptide 1 receptor agonists (GLP-1a), and the consequent impact on adverse outcomes, including hospitalizations and emergency department (ED) visits. RESEARCH DESIGN AND METHODS/METHODS:This retrospective cohort study used MarketScan Commercial data (2016-2021). The cohort included nonpregnant adults aged 18-64 years with type 2 diabetes who initiated GLP-1a therapy. Participants were continuously enrolled in the same private insurance plan for 6 months before the prescription date and 1 year thereafter. Exposures included average first 30-day OOP costs for GLP-1a, categorized into quartiles (lowest [Q1] to highest [Q4]). Primary outcomes were the annual proportion of days covered (PDC) for GLP-1a and nonadherence, defined as PDC <0.8. Secondary outcomes included diabetes-related and all-cause hospitalizations and ED visits 1 year post-GLP-1a initiation. RESULTS:Among 61,907 adults who initiated GLP-1a, higher 30-day OOP costs were associated with decreased adherence. Patients in the highest OOP cost quartile (Q4: $80-$3,375) had significantly higher odds of nonadherence (odds ratio [OR]1.25; 95% CI 1.19-1.31) compared with those in Q1 ($0-$21). Nonadherence was linked to increased incidence rates of diabetes-related hospitalizations or ED visits (incidence rate ratio [IRR] 1.86; 95% CI 1.43-2.42), cumulative length of hospitalization (IRR 1.56; 95% CI 1.41-1.72), all-cause ED visits (IRR 1.38; 95% CI 1.32-1.45), and increased ED-related costs ($69.81, 95% CI $53.54-$86.08). CONCLUSIONS:Higher OOP costs for GLP-1a were associated with reduced adherence and increased rates of adverse outcomes among type 2 diabetes patients.