McCoy, Rozalina G.

Background: Acute kidney injury (AKI) survivors are at heightened risk for poor short- and long-term health outcomes. Even among those who recover after an AKI episode, the risk for chronic kidney disease is 4- to 6-fold higher than in patients without AKI, underscoring the importance of identifying methods to improve AKI survivorship. Objective: The purpose of this report was to describe the development and feasibility of a novel multidisciplinary approach to caring for AKI survivors at care transitions (ACT). Design: Observational process improvement initiative. Setting: Single academic medical center in the United States. Patients: The studied population was adults with stage 3 AKI not discharging on dialysis who were established with a primary care provider (PCP) at our institution. Methods: An electronic health record tool was developed prior to implementation to identify AKI survivors. The ACT program encompassed engaging patients in the hospital, delivering education by nephrology-trained nurses before discharge, completing recommended laboratory testing after discharge, and conducting structured kidney-focused follow-up with a pharmacist and a PCP within 7 to 14 days after discharge. Patients could be referred for nephrology evaluation at the discretion of the PCP. Results: Preliminary data demonstrated that most AKI survivors of interest could be identified, educated, and followed up with this model. This strategy appeared feasible, scalable, and maximized the unique expertise of each member of the multidisciplinary team. Limitations: Small sample size, future assessment of process, clinical, and patient-reported outcomes needed. Conclusions: The multidisciplinary ACT workflow supported by clinical decision support was feasible and addressed gaps in existing care transition models. Team-based care delivery in primary care appears to be a mechanism to extend the capacity for kidney health monitoring for AKI survivors.

OBJECTIVE We characterized annual trends of severe hypoglycemic and hyperglycemic crises (diabetic ketoacidosis/hyperglycemic hyperosmolar state) in patients with diabetes and end-stage kidney disease (ESKD). RESEARCH DESIGN AND METHODS This was a nationwide, retrospective study of adults (≥18 years old) with diabetes/ESKD, from the United States Renal Data System registry, between 2013 and 2017. Primary outcome was annual rates of emergency department visits or hospitalizations for hypoglycemic and hyperglycemic crises, reported as number of events/1,000 person-years. Event rates and risk factors were adjusted for patient age, sex, race/ethnicity, dialysis modality, comorbidities, treatment regimen, and U.S. region. RESULTS Among 521,789 adults with diabetes/ESKD (median age 65 years [interquartile range 57–73], 56.1% male, and 46% White), overall adjusted rates of hypoglycemic and hyperglycemic crises were 53.64 and 18.24 per 1,000 person-years, respectively. For both hypoglycemia and hyperglycemia crises, respectively, the risks decreased with age and were lowest in older patients (≥75 vs. 18–44 years old: incidence rate ratio 0.35, 95% CI 0.33–0.37, and 0.03, 0.02–0.03), women (1.09, 1.06–1.12, and 1.44, 1.35–1.54), and those with smoking (1.36, 1.28–1.43, and 1.71, 1.53–1.91), substance abuse (1.27, 1.15–1.42, and 1.53, 1.23–1.9), retinopathy (1.10, 1.06–1.15, and 1.36, 1.26–1.47), and insulin therapy (vs. no therapy; 0.60, 0.59–0.63, and 0.44, 0.39–0.48). For hypoglycemia, specifically, additional risk was conferred by Black race (1.11, 1.08–1.15) and amputation history (1.20, 1.13–1.27). CONCLUSIONS In this nationwide study of patients with diabetes/ESKD, hypoglycemic crises were threefold more common than hyperglycemic crises, greatly exceeding national reports in nondialysis patients with chronic kidney disease. Young, Black, and female patients were disproportionately affected.

Research ObjectiveThroughout the spring of 2020, stay‐at‐home orders were imposed to curb the spread of the coronavirus disease 2019 (COVID‐19) in the United States. However, there is limited data and evidence on their effectiveness, especially in rural areas as compared to urban areas. Therefore, we examined the association between stay‐at‐home order implementation and the incidence of COVID‐19 cases in rural and urban counties.Study DesignWe conducted an interrupted time series analysis using a mixed effects zero‐inflated Poisson model with random intercept by county and standardized by population, which examined the association between stay‐at‐home orders and 14‐day lagged county‐level counts of daily new COVID‐19 cases in rural vs. urban counties between 1/22/2020 and 6/10/2020. To further bolster our findings, the association between stay‐at‐home orders and mobility in rural vs. urban counties was assessed using Google Community Mobility Reports.Population StudiedWe examined the COVID‐19 daily incident and stay‐at‐home orders for all 3142 United States counties. Each county was categorized as urban or rural using the 2013 county‐level Rural–Urban Continuum Codes per federal standards. There are 1976 rural and 1166 urban counties in the United States.Principal FindingsAs of June 10, 2020, there were 1,786,886 cases of COVID‐19 in the U.S., of which 9.0% (N = 161,452) were in rural counties. During the study period, 1854 (93.8%) rural counties and 1075 (92.2%) urban counties were covered by a stay‐at‐home order. Stay‐at‐home orders were implemented later (median March 30 vs. March 28) and were shorter (median 35 vs. 54 days) in rural than urban counties. Indoor mobility was, on average, 2.6–6.9% higher in rural than urban counties during and after stay‐at‐home orders. Compared to the pre‐stay‐at‐home baseline period, the number of new COVID‐19 cases increased under stay‐at‐home by IRR 1.60 (95% CI, 1.57–1.64) in rural and 1.36 (95% CI, 1.30–1.42) in urban counties. For each day under stay‐at‐home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981–0.982) in rural and 0.952 (95% CI, 0.951–0.953) in urban counties. Each day after stay‐at‐home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994–0.995) in rural and 0.997 (95% CI, 0.995–0.999) in urban counties.ConclusionsStay‐at‐home orders decreased mobility and slowed the spread of COVID‐19 less effectively in rural than urban counties.Implications for Policy or PracticeAs states are considering implementing a new round of stay‐at‐home orders, our findings reveal several ways to improve the implementation, enforcement, and adherence. First, they should be implemented earlier and maintained longer for optimal effectiveness. There was marked heterogeneity in the timing and duration of stay‐at‐home orders and our data reinforces the need for multi‐jurisdictional, ideally federal, infection control mandates. Any new stay‐at‐home orders should be gradually implemented to avoid the pre‐stay‐at‐home surge of mobility and subsequent spike in COVID‐19 cases. To better encourage and facilitate compliance, leaders at all levels need to use scientific evidence to advocate for the importance of stay‐at‐home orders, set personal examples, and develop employment, housing, educational, and healthcare assistance for the most vulnerable.Primary Funding SourceNational Institutes of Health.

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.