Behnken, Emma

Abstract Delirium is a common yet underdiagnosed medical emergency involving acute deficits in cognition and attention. It affects over 20% of hospitalized patients and up to 80% of ICU patients, with increased prevalence in older adults. Delirium is associated with increased mortality, prolonged hospitalization, long-term cognitive decline, and substantial healthcare costs. However, current screening tools are subjective and difficult to implement consistently. Traditional EEGs show diagnostic utility; however, they are costly and require specialized interpretation. We conducted a prospective pilot study evaluating a point-of-care, single-channel EEG device for delirium detection in hospitalized older adults. EEG spectra were utilized to calculate a ‘bispectral EEG’ (BSEEG) score reflecting the ratio of slow to fast brain waves. Twenty-two postoperative patients (mean age 75; 59.09% female) were assessed daily for up to seven days using both the BSEEG and the gold standard 3-Minute Diagnostic Interview for Confusion Assessment Method (3D-CAM). ROC analysis was utilized to determine an optimal threshold BSEEG score to indicate delirium positivity. BSEEG scores were significantly higher in the 3D-CAM-positive group than in the 3D-CAM-negative group (1.60 and 1.45, respectively; p < 0.05). A BSEEG threshold of 1.5 demonstrated 77.78% sensitivity and 56.10% specificity for delirium. In contrast, route clinical screenings (Delirium Triage Screen and Brief CAM) showed 0.00% sensitivity and 96.30% specificity, and failed to detect all true delirium cases. Although further validation in larger cohorts is needed, these findings suggest that the point-of-care single-channel EEG is a promising, non-invasive screening tool for delirium screening, particularly in fast-paced clinical settings.

Abstract Background Hospital‐at‐home programs (HaH) in the United States have evolved to include a virtual‐hybrid delivery model, where all physician encounters are virtual and partnered with a home care team. Objective To examine whether a virtual hybrid HaH program enabled by technology has similar clinical outcomes to traditional brick‐and‐mortar (B&M) hospital care. Methods We conducted a pragmatic trial at three hospitals, randomizing 1150 acutely ill patients requiring hospital care between July 10, 2023, and October 31, 2023 one‐to‐one into two groups: intervention (HaH) and control (B&M). The primary analysis was an intention‐to‐treat non‐inferiority analysis of the primary outcome, which was a composite of 30‐day all‐cause mortality and unplanned readmissions. Secondary outcomes included 30‐day readmission, all‐cause mortality, and patient experience. Results The mean age was 67.8 (standard deviation [SD] 16.3) years, and 52.2% were female. The primary outcome occurred in 99 (17.3%) HaH patients and 114 (19.8%) B&M patients (odds ratio [OR] 0.85, 95% confidence interval [CI] 0.63–1.14, p = .28), meeting the non‐inferiority criterion. Thirty‐day unplanned readmission occurred in 84 (14.7%) HaH patients and 101 (17.5%) B&M patients (OR 0.81, 95% CI 0.59–1.11, p = .19). Thirty‐day all‐cause mortality occurred in 25 (4.4%) HaH patients and 19 (3.3%) B&M patients (OR 1.34, 95% CI 0.73–2.46, p = .35). No HaH patients died while receiving their hospital care at home. HaH program was associated with a higher likelihood of patients reporting feeling extremely comfortable or very comfortable (84.4% HaH; 60.9% B&M, p = .001). Conclusion A hybrid HaH model is a safe and comfortable alternative to traditional B&M hospital care.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

Introduction: Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations. Hypothesis: An AI-guided, targeted screening approach could improve the diagnosis of AF. Methods We conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population. Results A total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p<0.001). Compared to usual care, AI-guided AF screening was associated with increased detection of AF (high-risk group: 4.2% vs. 11.1%, p<0.001; low-risk group: 0.9% vs. 2.4%, p=0.12) over a median follow-up of 10 months. Conclusions A prospective pragmatic study found that the AI a can risk-stratify a relatively uniform population (i.e., older adults at risk for stroke) to detect AF during short-term cardiac monitoring. Furthermore, when compared with usual care, AI-guided cardiac monitoring was associated with increased AF detection. As such, an AI-guided AF screening approach, leveraging existing EHR data and infrastructure, could be effective, patient-centered, and massively scalable, thereby reducing unnecessary health utilization and diagnosis-related anxiety (Clinicaltrials.gov: NCT04208971).

Introduction Diabetes is one of the most common serious chronic health conditions in the USA. People living with diabetes face multiple barriers to optimal diabetes care, including gaps in access to medical care and self-management education, diabetes distress, and high burden of treatment. Community paramedics (CPs) are uniquely positioned to support multidisciplinary care for patients with diabetes by delivering focused diabetes self-management education and support and bridging the gaps between patients and the clinical and community resources they need to live well with their disease. Methods and analysis We will conduct a pragmatic single-arm prospective trial of a CP-led Diabetes Rescue, Engagement and Management (D-REM) programme that seeks to reduce diabetes distress. We will enrol 70 adults (≥18 years) with diabetes who have haemoglobin A1c (HbA1c)≥9.0%, experienced an emergency department (ED) visit or hospitalisation for any cause within the prior 6 months, and reside in areas with available CP support in Southeast Minnesota (Olmsted, Freeborn and Mower counties) and Northwest Wisconsin (Barron, Rusk and Dunn counties). Participants will be identified using Mayo Clinic electronic health records, contacted for consent and enrolled into the D-REM programme. Visit frequency will be individualised for each patient, but will be an average of four CP visits over the course of approximately 1 month. Outcomes will be change in diabetes distress (primary outcome), confidence in diabetes self-management, health-related quality of life, self-reported hypoglycaemia and hyperglycaemia, HbA1c, ED visits and hospitalisations. Outcomes will be assessed on enrolment, programme completion and 3 months after programme completion. Ethics and dissemination The study was approved by Mayo Clinic Institutional Review Board. Findings will be disseminated through peer-reviewed publications and presentations. If demonstrated to be successful, this model of care can be implemented across diverse settings and populations to support patients living with diabetes. Trial registration number NCT04385758 .

Background Guidelines promote shared decision‐making (SDM) for anticoagulation in patients with atrial fibrillation. We recently showed that adding a within‐encounter SDM tool to usual care (UC) increases patient involvement in decision‐making and clinician satisfaction, without affecting encounter length. We aimed to estimate the extent to which use of an SDM tool changed adherence to the decided care plan and clinical safety end points. Methods and Results We conducted a multicenter, encounter‐level, randomized trial assessing the efficacy of UC with versus without an SDM conversation tool for use during the clinical encounter (Anticoagulation Choice) in patients with nonvalvular atrial fibrillation considering starting or reviewing anticoagulation treatment. We conducted a chart and pharmacy review, blinded to randomization status, at 10 months after enrollment to assess primary adherence (proportion of patients who were prescribed an anticoagulant who filled their first prescription) and secondary adherence (estimated using the proportion of days for which treatment was supplied and filled for direct oral anticoagulant, and as time in therapeutic range for warfarin). We also noted any strokes, transient ischemic attacks, major bleeding, or deaths as safety end points. We enrolled 922 evaluable patient encounters (Anticoagulation Choice=463, and UC=459), of which 814 (88%) had pharmacy and clinical follow‐up. We found no differences between arms in either primary adherence (78% of patients in the SDM arm filled their first prescription versus 81% in UC arm) or secondary adherence to anticoagulation (percentage days covered of the direct oral anticoagulant was 74.1% in SDM versus 71.6% in UC; time in therapeutic range for warfarin was 66.6% in SDM versus 64.4% in UC). Safety outcomes, mostly bleeds, occurred in 13% of participants in the SDM arm and 14% in the UC arm. Conclusions In this large, randomized trial comparing UC with a tool to promote SDM against UC alone, we found no significant differences between arms in primary or secondary adherence to anticoagulation or in clinical safety outcomes. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: clinicaltrials.gov. Identifier: NCT02905032.

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.

Abstract Context Current evidence on determinants of adverse health outcomes in patients with adrenal insufficiency (AI) is scarce, especially in regards to AI subtypes. Objective To determine predictors of adverse outcomes in different subtypes of AI. Design and Setting Cross-sectional survey study at 2 tertiary centers. Participants A total of 696 patients with AI: primary AI (PAI, 42%), secondary AI (SAI, 32%), and glucocorticoid-induced AI (GIAI, 26%). Intervention Patient-centered questionnaire. Main Outcome Measures Patients’ knowledge, self-management of AI, self-perceived health, and adverse outcomes. Results The incidence rate of adrenal crisis was 24/100 patient-years with 44% experiencing at least 1 adrenal crisis since diagnosis (59% in PAI vs 31% in SAI vs 37% in GIAI, P &lt; .0001). All patients described high degrees of discomfort with self-management and receiving prompt treatment. Patients with PAI were most likely to develop adrenal crises (adjusted OR 2.8, 95% CI 1.9-4.0) despite reporting better self-perceived health (adjusted OR 3.3, 95% CI 2.1-5.3), understanding of their diagnosis (89% vs 74-81% in other subtypes, P = .002), higher comfort with self-management (62% vs 52-61% in other sub types, P = .005), and higher likelihood to receive prompt treatment for adrenal crises in the emergency department (42% vs 19-30% in other subtypes, P &lt; .0001). Conclusions Patients with AI reported high degrees of discomfort with self-management and treatment delays when presenting with adrenal crises. Despite better self-perceived health and understanding of diagnosis, patients with PAI experienced the highest frequency of adrenal crises. A multidimensional educational effort is needed for patients and providers to improve the outcomes of all subtypes of AI.