Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

The Development of a Novel Outpatient Infusion Therapy Center for Treatment of COVID-19 in a Rural Healthcare System.

Therapeutic interventions to manage symptoms of COVID-19 are continually evolving and being used in a variety of settings. In an attempt to reduce the potential for a high influx of hospital admissions for COVID-19 and mitigate the advancement of COVID-19 disease in infected patients, an outpatient therapy clinic for infusion therapy was established. The focus of the current paper is to outline the development of the outpatient treatment center, provide a detailed summary of workflow and discuss operational challenges and directions for the future.

Vasopressor magnitude predicts poor outcome in adults with congenital heart disease after cardiac surgery.

BACKGROUND: High levels of vasoactive inotrope support (VIS) after congenital heart surgery are predictive of morbidity in pediatric patients. We sought to discern if this relationship applies to adults with congenital heart disease (ACHD). METHODS: We retrospectively studied adult patients (≥18 years old) admitted to the intensive care unit after cardiac surgery for congenital heart disease from 2002 to 2013 at Mayo Clinic. Vasoactive medication dose values within 96 hours of admission were examined to determine the relationship between VIS score and poor outcome of early mortality, early morbidity, or complication related morbidity. RESULTS: Overall, 1040 ACHD patients had cardiac surgery during the study time frame; 243 (23.4%) met study inclusion criteria. Sixty-two patients (25%), experienced composite poor outcome [including eight deaths within 90 days of hospital discharge (3%)]. Thirty-eight patients (15%) endured complication related early morbidity. The maximum VIS (maxVIS) score area under the curve was 0.92 (95% CI: 0.86-0.98) for in-hospital mortality; and 0.82 (95% CI: 0.76-0.89) for combined poor clinical outcome. On univariate analysis, maxVIS score ≥3 was predictive of composite adverse outcome (OR: 14.2, 95% CI: 7.2-28.2; P < 0.001), prolonged ICU LOS ICU LOS (OR: 19.2; 95% CI: 8.7-42.1; P < 0.0001), prolonged mechanical ventilation (OR: 13.6; 95% CI: 4.4-41.8; P < 0.0001) and complication related morbidity (OR: 7.3; 95% CI: 3.4-15.5; P < 0.0001). CONCLUSIONS: MaxVIS score strongly predicted adverse outcomes and can be used as a risk prediction tool to facilitate early intervention that may improve outcome and assist with clinical decision making for ACHD patients after cardiac surgery.