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BackgroundSustained lymph stagnation engenders a pathological response that is complex and not well characterized. Tissue inflammation in lymphedema may reflect either an active or passive consequence of impaired immune traffic.Methods and FindingsWe studied an experimental model of acute post-surgical lymphedema in the tails of female hairless, immunocompetent SKH-1 mice. We performed in vivo imaging of impaired immune traffic in experimental, murine acquired lymphatic insufficiency. We demonstrated impaired mobilization of immunocompetent cells from the lymphedematous region. These findings correlated with histopathological alterations and large-scale transcriptional profiling results. We found intense inflammatory changes in the dermis and the subdermis. The molecular pattern in the RNA extracted from the whole tissue was dominated by the upregulation of genes related to acute inflammation, immune response, complement activation, wound healing, fibrosis, and oxidative stress response.ConclusionsWe have characterized a mouse model of acute, acquired lymphedema using in vivo functional imaging and histopathological correlation. The model closely simulates the volume response, histopathology, and lymphoscintigraphic characteristics of human acquired lymphedema, and the response is accompanied by an increase in the number and size of microlymphatic structures in the lymphedematous cutaneous tissues. Molecular characterization through clustering of genes with known functions provides insights into processes and signaling pathways that compose the acute tissue response to lymph stagnation. Further study of genes identified through this effort will continue to elucidate the molecular mechanisms and lead to potential therapeutic strategies for lymphatic vascular insufficiency.

The post-surgical murine tail model of lymphedema closely simulates attributes of human lymphedema and provides the requisite sensitivity to detect therapeutically induced functional and structural alterations. It can, therefore, be used as an investigative platform to assess mechanisms of disease and its responses to candidate therapies, such as therapeutic lymphangiogenesis.

Objective Telemedicine practice has been shown to vary from clinical guidelines. Variations in practice patterns may be caused by disruptions in the continuity of care between traditional and telemedicine providers. This study compares virtual and in-person visits in Stanford’s ClickWell Care (CWC) – where patients see the same provider for both visit modalities. Methods Clinical data for two years of patient encounters at CWC from January 2015–2017 (5772 visits) were obtained through Stanford STRIDE. For the 20 most common visit categories, including 17 specific diagnoses, we compared the frequency of prescriptions, labs, procedures, and images ordered, as well as rates of repeat visits. Results For the 17 specific diagnoses, there are no differences in labs ordered. Two diagnoses show differences in images ordered, and four differences in prescriptions. Overall, there are more labs (0.16 virtual, 0.33 in-person p < 0.0001) and images ordered (0.07 virtual, 0.16 in-person, p < 0.0001) for in-person visits – due mainly to general medical exam visits. Repeat visits were more likely after in-person visits (19% virtual, 38% in-person, p < 0.0001), 10 out of 17 specific diagnoses showed differences in visit frequency between visit modalities. Visits for both anxiety (5.3x, p < 0.0001) and depression (5.1x, p < 0.0001) were much more frequent in the virtual setting. Conclusions Prescriptions, labs, and images ordered were similar between in-person and virtual visits for most diagnoses. Overall however, for in-person visits we find increased orders for labs and images, primarily from general medical exams. Finally, for anxiety and depression patients show clear preferences for virtual visits.

Background: The Apple watch irregular pulse detection algorithm was found to have a positive predictive value of 0.84 for identification of atrial fibrillation (AF). We sought to describe the prevalence of arrhythmias other than AF in those with an irregular pulse detected on a smartwatch. Methods: The Apple Heart Study investigated a smartwatch-based irregular pulse notification algorithm to identify AF. For this secondary analysis, we analyzed participants who received an ambulatory ECG patch after index irregular pulse notification. We excluded participants with AF identified on ECG patch and described the prevalence of other arrhythmias on the remaining participant ECG patches. We also reported the proportion of participants self-reporting subsequent AF diagnosis. Results: Among 419 297 participants enrolled in the Apple Heart Study, 450 participant ECG patches were analyzed, with no AF on 297 ECG patches (66%). Non-AF arrhythmias (excluding supraventricular tachycardias <30 beats and pauses <3 seconds) were detected in 119 participants (40.1%) with ECG patches without AF. The most common arrhythmias were frequent PACs (burden ≥1% to <5%, 15.8%; ≥5% to <15%, 8.8%), atrial tachycardia (≥30 beats, 5.4%), frequent PVCs (burden ≥1% to <5%, 6.1%; ≥5% to <15%, 2.7%), and nonsustained ventricular tachycardia (4–7 beats, 6.4%; ≥8 beats, 3.7%). Of 249 participants with no AF detected on ECG patch and patient-reported data available, 76 participants (30.5%) reported subsequent AF diagnosis. Conclusions: In participants with an irregular pulse notification on the Apple Watch and no AF observed on ECG patch, atrial and ventricular arrhythmias, mostly PACs and PVCs, were detected in 40% of participants. Defining optimal care for patients with detection of incidental arrhythmias other than AF is important as AF detection is further investigated, implemented, and refined.