Willingness to HPV self-sampling for cervical cancer screening and its predictors among women attending outpatient clinics in Meru District, Arusha Region, Northern Tanzania

Background: The ability for women to self-collect human papillomavirus (HPV) samples can potentially reduce the risk of cervical cancer and increase screening coverage. Objectives: To assess the willingness to HPV self-sampling for cervical cancer screening and its predictors among women attending outpatient clinics in Arusha region, northern Tanzania. Methods: A hospital-based cross-sectional study was conducted among 706 women aged 18-55 years in Meru District Hospital and Usa River Health Centre from March to April 2019. Face-to-face intervies were conducted using a questionnaire. Data analysis was performed using Stata version 14.0. The log-binomial regression was used to determine factors associated with willingness to self-collection of HPV samples. Results: Majority (70%) of the women were willing to self-collection of HPV samples for cervical cancer screening and was associated with attending Meru District hospital (PR=2.02, 95%CI 1.77-2.31); good knowledge about cervical cancer warning signs (PR=1.11, 95%CI 1.01-1.22), prevention (PR=1.13, 95%CI 1.04-1.20), and symptoms (PR=1.61, 95%CI 1.33-1.93); and having formal employment (PR=1.22, 95%CI 1.07-1.37). Conclusion: The majority of women were willing to self-collect HPV samples for cervical cancer screening. Self-collection is, therefore, an acceptable and viable means of screening for cervical cancer, which has great implications for Tanzania from a health policy perspective. Keywords: Cervical cancer; HPV testing; HPV self-collection; Tanzania

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Abstract Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. Methods A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. Results Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). Conclusion Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets

Impact of acute TTE-evidenced cardiac dysfunction on in-hospital and outpatient mortality: A multicenter NYC COVID-19 registry study.

BackgroundCOVID-19 is associated with cardiac dysfunction. This study tested the relative prognostic role of left (LV), right and bi- (BiV) ventricular dysfunction on mortality in a large multicenter cohort of patients during and after acute COVID-19 hospitalization.Methods/resultsAll hospitalized COVID-19 patients who underwent clinically indicated transthoracic echocardiography within 30 days of admission at four NYC hospitals between March 2020 and January 2021 were studied. Images were re-analyzed by a central core lab blinded to clinical data. Nine hundred patients were studied (28% Hispanic, 16% African-American), and LV, RV and BiV dysfunction were observed in 50%, 38% and 17%, respectively. Within the overall cohort, 194 patients had TTEs prior to COVID-19 diagnosis, among whom LV, RV, BiV dysfunction prevalence increased following acute infection (pConclusionsLV, RV and BiV function declines during acute COVID-19 infection with each contributing to increased in- and out-patient mortality risk. RV dysfunction independently increases mortality risk