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  1. Roslan AB, Naser JA, Nkomo VT, Padang R, Lin G, Pislaru C, et al.
    J Am Soc Echocardiogr, 2022 Feb 11.
    PMID: 35158051 DOI: 10.1016/j.echo.2022.01.019
    BACKGROUND: Bioprosthetic aortic valve dysfunction (BAVD) is a challenging diagnosis. Commonly used algorithms to classify high-gradient BAVD are the 2009 American Society of Echocardiography (ASE), 2014 Blauwet-Miller, and 2016 European Association of Cardiovascular Imaging (EACVI). We sought 1) to evaluate the accuracy of existing algorithms against objectively proven BAVD and 2) to propose an improved algorithm.

    METHODS: Retrospective study of 266 patients with objectively proven BAVD (pathology of explanted valves, 4D-CT prior to transcatheter valve-in-valve, or therapeutically confirmed bioprosthetic thrombosis) who were treated. Of those, 191 had obstruction, 48 had regurgitation, 15 had mixed stenosis and regurgitation, and 12 had patient-prosthesis mismatch (PPM). Normal controls were matched 1:1 (age, prosthesis size and type), of which 43 had high gradients (PPM in 30, high flow in 9 and normal prosthesis in 9). Algorithm assignment was based on the echocardiogram leading to BAVD diagnosis and the pre-discharge "fingerprint" echocardiogram after surgical or transcatheter aortic valve replacement. A novel algorithm (Mayo Clinic algorithm) incorporating valve appearance in addition to Doppler parameters was developed to improve observed deficiencies.

    RESULTS: The accuracy of existing algorithms was suboptimal (2009 ASE: 62%; 2014 Blauwet-Miller: 62%; 2016 EACVI: 57%). The most common overdiagnosis was PPM (22-29% of patients and controls with high gradients). The novel Mayo Clinic algorithm correctly identified the mechanism in 256 of 307 patients and controls (83%). Recognition of regurgitation was substantially improved (42 of 47 patients, 89%) and the number of PPM misdiagnoses significantly reduced (5 patients).

    CONCLUSION: Currently recommended algorithms misclassify a significant number of BAVD patients. The accuracy was improved by a newly proposed algorithm.

  2. Gunasekara YD, Kottawatta SA, Nisansala T, Wijewickrama IJB, Basnayake YI, Silva-Fletcher A, et al.
    Zoonoses Public Health, 2024 Feb;71(1):84-97.
    PMID: 37880923 DOI: 10.1111/zph.13087
    This study aimed to investigate and compare the proportion of AMR Escherichia coli (E. coli) between urban (Dompe in the Western province) and rural (Dambana in the Sabaragamuwa province) areas in Sri Lanka. The overall hypothesis of the study is that there is a difference in the proportion of AMR E. coli between the urban and the rural areas. Faecal samples were collected from healthy humans (n = 109), dairy animals (n = 103), poultry (n = 35), wild mammals (n = 81), wild birds (n = 76), soil (n = 80) and water (n = 80) from both areas. A total of 908 E. coli isolates were tested for susceptibility to 12 antimicrobials. Overall, E. coli isolated from urban area was significantly more likely to be resistant than those isolated from rural area. The human domain of the area had a significantly higher prevalence of AMR E. coli, but it was not significantly different in urban (98%) and rural (97%) areas. AMR E. coli isolated from dairy animals, wild animals and water was significantly higher in the urban area compared with the rural area. There was no significant difference in the proportion of multidrug resistance (MDR) E. coli isolated from humans, wild animals and water between the two study sites. Resistant isolates found from water and wild animals suggest contamination of the environment. A multi-sectorial One Health approach is urgently needed to control the spread of AMR and prevent the occurrences of AMR in Sri Lanka.
  3. Alsharqi M, Lapidaire W, Iturria-Medina Y, Xiong Z, Williamson W, Mohamed A, et al.
    Eur Heart J Imaging Methods Pract, 2023 Sep;1(2):qyad029.
    PMID: 37818310 DOI: 10.1093/ehjimp/qyad029
    AIMS: Accurate staging of hypertension-related cardiac changes, before the development of significant left ventricular hypertrophy, could help guide early prevention advice. We evaluated whether a novel semi-supervised machine learning approach could generate a clinically meaningful summary score of cardiac remodelling in hypertension.

    METHODS AND RESULTS: A contrastive trajectories inference approach was applied to data collected from three UK studies of young adults. Low-dimensional variance was identified in 66 echocardiography variables from participants with hypertension (systolic ≥160 mmHg) relative to a normotensive group (systolic < 120 mmHg) using a contrasted principal component analysis. A minimum spanning tree was constructed to derive a normalized score for each individual reflecting extent of cardiac remodelling between zero (health) and one (disease). Model stability and clinical interpretability were evaluated as well as modifiability in response to a 16-week exercise intervention. A total of 411 young adults (29 ± 6 years) were included in the analysis, and, after contrastive dimensionality reduction, 21 variables characterized >80% of data variance. Repeated scores for an individual in cross-validation were stable (root mean squared deviation = 0.1 ± 0.002) with good differentiation of normotensive and hypertensive individuals (area under the receiver operating characteristics 0.98). The derived score followed expected hypertension-related patterns in individual cardiac parameters at baseline and reduced after exercise, proportional to intervention compliance (P = 0.04) and improvement in ventilatory threshold (P = 0.01).

    CONCLUSION: A quantitative score that summarizes hypertension-related cardiac remodelling in young adults can be generated from a computational model. This score might allow more personalized early prevention advice, but further evaluation of clinical applicability is required.

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