<div><h4>Use of coronarycomputed tomography for cardiovascular risk assessment in immune-mediated inflammatory diseases.</h4><i>Peverelli M, Maughan RT, Gopalan D, Dweck MR, ... Rudd JHF, Tarkin JM</i><br /><AbstractText>Immune-mediated inflammatory diseases (IMIDs) are recognised risk factors for accelerated atherosclerotic cardiovascular disease (CVD), particularly in younger individuals and women who lack traditional CVD risk factors. Reflective of the critical role that inflammation plays in the formation, progression and rupture of atherosclerotic plaques, research into immune mechanisms of CVD has led to the identification of a range of therapeutic targets that are the subject of ongoing clinical trials. Several key inflammatory pathways implicated in the pathogenesis of atherosclerosis are targeted in people with IMIDs. However, cardiovascular risk continues to be systematically underestimated by conventional risk assessment tools in the IMID population, resulting in considerable excess CVD burden and mortality. Hence, there is a pressing need to improve methods for CVD risk-stratification among patients with IMIDs, to better guide the use of statins and other prognostic interventions. CT coronary angiography (CTCA) is the current first-line investigation for diagnosing and assessing the severity of coronary atherosclerosis in many individuals with suspected angina. Whether CTCA is also useful in the general population for reclassifying asymptomatic individuals and improving long-term prognosis remains unknown. However, in the context of IMIDs, it is conceivable that the information provided by CTCA, including state-of-the-art assessments of coronary plaque, could be an important clinical adjunct in this high-risk patient population. This narrative review discusses the current literature about the use of coronary CT for CVD risk-stratification in three of the most common IMIDs including rheumatoid arthritis, psoriasis and systemic lupus erythematosus.</AbstractText><br /><br />© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.<br /><br /><small>Heart: 22 Mar 2024; 110:545-551</small></div>

<div><h4>Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study.</h4><i>Zhou T, Gu S, Shao F, Li P, ... Gao P, Hua X</i><br /><b>Introduction</b><br />Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnancies.<br /><b>Methods</b><br />This prospective cohort study was conducted at Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Eligible participants included singleton pregnancies who presented for prenatal visits before 14 weeks of gestation from September 1, 2020, to February 1, 2022. Retinal fundus images were obtained using a nonmydriatic digital retinal camera during their initial prenatal visit upon admission before 20 weeks of gestation. In addition, we generated fundus scores, which indicated the predictive value of hypertension, using a hypertension detection model. To evaluate the predictive value of the retinal fundus image-based deep learning algorithm for preeclampsia, we conducted stratified analyses and measured the area under the curve (AUC), sensitivity, and specificity. We then conducted sensitivity analyses for validation.<br /><b>Results</b><br />Our study analyzed a total of 1138 women, 92 pregnancies developed into hypertension disorders of pregnancy (HDP), including 26 cases of gestational hypertension and 66 cases of preeclampsia. The adjusted odds ratio (aOR) of the fundus scores was 2.582 (95% CI, 1.883-3.616; P  < 0.001). Otherwise, in the categories of prepregnancy BMI less than 28.0 and at least 28.0, the aORs were 3.073 (95%CI, 2.265-4.244; P  < 0.001) and 5.866 (95% CI, 3.292-11.531; P  < 0.001). In the categories of maternal age less than 35.0 and at least 35.0, the aORs were 2.845 (95% CI, 1.854-4.463; P  < 0.001) and 2.884 (95% CI, 1.794-4.942; P  < 0.001). The AUC of the fundus score combined with risk factors was 0.883 (sensitivity, 0.722; specificity, 0.934; 95% CI, 0.834-0.932) for predicting preeclampsia.<br /><b>Conclusion</b><br />Our study demonstrates that the use of deep learning algorithm-based retinal fundus images offers promising predictive value for the early detection of preeclampsia.<br /><br />Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.<br /><br /><small>J Hypertens: 01 Apr 2024; 42:701-710</small></div>