EClinicalMedicine最新文献

{"title":"The diagnosis of mucormycosis by PCR in patients at risk: a systematic review and meta-analysis.","authors":"Lottie Brown, Lena Tschiderer, Alexandre Alanio, Rosemary A Barnes, Sharon C-A Chen, Massimo Cogliati, Mario Cruciani, J Peter Donnelly, Ferry Hagen, Catriona Halliday, Lena Klingspor, Katrien Lagrou, Willem Melchers, Laurence Millon, Florent Morio, Elena Salvador, Giacomo Stroffolini, Markus Ruhnke, Stephanie Toepfer, Karin van Dijk, Andrew M Borman, María José Buitrago, Rebecca Gorton, Jürgen Löffller, Riina Rautemaa-Richardson, Boualem Sendid, Peter Willeit, P Lewis White, Michaela Lackner","doi":"10.1016/j.eclinm.2025.103115","DOIUrl":"10.1016/j.eclinm.2025.103115","url":null,"abstract":"<p><strong>Background: </strong>This systematic review and meta-analysis aimed to examine the performance of polymerase chain reaction (PCR) assays for diagnosing mucormycosis.</p><p><strong>Methods: </strong>A standardised search was conducted from conception to December 3rd 2024 using PubMed, Embase, Global Health, and Cochrane library. Original studies that used PCR-based methods on any human specimen to diagnose mucormycosis were analysed for eligibility. Using a bivariate meta-analysis, the diagnostic performance of PCR was examined against the European Organisation for Research and Treatment of Cancer-Mycoses Study Group Education and Research Consortium 2020 (EORTC-MSGERC) definitions of proven and probable invasive mould disease, which was modified to include all patients at risk of mucormycosis. The study protocol was registered on the PROSPERO database (CRD42023478667).</p><p><strong>Findings: </strong>Of 4855 articles, a total of 30 met inclusion criteria, including 5920 PCR reactions on 5147 non-duplicate specimens from 819 cases of proven/probable mucormycosis and 4266 patients who did not meet the EORTC-MSGERC 2020 criteria. According to specimen type, sensitivity of PCR varied (p < 0.001) whereas specificity was similar (p = 0.662). Bronchoalveolar lavage fluid offered the highest sensitivity of 97.5% (95% CI 83.7-99.7%), specificity of 95.8% (95% CI 89.6-98.4%), positive likelihood ratio (LR+) of 23.5, and negative likelihood ratio (LR-) of 0.03. Tissue provided sensitivity of 86.4% (95% CI 78.9-91.5%), specificity of 90.6% (95% CI 78.1-96.3%), LR+ of 9.2, and LR- of 0.15. Blood provided reduced sensitivity of 81.6% (95% CI 70.1-89.4%), specificity of 95.5% (95% CI 87.4-98.5%), DOR of 95, LR+ of 18.3, and LR- of 0.19. Formalin-fixed paraffin-embedded specimens yielded the lowest sensitivity of 73.0% (95% CI 61.0-82.3%), highest specificity of 96.4% (CI 95% 87.5-99.0%), LR+ of 20.2, and LR- of 0.28. The covariates best explaining heterogeneity of the overall analysis were specimen type, study design (cohort <i>versus</i> case-control) and disease prevalence while patient population (COVID-19 <i>versus</i> other) and PCR (conventional <i>versus</i> quantitative) had less impact on heterogeneity.</p><p><strong>Interpretation: </strong>This meta-analysis confirms the high performance of PCR for diagnosing mucormycosis and supports the instatement of PCR detection of free-DNA in blood, BALF and tissue into future updated definitions and diagnostic guidelines for mucormycosis.</p><p><strong>Funding: </strong>None.</p>","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103115"},"PeriodicalIF":9.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Global Fund, Cervical Cancer, and HPV infections: what can low- and middle-income countries do to accelerate progress by 2030?","authors":"Runcie C W Chidebe, Alile Osayi, Julie S Torode","doi":"10.1016/j.eclinm.2025.103127","DOIUrl":"10.1016/j.eclinm.2025.103127","url":null,"abstract":"<p><p>The footprint of cervical cancer mirrors the impact of global inequity and inequality on the right to health for girls and women. While today, cervical cancer is a relatively rare cause of death in Europe, North America, and Australia, almost 94% of deaths in 2022 occurred in low- and middle-income countries (LMICs). Governments adopted the WHO global strategy to eliminate cervical cancer. Still, the stark reality is that many countries may not reach the 90:70:90 targets by 2030 without political commitment and a sense of urgency. We call for enhanced advocacy for the right to prevention services and political actions to mobilise global funding, local philanthropic support, and innovative financing. During the COVID-19 pandemic, an African coalition raised over $20 million to mitigate the impact of the pandemic. Positive lessons from this response should be replicated to save millions of women and girls at risk of cervical cancer in LMICs. There is a need for a global fund for cancer; regional blocs like the African Union need to recognise the disproportionate burden and establish continental funding mechanisms to enable high-burden countries to make crucial upfront health systems investments that will put their countries on the pathway to cervical cancer elimination.</p><p><strong>Funding: </strong>This study was not funded.</p>","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103127"},"PeriodicalIF":9.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence assisted identification of newborn auricular deformities via smartphone application.","authors":"Liu-Jie Ren, Rui-Jie Yang, Li-Li Chen, Shu-Yue Wang, Chen-Long Li, Yuan Huang, Tian-Yu Zhang, Yao-Yao Fu, Shuo Wang","doi":"10.1016/j.eclinm.2025.103124","DOIUrl":"10.1016/j.eclinm.2025.103124","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Auricular deformities are common in newborns and require early diagnosis and timely intervention. Several factors highlight the necessity of a machine learning-based diagnostic solution: the high prevalence of these conditions, the narrow time window for effective non-surgical treatment, limited medical resources, and the importance of both physical and mental well-being. This study presents a novel artificial intelligence (AI) model to identify and classify common sub-types of auricle deformities, using photos taken with mobile devices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The dataset was made up of the open-source dataset named BabyEar4k, which contains 3852 auricle images with diagnosis data, and another private dataset containing 104 microtia ears added from ENT Hospital of Fudan University. All the training photos were pre-processed to 800 × 800 RGB images, with the auricles located at the centers. The dataset was divided into two parts, 3835 samples for training/validation and 120 (20 for each class) for testing, i.e., the internal test dataset. 15% of the training data were used for validation during the training process. External validation was conducted on data from three centres across China (Xinjiang N = 252, Guizhou N = 186, and Fujian N = 252). The performance of the model was evaluated by comparative analyses with human volunteers. A prospective test set was collected in Shanghai (Obstetrics & Gynecology Hospital of Fudan University, from 2023/10/17 to 2023/12/29; N = 272). Given the significant variation in the distribution of sub-types, accuracy and weighted F1-score were chosen as primary evaluation metrics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Four different backbone architectures were evaluated: ResNet50, DenseNet121, EfficientNet, and RegNet. On the internal test set, the model achieved an accuracy of 0.83-0.85 for six-class classification and 0.94-0.98 for binary classification. ResNet50 backbone had the most consistent performance. Multi-center real-world data validation demonstrated satisfactory accuracy, with a range of 0.74-0.82 for six-class classification and 0.79-0.86 for normal/abnormal classification, indicating strong generalizability. In comparative analyses with volunteers, the professionals achieved an accuracy of 0.7-0.8 in the six-class classification task, while the related fellows scored 0.45-0.65, and the laypeople scored 0.45-0.55.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;The developed system offers an efficient and cost-effective solution for clinical applications, including early diagnosis of newborn auricular deformities, monitoring treatment progress, and educational purposes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Funding: &lt;/strong&gt;This study was supported by Shanghai Science and Technology Innovation Action Plan (23Y21900200, 21DZ2200700, T-Y Zhang) and Medical Engineering Fund of Fudan University (Y-Y Fu). S Wang was supported by the Shanghai Sailing Program (22YF1409300) and China Computer Federation (C","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103124"},"PeriodicalIF":9.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bronchodilator responsiveness and future chronic airflow obstruction: a multinational longitudinal study.","authors":"Ben Knox-Brown, Fahad Algharbi, Octavia Mulhern, James Potts, Imed Harrabi, Christer Janson, Rune Nielsen, Dhiraj Agarwal, Andrei Malinovschi, Sanjay Juvekar, Miriam Denguezli, Thorarinn Gíslason, Rana Ahmed, Asaad Nafees, Parvaiz A Koul, Daniel Obaseki, Mahesh Padukudru Anand, Li Cher Loh, Hermínia Brites Dias, Fátima Rodrigues, David Mannino, Mohammed Elbiaze, Karima El Rhazi, Filip Mejza, Graham Devereux, Frits M E Franssen, Asma El Sony, Emiel Wouters, Mohammed Al Ghobain, Kevin Mortimer, Abdul Rashid, Rashid Osman, Michael Studnicka, Joao Cardoso, Peter Burney, Andre F S Amaral","doi":"10.1016/j.eclinm.2025.103123","DOIUrl":"10.1016/j.eclinm.2025.103123","url":null,"abstract":"<p><strong>Background: </strong>Bronchodilator responsiveness testing is mainly used for diagnosing asthma. We aimed to investigate whether it is associated with progression to chronic airflow obstruction over time.</p><p><strong>Methods: </strong>The multinational Burden of Obstructive Lung Disease cohort study surveyed adults, aged 40 years and above, at baseline and followed them up after a mean of 9.1 years. Recruitment took place between January 2, 2003 and December 26, 2016. Follow-up measurements were collected between January 29, 2019 and October 24, 2021. On both occasions, study participants provided information on respiratory symptoms, health status and several environmental and lifestyle exposures. They also underwent pre- and post-bronchodilator spirometry. We defined bronchodilator responsiveness at baseline using the American Thoracic Society and European Respiratory Society (ATS/ERS) 2022 definition, and the presence of chronic airflow obstruction at follow-up as a post-bronchodilator forced expiratory volume in 1 s to forced vital capacity ratio (FEV₁/FVC) less than the lower limit of normal. We used multi-level regression models to estimate the association between baseline bronchodilator responsiveness and incident chronic airflow obstruction. We stratified analyses by gender and performed a sensitivity analysis in never smokers.</p><p><strong>Findings: </strong>We analysed data from 3701 adults with 56% being women. Compared to those without bronchodilator responsiveness at baseline, those with bronchodilator responsiveness had 36% increased risk of developing chronic airflow obstruction (RR: 1.36, 95%CI 1.04, 1.80). This effect was stronger in women (RR: 1.45, 95%CI 1.09, 1.91) than men (RR: 1.07, 95%CI 0.51, 2.24). Never smokers with bronchodilator responsiveness also were at greater risk of incident chronic airflow obstruction (RR: 1.48, 95%CI 1.01, 2.20).</p><p><strong>Interpretation: </strong>Bronchodilator responsiveness appears to be a risk factor for incident chronic airflow obstruction. It is important that future studies in other large population-based cohorts replicate these findings.</p><p><strong>Funding: </strong>National Heart and Lung Institute, UK Medical Research Council, and Wellcome Trust.</p>","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103123"},"PeriodicalIF":9.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143623949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool.","authors":"Kaitlin Swinnerton, Nathanael R Fillmore, Austin Vo, Jennifer La, Danne Elbers, Mary Brophy, Nhan V Do, Paul A Monach, Westyn Branch-Elliman","doi":"10.1016/j.eclinm.2025.103114","DOIUrl":"10.1016/j.eclinm.2025.103114","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Novel strategies that account for population-level changes in dominant variants, immunity, testing practices and changes in individual risk profiles are needed to identify patients who remain at high risk of severe COVID-19. The aim of this study was to develop and prospectively validate a tool to predict absolute risk of severe COVID-19 incorporating dynamic parameters at the patient and population levels that could be used to inform clinical care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A retrospective cohort of vaccinated US Veterans with SARS-CoV-2 from July 1, 2021, through August 25, 2023 was created. Models were estimated using logistic-regression-based machine learning with backward selection and included a variable with fluctuating absolute risk of severe COVID-19 to account for temporal changes. Age, sex, vaccine type, fully boosted status, and prior infection before vaccination were included &lt;i&gt;a priori&lt;/i&gt;. Variations in individual risk over time, e.g., due to receipt of immune suppressive medications, were also potentially included. The model was developed using data from July 1, 2021, through August 31, 2022 and prospectively validated on a subsequent second cohort (September 1, 2022, through August 25, 2023). Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and calibration by Brier score. The final model was used to compare observed rates of severe disease to predicted rates among patients who received oral antivirals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;216,890 SARS-CoV-2 infections in Veterans not treated with oral antivirals were included (median age, 65; 88% male). The development cohort included 165,303 patients (66,121 in the training set, 49,591 in the tuning set, and 49,591 in the testing set) and the prospective validation cohort included 51,587 patients. The percentage of severe infections ranged from 5% to 25%. Model performance improved until 24 clinical predictor variables including age, co-morbidities, and immune-suppressive medications plus a 30-day rolling risk window were included (AUC in development cohort, 0.88 (95% CI, 0.87-0.88), AUC in prospective validation, 0.85 (95% CI, 0.84-0.85), Brier Score, 0.13). The most important variables for predicting severe disease included age, chronic kidney disease, chronic obstructive pulmonary disease, Alzheimer's disease, heart failure, and anaemia. Glucocorticoid use during the one-month prior to COVID-19 diagnosis was the next most important predictor. Models that included a near-real time fluctuating population risk variable performed better than models stratified by circulating variant and models with dominant variant included as a predictor. Patients with predicted severe disease risk &gt;3% who received oral antivirals had approximately 4-fold lower rates of severe COVID-19 untreated patients at a similar risk level.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;Our novel risk prediction tool uses a simple","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103114"},"PeriodicalIF":9.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is it underestimated or overestimated?","authors":"Zhuoqiao He, Xuerui Tan","doi":"10.1016/j.eclinm.2025.103117","DOIUrl":"10.1016/j.eclinm.2025.103117","url":null,"abstract":"","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103117"},"PeriodicalIF":9.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review.","authors":"Qianhui Yang, Yong Mong Bee, Ciwei Cynthia Lim, Charumathi Sabanayagam, Carol Yim-Lui Cheung, Tien Yin Wong, Daniel S W Ting, Lee-Ling Lim, HuaTing Li, Mingguang He, Aaron Y Lee, A Jonathan Shaw, Yeo Khung Keong, Gavin Siew Wei Tan","doi":"10.1016/j.eclinm.2025.103089","DOIUrl":"10.1016/j.eclinm.2025.103089","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Artificial Intelligence (AI) has been used to automate detection of retinal diseases from retinal images with great success, in particular for screening for diabetic retinopathy, a major complication of diabetes. Since persons with diabetes routinely receive retinal imaging to evaluate their diabetic retinopathy status, AI-based retinal imaging may have potential to be used as an opportunistic comprehensive screening for multiple systemic micro- and macro-vascular complications of diabetes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a qualitative systematic review on published literature using AI on retina images to detect systemic diabetes complications. We searched three main databases: PubMed, Google Scholar, and Web of Science (January 1, 2000, to October 1, 2024). Research that used AI to evaluate the associations between retinal images and diabetes-associated complications, or research involving diabetes patients with retinal imaging and AI systems were included. Our primary focus was on articles related to AI, retinal images, and diabetes-associated complications. We evaluated each study for the robustness of the studies by development of the AI algorithm, size and quality of the training dataset, internal validation and external testing, and the performance. Quality assessments were employed to ensure the inclusion of high-quality studies, and data extraction was conducted systematically to gather pertinent information for analysis. This study has been registered on PROSPERO under the registration ID CRD42023493512.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;From a total of 337 abstracts, 38 studies were included. These studies covered a range of topics related to prediction of diabetes from pre-diabetes or non-diabeticindividuals (n = 4), diabetes related systemic risk factors (n = 10), detection of microvascular complications (n = 8) and detection of macrovascular complications (n = 17). Most studies (n = 32) utilized color fundus photographs (CFP) as retinal image modality, while others employed optical coherence tomography (OCT) (n = 6). The performance of the AI systems varied, with an AUC ranging from 0.676 to 0.971 in prediction or identification of different complications. Study designs included cross-sectional and cohort studies with sample sizes ranging from 100 to over 100,000 participants. Risk of bias was evaluated by using the Newcastle-Ottawa Scale and AXIS, with most studies scoring as low to moderate risk.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;Our review highlights the potential for the use of AI algorithms applied to retina images, particularly CFP, to screen, predict, or diagnose the various microvascular and macrovascular complications of diabetes. However, we identified few studies with longitudinal data and a paucity of randomized control trials, reflecting a gap between the development of AI algorithms and real-world implementation and translational studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Funding: &lt;/strong&gt;D","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103089"},"PeriodicalIF":9.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11883405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First-Episode Psychosis incidence pre-, during, and post-COVID-19 pandemic: a six-year natural quasi-experimental study in South London.","authors":"Andrea Quattrone, Eleni Petkari, Edoardo Spinazzola, Perry B M Leung, Zhikun Li, Robert Stewart, Diego Quattrone, Marta Di Forti, Robin M Murray, Mariana Pinto da Costa","doi":"10.1016/j.eclinm.2025.103086","DOIUrl":"10.1016/j.eclinm.2025.103086","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic may have been accompanied by an increased exposure to psychosis risk factors. We used a pre-during-post study design to examine variations in the incidence of First-Episode Psychosis (FEP) before, during, and after the COVID-19 pandemic in South London. We hypothesised that FEP rates rose during the pandemic and subsequently returned to pre-pandemic levels.</p><p><strong>Methods: </strong>Using the Clinical Record Interactive Search (CRIS) system, we screened individuals referred for FEP to Early Intervention Services for Psychosis (EISs) of the South London and Maudsley NHS Foundation Trust (SLaM) from 1 March 2018 to 29 February 2024. Population data for the SLaM catchment area were obtained from the Office for National Statistics (ONS). We calculated crude incidence rates and used Poisson regression models to estimate age-sex-ethnicity-adjusted variation in incidence by year (March-to-February) expressed as Incidence Rate Ratios (IRR).</p><p><strong>Findings: </strong>A total of 3752 individuals experienced FEP during 5,487,858 person-years at risk, with a mean crude incidence of 68.4 per 100,000 person-years (95% CI: 66.2-70.6). The Poisson model showed a deviation from this mean at the peak of the COVID-19 pandemic in 2020/21, with FEP rates rising to 77.5 per 100,000 person-years (95% CI: 71.8-83.2) and similar rates in 2021/22. FEP incidence gradually returned to the pre-pandemic levels in the following years. During the COVID-19 pandemic, individuals of Black ethnicity experienced the greatest FEP increase, with an IRR of 1.45 (95% CI: 1.29-1.61) in 2020/21 and similar ratios in 2021/22. An increase was also observed in Asian individuals, with an IRR of 1.54 (95% CI: 1.20-1.88) in 2021/22, whereas no significant changes in incidence were observed for other ethnic groups across the pre-, during-, and post-pandemic periods.</p><p><strong>Interpretation: </strong>FEP incidence in South London increased during the peak of the COVID-19 pandemic, particularly among Black and Asian individuals.</p><p><strong>Funding: </strong>None.</p>","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103086"},"PeriodicalIF":9.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effectiveness of an outreach program for HCC screening in patients with cirrhosis: a microsimulation modeling study.","authors":"Tami Gurley, Ruben Hernaez, Vanessa Cerda, Tynaje Thomas, Manasa Narasimman, Sukul Mittal, Mohammed Al-Hasan, Darine Daher, Amit G Singal","doi":"10.1016/j.eclinm.2025.103113","DOIUrl":"10.1016/j.eclinm.2025.103113","url":null,"abstract":"<p><strong>Background: </strong>Patients with cirrhosis are at high risk for hepatocellular carcinoma (HCC), but few undergo guideline-recommended semi-annual screening. Randomized clinical trials (RCTs) demonstrate that mailed outreach can increase screening versus visit-based screening. We estimated the costs and cost-effectiveness of an outreach strategy versus usual care.</p><p><strong>Methods: </strong>We built a 10-year Markov chain Monte Carlo microsimulation model to conduct a cost-effectiveness analysis comparing a mailed outreach program versus usual care for HCC screening in a cohort of 10,000 patients with cirrhosis. Model inputs were based on literature review (2005-current), and costs were based on inflation-adjusted estimates from Surveillance, Epidemiology, and End Results (SEER)-Medicare claims data. We conducted one-way sensitivity analyses for HCC incidence, outreach costs, efficacy of the outreach strategy to increase screening, and efficacy of curative (versus palliative) HCC treatments.</p><p><strong>Findings: </strong>Mailed outreach was estimated to cost $32.45 per patient in the first year and $21.90 per patient in subsequent years. The outreach program increased the number of HCC patients detected at an early stage by 48.4% and increased quality-adjusted life years (QALYs) by 300. Cost savings from these increases offset the costs of mailed outreach. Mailed outreach remained cost-effective across a wide range of HCC incidence rates, outreach costs, efficacy of the outreach strategy to increase screening, and the efficacy of curative HCC treatments. Annual out-of-pocket patient costs in the outreach arm were low at $13 per year.</p><p><strong>Interpretation: </strong>Mailed outreach to encourage HCC screening in patients with cirrhosis dominates usual care and should be considered for implementation in routine practice.</p><p><strong>Funding: </strong>National Cancer Institute and Cancer Prevention Research Institute of Texas.</p>","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103113"},"PeriodicalIF":9.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysis.","authors":"Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang","doi":"10.1016/j.eclinm.2025.103118","DOIUrl":"10.1016/j.eclinm.2025.103118","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Atrial fibrillation (AF) represents a major risk factor of ischemic stroke recurrence with serious management implications. However, it often remains undiagnosed due to lack of standard or prolonged cardiac rhythm monitoring. We aim to create a novel end-to-end artificial intelligence (AI) model that uses MRI data to rapidly identify high AF risk in patients who suffer from an acute ischemic stroke.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study comprises an internal retrospective cohort and a prospective cohort from Shanghai sixth people's hospital to train and validate an MRI-based AI model. Between January 1, 2018 and December 31, 2021, 510 patients were retrospectively enrolled for algorithm development and performance was measured using fivefold cross-validation. Patients from this trial were registered with http://www.chictr.org.cn, ChiCTR2200056385. Between September 1, 2022 and July 31, 2023, 73 patients were prospectively enrolled for algorithm test. An external cohort of 175 patients from Huashan Hospital, Minhang Hospital, and Shanghai Tenth People's Hospital was also enrolled retrospectively for further model validation. A combined classifier leveraging pre-defined radiomics features and &lt;i&gt;de novo&lt;/i&gt; features extracted by convolutional neural network (CNN) was proposed to identify underlying AF in acute ischemic stroke patients. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated for model evaluation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;The top-performing combined classifier achieved an AUC of 0.94 (95% CI, 0.90-0.98) in the internal retrospective validation group, 0.85 (95% CI, 0.79-0.91) in the external validation group, and 0.87 (95% CI, 0.90-0.98) in the prospective test group. Based on subgroup analysis, the AI model performed well in female patients, patients with NIHSS &gt; 4 or CHA&lt;sub&gt;2&lt;/sub&gt;DS&lt;sub&gt;2&lt;/sub&gt;-VASc ≤ 3, with the AUC of 0.91, 0.94, and 0.90, respectively. More importantly, our proposed model identified all the AF patients that were diagnosed with Holter monitoring during index stroke admission.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;Our work suggested a potential association between brain ischemic lesion pattern on MR images and underlying AF. Furthermore, with additional validation, the AI model we developed may serve as a rapid screening tool for AF in clinical practice of stroke units.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Funding: &lt;/strong&gt;This work was supported by grants from the National Natural Science Foundation of China (NSFC, Grant Number: 81871102 and 82172068); Shanghai Jiao Tong University School of Medicine, Two-Hundred Talent Program as Research Doctor (Grant Number: SBR202204); Municipal Science and Technology Commission Medical Innovation Project of Shanghai, (Grant/Award Number: 20Y11910200); Research Physician Program of Shanghai Shen Kang Hospital Development Center (Grant Number: SHD2022CRD039) to Dr. Dong Hu","PeriodicalId":11393,"journal":{"name":"EClinicalMedicine","volume":"81 ","pages":"103118"},"PeriodicalIF":9.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}