Lancet Digital Health最新文献

{"title":"Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis","authors":"Uri Nahum PhD , Olga Gorlanova MD , Fabienne Decrue PhD , Heide Oller MSc , Edgar Delgado-Eckert PhD , Andreas Böck MD , Prof Sven Schulzke PhD , Prof Philipp Latzin PhD , Bianca Schaub PhD , Prof Anne M Karvonen PhD , Prof Roger Lauener MD , Prof Amandine Divaret-Chauveau PhD , Sabina Illi PhD , Caroline Roduit PhD , Prof Erika von Mutius MD , Prof Urs Frey PhD , BILD study group , PASTURE study group","doi":"10.1016/S2589-7500(24)00147-X","DOIUrl":"10.1016/S2589-7500(24)00147-X","url":null,"abstract":"<div><h3>Background</h3><div>Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma.</div></div><div><h3>Methods</h3><div>In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0–4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0–12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host–environment correlation network, quantified by a simple descriptor: trajectory function <em>G(t)</em>. Wheeze outcomes at age 2–6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants.</div></div><div><h3>Findings</h3><div>CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, <em>G(t)</em> distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and <em>G(t)</em> was also able to distinguish between the farming and non-farming exposure groups (p<0·0001).</div></div><div><h3>Interpretation</h3><div>Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host–environment correlation network properties (<em>G(t)</em>) showed excellent discriminative ability for identifying ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e718-e728"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320343","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":"In the era of digitalisation and biosignatures, is C-reactive protein still the one to beat?","authors":"Danilo Buonsenso","doi":"10.1016/S2589-7500(24)00177-8","DOIUrl":"10.1016/S2589-7500(24)00177-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e677"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320337","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":"Highly sensitive detection platform-based diagnosis of oesophageal squamous cell carcinoma in China: a multicentre, case–control, diagnostic study","authors":"Yu Wang MM , Shan Xing MM , Prof Yi-Wei Xu PhD , Prof Qing-Xia Xu MS , Prof Ming-Fang Ji MM , Prof Yu-Hui Peng MS , Ya-Xian Wu MS , Meng Wu BS , Ning Xue MS , Biao Zhang MS , Shang-Hang Xie MS , Rui-Dan Zhu BS , Xin-Yuan Ou BS , Qi Huang MS , Bo-Yu Tian MS , Hui-Lan Li MS , Yu Jiang MS , Xiao-Bin Yao MS , Jian-Pei Li MM , Prof Li Ling PhD , Prof Mu-Sheng Zeng PhD","doi":"10.1016/S2589-7500(24)00153-5","DOIUrl":"10.1016/S2589-7500(24)00153-5","url":null,"abstract":"<div><h3>Background</h3><div>Early detection and screening of oesophageal squamous cell carcinoma rely on upper gastrointestinal endoscopy, which is not feasible for population-wide implementation. Tumour marker-based blood tests offer a potential alternative. However, the sensitivity of current clinical protein detection technologies is inadequate for identifying low-abundance circulating tumour biomarkers, leading to poor discrimination between individuals with and without cancer. We aimed to develop a highly sensitive blood test tool to improve detection of oesophageal squamous cell carcinoma.</div></div><div><h3>Methods</h3><div>We designed a detection platform named SENSORS and validated its effectiveness by comparing its performance in detecting the selected serological biomarkers MMP13 and SCC against ELISA and electrochemiluminescence immunoassay (ECLIA). We then developed a SENSORS-based oesophageal squamous cell carcinoma adjunct diagnostic system (with potential applications in screening and triage under clinical supervision) to classify individuals with oesophageal squamous cell carcinoma and healthy controls in a retrospective study including participants (cohort I) from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China), Henan Cancer Hospital (HNCH; Zhengzhou, China), and Cancer Hospital of Shantou University Medical College (CHSUMC; Shantou, China). The inclusion criteria were age 18 years or older, pathologically confirmed primary oesophageal squamous cell carcinoma, and no cancer treatments before serum sample collection. Participants without oesophageal-related diseases were recruited from the health examination department as the control group. The SENSORS-based diagnostic system is based on a multivariable logistic regression model that uses the detection values of SENSORS as the input and outputs a risk score for the predicted likelihood of oesophageal squamous cell carcinoma. We further evaluated the clinical utility of the system in an independent prospective multicentre study with different participants selected from the same three institutions. Patients with newly diagnosed oesophageal-related diseases without previous cancer treatment were enrolled. The inclusion criteria for healthy controls were no obvious abnormalities in routine blood and tumour marker tests, no oesophageal-associated diseases, and no history of cancer. Finally, we assessed whether classification could be improved by integrating machine-learning algorithms with the system, which combined baseline clinical characteristics, epidemiological risk factors, and serological tumour marker concentrations. Retrospective SYSUCC cohort I (randomly assigned [7:3] to a training set and an internal validation set) and three prospective validation sets (SYSUCC cohort II [internal validation], HNCH cohort II [external validation], and CHSUMC cohort II [external validation]) were used in this step. Six machine-learning algorithms were compared (the least a","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e705-e717"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320342","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":"In the era of digitalisation and biosignatures, is C-reactive protein still the one to beat? – Authors' reply","authors":"Myrsini Kaforou , Heather R Jackson , Taco W Kuijpers , Marien I de Jonge , Michael Levin","doi":"10.1016/S2589-7500(24)00178-X","DOIUrl":"10.1016/S2589-7500(24)00178-X","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e678-e679"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320338","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":"Correction to Lancet Digit Health 2024; 6: e755–66","authors":"","doi":"10.1016/S2589-7500(24)00201-2","DOIUrl":"10.1016/S2589-7500(24)00201-2","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e680"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320339","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":"Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist","authors":"Yilin Ning PhD ,&nbsp;Salinelat Teixayavong BSS ,&nbsp;Yuqing Shang MSc ,&nbsp;Prof Julian Savulescu PhD ,&nbsp;Vaishaanth Nagaraj ,&nbsp;Di Miao MSc ,&nbsp;Mayli Mertens PhD ,&nbsp;Daniel Shu Wei Ting PhD ,&nbsp;Jasmine Chiat Ling Ong PharmD ,&nbsp;Mingxuan Liu MSc ,&nbsp;Prof Jiuwen Cao PhD ,&nbsp;Michael Dunn PhD ,&nbsp;Prof Roger Vaughan PhD ,&nbsp;Prof Marcus Eng Hock Ong MPH ,&nbsp;Prof Joseph Jao-Yiu Sung MD ,&nbsp;Prof Eric J Topol MD ,&nbsp;Nan Liu PhD","doi":"10.1016/S2589-7500(24)00143-2","DOIUrl":"10.1016/S2589-7500(24)00143-2","url":null,"abstract":"<div><div>The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e848-e856"},"PeriodicalIF":23.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251498","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":"Correction to Lancet Digit Health 2024; 6: e281–90","authors":"","doi":"10.1016/S2589-7500(24)00195-X","DOIUrl":"10.1016/S2589-7500(24)00195-X","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e680"},"PeriodicalIF":23.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134234","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":"Digital technology and new care pathways will redefine the cardiovascular workforce","authors":"Virimchi Pillutla ,&nbsp;Adam B Landman ,&nbsp;Jagmeet P Singh","doi":"10.1016/S2589-7500(24)00193-6","DOIUrl":"10.1016/S2589-7500(24)00193-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e674-e676"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113571","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":"Promises and challenges of digital tools in cardiovascular care","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00194-8","DOIUrl":"10.1016/S2589-7500(24)00194-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e673"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113574","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":"Digital tools in heart failure: addressing unmet needs","authors":"Prof Peder L Myhre MD PhD ,&nbsp;Jasper Tromp MD PhD ,&nbsp;Wouter Ouwerkerk MD PhD ,&nbsp;Daniel S W Ting MD PhD ,&nbsp;Kieran F Docherty MD PhD ,&nbsp;Prof C Michael Gibson MS MD ,&nbsp;Prof Carolyn S P Lam MD PhD","doi":"10.1016/S2589-7500(24)00158-4","DOIUrl":"10.1016/S2589-7500(24)00158-4","url":null,"abstract":"<div><div>This Series paper provides an overview of digital tools in heart failure care, encompassing screening, early diagnosis, treatment initiation and optimisation, and monitoring, and the implications these tools could have for research. The current medical environment favours the implementation of digital tools in heart failure due to rapid advancements in technology and computing power, unprecedented global connectivity, and the paradigm shift towards digitisation. Despite available effective therapies for heart failure, substantial inadequacies in managing the condition have hindered improvements in patient outcomes, particularly in low-income and middle-income countries. As digital health tools continue to evolve and exert a growing influence on both clinical care and research, establishing clinical frameworks and supportive ecosystems that enable their effective use on a global scale is crucial.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e755-e766"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113572","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}