Lancet Digital Health最新文献

{"title":"Overlooked and under-reported: the impact of cyberattacks on primary care in the UK National Health Service.","authors":"Kunal Rajput, Ara Darzi, Saira Ghafur","doi":"10.1016/j.landig.2025.100879","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100879","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100879"},"PeriodicalIF":23.8,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When data disappear: public health pays as US policy strays.","authors":"Thomas McAndrew, Andrew A Lover, Garrik Hoyt, Maimuna S Majumder","doi":"10.1016/j.landig.2025.100874","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100874","url":null,"abstract":"<p><p>Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources; and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100874"},"PeriodicalIF":23.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technology for global immunisation.","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100881","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100881","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100881"},"PeriodicalIF":23.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of digital health technologies in the management of inflammatory bowel disease: an umbrella review.","authors":"Marco Gasparetto, Priya Narula, Charlotte Wong, James Ashton, Jochen Kammermeier, Marieke Pierik, Uri Kopylov, Naila Arebi","doi":"10.1016/j.landig.2024.12.007","DOIUrl":"https://doi.org/10.1016/j.landig.2024.12.007","url":null,"abstract":"<p><p>The use of digital health technology (DHT) is increasing worldwide. Clinical trials assessing available health tools for the management of patients with inflammatory bowel disease (IBD) are sparse, with limited evidence-based outcome data. In this umbrella review, we investigated the effectiveness of DHT in the care of patients with IBD and identified areas for future research following the Joanna Briggs Institute methodology. Systematic reviews published between January, 2012, and September, 2024, were identified through searches across nine databases (Ovid Embase, Ovid MEDLINE, ProQuest PsycINFO, Epistemonikos, Cochrane, Health Evidence, DoPHER, PROSPERO, and CINAHL via EBSCO), and the results were imported into Covidence software. Inclusion criteria included systematic reviews of randomised controlled trials (RCTs) involving patients of all ages with Crohn's disease or ulcerative colitis, using DHT for diagnostics, treatment support, monitoring, self-management, or increasing participation in research studies, compared with standard care or alternative interventions. Outcomes included the efficacy and effectiveness of digital interventions, as reported in the studies. The primary outcome was clinical efficacy reported as one or more of the following: clinical response or remission, disease activity, flare-ups or relapses, and quality of life. Secondary outcomes included medication adherence, number of health-care visits, patient engagement (satisfaction and adherence or compliance with interventions), attendance for all terms of engagement, rate of interactions, knowledge improvement, psychological outcomes, and cost or cost-time effectiveness. The review protocol was registered in PROSPERO (registration number: CRD42023417525). AMSTAR-2 was used for methodological quality assessment. Nine relevant reviews were included, including five with meta-analyses comprising 13-19 RCTs in each review; four reviews were rated as high quality and five as critically low quality. DHT was not directly beneficial in achieving or maintaining clinical remission in IBD. In four trials, DHT use was associated with a reduced number of hospital attendances and increased treatment adherence, supporting its role as an adjuvant to standard clinical practice in IBD. Although current evidence from several RCTs and systematic reviews does not indicate better clinical outcomes with DHT in maintaining IBD remission and reducing relapse rates, DHT could be used as an adjuvant resource contributing towards treatment adherence and reducing hospital visits.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100843"},"PeriodicalIF":23.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming women's health, empowerment, and gender equality with digital health: evidence-based policy and practice.","authors":"Israel Júnior Borges do Nascimento, Hebatullah Mohamed Abdulazeem, Ishanka Weerasekara, Jodie Marquez, Lenny T Vasanthan, Genevieve Deeken, Rosemary Morgan, Heang-Lee Tan, Isabel Yordi Aguirre, Lasse Østeengaard, Indunil Kularathne, Natasha Azzopardi-Muscat, Robin van Kessel, Edson Zangiacomi Martinez, Govin Permanand, David Novillo-Ortiz","doi":"10.1016/j.landig.2025.01.014","DOIUrl":"https://doi.org/10.1016/j.landig.2025.01.014","url":null,"abstract":"<p><p>We evaluated the effects of digital health technologies (DHTs) on women's health, empowerment, and gender equality, using the scoping review method. Following a search across five databases and grey literature, we analysed 80 studies published up to Aug 18, 2023. The thematic appraisal and quantitative analysis found that DHTs positively affect women's access to health-care services, self-care, and tailored self-monitoring enabling the acquisition of health-related interventions. Use of these technologies is beneficial across various medical fields, including gynaecology, endocrinology, and psychiatry. DHTs also improve women's empowerment and gender equality by facilitating skills acquisition, health education, and social interaction, while allowing cost-effective health services. Overall, DHTs contribute to better health outcomes for women and support the UN Sustainable Development Goals by improving access to health care and financial literacy.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.","authors":"Jirong Yi, Anna M Marcinkiewicz, Aakash Shanbhag, Robert J H Miller, Jolien Geers, Wenhao Zhang, Aditya Killekar, Nipun Manral, Mark Lemley, Mikolaj Buchwald, Jacek Kwiecinski, Jianhang Zhou, Paul B Kavanagh, Joanna X Liang, Valerie Builoff, Terrence D Ruddy, Andrew J Einstein, Attila Feher, Edward J Miller, Albert J Sinusas, Daniel S Berman, Damini Dey, Piotr J Slomka","doi":"10.1016/j.landig.2025.02.002","DOIUrl":"10.1016/j.landig.2025.02.002","url":null,"abstract":"<p><strong>Background: </strong>CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification.</p><p><strong>Methods: </strong>We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves.</p><p><strong>Findings: </strong>The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<0·0001, 1·55, 1·26-1·90; p<0·0001, and 1·30, 1·06-1·60; p=0·012, respectively). Patients with high bone attenuation were at reduced risk of death (0·77, 0·62-0·95; p=0·016). Likewise, high skeletal muscle volume index was associated with a reduced risk of death (0·56, 0·44-0·71; p<0·0001).</p><p><strong>Interpretation: </strong>CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers that can be automatically measured and offer important additional prognostic value.</p><p><strong>Funding: </strong>The National Heart, Lung, and Blood Institute, National Institutes of Health.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100862"},"PeriodicalIF":23.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?","authors":"Mohan Pammi, Prakesh S Shah, Liu K Yang, Joseph Hagan, Nima Aghaeepour, Josef Neu","doi":"10.1016/j.landig.2025.01.007","DOIUrl":"https://doi.org/10.1016/j.landig.2025.01.007","url":null,"abstract":"<p><p>Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data-namely, digital twins, synthetic patient data, and in-silico trials-are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data to empower paediatric trials. The use of virtual patient data has the advantages of decreased exposure of children to potentially ineffective or risky interventions, shorter trial durations leading to more rapid ascertainment of safety and effectiveness of interventions, and faster drug approvals. Use of virtual patient data could lead to more personalised treatment options with low costs and could result in faster clinical implementation of interventions in children. However, ethical and regulatory concerns, including replacing humans with digital data, data privacy, and security should be addressed and the safety and sustainability of digital data innovation ensured before virtual patient data are adopted widely.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100851"},"PeriodicalIF":23.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study.","authors":"Dennis Bontempi, Osbert Zalay, Danielle S Bitterman, Nicolai Birkbak, Derek Shyr, Fridolin Haugg, Jack M Qian, Hannah Roberts, Subha Perni, Vasco Prudente, Suraj Pai, Andre Dekker, Benjamin Haibe-Kains, Christian Guthier, Tracy Balboni, Laura Warren, Monica Krishan, Benjamin H Kann, Charles Swanton, Dirk De Ruysscher, Raymond H Mak, Hugo J W L Aerts","doi":"10.1016/j.landig.2025.03.002","DOIUrl":"https://doi.org/10.1016/j.landig.2025.03.002","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;As humans age at different rates, physical appearance can yield insights into biological age and physiological health more reliably than chronological age. In medicine, however, appearance is incorporated into medical judgements in a subjective and non-standardised way. In this study, we aimed to develop and validate FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;FaceAge was trained on data from 58 851 presumed healthy individuals aged 60 years or older: 56 304 individuals from the IMDb-Wiki dataset (training) and 2547 from the UTKFace dataset (initial validation). Clinical utility was evaluated on data from 6196 patients with cancer diagnoses from two institutions in the Netherlands and the USA: the MAASTRO, Harvard Thoracic, and Harvard Palliative cohorts FaceAge estimates in these cancer cohorts were compared with a non-cancerous reference cohort of 535 individuals. To assess the prognostic relevance of FaceAge, we performed Kaplan-Meier survival analysis and Cox modelling, adjusting for several clinical covariates. We also assessed the performance of FaceAge in patients with metastatic cancer receiving palliative treatment at the end of life by incorporating FaceAge into clinical prediction models. To evaluate whether FaceAge has the potential to be a biomarker for molecular ageing, we performed a gene-based analysis to assess its association with senescence genes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;FaceAge showed significant independent prognostic performance in various cancer types and stages. Looking older was correlated with worse overall survival (after adjusting for covariates per-decade hazard ratio [HR] 1·151, p=0·013 in a pan-cancer cohort of n=4906; 1·148, p=0·011 in a thoracic cohort of n=573; and 1·117, p=0·021 in a palliative cohort of n=717). We found that, on average, patients with cancer looked older than their chronological age (mean increase of 4·79 years with respect to non-cancerous reference cohort, p&lt;0·0001). We found that FaceAge can improve physicians' survival predictions in patients with incurable cancer receiving palliative treatments (from area under the curve 0·74 [95% CI 0·70-0·78] to 0·8 [0·76-0·83]; p&lt;0·0001), highlighting the clinical use of the algorithm to support end-of-life decision making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, whereas age was not.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;Our results suggest that a deep learning model can estimate biological age from face photographs and thereby enhance survival prediction in patients with cancer. Further research, including validation in larger cohorts, is needed to verify these findings in patients with cancer and to establish whether the findings extend to patients with other diseases. Subject to further testing and validation, approaches such as FaceAge could be ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100870"},"PeriodicalIF":23.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144035203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study.","authors":"Ziyao Meng, Zhouyu Guan, Shujie Yu, Yilan Wu, Yaoning Zhao, Jie Shen, Cynthia Ciwei Lim, Tingli Chen, Dawei Yang, An Ran Ran, Feng He, Haslina Hamzah, Sarkaaj Singh, Anis Syazwani Abd Raof, Jian Wen Samuel Lee-Boey, Soo-Kun Lim, Xufang Sun, Shuwang Ge, Gang Xu, Hua Su, Yang Cheng, Feng Lu, Xiaofei Liao, Hai Jin, Chenxin Deng, Lei Ruan, Cuntai Zhang, Chan Wu, Rongping Dai, Yixiao Jin, Wenxiao Wang, Tingyao Li, Ruhan Liu, Jiajia Li, Jia Shu, Yuwei Lu, Xiangning Wang, Qiang Wu, Yiming Qin, Jin Tang, Xiaohua Sheng, Qiong Jiao, Xiaokang Yang, Minyi Guo, Gareth J McKay, Ruth E Hogg, Gerald Liew, Evelyn Yi Lyn Chee, Wynne Hsu, Mong Li Lee, Simon Szeto, Andrea O Y Luk, Juliana C N Chan, Carol Y Cheung, Gavin Siew Wei Tan, Yih-Chung Tham, Ching-Yu Cheng, Charumathi Sabanayagam, Lee-Ling Lim, Weiping Jia, Huating Li, Bin Sheng, Tien Yin Wong","doi":"10.1016/j.landig.2025.02.008","DOIUrl":"https://doi.org/10.1016/j.landig.2025.02.008","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD; and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838-0·846) on the internal validation dataset and AUCs of 0·791-0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825-0·966) on the internal validation dataset and AUCs of 0·733-0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD with higher sensitivity (89·8% vs 66·3%, p&lt;0·0001). In the longitudinal study, participants with isolated diabetic nephropathy and participants with NDKD identified by DeepDKD had a significant difference in renal function outcomes (proportion of estimated glomerular filtration rate decline: 27·45% vs 52·56%, p=0·0010).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;Among diverse multi-ethnic populations with diabetes, a retinal image-based AI-deep learning system showed its potential for detecting DKD and differentiating isolated diabetic nephropathy from NDKD in clinical practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Funding: &lt;/strong&gt;National Key R & D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Shanghai Municipal Key Clinical Specialty, Shanghai Research Centre for Endocrine and Metabolic Diseases, Innovative research team of high-level local universities in Shanghai, Noncommunicable Chronic Di","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100868"},"PeriodicalIF":23.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of mobile health technology combining telemonitoring and teleintervention versus usual care in vulnerable-phase heart failure management (HERMeS): a multicentre, randomised controlled trial.","authors":"Sergi Yun, Josep Comín-Colet, Esther Calero-Molina, Encarnación Hidalgo, Núria José-Bazán, Marta Cobo Marcos, Teresa Soria, Pau Llàcer, Cristina Fernández, José Manuel García-Pinilla, Concepción Cruzado, Álvaro González-Franco, Eva María García-Marina, José Luis Morales-Rull, Cristina Solé, Elena García-Romero, Julio Núñez, José Civera, Coral Fernández, Mercedes Faraudo, Pedro Moliner, Francesc Formiga, Javier de-Juan Bagudá, Isabel Zegri-Reiriz, José María Verdú-Rotellar, Emili Vela, David Monterde, Jordi Piera-Jiménez, Gerard Carot-Sans, Cristina Enjuanes","doi":"10.1016/j.landig.2025.02.006","DOIUrl":"https://doi.org/10.1016/j.landig.2025.02.006","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The potential of mobile health (mHealth) technology combining telemonitoring and teleintervention as a non-invasive intervention to reduce the risk of cardiovascular events in patients with heart failure during the early post-discharge period (ie, the vulnerable phase) has not been evaluated to our knowledge. We investigated the efficacy of incorporating mHealth into routine heart failure management in vulnerable-phase patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The Heart Failure Events Reduction with Remote Monitoring and eHealth Support (HERMeS) trial was a 24-week, randomised, controlled, open-label with masked endpoint adjudication, phase 3 trial conducted in ten centres (hospitals [n=9] and a primary care service [n=1]) experienced in heart failure management in Spain. We enrolled adults (aged ≥18 years) with heart failure diagnosed according to the 2016 European Society of Cardiology criteria (then-current clinical practice guidelines at the initiation of the trial) who had recently been discharged (within the preceding 30 days of enrolment) from a hospital admission that was due to heart failure decompensation, or who were in the process of discharge planning. After discharge, participants were centrally randomly assigned (1:1) via a web-based system to mHealth, comprising telemonitoring and preplanned structured health-care follow-up via videoconference, or usual care according to each centre's heart failure care framework including a nurse-led educational programme. The primary outcome was a composite of the occurrence of cardiovascular death or worsening heart failure events during the 6-month follow-up period, assessed by time-to-first-event analysis in the full analysis set by the intention-to-treat principle. No prospective systematic collection of harms information was planned. The HERMeS trial is registered with ClinicalTrials.gov, NCT03663907, and is completed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;From May 15, 2018, to April 4, 2022, 506 participants (207 [41%] women and 299 [59%] men) were randomly assigned: 255 to mHealth and 251 to usual care. The mean age of participants was 73 years (SD 13). Follow-up ended prematurely in 51 (20%) of 255 participants in the mHealth group and 36 (14%) of 251 in the usual care group. During follow-up in the mHealth group, cardiovascular death or a worsening heart failure event occurred in 43 (17%) of 255 participants, compared with 102 (41%) of 251 in the usual care group (hazard ratio for time to first event 0·35 [95% CI 0·24-0·50]; p&lt;0·0001; relative risk reduction 65% [95% CI 50-76]). No spontaneously reported harms were reported in either group during follow-up.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;mHealth-based heart failure care combining teleintervention and telemonitoring reduced the risk of new fatal and non-fatal cardiovascular events compared with usual care in people with a recent hospital admission due to heart failure decompensation. The current finding","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100866"},"PeriodicalIF":23.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}