Lancet Digital Health最新文献

{"title":"Proteomic organ-specific ageing signatures and 20-year risk of age-related diseases: the Whitehall II observational cohort study","authors":"Prof Mika Kivimäki FMedSci ,&nbsp;Philipp Frank PhD ,&nbsp;Jaana Pentti MSc ,&nbsp;Prof Markus Jokela PhD ,&nbsp;Solja T Nyberg PhD ,&nbsp;Acer Blake MSc ,&nbsp;Joni V Lindbohm MD PhD ,&nbsp;Hamilton Se-Hwee Oh PhD ,&nbsp;Prof Archana Singh-Manoux PhD ,&nbsp;Prof Tony Wyss-Coray PhD ,&nbsp;Prof Linda Partridge FMedSci","doi":"10.1016/j.landig.2025.01.006","DOIUrl":"10.1016/j.landig.2025.01.006","url":null,"abstract":"<div><h3>Background</h3><div>Biological ageing is known to vary among different organs within an individual, but the extent to which advanced ageing of specific organs increases the risk of age-related diseases in the same and other organs remains poorly understood.</div></div><div><h3>Methods</h3><div>In this observational cohort study, to assess the biological age of an individual's organs relative to those of same-aged peers, ie, organ age gaps, we collected plasma samples from 6235 middle-aged (age 45–69 years) participants of the Whitehall II prospective cohort study in London, UK, in 1997–99. Age gaps of nine organs were determined from plasma proteins via SomaScan (SomaLogic; Boulder, CO, USA) using the Python package organage. Following this assessment, we tracked participants for 20 years through linkage to national health records. Study outcomes were 45 individual age-related diseases and multimorbidity.</div></div><div><h3>Findings</h3><div>Over 123 712 person-years of observation (mean follow-up 19·8 years [SD 3·6]), after excluding baseline disease cases and adjusting for age, sex, ethnicity, and age gaps in organs other than the one under investigation, individuals with large organ age gaps showed an increased risk of 30 diseases. Six diseases were exclusively associated with accelerated ageing of their respective organ: liver failure (hazard ratio [HR] per SD increment in the organ age gap 2·13 [95% CI 1·41–3·22]), dilated cardiomyopathy (HR 1·65 [1·28–2·12]), chronic heart failure (HR 1·52 [1·40–1·65]), lung cancer (HR 1·29 [1·04–1·59]), agranulocytosis (HR 1·27 [1·07–1·51]), and lymphatic node metastasis (HR 1·23 [1·06–1·43]). 24 diseases were associated with more than one organ age gap or with organ age gaps not directly related to the disease location. Larger age gaps were also associated with elevated HRs of developing two or more diseases affecting different organs within the same individual (ie, multiorgan multimorbidity): 2·03 (1·51–2·74) for the arterial age gap, 1·78 (1·48–2·14) for the kidney age gap, 1·52 (1·38–1·68) for the heart age gap, 1·52 (1·12–2·06) for the brain age gap, 1·43 (1·16–1·78) for the pancreas age gap, 1·37 (1·17–1·61) for the lung age gap, 1·36 (1·26–1·46) for the immune system age gap, and 1·30 (1·18–1·42) for the liver age gap.</div></div><div><h3>Interpretation</h3><div>Advanced proteomic organ ageing is associated with the long-term risk of age-related diseases. In most cases, faster ageing of a specific organ increases susceptibility to morbidity affecting multiple organs.</div></div><div><h3>Funding</h3><div>Wellcome Trust, UK Medical Research Council, National Institute for Aging, Academy of Finland.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e195-e204"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488606","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":"Passive sensing at scale to transform understanding of poor mental health","authors":"Aiden Doherty ,&nbsp;Sandra Bucci ,&nbsp;Alexandra Kenny ,&nbsp;Roman Kotov ,&nbsp;Gosia Lipinska ,&nbsp;Laura Ospina-Pinillos ,&nbsp;Katharina Schultebraucks","doi":"10.1016/j.landig.2025.01.002","DOIUrl":"10.1016/j.landig.2025.01.002","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e172-e174"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488109","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-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study","authors":"Arunashis Sau PhD ,&nbsp;Ewa Sieliwonczyk PhD ,&nbsp;Konstantinos Patlatzoglou PhD ,&nbsp;Libor Pastika MBBS ,&nbsp;Kathryn A McGurk PhD ,&nbsp;Antônio H Ribeiro PhD ,&nbsp;Prof Antonio Luiz P Ribeiro MD ,&nbsp;Jennifer E Ho MD ,&nbsp;Prof Nicholas S Peters MD ,&nbsp;Prof James S Ware PhD ,&nbsp;Upasana Tayal PhD ,&nbsp;Daniel B Kramer MD ,&nbsp;Jonathan W Waks MD ,&nbsp;Fu Siong Ng PhD","doi":"10.1016/j.landig.2024.12.003","DOIUrl":"10.1016/j.landig.2024.12.003","url":null,"abstract":"<div><h3>Background</h3><div>Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enhanced electrocardiography (AI-ECG) model to investigate sex-specific cardiovascular risk.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, we trained a convolutional neural network to classify sex using the 12-lead electrocardiogram (ECG). The Beth Israel Deaconess Medical Center (BIDMC) secondary care dataset, comprising data from individuals who had clinically indicated ECGs performed in a hospital setting in Boston, MA, USA collected between May, 2000, and March, 2023, was the derivation cohort (1 163 401 ECGs). 50% of this dataset was used for model training, 10% for validation, and 40% for testing. External validation was performed using the UK Biobank cohort, comprising data from volunteers aged 40–69 years at the time of enrolment in 2006–10 (42 386 ECGs). We examined the difference between AI-ECG-predicted sex (continuous) and biological sex (dichotomous), termed sex discordance score.</div></div><div><h3>Findings</h3><div>AI-ECG accurately identified sex (area under the receiver operating characteristic 0·943 [95% CI 0·942–0·943] for BIDMC and 0·971 [0·969–0·972] for the UK Biobank). In BIDMC outpatients with normal ECGs, an increased sex discordance score was associated with covariate-adjusted increased risk of cardiovascular death in females (hazard ratio [HR] 1·78 [95% CI 1·18–2·70], p=0·006) but not males (1·00 [0·63–1·58], p=0·996). In the UK Biobank cohort, the same pattern was seen (HR 1·33 [95% CI 1·06–1·68] for females, p=0·015; 0·98 [0·80–1·20] for males, p=0·854). Females with a higher sex discordance score were more likely to have future heart failure or myocardial infarction in the BIDMC cohort and had more male cardiac (increased left ventricular mass and chamber volumes) and non-cardiac phenotypes (increased muscle mass and reduced body fat percentage) in both cohorts.</div></div><div><h3>Interpretation</h3><div>Sex discordance score is a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. AI-ECG has the potential to identify female patients who could benefit from enhanced risk factor modification or surveillance.</div></div><div><h3>Funding</h3><div>British Heart Foundation.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e184-e194"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488605","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":"Accelerating action for gender equality in health","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.02.005","DOIUrl":"10.1016/j.landig.2025.02.005","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Page e167"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488604","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":"Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study","authors":"Damien K Ming PhD ,&nbsp;Vasin Vasikasin PhD ,&nbsp;Timothy M Rawson PhD ,&nbsp;Prof Pantelis Georgiou PhD ,&nbsp;Frances J Davies PhD ,&nbsp;Prof Alison H Holmes FMedSci ,&nbsp;Bernard Hernandez PhD","doi":"10.1016/j.landig.2025.01.010","DOIUrl":"10.1016/j.landig.2025.01.010","url":null,"abstract":"<div><h3>Background</h3><div>Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24–48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infections in patients admitted to hospital.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, we used routinely collected blood biomarkers and demographic data from patients who underwent blood sample collection for testing via culture between March 3, 2014, and Dec 1, 2021, at Imperial College Healthcare NHS Trust (London, UK) as model features. Data up to 14 days before blood sample collection were provided to long short-term memory (LSTM) or static logistic regression models. The primary outcome was prediction of blood culture results, defined as a pathogenic bloodstream infection (ie, isolation of pathogenic bacteria of interest) or no bloodstream infection (ie, no growth or contamination). Data collected up to Feb 28, 2021 (n=15 212) comprised the training set and were evaluated against a temporal hold-out test set comprising patients who were sampled after March 1, 2021 (n=5638).</div></div><div><h3>Findings</h3><div>Among 20 850 patients with available data, pathogenic bacteria were observed in the cultured blood samples of 3866 (18·5%) patients. 2920 (62·2%) of 4897 patients who had their blood samples taken more than 48 h after admission to hospital had pathogenic bloodstream infections, and so were defined as having hospital-acquired bloodstream infections. Including data from the 7 days before admission (7-day window approach) and using five-fold cross validation in the training set gave an area under receiver operator curve (AUROC) of 0·75 (IQR 0·68–0·82) and an area under the precision recall curve (AUPRC) of 0·58 (0·46–0·77) for static models and an AUROC of 0·92 (0·91–0·93) and AUPRC of 0·75 (0·72–0·76) for the LSTM model. In the hold-out test set performances were: AUROC of 0·74 (95% CI 0·70–0·78) and AUPRC of 0·48 (0·43–0·53) for static models and AUROC of 0·97 (0·96–0·97) and AUPRC of 0·65 (0·60–0·70) for LSTM. Removal of time series information resulted in lower model performance, particularly for hospital-acquired bloodstream infections. Dynamics of C-reactive protein concentration, eosinophil count, and platelet count were important features for prediction of blood culture results.</div></div><div><h3>Interpretation</h3><div>Deep learning models accounting for longitudinal changes could support individualised clinical decision making for patients at risk of bloodstream infections. Appropriate implementation into existing diagnostic pathways could enhance diagnostic stewardship and reduce unnecessary antimicrobial prescribing.</div></div><div><h3>Funding</h3><div>UK Department of Health and Social Care, the National Institute for Health and Care Research, and the Wellcome Trust.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e205-e215"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487939","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":"Unmasking hidden risk: an AI approach to improve cardiovascular risk assessment in females","authors":"Demilade A Adedinsewo ,&nbsp;Chrisandra L Shufelt ,&nbsp;Rickey E Carter","doi":"10.1016/j.landig.2025.01.015","DOIUrl":"10.1016/j.landig.2025.01.015","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e170-e171"},"PeriodicalIF":23.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488108","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":"Governing synthetic data in medical research: the time is now","authors":"Daniela Boraschi ,&nbsp;Mihaela van der Schaar ,&nbsp;Alessia Costa ,&nbsp;Richard Milne","doi":"10.1016/j.landig.2025.01.012","DOIUrl":"10.1016/j.landig.2025.01.012","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e233-e234"},"PeriodicalIF":23.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473358","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":"Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study","authors":"Veronica Hernström MD ,&nbsp;Viktoria Josefsson MD ,&nbsp;Hanna Sartor MD PhD ,&nbsp;David Schmidt MD ,&nbsp;Anna-Maria Larsson MD PhD ,&nbsp;Prof Solveig Hofvind PhD ,&nbsp;Ingvar Andersson MD PhD ,&nbsp;Aldana Rosso MSc PhD ,&nbsp;Oskar Hagberg MSc PhD ,&nbsp;Kristina Lång MD PhD","doi":"10.1016/S2589-7500(24)00267-X","DOIUrl":"10.1016/S2589-7500(24)00267-X","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Emerging evidence suggests that artificial intelligence (AI) can increase cancer detection in mammography screening while reducing screen-reading workload, but further understanding of the clinical impact is needed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;In this randomised, controlled, parallel-group, non-inferiority, single-blinded, screening-accuracy study, done within the Swedish national screening programme, women recruited at four screening sites in southwest Sweden (Malmö, Lund, Landskrona, and Trelleborg) who were eligible for mammography screening were randomly allocated (1:1) to AI-supported screening or standard double reading. The AI system (Transpara version 1.7.0 ScreenPoint Medical, Nijmegen, Netherlands) was used to triage screening examinations to single or double reading and as detection support highlighting suspicious findings. This is a protocol-defined analysis of the secondary outcome measures of recall, cancer detection, false-positive rates, positive predictive value of recall, type and stage of cancer detected, and screen-reading workload. This trial is registered at &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT04838756&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt; and is closed to accrual.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between April 12, 2021, and Dec 7, 2022, 105 934 women were randomly assigned to the intervention or control group. 19 women were excluded from the analysis. The median age was 53·7 years (IQR 46·5–63·2). AI-supported screening among 53 043 participants resulted in 338 detected cancers and 1110 recalls. Standard screening among 52 872 participants resulted in 262 detected cancers and 1027 recalls. Cancer-detection rates were 6·4 per 1000 (95% CI 5·7–7·1) screened participants in the intervention group and 5·0 per 1000 (4·4–5·6) in the control group, a ratio of 1·29 (95% CI 1·09–1·51; p=0·0021). AI-supported screening resulted in an increased detection of invasive cancers (270 &lt;em&gt;vs&lt;/em&gt; 217, a proportion ratio of 1·24 [95% CI 1·04–1·48]), wich were mainly small lymph-node negative cancers (58 more T1, 46 more lymph-node negative, and 21 more non-luminal A). AI-supported screening also resulted in an increased detection of in situ cancers (68 &lt;em&gt;vs&lt;/em&gt; 45, a proportion ratio of 1·51 [1·03–2·19]), with about half of the increased detection being high-grade in situ cancer (12 more nuclear grade III, and no increase in nuclear grade I). The recall and false-positive rate were not significantly higher in the intervention group (a ratio of 1·08 [95% CI 0·99–1·17; p=0·084] and 1·01 [0·91–1·11; p=0·92], respectively). The positive predictive value of recall was significantly higher in the intervention group compared with the control group, with a ratio of 1·19 (95% CI 1·04–1·37; p=0·012). There were 61 248 screen readings in the intervention group and 109 692 in the control group, resulting in a 44·2% reduction in the screen-reading workload.&lt;/di","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e175-e183"},"PeriodicalIF":23.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190963","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":"AI for mammography: making double screen-reading history","authors":"Nehmat Houssami ,&nbsp;M Luke Marinovich","doi":"10.1016/j.landig.2025.01.004","DOIUrl":"10.1016/j.landig.2025.01.004","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e168-e169"},"PeriodicalIF":23.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190962","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":"Exploring electronic health records to study rare diseases","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.01.008","DOIUrl":"10.1016/j.landig.2025.01.008","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Page e103"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075878","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}