Pub Date : 2024-10-07DOI: 10.1016/S2589-7500(24)00197-3
{"title":"Combating medical misinformation and rebuilding trust in the USA","authors":"","doi":"10.1016/S2589-7500(24)00197-3","DOIUrl":"10.1016/S2589-7500(24)00197-3","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394366","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}
Pub Date : 2024-10-04DOI: 10.1016/S2589-7500(24)00149-3
Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor
<p><strong>Background: </strong>Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people.</p><p><strong>Methods: </strong>This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16-22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick-Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.</p><p><strong>Findings: </strong>Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS -0·21 [95% CI -1·08 to 0·66]) or the self-monitoring app (0·32 [-0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [-0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related reasons, which were considered unrelated to the interventions (five participants in the emotional competence
{"title":"Emotional competence self-help mobile phone app versus cognitive behavioural self-help app versus self-monitoring app to promote mental wellbeing in healthy young adults (ECoWeB PROMOTE): an international, multicentre, parallel, open-label, randomised controlled trial.","authors":"Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor","doi":"10.1016/S2589-7500(24)00149-3","DOIUrl":"10.1016/S2589-7500(24)00149-3","url":null,"abstract":"<p><strong>Background: </strong>Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people.</p><p><strong>Methods: </strong>This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16-22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick-Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.</p><p><strong>Findings: </strong>Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS -0·21 [95% CI -1·08 to 0·66]) or the self-monitoring app (0·32 [-0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [-0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related reasons, which were considered unrelated to the interventions (five participants in the emotional competence","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378382","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}
Pub Date : 2024-10-04DOI: 10.1016/S2589-7500(24)00148-1
Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor
<p><strong>Background: </strong>Effective, scalable interventions are needed to prevent poor mental health in young people. Although mental health apps can provide scalable prevention, few have been rigorously tested in high-powered trials built on models of healthy emotional functioning or tailored to individual profiles. We aimed to test a personalised emotional competence app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to prevent an increase in depression symptoms in young people.</p><p><strong>Methods: </strong>This multicentre, parallel, open-label, randomised controlled trial, within a cohort multiple randomised trial (including a parallel trial of wellbeing promotion) was done at four university trial sites in the UK, Germany, Spain, and Belgium. Participants were recruited from schools, universities, and social media from the four respective countries. Eligible participants were aged 16-22 years with increased vulnerability indexed by baseline emotional competence profile, without current or past diagnosis of major depression. Participants were randomly assigned (1:1:1) to usual practice plus either the personalised emotional competence self-help app, the generic CBT self-help app, or the self-monitoring app by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. Outcome assessors were masked to group allocation. The primary outcome was depression symptoms (according to Patient Health Questionnaire-9 [PHQ-9]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.</p><p><strong>Findings: </strong>Between Oct 15, 2020, and Aug 3, 2021, 1262 participants were enrolled, including 417 to the emotional competence app, 423 to the CBT app, and 422 to the self-monitoring app. Mean age was 18·8 years (SD 2·0). Of 1262 participants self-reporting gender, 984 (78·0%) were female, 253 (20·0%) were male, 15 (1·2%) were neither, and ten (0·8%) were both. 178 participants in the emotional competence app group, 191 in the CBT app group, and 199 in the self-monitoring app group completed the follow-up assessment at 3 months. At 3 months, depression symptoms were lower with the CBT app than the self-monitoring app (mean difference in PHQ-9 -1·18 [95% CI -2·01 to -0·34]; p=0·006), but depression symptoms did not differ between the emotional competence app and the CBT app (0·63 [-0·22 to 1·49]; p=0·15) or the self-monitoring app and emotional competence app (-0·54 [-1·39 to 0·31]; p=0·21). 31 of the 541 participants who completed any of the follow-up assessments received treatment in hospital or were admitted to hospital for mental health-related reasons considered unrelated to interventions (eight in the emotional competence app group, 15 in the CBT app group, and eight in the self-monitoring app group). No deaths o
{"title":"Emotional competence self-help app versus cognitive behavioural self-help app versus self-monitoring app to prevent depression in young adults with elevated risk (ECoWeB PREVENT): an international, multicentre, parallel, open-label, randomised controlled trial.","authors":"Edward R Watkins, Fiona C Warren, Alexandra Newbold, Claire Hulme, Timothy Cranston, Benjamin Aas, Holly Bear, Cristina Botella, Felix Burkhardt, Thomas Ehring, Mina Fazel, Johnny R J Fontaine, Mads Frost, Azucena Garcia-Palacios, Ellen Greimel, Christiane Hößle, Arpine Hovasapian, Veerle E I Huyghe, Kostas Karpouzis, Johanna Löchner, Guadalupe Molinari, Reinhard Pekrun, Belinda Platt, Tabea Rosenkranz, Klaus R Scherer, Katja Schlegel, Bjorn W Schuller, Gerd Schulte-Korne, Carlos Suso-Ribera, Varinka Voigt, Maria Voß, Rod S Taylor","doi":"10.1016/S2589-7500(24)00148-1","DOIUrl":"10.1016/S2589-7500(24)00148-1","url":null,"abstract":"<p><strong>Background: </strong>Effective, scalable interventions are needed to prevent poor mental health in young people. Although mental health apps can provide scalable prevention, few have been rigorously tested in high-powered trials built on models of healthy emotional functioning or tailored to individual profiles. We aimed to test a personalised emotional competence app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to prevent an increase in depression symptoms in young people.</p><p><strong>Methods: </strong>This multicentre, parallel, open-label, randomised controlled trial, within a cohort multiple randomised trial (including a parallel trial of wellbeing promotion) was done at four university trial sites in the UK, Germany, Spain, and Belgium. Participants were recruited from schools, universities, and social media from the four respective countries. Eligible participants were aged 16-22 years with increased vulnerability indexed by baseline emotional competence profile, without current or past diagnosis of major depression. Participants were randomly assigned (1:1:1) to usual practice plus either the personalised emotional competence self-help app, the generic CBT self-help app, or the self-monitoring app by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. Outcome assessors were masked to group allocation. The primary outcome was depression symptoms (according to Patient Health Questionnaire-9 [PHQ-9]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed.</p><p><strong>Findings: </strong>Between Oct 15, 2020, and Aug 3, 2021, 1262 participants were enrolled, including 417 to the emotional competence app, 423 to the CBT app, and 422 to the self-monitoring app. Mean age was 18·8 years (SD 2·0). Of 1262 participants self-reporting gender, 984 (78·0%) were female, 253 (20·0%) were male, 15 (1·2%) were neither, and ten (0·8%) were both. 178 participants in the emotional competence app group, 191 in the CBT app group, and 199 in the self-monitoring app group completed the follow-up assessment at 3 months. At 3 months, depression symptoms were lower with the CBT app than the self-monitoring app (mean difference in PHQ-9 -1·18 [95% CI -2·01 to -0·34]; p=0·006), but depression symptoms did not differ between the emotional competence app and the CBT app (0·63 [-0·22 to 1·49]; p=0·15) or the self-monitoring app and emotional competence app (-0·54 [-1·39 to 0·31]; p=0·21). 31 of the 541 participants who completed any of the follow-up assessments received treatment in hospital or were admitted to hospital for mental health-related reasons considered unrelated to interventions (eight in the emotional competence app group, 15 in the CBT app group, and eight in the self-monitoring app group). No deaths o","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378381","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}
Pub Date : 2024-10-01DOI: 10.1016/S2589-7500(24)00170-5
The PhysioNet open access database (PND) is one of the world's largest and most comprehensive repositories of biosignal data and is widely used by researchers to develop, train, and validate algorithms. To contextualise the results of such algorithms, understanding the underlying demographic distribution of the data is crucial—specifically, the race, ethnicity, sex or gender, and age of study participants. We sought to understand the underlying reporting patterns and characteristics of the demographic data of the datasets available on PND. Of the 181 unique datasets present in the PND as of July 6, 2023, 175 involved human participants, with less than 7% of studies reporting on all four of the key demographic variables. Furthermore, we found a higher rate of reporting sex or gender and age than race and ethnicity. In the studies that did include participant sex or gender, the samples were mostly male. Additionally, we found that most studies were done in North America, particularly in the USA. These imbalances and poor reporting of representation raise concerns regarding potential embedded biases in the algorithms that rely on these datasets. They also underscore the need for universal and comprehensive reporting practices to ensure equitable development and deployment of artificial intelligence and machine learning tools in medicine.
{"title":"Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database","authors":"","doi":"10.1016/S2589-7500(24)00170-5","DOIUrl":"10.1016/S2589-7500(24)00170-5","url":null,"abstract":"<div><div>The PhysioNet open access database (PND) is one of the world's largest and most comprehensive repositories of biosignal data and is widely used by researchers to develop, train, and validate algorithms. To contextualise the results of such algorithms, understanding the underlying demographic distribution of the data is crucial—specifically, the race, ethnicity, sex or gender, and age of study participants. We sought to understand the underlying reporting patterns and characteristics of the demographic data of the datasets available on PND. Of the 181 unique datasets present in the PND as of July 6, 2023, 175 involved human participants, with less than 7% of studies reporting on all four of the key demographic variables. Furthermore, we found a higher rate of reporting sex or gender and age than race and ethnicity. In the studies that did include participant sex or gender, the samples were mostly male. Additionally, we found that most studies were done in North America, particularly in the USA. These imbalances and poor reporting of representation raise concerns regarding potential embedded biases in the algorithms that rely on these datasets. They also underscore the need for universal and comprehensive reporting practices to ensure equitable development and deployment of artificial intelligence and machine learning tools in medicine.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367058","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}
Pub Date : 2024-09-26DOI: 10.1016/S2589-7500(24)00119-5
This umbrella review of 34 meta-analyses, representing 235 randomised controlled trials done across 52 countries and 48 957 participants and ten chronic conditions, aimed to evaluate evidence on the efficacy of mobile phone interventions for populations with chronic diseases. We evaluated the strengths of evidence via the Fusar-Poli and Radua methodology. Compared with usual care, mobile apps had convincing effects on glycated haemoglobin reduction among adults with type 2 diabetes (d=0·44). Highly suggestive effects were found for both text messages and apps on various outcomes, including medication adherence (among patients with HIV in sub-Saharan Africa and people with cardiovascular disease), glucose management in type 2 diabetes, and blood pressure reduction in hypertension. Many effects (42%) were non-significant. Various gaps were identified, such as a scarcity of reporting on moderators and publication bias by meta-analyses, little research in low-income and lower-middle-income countries, and little reporting on adverse events.
{"title":"Mobile phone interventions to improve health outcomes among patients with chronic diseases: an umbrella review and evidence synthesis from 34 meta-analyses","authors":"","doi":"10.1016/S2589-7500(24)00119-5","DOIUrl":"10.1016/S2589-7500(24)00119-5","url":null,"abstract":"<div><div>This umbrella review of 34 meta-analyses, representing 235 randomised controlled trials done across 52 countries and 48 957 participants and ten chronic conditions, aimed to evaluate evidence on the efficacy of mobile phone interventions for populations with chronic diseases. We evaluated the strengths of evidence via the Fusar-Poli and Radua methodology. Compared with usual care, mobile apps had convincing effects on glycated haemoglobin reduction among adults with type 2 diabetes (d=0·44). Highly suggestive effects were found for both text messages and apps on various outcomes, including medication adherence (among patients with HIV in sub-Saharan Africa and people with cardiovascular disease), glucose management in type 2 diabetes, and blood pressure reduction in hypertension. Many effects (42%) were non-significant. Various gaps were identified, such as a scarcity of reporting on moderators and publication bias by meta-analyses, little research in low-income and lower-middle-income countries, and little reporting on adverse events.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356149","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}
Pub Date : 2024-09-25DOI: 10.1016/S2589-7500(24)00150-X
<div><h3>Background</h3><div>Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir–ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores—the current clinical gold standard for breast cancer risk prediction—for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer).</div></div><div><h3>Findings</h3><div>4382 female donors (median age at donation 45 years [IQR 34–57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7–11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10–2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36–0·88]; p=0·013). For the othe
{"title":"Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study","authors":"","doi":"10.1016/S2589-7500(24)00150-X","DOIUrl":"10.1016/S2589-7500(24)00150-X","url":null,"abstract":"<div><h3>Background</h3><div>Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir–ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores—the current clinical gold standard for breast cancer risk prediction—for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer).</div></div><div><h3>Findings</h3><div>4382 female donors (median age at donation 45 years [IQR 34–57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7–11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10–2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36–0·88]; p=0·013). For the othe","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320340","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}
Pub Date : 2024-09-25DOI: 10.1016/S2589-7500(24)00152-3
<div><h3>Background</h3><div>The COVID-19 pandemic resulted in the widespread disruption of cancer health provision services across the entirety of the cancer care pathway in the UK, from screening to treatment. The potential long-term health implications, including increased mortality for individuals who missed diagnoses or appointments, are concerning. However, the precise impact of lockdown policies on national cancer health service provision across diagnostic groups is understudied. We aimed to systematically evaluate changes in patterns of attendance for groups of individuals diagnosed with cancer, including the changes in attendance volume and consultation rates, stratified by both time-based exposures and by patient-based exposures and to better understand the impact of such changes on cancer-specific mortality.</div></div><div><h3>Methods</h3><div>In this retrospective, cross-sectional, phase-by-phase time-series analysis, by using primary care records linked to hospitals and the death registry from Jan 1, 1998, to June 17, 2021, we conducted descriptive analyses to quantify attendance changes for groups stratified by patient-based exposures (Index of Multiple Deprivation, ethnicity, age, comorbidity count, practice region, diagnosis time, and cancer subtype) across different phases of the COVID-19 pandemic in England, UK. In this study, we defined the phases of the COVID-19 pandemic as: pre-pandemic period (Jan 1, 2018, to March 22, 2020), lockdown 1 (March 23 to June 21, 2020), minimal restrictions (June 22 to Sept 20, 2020), lockdown 2 (Sept 21, 2020, to Jan 3, 2021), lockdown 3 (Jan 4 to March 21, 2021), and lockdown restrictions lifted (March 22 to March 31, 2021). In the analyses we examined changes in both attendance volume and consultation rate. We further compared changes in attendance trends to cancer-specific mortality trends. Finally, we conducted an interrupted time-series analysis with the lockdown on March 23, 2020, as the intervention point using an autoregressive integrated moving average model.</div></div><div><h3>Findings</h3><div>From 561 611 eligible individuals, 7 964 685 attendances were recorded. During the first lockdown, the median attendance volume decreased (–35·30% [IQR –36·10 to –34·25]) compared with the preceding pre-pandemic period, followed by a median change of 4·38% (2·66 to 5·15) during minimal restrictions. More drastic reductions in attendance volume were seen in the second (–48·71% [–49·54 to –48·26]) and third (–71·62% [–72·23 to –70·97]) lockdowns. These reductions were followed by a 4·48% (3·45 to 7·10) increase in attendance when lockdown restrictions were lifted. The median consultation rate change during the first lockdown was 31·32% (25·10 to 33·60), followed by a median change of –0·25% (–1·38 to 1·68) during minimal restrictions. The median consultation rate decreased in the second (–33·89% [–34·64 to –33·18]) and third (–4·98% [–5·71 to –4·00]) lockdowns, followed by a 416·16% increase (40
{"title":"Impact of the COVID-19 pandemic on health-care use among patients with cancer in England, UK: a comprehensive phase-by-phase time-series analysis across attendance types for 38 cancers","authors":"","doi":"10.1016/S2589-7500(24)00152-3","DOIUrl":"10.1016/S2589-7500(24)00152-3","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic resulted in the widespread disruption of cancer health provision services across the entirety of the cancer care pathway in the UK, from screening to treatment. The potential long-term health implications, including increased mortality for individuals who missed diagnoses or appointments, are concerning. However, the precise impact of lockdown policies on national cancer health service provision across diagnostic groups is understudied. We aimed to systematically evaluate changes in patterns of attendance for groups of individuals diagnosed with cancer, including the changes in attendance volume and consultation rates, stratified by both time-based exposures and by patient-based exposures and to better understand the impact of such changes on cancer-specific mortality.</div></div><div><h3>Methods</h3><div>In this retrospective, cross-sectional, phase-by-phase time-series analysis, by using primary care records linked to hospitals and the death registry from Jan 1, 1998, to June 17, 2021, we conducted descriptive analyses to quantify attendance changes for groups stratified by patient-based exposures (Index of Multiple Deprivation, ethnicity, age, comorbidity count, practice region, diagnosis time, and cancer subtype) across different phases of the COVID-19 pandemic in England, UK. In this study, we defined the phases of the COVID-19 pandemic as: pre-pandemic period (Jan 1, 2018, to March 22, 2020), lockdown 1 (March 23 to June 21, 2020), minimal restrictions (June 22 to Sept 20, 2020), lockdown 2 (Sept 21, 2020, to Jan 3, 2021), lockdown 3 (Jan 4 to March 21, 2021), and lockdown restrictions lifted (March 22 to March 31, 2021). In the analyses we examined changes in both attendance volume and consultation rate. We further compared changes in attendance trends to cancer-specific mortality trends. Finally, we conducted an interrupted time-series analysis with the lockdown on March 23, 2020, as the intervention point using an autoregressive integrated moving average model.</div></div><div><h3>Findings</h3><div>From 561 611 eligible individuals, 7 964 685 attendances were recorded. During the first lockdown, the median attendance volume decreased (–35·30% [IQR –36·10 to –34·25]) compared with the preceding pre-pandemic period, followed by a median change of 4·38% (2·66 to 5·15) during minimal restrictions. More drastic reductions in attendance volume were seen in the second (–48·71% [–49·54 to –48·26]) and third (–71·62% [–72·23 to –70·97]) lockdowns. These reductions were followed by a 4·48% (3·45 to 7·10) increase in attendance when lockdown restrictions were lifted. The median consultation rate change during the first lockdown was 31·32% (25·10 to 33·60), followed by a median change of –0·25% (–1·38 to 1·68) during minimal restrictions. The median consultation rate decreased in the second (–33·89% [–34·64 to –33·18]) and third (–4·98% [–5·71 to –4·00]) lockdowns, followed by a 416·16% increase (40","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320341","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}
Pub Date : 2024-09-25DOI: 10.1016/S2589-7500(24)00147-X
<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
{"title":"Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis","authors":"","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":null,"pages":null},"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}
Pub Date : 2024-09-25DOI: 10.1016/S2589-7500(24)00177-8
{"title":"In the era of digitalisation and biosignatures, is C-reactive protein still the one to beat?","authors":"","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":null,"pages":null},"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}
Pub Date : 2024-09-25DOI: 10.1016/S2589-7500(24)00153-5
<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
{"title":"Highly sensitive detection platform-based diagnosis of oesophageal squamous cell carcinoma in China: a multicentre, case–control, diagnostic study","authors":"","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":null,"pages":null},"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}
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