Pub Date : 2025-02-01DOI: 10.1016/S2589-7500(24)00196-1
Anmol Arora MBBChir MA , Siegfried Karl Wagner PhD FRCOphth , Robin Carpenter BSc , Rajesh Jena MD , Pearse A Keane MD FRCOphth
Synthetic data, generated through artificial intelligence technologies such as generative adversarial networks and latent diffusion models, maintain aggregate patterns and relationships present in the real data the technologies were trained on without exposing individual identities, thereby mitigating re-identification risks. This approach has been gaining traction in biomedical research because of its ability to preserve privacy and enable dataset sharing between organisations. Although the use of synthetic data has become widespread in other domains, such as finance and high-energy physics, use in medical research raises novel issues. The use of synthetic data as a method of preserving the privacy of data used to train models requires that the data are high fidelity with the original data to preserve utility, but must be sufficiently different as to protect against adversarial or accidental re-identification. There is a need for the development of standards for synthetic data generation and consensus standards for its evaluation. As synthetic data applications expand, ongoing legal and ethical evaluations are crucial to ensure that they remain a secure and effective tool for advancing medical research without compromising individual privacy.
{"title":"The urgent need to accelerate synthetic data privacy frameworks for medical research","authors":"Anmol Arora MBBChir MA , Siegfried Karl Wagner PhD FRCOphth , Robin Carpenter BSc , Rajesh Jena MD , Pearse A Keane MD FRCOphth","doi":"10.1016/S2589-7500(24)00196-1","DOIUrl":"10.1016/S2589-7500(24)00196-1","url":null,"abstract":"<div><div>Synthetic data, generated through artificial intelligence technologies such as generative adversarial networks and latent diffusion models, maintain aggregate patterns and relationships present in the real data the technologies were trained on without exposing individual identities, thereby mitigating re-identification risks. This approach has been gaining traction in biomedical research because of its ability to preserve privacy and enable dataset sharing between organisations. Although the use of synthetic data has become widespread in other domains, such as finance and high-energy physics, use in medical research raises novel issues. The use of synthetic data as a method of preserving the privacy of data used to train models requires that the data are high fidelity with the original data to preserve utility, but must be sufficiently different as to protect against adversarial or accidental re-identification. There is a need for the development of standards for synthetic data generation and consensus standards for its evaluation. As synthetic data applications expand, ongoing legal and ethical evaluations are crucial to ensure that they remain a secure and effective tool for advancing medical research without compromising individual privacy.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e157-e160"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142740999","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 : 2025-02-01DOI: 10.1016/S2589-7500(24)00249-8
Evangelos K Oikonomou MD , Akhil Vaid MD , Gregory Holste BA , Andreas Coppi PhD , Robert L McNamara MD , Cristiana Baloescu MD , Harlan M Krumholz MD , Zhangyang Wang PhD , Donald J Apakama MD , Girish N Nadkarni MD , Rohan Khera MD
Background
Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed to develop and test artificial intelligence (AI) models to screen for under-diagnosed cardiomyopathies from cardiac POCUS.
Methods
In a development set of 290 245 transthoracic echocardiographic videos across the Yale–New Haven Health System (YNHHS), we used augmentation approaches, and a customised loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network that discriminates hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy from controls without known disease. We evaluated the model across independent, internal, and external, retrospective cohorts of individuals undergoing cardiac POCUS across YNHHS and the Mount Sinai Health System (MSHS) emergency departments (between 2012 and 2024) to prioritise key views and validate the diagnostic and prognostic performance of single-view screening protocols.
Findings
Between Nov 1, 2023, and March 28, 2024, we identified 33 127 patients (mean age 58·9 [SD 20·5] years, 17 276 [52·2%] were female, 14 923 [45·0%] were male, and for 928 [2·8%] sex was recorded as unknown) at YNHHS and 5624 patients (mean age 56·0 [20·5] years, 1953 [34·7%] were female, 2470 [43·9%] were male, and for 1201 [21·4%] sex was recorded as unknown) at MSHS with 78 054 and 13 796 eligible cardiac POCUS videos, respectively. AI deployed to single-view POCUS videos successfully discriminated hypertrophic cardiomyopathy (eg, area under the receiver operating characteristic curve 0·903 [95% CI 0·795–0·981] in YNHHS; 0·890 [0·839–0·938] in MSHS for apical-4-chamber acquisitions) and transthyretin amyloid cardiomyopathy (0·907 [0·874–0·932] in YNHHS; 0·972 [0·959–0·983] in MSHS for parasternal acquisitions). In YNHHS, 40 (58%) of 69 hypertrophic cardiomyopathy cases and 22 (46%) of 48 transthyretin amyloid cardiomyopathy cases would have had a positive screen by AI-POCUS at a median of 2·1 (IQR 0·9–4·5) years and 1·9 (0·6–3·5) years before diagnosis. Moreover, among 25 261 participants without known cardiomyopathy followed up over a median of 2·8 (1·2–6·4) years, AI-POCUS probabilities in the highest (vs lowest) quintile for hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy conferred a 17% (adjusted hazard ratio 1·17, 95% CI 1·06–1·29; p=0·0022) and 32% (1·39, 1·19–1·46; p<0·0001) higher adjusted mortality risk, respectively.
Interpretation
We developed and validated an AI framework that enables scalable, opportunistic screening of under-recognised cardiomyopathies through simple POCUS acquisitions.
Funding
National Heart, Lung, and Blood Institute, Doris Duke Charitable Foundation, and BridgeBio.
{"title":"Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study","authors":"Evangelos K Oikonomou MD , Akhil Vaid MD , Gregory Holste BA , Andreas Coppi PhD , Robert L McNamara MD , Cristiana Baloescu MD , Harlan M Krumholz MD , Zhangyang Wang PhD , Donald J Apakama MD , Girish N Nadkarni MD , Rohan Khera MD","doi":"10.1016/S2589-7500(24)00249-8","DOIUrl":"10.1016/S2589-7500(24)00249-8","url":null,"abstract":"<div><h3>Background</h3><div>Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed to develop and test artificial intelligence (AI) models to screen for under-diagnosed cardiomyopathies from cardiac POCUS.</div></div><div><h3>Methods</h3><div>In a development set of 290 245 transthoracic echocardiographic videos across the Yale–New Haven Health System (YNHHS), we used augmentation approaches, and a customised loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network that discriminates hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy from controls without known disease. We evaluated the model across independent, internal, and external, retrospective cohorts of individuals undergoing cardiac POCUS across YNHHS and the Mount Sinai Health System (MSHS) emergency departments (between 2012 and 2024) to prioritise key views and validate the diagnostic and prognostic performance of single-view screening protocols.</div></div><div><h3>Findings</h3><div>Between Nov 1, 2023, and March 28, 2024, we identified 33 127 patients (mean age 58·9 [SD 20·5] years, 17 276 [52·2%] were female, 14 923 [45·0%] were male, and for 928 [2·8%] sex was recorded as unknown) at YNHHS and 5624 patients (mean age 56·0 [20·5] years, 1953 [34·7%] were female, 2470 [43·9%] were male, and for 1201 [21·4%] sex was recorded as unknown) at MSHS with 78 054 and 13 796 eligible cardiac POCUS videos, respectively. AI deployed to single-view POCUS videos successfully discriminated hypertrophic cardiomyopathy (eg, area under the receiver operating characteristic curve 0·903 [95% CI 0·795–0·981] in YNHHS; 0·890 [0·839–0·938] in MSHS for apical-4-chamber acquisitions) and transthyretin amyloid cardiomyopathy (0·907 [0·874–0·932] in YNHHS; 0·972 [0·959–0·983] in MSHS for parasternal acquisitions). In YNHHS, 40 (58%) of 69 hypertrophic cardiomyopathy cases and 22 (46%) of 48 transthyretin amyloid cardiomyopathy cases would have had a positive screen by AI-POCUS at a median of 2·1 (IQR 0·9–4·5) years and 1·9 (0·6–3·5) years before diagnosis. Moreover, among 25 261 participants without known cardiomyopathy followed up over a median of 2·8 (1·2–6·4) years, AI-POCUS probabilities in the highest (<em>vs</em> lowest) quintile for hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy conferred a 17% (adjusted hazard ratio 1·17, 95% CI 1·06–1·29; p=0·0022) and 32% (1·39, 1·19–1·46; p<0·0001) higher adjusted mortality risk, respectively.</div></div><div><h3>Interpretation</h3><div>We developed and validated an AI framework that enables scalable, opportunistic screening of under-recognised cardiomyopathies through simple POCUS acquisitions.</div></div><div><h3>Funding</h3><div>National Heart, Lung, and Blood Institute, Doris Duke Charitable Foundation, and BridgeBio.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e113-e123"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075873","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 : 2025-02-01DOI: 10.1016/j.landig.2025.01.008
The Lancet Digital Health
{"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}
Pub Date : 2025-02-01DOI: 10.1016/j.landig.2025.01.005
Olga Kostopoulou , Brendan Delaney
{"title":"AI for medical diagnosis: does a single negative trial mean it is ineffective?","authors":"Olga Kostopoulou , Brendan Delaney","doi":"10.1016/j.landig.2025.01.005","DOIUrl":"10.1016/j.landig.2025.01.005","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e108-e109"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075869","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 : 2025-02-01DOI: 10.1016/j.landig.2024.12.008
Shishir Rao , Kazem Rahimi
{"title":"Prediction of emergency admissions: trade-offs between model simplicity and performance","authors":"Shishir Rao , Kazem Rahimi","doi":"10.1016/j.landig.2024.12.008","DOIUrl":"10.1016/j.landig.2024.12.008","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e106-e107"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075845","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 : 2025-02-01DOI: 10.1016/S2589-7500(24)00250-4
Wolf E Hautz, Thimo Marcin, Stefanie C Hautz, Stefan K Schauber, Gert Krummrey, Martin Müller, Thomas C Sauter, Cornelia Lambrigger, David Schwappach, Mathieu Nendaz, Gregor Lindner, Simon Bosbach, Ines Griesshammer, Philipp Schönberg, Emanuel Plüss, Valerie Romann, Svenja Ravioli, Nadine Werthmüller, Fabian Kölbener, Aristomenis K Exadaktylos, Hardeep Singh, Laura Zwaan
Background: Diagnostic error is a frequent and clinically relevant health-care problem. Whether computerised diagnostic decision support systems (CDDSSs) improve diagnoses is controversial, and prospective randomised trials investigating their effectiveness in routine clinical practice are scarce. We hypothesised that diagnoses made with a CDDSS in the emergency department setting would be superior to unsupported diagnoses.
Methods: This multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial was done in four emergency departments in Switzerland. Eligible patients were adults (aged ≥18 years) presenting with abdominal pain, fever of unknown origin, syncope, or non-specific symptoms. Emergency departments were randomly assigned (1:1) to one of two predefined sequences of six alternating periods of intervention or control. Patients presenting during an intervention period were diagnosed with the aid of a CDDSS, whereas patients presenting during a control period were diagnosed without a CDDSS (usual care). Patients and personnel assessing outcomes were masked to group allocation; treating physicians were not. The primary binary outcome (false or true) was a composite score indicating a risk of reduced diagnostic quality, which was deemed to be present if any of the following occurred within 14 days: unscheduled medical care, a change in diagnosis, an unexpected intensive care unit admission within 24 h if initially admitted to hospital, or death. We assessed superiority of supported versus unsupported diagnoses in all consenting patients using a generalised linear mixed effects model. All participants who received any study treatment (including control) and completed the study were included in the safety analysis. This trial is registered with ClinicalTrials.gov (NCT05346523) and is closed to accrual.
Findings: Between June 9, 2022, and June 23, 2023, 15 845 patients were screened and 1204 (591 [49·1%] female and 613 [50·9%] male) were included in the primary efficacy analysis. The median age of participants was 53 years (IQR 34-69). Diagnostic quality risk was observed in 100 (18%) of 559 patients with CDDSS-supported diagnoses and 119 (18%) of 645 with unsupported diagnoses (adjusted odds ratio 0·96 [95% CI 0·71-1·3]). 94 (7·8%) patients suffered a serious adverse event, none related to the study.
Interpretation: Use of a CDDSS did not reduce the occurrence of diagnostic quality risk compared with the usual diagnostic process in adults presenting to emergency departments. Future research should aim to identify specific contexts in which CDDSSs are effective and how existing CDDSSs can be adapted to improve patient outcomes.
Funding: Swiss National Science Foundation and University Hospital Bern.
{"title":"Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial.","authors":"Wolf E Hautz, Thimo Marcin, Stefanie C Hautz, Stefan K Schauber, Gert Krummrey, Martin Müller, Thomas C Sauter, Cornelia Lambrigger, David Schwappach, Mathieu Nendaz, Gregor Lindner, Simon Bosbach, Ines Griesshammer, Philipp Schönberg, Emanuel Plüss, Valerie Romann, Svenja Ravioli, Nadine Werthmüller, Fabian Kölbener, Aristomenis K Exadaktylos, Hardeep Singh, Laura Zwaan","doi":"10.1016/S2589-7500(24)00250-4","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00250-4","url":null,"abstract":"<p><strong>Background: </strong>Diagnostic error is a frequent and clinically relevant health-care problem. Whether computerised diagnostic decision support systems (CDDSSs) improve diagnoses is controversial, and prospective randomised trials investigating their effectiveness in routine clinical practice are scarce. We hypothesised that diagnoses made with a CDDSS in the emergency department setting would be superior to unsupported diagnoses.</p><p><strong>Methods: </strong>This multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial was done in four emergency departments in Switzerland. Eligible patients were adults (aged ≥18 years) presenting with abdominal pain, fever of unknown origin, syncope, or non-specific symptoms. Emergency departments were randomly assigned (1:1) to one of two predefined sequences of six alternating periods of intervention or control. Patients presenting during an intervention period were diagnosed with the aid of a CDDSS, whereas patients presenting during a control period were diagnosed without a CDDSS (usual care). Patients and personnel assessing outcomes were masked to group allocation; treating physicians were not. The primary binary outcome (false or true) was a composite score indicating a risk of reduced diagnostic quality, which was deemed to be present if any of the following occurred within 14 days: unscheduled medical care, a change in diagnosis, an unexpected intensive care unit admission within 24 h if initially admitted to hospital, or death. We assessed superiority of supported versus unsupported diagnoses in all consenting patients using a generalised linear mixed effects model. All participants who received any study treatment (including control) and completed the study were included in the safety analysis. This trial is registered with ClinicalTrials.gov (NCT05346523) and is closed to accrual.</p><p><strong>Findings: </strong>Between June 9, 2022, and June 23, 2023, 15 845 patients were screened and 1204 (591 [49·1%] female and 613 [50·9%] male) were included in the primary efficacy analysis. The median age of participants was 53 years (IQR 34-69). Diagnostic quality risk was observed in 100 (18%) of 559 patients with CDDSS-supported diagnoses and 119 (18%) of 645 with unsupported diagnoses (adjusted odds ratio 0·96 [95% CI 0·71-1·3]). 94 (7·8%) patients suffered a serious adverse event, none related to the study.</p><p><strong>Interpretation: </strong>Use of a CDDSS did not reduce the occurrence of diagnostic quality risk compared with the usual diagnostic process in adults presenting to emergency departments. Future research should aim to identify specific contexts in which CDDSSs are effective and how existing CDDSSs can be adapted to improve patient outcomes.</p><p><strong>Funding: </strong>Swiss National Science Foundation and University Hospital Bern.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"e136-e144"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075876","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 : 2025-02-01DOI: 10.1016/j.landig.2025.01.009
The Lancet Digital Health Editors
{"title":"Thank you to The Lancet Digital Health's statistical and peer reviewers in 2024.","authors":"The Lancet Digital Health Editors","doi":"10.1016/j.landig.2025.01.009","DOIUrl":"https://doi.org/10.1016/j.landig.2025.01.009","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"e110-e112"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075967","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 : 2025-02-01DOI: 10.1016/S2589-7500(24)00218-8
Carmen Tamayo Cuartero DVM PhD , Anna C Carnegie MPP , Zulma M Cucunuba MD PhD , Anne Cori PhD , Sara M Hollis MSc , Rolina D Van Gaalen PhD , Amrish Y Baidjoe , Alexander F Spina MPH , John A Lees PhD , Simon Cauchemez PhD , Mauricio Santos PhD , Juan D Umaña MSc , Chaoran Chen PhD , Hugo Gruson PhD , Pratik Gupte PhD , Joseph Tsui MSc , Anita A Shah MPH , Geraldine Gomez Millan SEP , David Santiago Quevedo MSc , Neale Batra MSc , Prof Adam J Kucharski
Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response.
{"title":"From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics","authors":"Carmen Tamayo Cuartero DVM PhD , Anna C Carnegie MPP , Zulma M Cucunuba MD PhD , Anne Cori PhD , Sara M Hollis MSc , Rolina D Van Gaalen PhD , Amrish Y Baidjoe , Alexander F Spina MPH , John A Lees PhD , Simon Cauchemez PhD , Mauricio Santos PhD , Juan D Umaña MSc , Chaoran Chen PhD , Hugo Gruson PhD , Pratik Gupte PhD , Joseph Tsui MSc , Anita A Shah MPH , Geraldine Gomez Millan SEP , David Santiago Quevedo MSc , Neale Batra MSc , Prof Adam J Kucharski","doi":"10.1016/S2589-7500(24)00218-8","DOIUrl":"10.1016/S2589-7500(24)00218-8","url":null,"abstract":"<div><div>Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e161-e166"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873186","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 : 2025-02-01DOI: 10.1016/j.landig.2024.12.006
Roberto Sciagrà
{"title":"Using artificial intelligence to switch from accident to sagacity in the serendipitous detection of uncommon diseases.","authors":"Roberto Sciagrà","doi":"10.1016/j.landig.2024.12.006","DOIUrl":"https://doi.org/10.1016/j.landig.2024.12.006","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"e104-e105"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075969","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 : 2025-02-01DOI: 10.1016/S2589-7500(24)00253-X
Johan H Thygesen, Huayu Zhang, Hanane Issa, Jinge Wu, Tuankasfee Hama, Ana-Caterina Phiho-Gomes, Tudor Groza, Sara Khalid, Thomas R Lumbers, Mevhibe Hocaoglu, Kamlesh Khunti, Rouven Priedon, Amitava Banerjee, Nikolas Pontikos, Chris Tomlinson, Ana Torralbo, Paul Taylor, Cathie Sudlow, Spiros Denaxas, Harry Hemingway, Honghan Wu
<p><strong>Background: </strong>The Global Burden of Disease Study has provided key evidence to inform clinicians, researchers, and policy makers across common diseases, but no similar effort with a single-study design exists for hundreds of rare diseases. Consequently, for many rare conditions there is little population-level evidence, including prevalence and clinical vulnerability, resulting in an absence of evidence-based care that was prominent during the COVID-19 pandemic. We aimed to inform rare disease care by providing key descriptors from national data and explore the impact of rare diseases during the COVID-19 pandemic.</p><p><strong>Methods: </strong>In this nationwide retrospective observational cohort study, we used the electronic health records (EHRs) of more than 58 million people in England, linking nine National Health Service datasets spanning health-care settings for people who were alive on Jan 23, 2020. Starting with all rare diseases listed in Orphanet (an extensive online resource for rare diseases), we quality assured and filtered down to analyse 331 conditions mapped to ICD-10 or Systemized Nomenclature of Medicine-Clinical Terms that were clinically validated in our dataset. For all 331 rare diseases, we calculated population prevalences, analysed patients' clinical and demographic details, and investigated mortality with SARS-CoV-2. We assessed COVID-19-related mortality by comparing cohorts of patients for each rare disease and rare disease category with controls matched for age group, sex, ethnicity, and vaccination status, at a ratio of two controls per individual with a rare disease.</p><p><strong>Findings: </strong>Of 58 162 316 individuals, we identified 894 396 with at least one rare disease and assessed COVID-19-related mortality between Sept 1, 2020, and Nov 30, 2021. We calculated reproducible estimates, adjusted for age and sex, for all 331 rare diseases, including for 186 (56·2%) conditions without existing prevalence estimates in Orphanet. 49 rare diseases were significantly more frequent in female individuals than in male individuals, and 62 were significantly more frequent in male individuals than in female individuals; 47 were significantly more frequent in Asian or British Asian individuals than in White individuals; and 22 were significantly more frequent in Black or Black British individuals than in White individuals. 37 rare diseases were significantly more frequent in the White population compared with either the Black or Asian population. 7965 (0·9%) of 894 396 patients with a rare disease died from COVID-19, compared with 141 287 (0·2%) of 58 162 316 in the full study population. In fully vaccinated individuals, the risk of COVID-19-related mortality was significantly higher for eight rare diseases, with patients with bullous pemphigoid (hazard ratio 8·07, 95% CI 3·01-21·62) being at highest risk.</p><p><strong>Interpretation: </strong>Our study highlights that national-scale EHRs provide a uniqu
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