Pub Date : 2024-06-19DOI: 10.1016/S2589-7500(24)00100-6
Grace B Hatton , Christie Brooks
{"title":"A response to evaluating national data flows","authors":"Grace B Hatton , Christie Brooks","doi":"10.1016/S2589-7500(24)00100-6","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00100-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 7","pages":"Page e444"},"PeriodicalIF":30.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001006/pdfft?md5=71db10e05782e151870bf4bed71961b6&pid=1-s2.0-S2589750024001006-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428953","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 : 2024-06-19DOI: 10.1016/S2589-7500(24)00122-5
The Lancet Digital Health
{"title":"The lofty heights of digital health","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00122-5","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00122-5","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 7","pages":"Page e433"},"PeriodicalIF":30.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001225/pdfft?md5=0f5b1d023170bc6a9b888b87db25ca61&pid=1-s2.0-S2589750024001225-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428730","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 : 2024-06-19DOI: 10.1016/S2589-7500(24)00070-0
Prof Sebastian Kohlmann PhD , Franziska Sikorski MSc , Prof Hans-Helmut König MD , Marion Schütt MSc , Prof Antonia Zapf PhD , Prof Bernd Löwe MD
<div><h3>Background</h3><p>Despite the availability of effective treatments, most depressive disorders remain undetected and untreated. Internet-based depression screening combined with automated feedback of screening results could reach people with depression and lead to evidence-based care. We aimed to test the efficacy of two versions of automated feedback after internet-based screening on depression severity compared with no feedback.</p></div><div><h3>Methods</h3><p>DISCOVER was an observer-masked, three-armed, randomised controlled trial in Germany. We recruited individuals (aged ≥18 years) who were undiagnosed with depression and screened positive for depression on an internet-based self-report depression rating scale (Patient Health Questionnaire-9 [PHQ-9] ≥10 points). Participants were randomly assigned 1:1:1 to automatically receive no feedback, non-tailored feedback, or tailored feedback on the depression screening result. Randomisation was stratified by depression severity (moderate: PHQ-9 score 10–14 points; severe: PHQ-9 score ≥15 points). Participants could not be masked but were kept unaware of trial hypotheses to minimise expectancy bias. The non-tailored feedback included the depression screening result, a recommendation to seek professional diagnostic advice, and brief general information about depression and its treatment. The tailored feedback included the same basic information but individually framed according to the participants’ symptom profiles, treatment preferences, causal symptom attributions, health insurance, and local residence. Research staff were masked to group allocation and outcome assessment as these were done using online questionnaires. The primary outcome was change in depression severity, defined as change in PHQ-9 score 6 months after random assignment. Analyses were conducted following the intention-to-treat principle for participants with at least one follow-up visit. This trial was registered at <span>ClinicalTrials.gov</span><svg><path></path></svg>, <span>NCT04633096</span><svg><path></path></svg>.</p></div><div><h3>Findings</h3><p>Between Jan 12, 2021, and Jan 31, 2022, 4878 individuals completed the internet-based screening. Of these, 1178 (24%) screened positive for depression (mean age 37·1 [SD 14·2] years; 824 [70%] woman, 344 [29%] men, and 10 [1%] other gender identity). 6 months after random assignment, depression severity decreased by 3·4 PHQ-9 points in the no feedback group (95% CI 2·9–4·0; within-group d 0·67; 325 participants), by 3·5 points in the non-tailored feedback group (3·0–4·0; within-group d 0·74; 319 participants), and by 3·7 points in the tailored feedback group (3·2–4·3; within-group d 0·71; 321 participants), with no significant differences among the three groups (p=0·72). The number of participants seeking help for depression or initiating psychotherapy or antidepressant treatment did not differ among study groups. The results remained consistent when adjusted for fulfilli
{"title":"The efficacy of automated feedback after internet-based depression screening (DISCOVER): an observer-masked, three-armed, randomised controlled trial in Germany","authors":"Prof Sebastian Kohlmann PhD , Franziska Sikorski MSc , Prof Hans-Helmut König MD , Marion Schütt MSc , Prof Antonia Zapf PhD , Prof Bernd Löwe MD","doi":"10.1016/S2589-7500(24)00070-0","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00070-0","url":null,"abstract":"<div><h3>Background</h3><p>Despite the availability of effective treatments, most depressive disorders remain undetected and untreated. Internet-based depression screening combined with automated feedback of screening results could reach people with depression and lead to evidence-based care. We aimed to test the efficacy of two versions of automated feedback after internet-based screening on depression severity compared with no feedback.</p></div><div><h3>Methods</h3><p>DISCOVER was an observer-masked, three-armed, randomised controlled trial in Germany. We recruited individuals (aged ≥18 years) who were undiagnosed with depression and screened positive for depression on an internet-based self-report depression rating scale (Patient Health Questionnaire-9 [PHQ-9] ≥10 points). Participants were randomly assigned 1:1:1 to automatically receive no feedback, non-tailored feedback, or tailored feedback on the depression screening result. Randomisation was stratified by depression severity (moderate: PHQ-9 score 10–14 points; severe: PHQ-9 score ≥15 points). Participants could not be masked but were kept unaware of trial hypotheses to minimise expectancy bias. The non-tailored feedback included the depression screening result, a recommendation to seek professional diagnostic advice, and brief general information about depression and its treatment. The tailored feedback included the same basic information but individually framed according to the participants’ symptom profiles, treatment preferences, causal symptom attributions, health insurance, and local residence. Research staff were masked to group allocation and outcome assessment as these were done using online questionnaires. The primary outcome was change in depression severity, defined as change in PHQ-9 score 6 months after random assignment. Analyses were conducted following the intention-to-treat principle for participants with at least one follow-up visit. This trial was registered at <span>ClinicalTrials.gov</span><svg><path></path></svg>, <span>NCT04633096</span><svg><path></path></svg>.</p></div><div><h3>Findings</h3><p>Between Jan 12, 2021, and Jan 31, 2022, 4878 individuals completed the internet-based screening. Of these, 1178 (24%) screened positive for depression (mean age 37·1 [SD 14·2] years; 824 [70%] woman, 344 [29%] men, and 10 [1%] other gender identity). 6 months after random assignment, depression severity decreased by 3·4 PHQ-9 points in the no feedback group (95% CI 2·9–4·0; within-group d 0·67; 325 participants), by 3·5 points in the non-tailored feedback group (3·0–4·0; within-group d 0·74; 319 participants), and by 3·7 points in the tailored feedback group (3·2–4·3; within-group d 0·71; 321 participants), with no significant differences among the three groups (p=0·72). The number of participants seeking help for depression or initiating psychotherapy or antidepressant treatment did not differ among study groups. The results remained consistent when adjusted for fulfilli","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 7","pages":"Pages e446-e457"},"PeriodicalIF":30.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000700/pdfft?md5=339cfa330e95efe94536fa8c159d0a77&pid=1-s2.0-S2589750024000700-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428956","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 : 2024-06-06DOI: 10.1016/S2589-7500(24)00085-2
Jue Wang MS , Nafen Zheng BSc , Huan Wan BSc , Qinyue Yao MS , Shijun Jia MS , Xin Zhang MS , Sha Fu MD , Jingliang Ruan MD , Gui He BSc , Xulin Chen MS , Suiping Li MS , Rui Chen BSc , Boan Lai BSc , Jin Wang PhD , Prof Qingping Jiang MD , Prof Nengtai Ouyang MD , Yin Zhang PhD
<div><h3>Background</h3><p>Accurately distinguishing between malignant and benign thyroid nodules through fine-needle aspiration cytopathology is crucial for appropriate therapeutic intervention. However, cytopathologic diagnosis is time consuming and hindered by the shortage of experienced cytopathologists. Reliable assistive tools could improve cytopathologic diagnosis efficiency and accuracy. We aimed to develop and test an artificial intelligence (AI)-assistive system for thyroid cytopathologic diagnosis according to the Thyroid Bethesda Reporting System.</p></div><div><h3>Methods</h3><p>11 254 whole-slide images (WSIs) from 4037 patients were used to train deep learning models. Among the selected WSIs, cell level was manually annotated by cytopathologists according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) guidelines of the second edition (2017 version). A retrospective dataset of 5638 WSIs of 2914 patients from four medical centres was used for validation. 469 patients were recruited for the prospective study of the performance of AI models and their 537 thyroid nodule samples were used. Cohorts for training and validation were enrolled between Jan 1, 2016, and Aug 1, 2022, and the prospective dataset was recruited between Aug 1, 2022, and Jan 1, 2023. The performance of our AI models was estimated as the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The primary outcomes were the prediction sensitivity and specificity of the model to assist cyto-diagnosis of thyroid nodules.</p></div><div><h3>Findings</h3><p>The AUROC of TBSRTC III+ (which distinguishes benign from TBSRTC classes III, IV, V, and VI) was 0·930 (95% CI 0·921–0·939) for Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH) internal validation and 0·944 (0·929 – 0·959), 0·939 (0·924–0·955), 0·971 (0·938–1·000) for The First People's Hospital of Foshan (FPHF), Sichuan Cancer Hospital & Institute (SCHI), and The Third Affiliated Hospital of Guangzhou Medical University (TAHGMU) medical centres, respectively. The AUROC of TBSRTC V+ (which distinguishes benign from TBSRTC classes V and VI) was 0·990 (95% CI 0·986–0·995) for SYSMH internal validation and 0·988 (0·980–0·995), 0·965 (0·953–0·977), and 0·991 (0·972–1·000) for FPHF, SCHI, and TAHGMU medical centres, respectively. For the prospective study at SYSMH, the AUROC of TBSRTC III+ and TBSRTC V+ was 0·977 and 0·981, respectively. With the assistance of AI, the specificity of junior cytopathologists was boosted from 0·887 (95% CI 0·8440–0·922) to 0·993 (0·974–0·999) and the accuracy was improved from 0·877 (0·846–0·904) to 0·948 (0·926–0·965). 186 atypia of undetermined significance samples from 186 patients with <em>BRAF</em> mutation information were collected; 43 of them harbour the <em>BRAF</em><sup>V600E</sup> mutation. 91% (39/43) of <em>BRAF</em><sup>V600E</sup>-positive atypia o
{"title":"Deep learning models for thyroid nodules diagnosis of fine-needle aspiration biopsy: a retrospective, prospective, multicentre study in China","authors":"Jue Wang MS , Nafen Zheng BSc , Huan Wan BSc , Qinyue Yao MS , Shijun Jia MS , Xin Zhang MS , Sha Fu MD , Jingliang Ruan MD , Gui He BSc , Xulin Chen MS , Suiping Li MS , Rui Chen BSc , Boan Lai BSc , Jin Wang PhD , Prof Qingping Jiang MD , Prof Nengtai Ouyang MD , Yin Zhang PhD","doi":"10.1016/S2589-7500(24)00085-2","DOIUrl":"10.1016/S2589-7500(24)00085-2","url":null,"abstract":"<div><h3>Background</h3><p>Accurately distinguishing between malignant and benign thyroid nodules through fine-needle aspiration cytopathology is crucial for appropriate therapeutic intervention. However, cytopathologic diagnosis is time consuming and hindered by the shortage of experienced cytopathologists. Reliable assistive tools could improve cytopathologic diagnosis efficiency and accuracy. We aimed to develop and test an artificial intelligence (AI)-assistive system for thyroid cytopathologic diagnosis according to the Thyroid Bethesda Reporting System.</p></div><div><h3>Methods</h3><p>11 254 whole-slide images (WSIs) from 4037 patients were used to train deep learning models. Among the selected WSIs, cell level was manually annotated by cytopathologists according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) guidelines of the second edition (2017 version). A retrospective dataset of 5638 WSIs of 2914 patients from four medical centres was used for validation. 469 patients were recruited for the prospective study of the performance of AI models and their 537 thyroid nodule samples were used. Cohorts for training and validation were enrolled between Jan 1, 2016, and Aug 1, 2022, and the prospective dataset was recruited between Aug 1, 2022, and Jan 1, 2023. The performance of our AI models was estimated as the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The primary outcomes were the prediction sensitivity and specificity of the model to assist cyto-diagnosis of thyroid nodules.</p></div><div><h3>Findings</h3><p>The AUROC of TBSRTC III+ (which distinguishes benign from TBSRTC classes III, IV, V, and VI) was 0·930 (95% CI 0·921–0·939) for Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH) internal validation and 0·944 (0·929 – 0·959), 0·939 (0·924–0·955), 0·971 (0·938–1·000) for The First People's Hospital of Foshan (FPHF), Sichuan Cancer Hospital & Institute (SCHI), and The Third Affiliated Hospital of Guangzhou Medical University (TAHGMU) medical centres, respectively. The AUROC of TBSRTC V+ (which distinguishes benign from TBSRTC classes V and VI) was 0·990 (95% CI 0·986–0·995) for SYSMH internal validation and 0·988 (0·980–0·995), 0·965 (0·953–0·977), and 0·991 (0·972–1·000) for FPHF, SCHI, and TAHGMU medical centres, respectively. For the prospective study at SYSMH, the AUROC of TBSRTC III+ and TBSRTC V+ was 0·977 and 0·981, respectively. With the assistance of AI, the specificity of junior cytopathologists was boosted from 0·887 (95% CI 0·8440–0·922) to 0·993 (0·974–0·999) and the accuracy was improved from 0·877 (0·846–0·904) to 0·948 (0·926–0·965). 186 atypia of undetermined significance samples from 186 patients with <em>BRAF</em> mutation information were collected; 43 of them harbour the <em>BRAF</em><sup>V600E</sup> mutation. 91% (39/43) of <em>BRAF</em><sup>V600E</sup>-positive atypia o","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 7","pages":"Pages e458-e469"},"PeriodicalIF":30.8,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000852/pdfft?md5=d718eec693d6690f3aa369916941141d&pid=1-s2.0-S2589750024000852-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288784","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 : 2024-05-29DOI: 10.1016/S2589-7500(24)00090-6
Melany Gaetani , Mjaye Mazwi , Hadrian Balaci , Robert Greer , Christina Maratta
{"title":"Artificial intelligence in medicine and the pursuit of environmentally responsible science","authors":"Melany Gaetani , Mjaye Mazwi , Hadrian Balaci , Robert Greer , Christina Maratta","doi":"10.1016/S2589-7500(24)00090-6","DOIUrl":"10.1016/S2589-7500(24)00090-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 7","pages":"Pages e438-e440"},"PeriodicalIF":30.8,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000906/pdfft?md5=1aaa78fbfb7a49f99226ead1aa0c786c&pid=1-s2.0-S2589750024000906-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141180100","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 : 2024-05-22DOI: 10.1016/S2589-7500(24)00098-0
The Lancet Digital Health
{"title":"Machine learning to predict type 1 diabetes in children","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00098-0","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00098-0","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 6","pages":"Page e374"},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000980/pdfft?md5=22bc305e02fa97a5aacd3342ae7b180c&pid=1-s2.0-S2589750024000980-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084530","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 : 2024-05-22DOI: 10.1016/S2589-7500(24)00062-1
Alexander W Jung PhD , Peter C Holm MSc , Kumar Gaurav PhD , Jessica Xin Hjaltelin PhD , Davide Placido PhD , Prof Laust Hvas Mortensen PhD , Prof Ewan Birney PhD , Prof S⊘ren Brunak PhD , Prof Moritz Gerstung PhD
<div><h3>Background</h3><p>Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank.</p></div><div><h3>Methods</h3><p>In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16–86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16–75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50–75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance.</p></div><div><h3>Findings</h3><p>From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65–0·67] for cervix uteri cancer to 0·91 [0·90–0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44–0·66] for cervix uteri cancer to 0·78 [0·77–0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagn
{"title":"Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study","authors":"Alexander W Jung PhD , Peter C Holm MSc , Kumar Gaurav PhD , Jessica Xin Hjaltelin PhD , Davide Placido PhD , Prof Laust Hvas Mortensen PhD , Prof Ewan Birney PhD , Prof S⊘ren Brunak PhD , Prof Moritz Gerstung PhD","doi":"10.1016/S2589-7500(24)00062-1","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00062-1","url":null,"abstract":"<div><h3>Background</h3><p>Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank.</p></div><div><h3>Methods</h3><p>In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16–86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16–75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50–75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance.</p></div><div><h3>Findings</h3><p>From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65–0·67] for cervix uteri cancer to 0·91 [0·90–0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44–0·66] for cervix uteri cancer to 0·78 [0·77–0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagn","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 6","pages":"Pages e396-e406"},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000621/pdfft?md5=cd2cb89cd08e988338ffea88e7f08924&pid=1-s2.0-S2589750024000621-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084533","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 : 2024-05-22DOI: 10.1016/S2589-7500(24)00072-4
Katherine G Young , John M Dennis , Nicholas J M Thomas
{"title":"Challenges of detecting childhood diabetes in primary care","authors":"Katherine G Young , John M Dennis , Nicholas J M Thomas","doi":"10.1016/S2589-7500(24)00072-4","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00072-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 6","pages":"Pages e375-e376"},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000724/pdfft?md5=d78ef5bef50804109f1a0585472270cb&pid=1-s2.0-S2589750024000724-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084531","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 : 2024-05-22DOI: 10.1016/S2589-7500(24)00050-5
Prof Rhian Daniel PhD , Hywel Jones PGDip , Prof John W Gregory MD , Ambika Shetty MD , Prof Nick Francis PhD , Prof Shantini Paranjothy PhD , Julia Townson PhD
Background
Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with diabetic ketoacidosis, a proportion unchanged over 25 years. Our aim was to investigate whether a machine-learning algorithm could lead to earlier detection of type 1 diabetes in primary care.
Methods
We developed the predictive algorithm using Welsh primary care electronic health records (EHRs) linked to the Brecon Dataset, a register of children newly diagnosed with type 1 diabetes. Children were included from their first primary care record within the study period of Jan 1, 2000, to Dec 31, 2016, until either type 1 diabetes diagnosis, they turned 15 years of age, or study end. We developed an ensemble learner (SuperLearner) using 26 potential predictors. Validation of the algorithm was done in English EHRs from the Clinical Practice Research Datalink (primary care) and Hospital Episode Statistics, focusing on the ability of the algorithm to identify children who went on to develop type 1 diabetes and the time by which diagnosis could be anticipated.
Findings
The development dataset comprised 34 754 400 primary care contacts, relating to 952 402 children, and the validation dataset comprised 43 089 103 primary care contacts, relating to 1 493 328 children. Of these, 1829 (0·19%) children younger than 15 years in the development dataset, and 1516 (0·10%) in the validation dataset had a reliable date of type 1 diabetes diagnosis. If set to give an alert in 10% of contacts, an estimated 71·6% (95% CI 68·8–74·4) of the children with type 1 diabetes would receive an alert by the algorithm in the 90 days before diagnosis, with diagnosis anticipated, on average, by an estimated 9·34 days (95% CI 7·77–10·9).
Interpretation
If implemented into primary care settings, this predictive algorithm could substantially reduce the proportion of patients with new-onset type 1 diabetes presenting in diabetic ketoacidosis. Acceptability of alert thresholds should be explored in primary care.
{"title":"Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm","authors":"Prof Rhian Daniel PhD , Hywel Jones PGDip , Prof John W Gregory MD , Ambika Shetty MD , Prof Nick Francis PhD , Prof Shantini Paranjothy PhD , Julia Townson PhD","doi":"10.1016/S2589-7500(24)00050-5","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00050-5","url":null,"abstract":"<div><h3>Background</h3><p>Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with diabetic ketoacidosis, a proportion unchanged over 25 years. Our aim was to investigate whether a machine-learning algorithm could lead to earlier detection of type 1 diabetes in primary care.</p></div><div><h3>Methods</h3><p>We developed the predictive algorithm using Welsh primary care electronic health records (EHRs) linked to the Brecon Dataset, a register of children newly diagnosed with type 1 diabetes. Children were included from their first primary care record within the study period of Jan 1, 2000, to Dec 31, 2016, until either type 1 diabetes diagnosis, they turned 15 years of age, or study end. We developed an ensemble learner (SuperLearner) using 26 potential predictors. Validation of the algorithm was done in English EHRs from the Clinical Practice Research Datalink (primary care) and Hospital Episode Statistics, focusing on the ability of the algorithm to identify children who went on to develop type 1 diabetes and the time by which diagnosis could be anticipated.</p></div><div><h3>Findings</h3><p>The development dataset comprised 34 754 400 primary care contacts, relating to 952 402 children, and the validation dataset comprised 43 089 103 primary care contacts, relating to 1 493 328 children. Of these, 1829 (0·19%) children younger than 15 years in the development dataset, and 1516 (0·10%) in the validation dataset had a reliable date of type 1 diabetes diagnosis. If set to give an alert in 10% of contacts, an estimated 71·6% (95% CI 68·8–74·4) of the children with type 1 diabetes would receive an alert by the algorithm in the 90 days before diagnosis, with diagnosis anticipated, on average, by an estimated 9·34 days (95% CI 7·77–10·9).</p></div><div><h3>Interpretation</h3><p>If implemented into primary care settings, this predictive algorithm could substantially reduce the proportion of patients with new-onset type 1 diabetes presenting in diabetic ketoacidosis. Acceptability of alert thresholds should be explored in primary care.</p></div><div><h3>Funding</h3><p>Diabetes UK.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 6","pages":"Pages e386-e395"},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000505/pdfft?md5=76310061ae70a1b70541648aac85e4dd&pid=1-s2.0-S2589750024000505-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084532","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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