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":null,"pages":null},"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}
Pub Date : 2024-05-22DOI: 10.1016/S2589-7500(24)00063-3
David Hermann Lehmann MSc , Bruna Gomes MD , Niklas Vetter MD , Olivia Braun MD , Ali Amr MD , Thomas Hilbel MD , Jens Müller MSc , Prof Ulrich Köthe PhD , Christoph Reich MD , Elham Kayvanpour MD , Farbod Sedaghat-Hamedani MD , Manuela Meder MD , Jan Haas PhD , Prof Euan Ashley MD , Prof Wolfgang Rottbauer MD , Dominik Felbel MD , Raffi Bekeredjian MD , Heiko Mahrholdt MD , Prof Andreas Keller PhD , Peter Ong MD , Prof Benjamin Meder MD
Background
With increasing numbers of patients and novel drugs for distinct causes of systolic and diastolic heart failure, automated assessment of cardiac function is important. We aimed to provide a non-invasive method to predict diagnosis of patients undergoing cardiac MRI (cMRI) and to obtain left ventricular end-diastolic pressure (LVEDP).
Methods
For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital's AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done.
Findings
66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77–0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77–7·95), with a Pearson correlation of 0·57 (95% CI 0·56–0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79–0·84) for ischaemic cardiomyopathy and 0·92 (0·91–0·94) for hypertrophic cardiomyopathy.
Interpretation
Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function.
Funding
Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCa
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Pub Date : 2024-05-22DOI: 10.1016/S2589-7500(24)00068-2
Bekure Tamirat , Festo Mazuguni , Moses Bamutura , Kyeng Mercy , Kofi M Nyarko , Binyam Tilahun , Kokou N Alinon , Yenew K Tebeje
{"title":"Africa CDC spearheading the strengthening of health information exchange in Africa","authors":"Bekure Tamirat , Festo Mazuguni , Moses Bamutura , Kyeng Mercy , Kofi M Nyarko , Binyam Tilahun , Kokou N Alinon , Yenew K Tebeje","doi":"10.1016/S2589-7500(24)00068-2","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00068-2","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000682/pdfft?md5=f54d0db70f3659cdb0e61a9560dbf6e9&pid=1-s2.0-S2589750024000682-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141083299","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)00093-1
Mattias Johansson , Hilary A Robbins
{"title":"Harnessing population-wide health data to predict cancer risk","authors":"Mattias Johansson , Hilary A Robbins","doi":"10.1016/S2589-7500(24)00093-1","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00093-1","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000931/pdfft?md5=1f966bca28fe492d78b9b4f155660ee8&pid=1-s2.0-S2589750024000931-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084474","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}
Few people with problematic alcohol use reach treatment and dropout is frequent. Therapy for problematic alcohol use delivered via video conference (e-alcohol therapy) might overcome treatment barriers. In this randomised study, we tested whether proactive e-alcohol therapy outperformed face-to-face alcohol therapy (standard care) regarding treatment initiation, compliance, and weekly alcohol intake at 3-month and 12-month follow-up.
Methods
In this two-arm randomised controlled trial, we recruited individuals who had problematic alcohol use, defined as a score of 8 or more on the Alcohol Use Disorders Identification Test; were 18 years or older; and had access to a personal computer, smartphone, or tablet with internet access in Denmark through online advertisements. Participants were assigned to receive alcohol therapy delivered either face-to-face or via video conference. The number, frequency, and duration of therapy sessions were individualised in both groups. Data analysis was conducted using masked data. Primary analyses were based on an intention-to-treat sample. The study is registered with ClinicalTrials.gov (NCT03116282).
Findings
Between Jan 22, 2018, and June 29, 2020, 816 individuals signed up for the trial and 502 (63%) were assessed for eligibility. We randomly assigned 379 to proactive e-alcohol therapy (n=187) or standard care (n=192), of which, 170 (48%) participants were female and 186 (52%) were male. In the intervention group, more participants initiated treatment (155 [88%] of 177 vs 96 [54%] of 179; odds ratio [OR] 6·3; 95% CI 2·8 to 13·8; p<0·0001 at 3 months; 151 [85%] of 177 vs 115 [64%] of 179; OR 3·2; 95% CI 1·6 to 6·2; p=0·0007 at 12 months) and complied with treatment (130 [73%] of 177 vs 74 [41%] of 179; OR 4·0; 95% CI 2·2 to 7·2; p<0·0001 at 3 months; 140 [79%] of 177 vs 95 [53%] of 179; OR 3·4; 95% CI 1·8 to 6·3; p=0·0002 at 12 months). Weekly alcohol intake was significantly lower in the intervention group only after 3 months (13·0 standard drinks per week vs 21·3 standard drinks per week; adjusted difference –6·7; 95% CI –12·3 to –1·0; p=0·019).
Interpretation
Proactive e-alcohol therapy was associated with increased treatment initiation and compliance and is promising as an easily accessible and effective alcohol treatment for individuals with problematic alcohol use.
Funding
TrygFonden.
背景有酗酒问题的人很少接受治疗，而且经常辍学。通过视频会议（电子酒精疗法）对问题性饮酒进行治疗可能会克服治疗障碍。在这项随机研究中，我们测试了在 3 个月和 12 个月的随访中，积极主动的电子酒精疗法在治疗启动、依从性和每周酒精摄入量方面是否优于面对面酒精疗法（标准护理）。方法在这项双臂随机对照试验中，我们通过在线广告招募了有问题酒精使用的个人，其定义是酒精使用障碍识别测试中的得分达到或超过 8 分；年龄在 18 岁或以上；在丹麦可以使用个人电脑、智能手机或平板电脑上网。参与者被分配接受面对面或通过视频会议提供的酒精治疗。两组的治疗次数、频率和持续时间都是个性化的。数据分析采用蒙面数据。主要分析基于意向治疗样本。该研究已在ClinicalTrials.gov（NCT03116282）上注册。研究结果在2018年1月22日至2020年6月29日期间，共有816人报名参加试验，其中502人（63%）通过了资格评估。我们随机分配了379人接受主动电子酒精疗法（187人）或标准护理（192人），其中170人（48%）为女性，186人（52%）为男性。在干预组中，更多参与者开始接受治疗（177 人中的 155 [88%] 对 179 人中的 96 [54%]；3 个月时的几率比 [OR] 6-3；95% CI 2-8 到 13-8；p<0-0001；177 人中的 151 [85%] 对 179 人中的 115 [64%]；OR 3-2；95% CI 1-6 到 6-2；12个月时，p=0-0007）和坚持治疗（177人中有130人[73%] vs 179人中有74人[41%]；3个月时，OR 4-0；95% CI 2-2 to 7-2；p<0-0001；177人中有140人[79%] vs 179人中有95人[53%]；12个月时，OR 3-4; 95% CI 1-8 to 6-3；p=0-0002）。只有在3个月后，干预组的每周酒精摄入量才明显降低（每周13-0标准饮品 vs 每周21-3标准饮品；调整后差异为-6-7；95% CI为-12-3至-1-0；p=0-019）。释义积极的电子酒精疗法与治疗启动率和依从性的提高有关，有望成为针对酗酒问题患者的一种方便有效的酒精治疗方法。
{"title":"Effectiveness of proactive video therapy for problematic alcohol use on treatment initiation, compliance, and alcohol intake: a randomised controlled trial in Denmark","authors":"Kia Kejlskov Egan MSc , Prof Ulrik Becker DMSc , Sanne Pagh M⊘ller MSc , Veronica Pisinger PhD , Prof Janne Schurmann Tolstrup PhD","doi":"10.1016/S2589-7500(24)00067-0","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00067-0","url":null,"abstract":"<div><h3>Background</h3><p>Few people with problematic alcohol use reach treatment and dropout is frequent. Therapy for problematic alcohol use delivered via video conference (e-alcohol therapy) might overcome treatment barriers. In this randomised study, we tested whether proactive e-alcohol therapy outperformed face-to-face alcohol therapy (standard care) regarding treatment initiation, compliance, and weekly alcohol intake at 3-month and 12-month follow-up.</p></div><div><h3>Methods</h3><p>In this two-arm randomised controlled trial, we recruited individuals who had problematic alcohol use, defined as a score of 8 or more on the Alcohol Use Disorders Identification Test; were 18 years or older; and had access to a personal computer, smartphone, or tablet with internet access in Denmark through online advertisements. Participants were assigned to receive alcohol therapy delivered either face-to-face or via video conference. The number, frequency, and duration of therapy sessions were individualised in both groups. Data analysis was conducted using masked data. Primary analyses were based on an intention-to-treat sample. The study is registered with <span>ClinicalTrials.gov</span><svg><path></path></svg> (<span>NCT03116282</span><svg><path></path></svg>).</p></div><div><h3>Findings</h3><p>Between Jan 22, 2018, and June 29, 2020, 816 individuals signed up for the trial and 502 (63%) were assessed for eligibility. We randomly assigned 379 to proactive e-alcohol therapy (n=187) or standard care (n=192), of which, 170 (48%) participants were female and 186 (52%) were male. In the intervention group, more participants initiated treatment (155 [88%] of 177 <em>vs</em> 96 [54%] of 179; odds ratio [OR] 6·3; 95% CI 2·8 to 13·8; p<0·0001 at 3 months; 151 [85%] of 177 <em>vs</em> 115 [64%] of 179; OR 3·2; 95% CI 1·6 to 6·2; p=0·0007 at 12 months) and complied with treatment (130 [73%] of 177 <em>vs</em> 74 [41%] of 179; OR 4·0; 95% CI 2·2 to 7·2; p<0·0001 at 3 months; 140 [79%] of 177 <em>vs</em> 95 [53%] of 179; OR 3·4; 95% CI 1·8 to 6·3; p=0·0002 at 12 months). Weekly alcohol intake was significantly lower in the intervention group only after 3 months (13·0 standard drinks per week <em>vs</em> 21·3 standard drinks per week; adjusted difference –6·7; 95% CI –12·3 to –1·0; p=0·019).</p></div><div><h3>Interpretation</h3><p>Proactive e-alcohol therapy was associated with increased treatment initiation and compliance and is promising as an easily accessible and effective alcohol treatment for individuals with problematic alcohol use.</p></div><div><h3>Funding</h3><p>TrygFonden.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000670/pdfft?md5=6791fc7fb00477e6d6469dc31f400771&pid=1-s2.0-S2589750024000670-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084499","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 : 2024-04-25DOI: 10.1016/S2589-7500(24)00089-X
{"title":"Correction to Lancet Digit Health 2022; 4: e884–92","authors":"","doi":"10.1016/S2589-7500(24)00089-X","DOIUrl":"10.1016/S2589-7500(24)00089-X","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258975002400089X/pdfft?md5=be20618818232af8d0cdd76ba2e26ed9&pid=1-s2.0-S258975002400089X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869970","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 : 2024-04-24DOI: 10.1016/S2589-7500(24)00073-6
Rupa Sarkar, Diana Samuel, Lucy Dunbar, Gustavo Monnerat
{"title":"5 years of The Lancet Digital Health","authors":"Rupa Sarkar, Diana Samuel, Lucy Dunbar, Gustavo Monnerat","doi":"10.1016/S2589-7500(24)00073-6","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00073-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000736/pdfft?md5=f2e1486de590af503639a68ea97a3784&pid=1-s2.0-S2589750024000736-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644477","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 : 2024-04-24DOI: 10.1016/S2589-7500(24)00059-1
Anatol-Fiete Näher , Ivar Krumpal , Esther-Maria Antão , Erika Ong , Marina Rojo , Fred Kaggwa , Felix Balzer , Leo Anthony Celi , Katarina Braune , Lothar H Wieler , Louis Agha-Mir-Salim
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Pub Date : 2024-04-24DOI: 10.1016/S2589-7500(24)00021-9
Qi Wei PhD , Prof Philip J Mease MD , Michael Chiorean MD , Lulu Iles-Shih MD , Wanessa F Matos MD , Andrew Baumgartner PhD , Sevda Molani PhD , Yeon Mi Hwang MSc , Basazin Belhu BSc , Alexandra Ralevski PhD , Jennifer Hadlock MD
Background
In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investigate the effects of the pandemic time period and the risks associated with individual IMIDs, classes of immunomodulatory medications (IMMs), chronic comorbidities, and COVID-19 vaccination status.
Methods
In this retrospective cohort study, clinical data were derived from the electronic health records of an integrated health-care system serving patients in 51 hospitals and 1085 clinics across seven US states (Providence St Joseph Health). Data were observed for patients (no age restriction) with one or more IMID and for unmatched controls without IMIDs. COVID-19 was identified with a positive nucleic acid amplification test result for SARS-CoV-2. Two timeframes were analysed: March 1, 2020–Dec 25, 2021 (pre-omicron period), and Dec 26, 2021–Aug 30, 2022 (omicron-predominant period). Primary outcomes were hospitalisation, mechanical ventilation, and mortality in patients with COVID-19. Factors, including IMID diagnoses, comorbidities, long-term use of IMMs, and COVID-19 vaccination status, were analysed with multivariable logistic regression (LR) and extreme gradient boosting (XGB).
Findings
Of 2 167 656 patients tested for SARS-CoV-2, 290 855 (13·4%) had confirmed COVID-19: 15 397 (5·3%) patients with IMIDs and 275 458 (94·7%) without IMIDs. In the pre-omicron period, 169 993 (11·2%) of 1 517 295 people who were tested for COVID-19 tested positive, of whom 23 330 (13·7%) were hospitalised, 1072 (0·6%) received mechanical ventilation, and 5294 (3·1%) died. Compared with controls, patients with IMIDs and COVID-19 had higher rates of hospitalisation (1176 [14·6%] vs 22 154 [13·7%]; p=0·024) and mortality (314 [3·9%] vs 4980 [3·1%]; p<0·0001). In the omicron-predominant period, 120 862 (18·6%) of 650 361 patients tested positive for COVID-19, of whom 14 504 (12·0%) were hospitalised, 567 (0·5%) received mechanical ventilation, and 2001 (1·7%) died. Compared with controls, patients with IMIDs and COVID-19 (7327 [17·3%] of 42 249) had higher rates of hospitalisation (13 422 [11·8%] vs 1082 [14·8%]; p<0·0001) and mortality (1814 [1·6%] vs 187 [2·6%]; p<0·0001). Age was a risk factor for worse outcomes (adjusted odds ratio [OR] from 2·1 [95% CI 2·0–2·1]; p<0·0001 to 3·0 [2·9–3·0]; p<0·0001), whereas COVID-19 vaccination (from 0·082 [0·080–0·085]; p<0·0001 to 0·52 [0·50–0·53]; p<0·0001) and booster vaccination (from 2·1 [2·0–2·2]; p<0·0001 to 3·0 [2·9–3·0]; p<0·0001) status were associated with better outcomes. Seven chronic comorbidities were significant risk factors during both time periods for all three outcomes: atrial fibrillation, coronary artery disease, heart failure, chronic k
{"title":"Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system","authors":"Qi Wei PhD , Prof Philip J Mease MD , Michael Chiorean MD , Lulu Iles-Shih MD , Wanessa F Matos MD , Andrew Baumgartner PhD , Sevda Molani PhD , Yeon Mi Hwang MSc , Basazin Belhu BSc , Alexandra Ralevski PhD , Jennifer Hadlock MD","doi":"10.1016/S2589-7500(24)00021-9","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00021-9","url":null,"abstract":"<div><h3>Background</h3><p>In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investigate the effects of the pandemic time period and the risks associated with individual IMIDs, classes of immunomodulatory medications (IMMs), chronic comorbidities, and COVID-19 vaccination status.</p></div><div><h3>Methods</h3><p>In this retrospective cohort study, clinical data were derived from the electronic health records of an integrated health-care system serving patients in 51 hospitals and 1085 clinics across seven US states (Providence St Joseph Health). Data were observed for patients (no age restriction) with one or more IMID and for unmatched controls without IMIDs. COVID-19 was identified with a positive nucleic acid amplification test result for SARS-CoV-2. Two timeframes were analysed: March 1, 2020–Dec 25, 2021 (pre-omicron period), and Dec 26, 2021–Aug 30, 2022 (omicron-predominant period). Primary outcomes were hospitalisation, mechanical ventilation, and mortality in patients with COVID-19. Factors, including IMID diagnoses, comorbidities, long-term use of IMMs, and COVID-19 vaccination status, were analysed with multivariable logistic regression (LR) and extreme gradient boosting (XGB).</p></div><div><h3>Findings</h3><p>Of 2 167 656 patients tested for SARS-CoV-2, 290 855 (13·4%) had confirmed COVID-19: 15 397 (5·3%) patients with IMIDs and 275 458 (94·7%) without IMIDs. In the pre-omicron period, 169 993 (11·2%) of 1 517 295 people who were tested for COVID-19 tested positive, of whom 23 330 (13·7%) were hospitalised, 1072 (0·6%) received mechanical ventilation, and 5294 (3·1%) died. Compared with controls, patients with IMIDs and COVID-19 had higher rates of hospitalisation (1176 [14·6%] <em>vs</em> 22 154 [13·7%]; p=0·024) and mortality (314 [3·9%] <em>vs</em> 4980 [3·1%]; p<0·0001). In the omicron-predominant period, 120 862 (18·6%) of 650 361 patients tested positive for COVID-19, of whom 14 504 (12·0%) were hospitalised, 567 (0·5%) received mechanical ventilation, and 2001 (1·7%) died. Compared with controls, patients with IMIDs and COVID-19 (7327 [17·3%] of 42 249) had higher rates of hospitalisation (13 422 [11·8%] <em>vs</em> 1082 [14·8%]; p<0·0001) and mortality (1814 [1·6%] <em>vs</em> 187 [2·6%]; p<0·0001). Age was a risk factor for worse outcomes (adjusted odds ratio [OR] from 2·1 [95% CI 2·0–2·1]; p<0·0001 to 3·0 [2·9–3·0]; p<0·0001), whereas COVID-19 vaccination (from 0·082 [0·080–0·085]; p<0·0001 to 0·52 [0·50–0·53]; p<0·0001) and booster vaccination (from 2·1 [2·0–2·2]; p<0·0001 to 3·0 [2·9–3·0]; p<0·0001) status were associated with better outcomes. Seven chronic comorbidities were significant risk factors during both time periods for all three outcomes: atrial fibrillation, coronary artery disease, heart failure, chronic k","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000219/pdfft?md5=7f87bbc61788e0cf2d2147dbbc0ff7e1&pid=1-s2.0-S2589750024000219-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644479","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 : 2024-04-24DOI: 10.1016/S2589-7500(24)00066-9
Amin T Turki , Merlin Engelke , Marta Sobas
{"title":"Advances in decision support for diagnosis and early management of acute leukaemia","authors":"Amin T Turki , Merlin Engelke , Marta Sobas","doi":"10.1016/S2589-7500(24)00066-9","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00066-9","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":30.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000669/pdfft?md5=f2f2f5543d8901dc37dcd6a20dbc49c0&pid=1-s2.0-S2589750024000669-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644478","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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