JAMA Network Open最新文献

{"title":"Machine Learning for Predicting Critical Events Among Hospitalized Children.","authors":"Sierra Strutz, Huan Liang, Kyle Carey, Fereshteh Bashiri, Priti Jani, Emily Gilbert, Julie L Fitzgerald, Nicholas Kuehnel, Maya Dewan, L Nelson Sanchez-Pinto, Dana Edelson, Majid Afshar, Matthew Churpek, Anoop Mayampurath","doi":"10.1001/jamanetworkopen.2025.13149","DOIUrl":"10.1001/jamanetworkopen.2025.13149","url":null,"abstract":"<p><strong>Importance: </strong>Unrecognized deterioration among hospitalized children is associated with a high risk of mortality and morbidity. The current approach to pediatric risk stratification is fragmented, as each hospital unit (emergency, ward, or intensive care) uses different tools for predicting specific outcomes.</p><p><strong>Objective: </strong>To develop a machine learning model for the early detection of deterioration across all units, thereby enabling a unified risk assessment throughout the patient's hospital stay.</p><p><strong>Design, setting, and participants: </strong>This retrospective cohort study used data from pediatric (age <18 years) admissions to inpatient and intensive care units at 3 tertiary care academic hospitals. Data were analyzed from January 2024 to March 2025.</p><p><strong>Main outcomes and measures: </strong>The primary outcome was critical events, defined as invasive mechanical ventilation, administration of vasoactive medications, or death within 12 hours of an observation.</p><p><strong>Results: </strong>The cohort included 135 621 patients (mean [SD] age, 7 [6] years; 60 376 [44.5%] female). Patient age, hospital unit, vital signs, laboratory results, and prior comorbidities were used to derive a regression-based model, an extreme gradient-boosted machine (XGB) model, and 2 deep learning models. Data from 2 hospitals were used as a derivation cohort, while patients in the third hospital constituted the hold-out external test cohort. The XGB model was the best-performing machine learning model, outperforming 2 existing ward-focused models in terms of discrimination (C statistic: XGB, 0.86; ward-focused models, 0.82 [P < .001] and 0.70 [P < .001]) and the number needed to alert (at an example 80% sensitivity: XGB, 6 ward-focused models: 9 and 11). The deep learning models did not exhibit improved performance. The XGB model performed better or equivalent to models trained for a specific hospital unit.</p><p><strong>Conclusions and relevance: </strong>This retrospective cohort study describes the development of a novel hospitalwide model for continuously predicting the risk of critical events through the entirety of a child's stay. The model facilitated a unified framework for risk assessment in a pediatric hospital.</p>","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e2513149"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Error in Byline.","authors":"","doi":"10.1001/jamanetworkopen.2025.15507","DOIUrl":"https://doi.org/10.1001/jamanetworkopen.2025.15507","url":null,"abstract":"","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e2515507"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143971412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Impact of AI in Inpatient Diabetes Management.","authors":"Eileen R Faulds","doi":"10.1001/jamanetworkopen.2025.8924","DOIUrl":"https://doi.org/10.1001/jamanetworkopen.2025.8924","url":null,"abstract":"","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e258924"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988947","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}

{"title":"AI-Based Virtual Triage and Telemedical Health Care for Ukrainian War Refugees and Displaced Persons.","authors":"George A Gellert, Kacper Kuszczynski, Gabriel L Gellert, Aleksandra Kabat-Karabon, Anna Nowicka, Tim Price","doi":"10.1001/jamanetworkopen.2025.8839","DOIUrl":"https://doi.org/10.1001/jamanetworkopen.2025.8839","url":null,"abstract":"","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e258839"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12056562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes: A Randomized Clinical Trial.","authors":"Zhen Ying, Yujuan Fan, Congling Chen, Yuchen Liu, Qi Tang, Zhiwei Chen, Qian Yang, Hongmei Yan, Liming Wu, Jiaping Lu, Zhiwen Liu, Jun Liu, Xiaoying Li, Ying Chen","doi":"10.1001/jamanetworkopen.2025.8910","DOIUrl":"https://doi.org/10.1001/jamanetworkopen.2025.8910","url":null,"abstract":"<p><strong>Importance: </strong>Type 2 diabetes (T2D) is one of the most prevalent chronic diseases in the world. Insulin titration for glycemic control in T2D is crucial but limited by the lack of personalized and real-time tools.</p><p><strong>Objective: </strong>To examine whether an artificial intelligence-based insulin clinical decision support system (iNCDSS) for glycemic control in hospitalized patients with T2D is noninferior to standard insulin therapy administered by senior physicians.</p><p><strong>Design, setting, and participants: </strong>This multicenter, single-blind, parallel randomized clinical trial (RCT) was conducted between October 1, 2021, and September 8, 2022, in endocrinology wards of 3 medical centers. Eligible participants were adults (aged ≥18 years) with glycated hemoglobin levels between 7.0% and 11.0% who had received antidiabetic treatments in the previous 3 months.</p><p><strong>Interventions: </strong>Participants were randomized in a 1:1 ratio to receive insulin dosage titration by iNCDSS or senior endocrinology physicians for 5 consecutive days.</p><p><strong>Main outcomes and measures: </strong>The primary outcome was the proportion of time in the target glucose range (70-180 mg/dL) during the 5-day study period; the noninferiority margin was 6 percentage points. Secondary outcomes included other glycemic control measurements and adverse events.</p><p><strong>Results: </strong>A total of 149 participants (mean [SD] age, 64.2 [12.0] years; 84 male [56.4%]) were enrolled and randomized to the iNCDSS group (n = 75) or physician group (n = 74). The mean (SD) target glucose range (primary outcome) was 76.4% (16.4%) in the iNCDSS group and 73.6% (16.8%) in the physician group, which achieved the prespecified noninferiority criterion (estimated treatment difference, 2.7%; 95% CI, -2.7% to 8.0%). There were no significant differences in adverse events between the 2 groups. Most physicians were satisfied with the iNCDSS for its clear, time-saving, effective, and safe clinical support.</p><p><strong>Conclusions and relevance: </strong>In this RCT of an iNCDSS, the system demonstrated noninferiority to senior endocrinology physicians in insulin titration in an inpatient setting, indicating its potential as a favorable tool for insulin titration in patients with T2D.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov Identifier: NCT04642378.</p>","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e258910"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12059970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disparities in Early Lecanemab Uptake Among US Medicare Beneficiaries.","authors":"Frank F Zhou, Utibe R Essien, Jeffrey M Souza, Catherine A Sarkisian, Cheryl L Damberg, Bruce E Landon, John N Mafi","doi":"10.1001/jamanetworkopen.2025.11711","DOIUrl":"10.1001/jamanetworkopen.2025.11711","url":null,"abstract":"","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e2511711"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Referral Criteria for Specialist Palliative Care for Patients With Dementia.","authors":"Yuchieh Kathryn Chang, Jennifer Philip, Jenny T van der Steen, Lieve Van den Block, Allyn Yin Mei Hum, Pedro E Pérez-Cruz, Carlos Paiva, Masanori Mori, Ping-Jen Chen, Meera R Agar, Laura Hanson, Catherine J Evans, David Hui","doi":"10.1001/jamanetworkopen.2025.10298","DOIUrl":"10.1001/jamanetworkopen.2025.10298","url":null,"abstract":"<p><strong>Importance: </strong>Patients with dementia have considerable supportive care needs. Specialist palliative care may be beneficial, but it is unclear which patients are most appropriate for referral and when they should be referred.</p><p><strong>Objective: </strong>To identify a set of consensus referral criteria for specialist palliative care for patients with dementia.</p><p><strong>Design, setting, and participants: </strong>In this survey study using 3 rounds of Delphi surveys, an international, multidisciplinary panel of clinicians from 5 continents with expertise in the integration of dementia and palliative care were asked to rate 83 putative referral criteria (generated from a previous systematic review and steering committee discussion). Specialist palliative care was defined as an interdisciplinary team consisting of practitioners with advanced knowledge and skills in palliative medicine offering consultative services for specialist-level palliative care in (nonhospice) inpatient, outpatient, community, and home-based settings.</p><p><strong>Main outcomes and measures: </strong>Consensus was defined a priori as at least 70% agreement among experts. A criterion was coded as major if the experts advocated that meeting 1 criterion alone was satisfactory to justify a referral. Data were summarized using descriptive statistics.</p><p><strong>Results: </strong>Of the 63 invited and eligible panelists, the response rate was 58 (92.1%) in round 1, 58 (92.1%) in round 2, and 60 (95.2%) in round 3. Of the 58 panelists who provided demographic data in round 1, most were aged 40 to 49 years (28 of 58 [48.3%]), and 29 panelists (50%) each were men and women. Panelists achieved consensus on 15 major and 42 minor criteria for specialist palliative care referral. The 15 major criteria were grouped under 5 categories, including dementia type (eg, rapidly progressive dementia), symptom distress (eg, severe physical symptoms), psychosocial factors or decision-making (eg, request for hastened death, assisted suicide, or euthanasia), comorbidities or complications (eg, ≥2 episodes of aspiration pneumonia in the past 12 months); and hospital use (eg, ≥2 hospitalizations within the past 3 months).</p><p><strong>Conclusions and relevance: </strong>In this Delphi survey study, international experts reached consensus on a range of criteria for referral to specialist palliative care. With testing and validation, these criteria may be used to standardize specialist palliative care access for patients with dementia across various care settings.</p>","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e2510298"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12079294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Clinical Research Interaction Scale for Racial and Ethnic Minority Participants.","authors":"Jennifer Y Kim, Emily Botto, Ruby Madison Ford","doi":"10.1001/jamanetworkopen.2025.9481","DOIUrl":"10.1001/jamanetworkopen.2025.9481","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Importance: &lt;/strong&gt;Patient-staff interactions in clinical trials may influence future enrollment decisions among racial and ethnic minority patients, who remain underrepresented in clinical research. A scale that measures common patient-staff interactions encountered by racial and ethnic minority patients in clinical trials may help improve patient experience and enrollment outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To develop and validate a scale that measures common interactions encountered by racial and ethnic minority patients in clinical trials.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design, setting, and participants: &lt;/strong&gt;This mixed-methods survey study involved interviews and online surveys for data collection between April 1, 2023, and June 30, 2024. Adult (aged ≥18 years) racial and ethnic minority patients were interviewed to identify common interactions with research staff. The survey was validated across potential clinical trial participants and among former clinical trial participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main outcomes and measures: &lt;/strong&gt;Fit statistics for exploratory factor analysis and confirmatory factor analysis were used to confirm the validity of the scale. Structural equation modeling coefficients were used to assess the validity of the scale for measuring patients' trust toward the research staff and willingness to participate in future studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The sample include 1113 participants. The scale item derivation cohort comprised 16 racial and ethnic minority participants with clinical trial experience (mean [SD] age, 44.9 [12.9] years; 10 female [62.5%]; 3 identifying as Asian or Pacific Islander [18.8%], 9 as Black [56.3%], 3 as Latino [18.8%], and 1 as multiracial [6.3%]). The scale structure validation cohort of potential clinical trial participants comprised 479 survey respondents (mean [SD] age, 35.5 [11.9] years; 219 women [45.7%]; 1 identifying as American Indian [0.2%], 59 as Asian or Pacific Islander [12.3%], 266 as Black [55.5%], 59 as Latino [12.3%], and 86 as multiracial [19.7%]). The concurrent validation cohort included 618 participants (mean [SD] age, 45.3 [16.3] years; 53% male; 63 identifying as Asian or Pacific Islander [10.2%], 228 as Black [36.9%], 75 as Latino [12.1%], 223 as White [36.1%], and 29 as multiracial [4.7%]). The 22-item Clinical Research Interaction Scale had high reliability (α = 0.96) and validity (comparative fit index, 0.92; Tucker-Lewis index, 0.91; root mean square error of approximation, 0.08). Patient experience of frequent low-quality interactions was significantly associated with lowered trust toward research staff (β, -0.56; 95% CI, -0.74 to -0.37), which in turn significantly lowered patients' willingness to return to the site for future studies (β, 0.80; 95% CI, 0.70-0.90).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions and relevance: &lt;/strong&gt;These findings suggest that low-quality interactions with research staff may reduce racial and ethnic minority patients' willingness to retu","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e259481"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12076173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Missing Link in Behavioral Interventions to Raise Medication Adherence.","authors":"Iwan Barankay","doi":"10.1001/jamanetworkopen.2025.10170","DOIUrl":"10.1001/jamanetworkopen.2025.10170","url":null,"abstract":"","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e2510170"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001566","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}

{"title":"Sustained Midlife Weight Loss-A Pathway to Lower Chronic Disease and Mortality Risk?","authors":"Ga Eun Nam, Yong-Moon Mark Park","doi":"10.1001/jamanetworkopen.2025.11832","DOIUrl":"https://doi.org/10.1001/jamanetworkopen.2025.11832","url":null,"abstract":"","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 5","pages":"e2511832"},"PeriodicalIF":10.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150463","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}