NPJ Digital Medicine最新文献_第7页

Wearable health devices for pediatric ophthalmology 儿童眼科可穿戴健康设备

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-12 DOI: 10.1038/s41746-025-01718-8

Naira Ikram, Nimesh A. Patel, Joseph C. Kvedar

引用次数: 0

Economic analysis of an AI-enabled ECG alert system: impact on mortality outcomes from a pragmatic randomized trial 人工智能心电图报警系统的经济分析：一项实用的随机试验对死亡率结果的影响

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-11 DOI: 10.1038/s41746-025-01735-7

Ping-Hsuan Hsieh, Chin Lin, Chin-Sheng Lin, Wei-Ting Liu, Tsung-Kun Lin, Dung-Jang Tsai, Yi-Jen Hung, Yuan-Hao Chen, Chih-Yuan Lin, Shih-Hua Lin, Chien-Sung Tsai

{"title":"Economic analysis of an AI-enabled ECG alert system: impact on mortality outcomes from a pragmatic randomized trial","authors":"Ping-Hsuan Hsieh, Chin Lin, Chin-Sheng Lin, Wei-Ting Liu, Tsung-Kun Lin, Dung-Jang Tsai, Yi-Jen Hung, Yuan-Hao Chen, Chih-Yuan Lin, Shih-Hua Lin, Chien-Sung Tsai","doi":"10.1038/s41746-025-01735-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01735-7","url":null,"abstract":"Findings from a previous study (ClinicalTrials.gov: NCT05118035) demonstrated that an AI-enabled electrocardiogram (AI-ECG), combining AI reports and physician alerts, effectively identified hospitalized patients at high risk of mortality and reduced all-cause mortality. This study evaluates its cost-effectiveness from the health payer’s perspective in Taiwan over a 90-day post-intervention period. Cost data were obtained from electronic health records of participating hospitals, and incremental cost-effectiveness ratios (ICERs) per death averted were calculated. Non-parametric bootstrap techniques were used to address uncertainty. Among 15,965 patients, 90-day all-cause mortality was 3.6% in the intervention group versus 4.3% in controls. Medication and ICU costs were higher in the AI-ECG group, but overall medical cost was similar ($6204 vs. $5803). The ICER was $59,500 (95% CI: $-4657 to $385,950) per death averted. The cost-effectiveness acceptability curve showed that 95% of the probability mass lies below a willingness-to-pay threshold of $409,321, supporting favorable cost-effectiveness despite uncertainty.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"70 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260598","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}

引用次数: 0

Real-time digital intervention on oral pre-exposure prophylaxis adherence among MSM: randomized controlled trial MSM人群口服暴露前预防依从性的实时数字干预：随机对照试验

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-11 DOI: 10.1038/s41746-025-01743-7

Zhen-Xing Chu, Xia Jin, Ze-Hao Ye, Yan-Yan Zhu, Xiao-Jie Huang, Hui Wang, Yao-Kai Chen, Yu-Jing An, Zhen-Hao Wu, Yong-Jun Jiang, Qing-Hai Hu, Hong Shang

{"title":"Real-time digital intervention on oral pre-exposure prophylaxis adherence among MSM: randomized controlled trial","authors":"Zhen-Xing Chu, Xia Jin, Ze-Hao Ye, Yan-Yan Zhu, Xiao-Jie Huang, Hui Wang, Yao-Kai Chen, Yu-Jing An, Zhen-Hao Wu, Yong-Jun Jiang, Qing-Hai Hu, Hong Shang","doi":"10.1038/s41746-025-01743-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01743-7","url":null,"abstract":"Oral pre-exposure prophylaxis (PrEP) effectively prevents HIV among men who have sex with men, but its adherence faces significant hindrances. We evaluated the effectiveness of real-time digital intervention in promoting oral PrEP adherence through a randomized controlled trial using electronic medication monitors. The trial was registered on the Chinese Clinical Trial Registry (ChiCTR1900025604) and randomized 442 MSM to intervention (247) or control group (195). At the 6-month follow-up, the intervention group exhibited significantly higher oral PrEP adherence than the control (83.1% vs. 59.8%; adjusted net difference: 21.0%, 95% confidence interval: 12.9–29.2%, p < 0.001), while no differences were detected in the number of male sexual partners, condomless anal intercourse prevalence, or substance use disorder, with consistent results across both daily and event-driven oral PrEP regimens. Therefore, digital intervention significantly increased oral PrEP adherence over 6 months in the daily and event-driven subgroups but demonstrated no effect on high-risk behaviors.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"24 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260585","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}

引用次数: 0

Embodied artificial intelligence in ophthalmology 眼科学体现人工智能

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-11 DOI: 10.1038/s41746-025-01754-4

Yao Qiu, Xiaolan Chen, Xinyuan Wu, Yunqian Li, Pusheng Xu, Kai Jin, Xianwen Shang, Peranut Chotcomwongse, Mingguang He, Danli Shi

引用次数: 0

Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model 通过脓毒症机器学习模型的多站点外部验证来评估脓毒症观察的普遍性

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-11 DOI: 10.1038/s41746-025-01664-5

Bruno Valan, Anusha Prakash, William Ratliff, Michael Gao, Srikanth Muthya, Ajit Thomas, Jennifer L. Eaton, Matt Gardner, Marshall Nichols, Mike Revoir, Dustin Tart, Cara O’Brien, Manesh Patel, Suresh Balu, Mark Sendak

{"title":"Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model","authors":"Bruno Valan, Anusha Prakash, William Ratliff, Michael Gao, Srikanth Muthya, Ajit Thomas, Jennifer L. Eaton, Matt Gardner, Marshall Nichols, Mike Revoir, Dustin Tart, Cara O’Brien, Manesh Patel, Suresh Balu, Mark Sendak","doi":"10.1038/s41746-025-01664-5","DOIUrl":"https://doi.org/10.1038/s41746-025-01664-5","url":null,"abstract":"Sepsis accounts for a substantial portion of global deaths and healthcare costs. The objective of this reproducibility study is to validate Duke Health’s Sepsis Watch ML model, in a new community healthcare setting and assess its performance and clinical utility in early sepsis detection at Summa Health’s emergency departments. The study analyzed the model’s ability to predict sepsis using a combination of static and dynamic patient data using 205,005 encounters between 2020 and 2021 from 101,584 unique patients. 54.7% (n = 112,223) patients were female and the average age was 50 (IQR [38,71]). The AUROC ranged from 0.906 to 0.960, and the AUPRC ranged from 0.177 to 0.252 across the four sites. Ultimately, the reproducibility of the Sepsis Watch model in a community health system setting confirmed its strong and robust performance and portability across different geographical and demographic contexts with little variation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260174","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}

引用次数: 0

High-resolution lifestyle profiling and metabolic subphenotypes of type 2 diabetes 2型糖尿病的高分辨率生活方式分析和代谢亚表型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-11 DOI: 10.1038/s41746-025-01728-6

Heyjun Park, Ahmed A. Metwally, Alireza Delfarah, Yue Wu, Dalia Perelman, Caleb Mayer, Curtis McGinity, Majid Rodgar, Alessandra Celli, Tracey McLaughlin, Emmanuel Mignot, Michael Snyder

{"title":"High-resolution lifestyle profiling and metabolic subphenotypes of type 2 diabetes","authors":"Heyjun Park, Ahmed A. Metwally, Alireza Delfarah, Yue Wu, Dalia Perelman, Caleb Mayer, Curtis McGinity, Majid Rodgar, Alessandra Celli, Tracey McLaughlin, Emmanuel Mignot, Michael Snyder","doi":"10.1038/s41746-025-01728-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01728-6","url":null,"abstract":"Distinct metabolic susceptibilities (beta-cell dysfunction, insulin resistance (IR), and impaired incretin response) underlie type 2 diabetes (T2D). However, their relationships with habitual lifestyle behaviors are underexplored. This study integrated high-resolution lifestyle data from wearable devices, continuous glucose monitoring, and smartphone-based food logs with gold-standard physiological tests in 36 individuals at risk for T2D (ClinicalTrials.Gov; NCT03919877; 2019-04-18). Over 6400 timestamped records of diet, sleep, and physical activity were analyzed with in participants with measures of beta-cell function, tissue-specific IR (muscle, hepatic, adipose), and incretin response. We found that lifestyle timing and variability were strongly associated with metabolic subphenotypes: (1) eating timing was associated with muscle IR and incretin function; (2) irregular sleep correlated to IR and incretin function; and (3) Time-of-day effects of physical activity varied by subphenotype. These findings were validated in an independent cohort. Our results highlight novel physiological links between daily behaviors and metabolic risk, informing potential lifestyle modifications for T2D prevention.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"26 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260475","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}

引用次数: 0

Empirical evaluation of artificial intelligence distillation techniques for ascertaining cancer outcomes from electronic health records 从电子健康记录中确定癌症结果的人工智能蒸馏技术的经验评估

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-10 DOI: 10.1038/s41746-025-01646-7

Irbaz Bin Riaz, Syed Arsalan Ahmed Naqvi, Noman Ashraf, Gordon J. Harris, Kenneth L. Kehl

{"title":"Empirical evaluation of artificial intelligence distillation techniques for ascertaining cancer outcomes from electronic health records","authors":"Irbaz Bin Riaz, Syed Arsalan Ahmed Naqvi, Noman Ashraf, Gordon J. Harris, Kenneth L. Kehl","doi":"10.1038/s41746-025-01646-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01646-7","url":null,"abstract":"Phenotypic information for cancer research is embedded in unstructured electronic health records (EHR), requiring effort to extract. Deep learning models can automate this but face scalability issues due to privacy concerns. We evaluated techniques for applying a teacher-student framework to extract longitudinal clinical outcomes from EHRs. We focused on the challenging task of ascertaining two cancer outcomes—overall response and progression according to Response Evaluation Criteria in Solid Tumors (RECIST)—from free-text radiology reports. Teacher models with hierarchical Transformer architecture were trained on data from Dana-Farber Cancer Institute (DFCI). These models labeled public datasets (MIMIC-IV, Wiki-text) and GPT-4-generated synthetic data. “Student” models were then trained to mimic the teachers’ predictions. DFCI “teacher” models achieved high performance, and student models trained on MIMIC-IV data showed comparable results, demonstrating effective knowledge transfer. However, student models trained on Wiki-text and synthetic data performed worse, emphasizing the need for in-domain public datasets for model distillation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"21 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260591","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}

引用次数: 0

Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap 人工智能应该真正支持临床推理和决策，以弥合翻译差距

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-10 DOI: 10.1038/s41746-025-01725-9

Kacper Sokol, James Fackler, Julia E. Vogt

引用次数: 0

A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning 基于纵向预训练和标签分布学习的视网膜衰老生物标志物的跨群体研究

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-10 DOI: 10.1038/s41746-025-01751-7

Zhen Yu, Ruiye Chen, Peng Gui, Wei Wang, Imran Razzak, Hamid Alinejad-Rokny, Xiaomin Zeng, Xianwen Shang, Lei Zhang, Xiaohong Yang, Honghua Yu, Wenyong Huang, Huimin Lu, Peter van Wijngaarden, Mingguang He, Zhuoting Zhu, Zongyuan Ge

{"title":"A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning","authors":"Zhen Yu, Ruiye Chen, Peng Gui, Wei Wang, Imran Razzak, Hamid Alinejad-Rokny, Xiaomin Zeng, Xianwen Shang, Lei Zhang, Xiaohong Yang, Honghua Yu, Wenyong Huang, Huimin Lu, Peter van Wijngaarden, Mingguang He, Zhuoting Zhu, Zongyuan Ge","doi":"10.1038/s41746-025-01751-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01751-7","url":null,"abstract":"Retinal age has emerged as a promising biomarker of aging, offering a non-invasive and accessible assessment tool. We developed a deep learning model to estimate retinal age with enhanced accuracy, leveraging retinal images from diverse populations. Our approach integrates self-supervised learning to capture chronological information from both snapshot and sequential images, alongside a progressive label distribution learning module to model biological aging variability. Trained and validated on healthy cohorts (34,433 participants from the UK Biobank and three Chinese cohorts), the model achieved a mean absolute error of 2.79 years, surpassing previous methods. When applied to broader populations, analysis of the retinal age gap—the difference between retina-predicted and chronological age—revealed associations with increased risks of all-cause mortality and multiple age-related diseases. These findings highlight the potential of retinal age as a reliable biomarker for predicting survival and aging outcomes, supporting targeted risk management and precision health interventions.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252476","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}

引用次数: 0

Development and validation of an interpretable risk prediction model for the early classification of thalassemia 开发和验证可解释的地中海贫血早期分类风险预测模型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-10 DOI: 10.1038/s41746-025-01766-0

Jin-Xin Lai, Jia-Wei Tang, Shan-Shan Gong, Ming-Xiong Qin, Yu-Lu Zhang, Quan-Fa Liang, Li-Yan Li, Zhen Cai, Liang Wang

引用次数: 0