NPJ Digital Medicine最新文献_第4页

The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact 数字表型的综合临床效益：从广泛采用到全面影响

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-08 DOI: 10.1038/s41746-025-01602-5

Yingbo Zhang, Jiao Wang, Hui Zong, Rajeev K. Singla, Amin Ullah, Xingyun Liu, Rongrong Wu, Shumin Ren, Bairong Shen

引用次数: 0

A methodological systematic review of validation and performance of sepsis real-time prediction models 对脓毒症实时预测模型的有效性和性能进行方法学系统回顾

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-025-01587-1

Zichen Wang, Wen Wang, Che Sun, Jili Li, Shuangyi Xie, Jiayue Xu, Kang Zou, Yinghui Jin, Siyu Yan, Xuelian Liao, Yan Kang, Craig M. Coopersmith, Xin Sun

{"title":"A methodological systematic review of validation and performance of sepsis real-time prediction models","authors":"Zichen Wang, Wen Wang, Che Sun, Jili Li, Shuangyi Xie, Jiayue Xu, Kang Zou, Yinghui Jin, Siyu Yan, Xuelian Liao, Yan Kang, Craig M. Coopersmith, Xin Sun","doi":"10.1038/s41746-025-01587-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01587-1","url":null,"abstract":"Sepsis real-time prediction models (SRPMs) provide timely alerts and may improve patient outcomes but face limited clinical adoption due to inconsistent validation methods and potential biases. Comprehensive evaluation, including external full-window validation with model- and outcome-level metrics, is crucial for real-world effectiveness, yet performance evidence remains scarce. This study systematically reviewed SRPM performance across validation methods, analyzing 91 studies from multiple databases. Only 54.9% applied full-window validation with both metric types. Performance decreased under external and full-window validation, with median AUROCs of 0.886 and 0.861 at 6- and 12-hours pre-onset, dropping to 0.783 in full-window external validation. Median Utility Scores declined from 0.381 in internal to -0.164 in external validation. Combining AUROC and Utility Score identified top-performing SRPMs in 18.7% of studies. Hand-crafted features significantly improved performance. Future research should focus on multi-center datasets, hand-crafted features, multi-metric full-window validation, and prospective trials to support clinical implementation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789805","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

MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens 基于mri的数字双胞胎通过优化新辅助化疗方案来改善乳腺癌的治疗反应

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-025-01579-1

Chengyue Wu, Ernesto A. B. F. Lima, Casey E. Stowers, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov

{"title":"MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens","authors":"Chengyue Wu, Ernesto A. B. F. Lima, Casey E. Stowers, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov","doi":"10.1038/s41746-025-01579-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01579-1","url":null,"abstract":"We developed a practical framework to construct digital twins for predicting and optimizing triple-negative breast cancer (TNBC) response to neoadjuvant chemotherapy (NAC). This study employed 105 TNBC patients from the ARTEMIS trial (NCT02276443, registered on 10/21/2014) who received Adriamycin/Cytoxan (A/C)-Taxol (T). Digital twins were established by calibrating a biology-based mathematical model to patient-specific MRI data, which accurately predicted pathological complete response (pCR) with an AUC of 0.82. We then used each patient’s twin to theoretically optimize outcome by identifying their optimal A/C-T schedule from 128 options. The patient-specifically optimized treatment yielded a significant improvement in pCR rate of 20.95–24.76%. Retrospective validation was conducted by virtually treating the twins with AC-T schedules from historical trials and obtaining identical observations on outcomes: bi-weekly A/C-T outperforms tri-weekly A/C-T, and weekly/bi-weekly T outperforms tri-weekly T. This proof-of-principle study demonstrates that our digital twin framework provides a practical methodology to identify patient-specific TNBC treatment schedules.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"53 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789748","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

A scoping review of OMOP CDM adoption for cancer research using real world data 使用真实世界数据对癌症研究采用OMOP CDM进行范围审查

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-025-01581-7

Liwei Wang, Andrew Wen, Sunyang Fu, Xiaoyang Ruan, Ming Huang, Rui Li, Qiuhao Lu, Heather Lyu, Andrew E. Williams, Hongfang Liu

引用次数: 0

The role of open standards in catalysing knowledge transfer to deliver climate adaptive care 开放标准在促进知识转移以提供气候适应性护理方面的作用

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-024-01401-4

Maeghan Orton, Olivia Swann, Gabrielle Samuel, Peter Drury, Dimitrios Kalogeropoulos, David Pencheon, Kristina Celentano, Babajide Babayeju, Liz Grant

引用次数: 0

Telehealth virtual reality intervention reduces chronic pain in a randomized crossover study 远程医疗虚拟现实干预减少慢性疼痛在一项随机交叉研究

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-025-01553-x

Luana Colloca, Anna Han, Rachel Massalee, Nandini Raghuraman, Rachel L. Cundiff-O’Sullivan, Giancarlo Colloca, Yang Wang

引用次数: 0

Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults 成人1和2周非选择性住院和败血症住院风险的预测

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-025-01574-6

Vincent X. Liu, Gabriel J. Escobar, Liam O’Suilleabhain, Khanh K. Thai, David Schlessinger, Laura C. Myers, John D. Greene, Fernando Barreda, Lawrence D. Gerstley, Patricia Kipnis

{"title":"Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults","authors":"Vincent X. Liu, Gabriel J. Escobar, Liam O’Suilleabhain, Khanh K. Thai, David Schlessinger, Laura C. Myers, John D. Greene, Fernando Barreda, Lawrence D. Gerstley, Patricia Kipnis","doi":"10.1038/s41746-025-01574-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01574-6","url":null,"abstract":"We developed and validated models to predict 1- and 2-week risk of non-elective hospitalization (NEH) and sepsis hospitalization following outpatient clinic, emergency department treat and release (EDTR), or hospitalization encounters. We employed data from 4,488,579 adults with 1,481,430 hospital, 6,035,296 EDTR, and 86,013,893 clinic encounters. Predictors included administrative, clinical (laboratory tests, vital signs), utilization, and prescription pattern data. We employed 2012–2018 data for development and 2019 data for validation. In validation datasets, discrimination (area under the receiver operator characteristic curve) ranged from 0.687 for NEH within 1 week of hospital discharge to 0.904 for sepsis hospitalization within 2 weeks of clinic visits. At a sensitivity of 40%, numbers needed to evaluate (NNE) ranged from 4.3 for NEH within 2 weeks of hospitalization to 45 for sepsis hospitalization within 1 week of a clinic visit. Our models have potentially clinically actionable NNEs and could support clinical programs for the prevention of short-term hospitalizations and sepsis.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"87 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789802","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

Reaching the unreached through an integrated communicable disease dashboard 通过综合传染病仪表板帮助未被触及的人群

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-07 DOI: 10.1038/s41746-025-01558-6

Fukushi Morishita, Wei Su, James Kelley, Kiyohiko Izumi, Kalpeshsinh Rahevar, Kazim Hizbullah Sanikullah, Kyung Hyun Oh, Zoie Shui Yee Wong, Rajendra Prasad Yadav, Thi Giang Huong Tran

引用次数: 0

Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features 利用视网膜血管特征无创早期预测妊娠子痫前期

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-05 DOI: 10.1038/s41746-025-01582-6

Yuxuan Wu, Lixia Shen, Lanqin Zhao, Xiaohong Lin, Miaohong Xu, Zhenjun Tu, Yihong Huang, Lingyi Kong, Zhenzhe Lin, Duoru Lin, Lixue Liu, Xun Wang, Zizheng Cao, Xi Chen, Shengmei Zhou, Weiling Hu, Yunjian Huang, Shiyuan Chen, Meimei Dongye, Xulin Zhang, Dongni Wang, Danli Shi, Zilian Wang, Xiaohang Wu, Dongyu Wang, Haotian Lin

{"title":"Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features","authors":"Yuxuan Wu, Lixia Shen, Lanqin Zhao, Xiaohong Lin, Miaohong Xu, Zhenjun Tu, Yihong Huang, Lingyi Kong, Zhenzhe Lin, Duoru Lin, Lixue Liu, Xun Wang, Zizheng Cao, Xi Chen, Shengmei Zhou, Weiling Hu, Yunjian Huang, Shiyuan Chen, Meimei Dongye, Xulin Zhang, Dongni Wang, Danli Shi, Zilian Wang, Xiaohang Wu, Dongyu Wang, Haotian Lin","doi":"10.1038/s41746-025-01582-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01582-6","url":null,"abstract":"Preeclampsia (PE), a severe hypertensive disorder during pregnancy, significantly contributes to maternal and neonatal mortality. Existing prediction biomarkers are often invasive and expensive, hindering their widespread application. This study introduces PROMPT (Preeclampsia Risk factor + Ophthalmic data + Mean arterial pressure Prediction Test), an AI-driven model leveraging retinal photography for PE prediction, registered at ChiCTR (ChiCTR2100049850) in August 2021. Analyzing 1812 pregnancies before 14 gestational weeks, we extracted retinal parameters using a deep learning system. The PROMPT achieved an AUC of 0.87 (0.83–0.90) for PE prediction and 0.91 (0.85–0.97) for preterm PE prediction using machine learning, significantly outperforming the baseline model (p < 0.001). It also improved detection of severe adverse pregnancy outcomes from 35% to 41%. Economically, PROMPT was estimated to avert 1809 PE cases and saved over $50 million per 100,000 screenings. These results position PROMPT as a non-invasive and cost-effective tool for prenatal care, especially valuable in low- and middle-income countries.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"37 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782389","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

Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness 10种大型语言模型的检索增强生成及其在医学适应度评估中的推广

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-04-05 DOI: 10.1038/s41746-025-01519-z

Yu He Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Chang-Fu Kuo, Shao-Chun Wu, Vesela P. Kovacheva, Daniel Shu Wei Ting

{"title":"Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness","authors":"Yu He Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Chang-Fu Kuo, Shao-Chun Wu, Vesela P. Kovacheva, Daniel Shu Wei Ting","doi":"10.1038/s41746-025-01519-z","DOIUrl":"https://doi.org/10.1038/s41746-025-01519-z","url":null,"abstract":"Large Language Models (LLMs) hold promise for medical applications but often lack domain-specific expertise. Retrieval Augmented Generation (RAG) enables customization by integrating specialized knowledge. This study assessed the accuracy, consistency, and safety of LLM-RAG models in determining surgical fitness and delivering preoperative instructions using 35 local and 23 international guidelines. Ten LLMs (e.g., GPT3.5, GPT4, GPT4o, Gemini, Llama2, and Llama3, Claude) were tested across 14 clinical scenarios. A total of 3234 responses were generated and compared to 448 human-generated answers. The GPT4 LLM-RAG model with international guidelines generated answers within 20 s and achieved the highest accuracy, which was significantly better than human-generated responses (96.4% vs. 86.6%, p = 0.016). Additionally, the model exhibited an absence of hallucinations and produced more consistent output than humans. This study underscores the potential of GPT-4-based LLM-RAG models to deliver highly accurate, efficient, and consistent preoperative assessments.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"16 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782381","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