NPJ Digital Medicine最新文献_第2页

Predicting outcomes following endovascular aortoiliac revascularization using machine learning 利用机器学习预测血管内主动脉髂血管重建术后的预后

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-24 DOI: 10.1038/s41746-025-01865-y

Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran

{"title":"Predicting outcomes following endovascular aortoiliac revascularization using machine learning","authors":"Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran","doi":"10.1038/s41746-025-01865-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01865-y","url":null,"abstract":"Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day post-procedural outcomes. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent endovascular aortoiliac revascularization between 2011–2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day post-procedural major adverse limb event (MALE) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using pre-operative features. Overall, 6601 patients were included, and 30-day MALE/death occurred in 470 (7.1%) individuals. The best-performing model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93–0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.74 (0.73–0.76). The XGBoost model accurately predicted 30-day post-procedural outcomes, performing better than logistic regression.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"14 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693972","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

Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL) 基于动态队列集成学习（DynaCEL）的重症监护个性化实时血流动力学管理

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-24 DOI: 10.1038/s41746-025-01863-0

Lingzhong Meng, Jiangqiong Li, Xiang Liu, Yanhua Sun, Zuotian Li, Jinjin Cai, Ameya D. Parab, George Lu, Aishwarya Budhkar, Saravanan Kanakasabai, David C. Adams, Ziyue Liu, Xuhong Zhang, Jing Su

{"title":"Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL)","authors":"Lingzhong Meng, Jiangqiong Li, Xiang Liu, Yanhua Sun, Zuotian Li, Jinjin Cai, Ameya D. Parab, George Lu, Aishwarya Budhkar, Saravanan Kanakasabai, David C. Adams, Ziyue Liu, Xuhong Zhang, Jing Su","doi":"10.1038/s41746-025-01863-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01863-0","url":null,"abstract":"Effective hemodynamic management in the intensive care unit requires individualized targets that adapt to dynamic clinical conditions. We developed Dynamic Cohort Ensemble Learning (DynaCEL), a real-time framework that recommends personalized heart rate and systolic blood pressure targets by modeling each time point post-intensive care unit admission as a distinct temporal cohort. Trained on eICU data and validated on MIMIC-IV and Indiana University Health datasets, DynaCEL demonstrated robust predictive performance (AUCs 0.83–0.91). In the MIMIC-IV cohort, proximity to DynaCEL-predicted targets was associated with lower 24-hour mortality compared to fixed targets, after adjustment using propensity score matching. Dose-response and comparative analyses revealed that greater deviations from personalized targets were associated with higher mortality. Case studies illustrated temporal and inter-individual variation in optimal targets. DynaCEL offers interpretable and scalable support for exploring precision hemodynamic management, although its clinical utility remains to be established in prospective trials.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"19 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693965","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

Artificially intelligent nasal perception for rapid sepsis diagnostics. 用于脓毒症快速诊断的人工智能鼻腔感知。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-24 DOI: 10.1038/s41746-025-01851-4

Joonchul Shin,Gwang Su Kim,Seongmin Ha,Taehee Yoon,Junwoo Lee,Taehoon Lee,Woong Heo,Kyungyeon Lee,Seong Jun Park,Sunyoung Park,Jaewoo Song,Sunghoon Hur,Hyun-Cheol Song,Ji-Soo Jang,Jin-Sang Kim,Hyo-Il Jung,Chong-Yun Kang

{"title":"Artificially intelligent nasal perception for rapid sepsis diagnostics.","authors":"Joonchul Shin,Gwang Su Kim,Seongmin Ha,Taehee Yoon,Junwoo Lee,Taehoon Lee,Woong Heo,Kyungyeon Lee,Seong Jun Park,Sunyoung Park,Jaewoo Song,Sunghoon Hur,Hyun-Cheol Song,Ji-Soo Jang,Jin-Sang Kim,Hyo-Il Jung,Chong-Yun Kang","doi":"10.1038/s41746-025-01851-4","DOIUrl":"https://doi.org/10.1038/s41746-025-01851-4","url":null,"abstract":"Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due to its high morbidity and mortality rates. A rapid and precise diagnosis of sepsis is essential for better patient outcomes. However, conventional diagnostic methods, such as bacterial cultures, are time-consuming and can delay sepsis diagnosis. Considering these, researchers investigated alternative techniques that detect volatile organic compounds (VOCs) produced by bacteria. In this study, we designed colorimetric gas sensor arrays, which change color upon interaction with biomarkers, offer a direct visual signal, and demonstrate high sensitivity and specificity in detecting sepsis-related VOCs. Furthermore, an artificial intelligence (AI) based algorithm, Rapid Sepsis Boosting (RSBoost), was employed as an analytical technique to enhance diagnostic accuracy (96.2%) in blood sample. This approach significantly improves the speed and accuracy of sepsis diagnostics within 24 h, holding great potential for transforming clinical diagnostics, saving lives, and reducing healthcare costs.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"19 1","pages":"476"},"PeriodicalIF":15.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701293","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

Synthetic data trained open-source language models are feasible alternatives to proprietary models for radiology reporting 合成数据训练的开源语言模型是放射学报告专有模型的可行替代方案

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-23 DOI: 10.1038/s41746-025-01658-3

Aakriti Pandita, Angela Keniston, Nikhil Madhuripan

引用次数: 0

Effectiveness of digital healthcare to improve clinical outcomes in discharged patients with coronary artery disease 数字医疗对改善冠状动脉疾病出院患者临床结果的有效性

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-23 DOI: 10.1038/s41746-025-01655-6

Lanshu Yang, Zuoxiang Wang, Sheng Zhao, Mengyuan Liu, Yalin Zhu, Fenghuan Hu, Xiaojin Gao, Yongjian Wu

{"title":"Effectiveness of digital healthcare to improve clinical outcomes in discharged patients with coronary artery disease","authors":"Lanshu Yang, Zuoxiang Wang, Sheng Zhao, Mengyuan Liu, Yalin Zhu, Fenghuan Hu, Xiaojin Gao, Yongjian Wu","doi":"10.1038/s41746-025-01655-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01655-6","url":null,"abstract":"Post-discharge management of coronary artery disease (CAD) remains clinically challenging, with digital healthcare’s efficacy underexplored. This study analyzed 16,797 CAD patients enrolled in the HeartMed Digital Management System (June 2018–September 2022), comparing outcomes between a digital management (DM, n = 4,713) and conventional management (CM, n = 12,084) cohort over 12 months. Cox models adjusted for confounders revealed significantly reduced all-cause mortality in the DM group (1.6% vs. 2.7%; HR 0.58, 95% CI 0.45–0.75, p < 0.001) and lower risks for major adverse cardiovascular events (MACCE: 6.4% vs. 9.2%; HR 0.67, 0.59–0.77, p < 0.001), cardiovascular death (HR 0.70, 0.51–0.95), myocardial infarction (HR 0.38, 0.29–0.50), recurrent angina (HR 0.75, 0.65–0.87), revascularization (HR 0.84, 0.71–0.99), and readmissions (HR 0.76, 0.68–0.84) (p < 0.05 for all). Digital healthcare demonstrates superior post-discharge optimization of CAD outcomes, significantly attenuating mortality and morbidity.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"16 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684656","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

Returning to work in the digital age: why smartphone interventions must go further 在数字时代重返工作岗位：为什么智能手机干预必须更进一步

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-22 DOI: 10.1038/s41746-025-01894-7

Conor Wall, Andrej Kohont, Živa Kolbl, Alan Godfrey

{"title":"Returning to work in the digital age: why smartphone interventions must go further","authors":"Conor Wall, Andrej Kohont, Živa Kolbl, Alan Godfrey","doi":"10.1038/s41746-025-01894-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01894-7","url":null,"abstract":"Return-to-work (RTW) after long-term absence due to ill health (or other factors) can be fraught with psychological, physical, and organisational challenges which may require continuous management to ensure successful employee reintegration. While digital interventions have emerged to support reintegration, a recent systematic review revealed that few explicitly address RTW needs, despite growing interest in e-mental health. Early online interventions demonstrate promise in improving psychological outcomes, yet face limitations in scalability, personalisation, and integration into workplace systems. Smartphone-based interventions via applications/apps offer a scalable alternative, leveraging ubiquitous technology to deliver support beyond bespoke settings through self-monitoring, continuous learning, and communication tools. However, existing RTW-focused apps remain narrowly tailored to specific conditions, with limited adaptation to individual needs and insufficient evaluation of long-term effectiveness. Future developments must prioritise personalisation, rigorous evaluation in diverse populations, and integration within occupational health and real-world employer systems with organisational support. Addressing these gaps is essential to fully realise the potential of digital solutions in supporting sustainable work reintegration that is respectful and compassionate.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"52 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677949","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

Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury 常规血液检查作为脊髓损伤预后动态生物标志物的建模轨迹

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-22 DOI: 10.1038/s41746-025-01782-0

Marzieh Mussavi Rizi, Daniel Fernández, John L. K. Kramer, Rajiv Saigal, Anthony M. DiGiorgio, Michael S. Beattie, Adam R. Ferguson, Nikos Kyritsis, Abel Torres-Espín

引用次数: 0

A randomized controlled trial of mobile intervention using health support bubbles to prevent social frailty 一项使用健康支持泡泡预防社会脆弱的流动干预的随机对照试验

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-22 DOI: 10.1038/s41746-025-01873-y

Chisato Hayashi, Nanae Tanemura, Maki Taniguchi, Tadashi Okano, Hiromitsu Toyoda, Sonoe Mashino, Soshiro Ogata

{"title":"A randomized controlled trial of mobile intervention using health support bubbles to prevent social frailty","authors":"Chisato Hayashi, Nanae Tanemura, Maki Taniguchi, Tadashi Okano, Hiromitsu Toyoda, Sonoe Mashino, Soshiro Ogata","doi":"10.1038/s41746-025-01873-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01873-y","url":null,"abstract":"Mobile health (mHealth) is gaining interest worldwide. This randomized trial aimed to test the effects of family companionship on the prevention of social frailty among Japanese individuals aged ≥40 years using a mHealth application. We used a participant-coaching system, which included a mobile nutrition management software application and a web portal. We assessed social frailty using Bunt’s framework. We enrolled 101 participants who used the application either alone (control group) or with family members (intervention group). The intervention arm showed greater improvement in social behavior and leisure activities (p = 0.004) and the total frailty score (p = 0.037). However, the social isolation did not improve with intervention. Our results suggest that mHealth can influence behavior change, but it does not resolve social isolation. While it provides convenience and enhanced access to healthcare, there is a need to balance digital efficiency with human interaction, ensuring that mHealth solutions complement, rather than replace, personal connections.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"53 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678022","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

Pitfalls of large language models in medical ethics reasoning 大型语言模型在医学伦理推理中的缺陷

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-22 DOI: 10.1038/s41746-025-01792-y

Shelly Soffer, Vera Sorin, Girish N. Nadkarni, Eyal Klang

引用次数: 0

Re-identification of patients from imaging features extracted by foundation models 根据基础模型提取的影像特征对患者进行再识别

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-22 DOI: 10.1038/s41746-025-01801-0

Giacomo Nebbia, Sourav Kumar, Stephen Michael McNamara, Christopher Bridge, J. Peter Campbell, Michael F. Chiang, Naresh Mandava, Praveer Singh, Jayashree Kalpathy-Cramer

{"title":"Re-identification of patients from imaging features extracted by foundation models","authors":"Giacomo Nebbia, Sourav Kumar, Stephen Michael McNamara, Christopher Bridge, J. Peter Campbell, Michael F. Chiang, Naresh Mandava, Praveer Singh, Jayashree Kalpathy-Cramer","doi":"10.1038/s41746-025-01801-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01801-0","url":null,"abstract":"Foundation models for medical imaging are a prominent research topic, but risks associated with the imaging features they can capture have not been explored. We aimed to assess whether imaging features from foundation models enable patient re-identification and to relate re-identification to demographic features prediction. Our data included Colour Fundus Photos (CFP), Optical Coherence Tomography (OCT) b-scans, and chest x-rays and we reported re-identification rates of 40.3%, 46.3%, and 25.9%, respectively. We reported varying performance on demographic features prediction depending on re-identification status (e.g., AUC-ROC for gender from CFP is 82.1% for re-identified images vs. 76.8% for non-re-identified ones). When training a deep learning model on the re-identification task, we reported performance of 82.3%, 93.9%, and 63.7% at image level on our internal CFP, OCT, and chest x-ray data. We showed that imaging features extracted from foundation models in ophthalmology and radiology include information that can lead to patient re-identification.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"14 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677957","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