Mayo Clinic Proceedings. Digital health最新文献

{"title":"Real-World Body Composition and Blood Pressure Changes Following Glucagon-Like Peptide 1 Receptor Agonist Initiation: A Causal Inference Study of Connected Device Data","authors":"Agathe Chabassier MSc,&nbsp;Benjamin Vittrant PhD,&nbsp;Pierre Binon MSc,&nbsp;Erwan Scornet PhD,&nbsp;Julie Josse PhD","doi":"10.1016/j.mcpdig.2026.100375","DOIUrl":"10.1016/j.mcpdig.2026.100375","url":null,"abstract":"<div><h3>Objective</h3><div>To characterize real-world body composition and blood pressure changes following glucagon-like peptide (GLP)-1 receptor agonist (RA) initiation and identify predictors of weight loss quality using connected device data.</div></div><div><h3>Patients and Methods</h3><div>In this retrospective cohort study, adult US users completing an in-application GLP-1 RA survey (June 8, 2023, to July 4, 2024) were followed up using longitudinal device data (December 2021 to September 2025). We used a distributional causal inference framework to match GLP-1 RA users to nonusers. Primary estimands were the distributional average treatment effect on the treated and median differences over months 10 to 14.</div></div><div><h3>Results</h3><div>Among 2031 users for body composition (396 exposed) and 672 for blood pressure (BP; 148 exposed), GLP-1 RA initiation was associated with a 9.8% median weight reduction (95% CI, 9.8-9.9), driven by fat mass loss (7.5%; 95% CI, 7.5-7.8) with smaller muscle mass loss (2.3%; 95% CI, 2.1-2.4). Sustained reductions were observed in systolic (2.5 mm Hg; 95% CI, 1.5-2.9) and diastolic BP (1.5 mm Hg; 95% CI, 1.2-1.9). Baseline muscle-to-fat ratio (MFR) was the strongest predictor of weight loss quality, with lower MFR individuals losing more fat and sparing more muscle.</div></div><div><h3>Conclusion</h3><div>In a real-world setting, GLP-1 RA initiation was associated with clinically significant fat-predominant weight loss and sustained BP improvements. Baseline MFR predicted weight loss quality, suggesting that pretreatment body composition may help clinicians anticipate individual responses—a step toward precision medicine in GLP-1 RA therapy.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 3","pages":"Article 100375"},"PeriodicalIF":5.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case","authors":"Jorge A. Rios-Duarte MD,&nbsp;Taylor L. Pick RN,&nbsp;Trece N. Robson RN, MSN,&nbsp;Raymond J. Lin BA,&nbsp;Austin Todd MS,&nbsp;Santiago Romero-Brufau MD, PhD,&nbsp;Nahid Y. Vidal MD","doi":"10.1016/j.mcpdig.2026.100394","DOIUrl":"10.1016/j.mcpdig.2026.100394","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a data science pipeline for data extraction and collection to conduct an observational study using institutional medical informatics and artificial intelligence tools.</div></div><div><h3>Patients and Methods</h3><div>Skin excision cases exposed to intra-incisional clindamycin (from February 5 to October 14, 2025) and nonexposed (from January 1 to December 31, 2022) were defined as our cohorts of interest. The outcome of our study was surgical site infection (SSI) 30 days after surgery. A large language model (LLM)-supported data science pipeline was used for cohort identification, case screening, and extraction of procedural information. LLM screening and data extraction were validated in 300 random cases. A generalized estimating equation model was used to analyze the effect of intra-incisional clindamycin on SSI.</div></div><div><h3>Results</h3><div>The LLM achieved high accuracy for screening cases, with accurate identification of procedures done in the head (accuracy, 99.0%; 95% CI, 97.1%-99.8%) and skin excisions (97.0%; 95% CI, 94.4%-98.6%). In addition, it achieved remarkable performance for extraction of clinical information, with the highest accuracy observed for extraction of anatomical location associated with the procedure (accuracy, 100.0%; 95% CI, 98.8%-100.0%). The final dataset included 2247 skin excision cases (594 exposed and 1653 nonexposed) from 1923 patients. Cases that received intra-incisional clindamycin had lower odds of SSI; however, this association was not statistically significant (aOR, 0.57; 95% CI, 0.31-1.07).</div></div><div><h3>Conclusion</h3><div>The implementation of medical informatics tools and robust artificial intelligence algorithms will enable the conduct of end-to-end research pipelines. However, it is important to always consider humans in the loop, as oversight ensures reliability, accuracy, and robustness.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 3","pages":"Article 100394"},"PeriodicalIF":5.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Extended Reality in Perioperative Medicine: A Scoping Review of Intervention Development, Feasibility Evaluation, and Translational Readiness","authors":"Megan Crooks MA,&nbsp;Kailey E. Penner BA (Hons),&nbsp;Hilary Johnson MPT,&nbsp;Thomas J. Caruso MD, PhD,&nbsp;Vanessa A. Olbrecht MD, MBA,&nbsp;Hunter G. Hoffman PhD,&nbsp;Elizabeth Rossmann Beel MD, MPH,&nbsp;Kathleen Chen MD, MS,&nbsp;Sam R. Sharar MD,&nbsp;Jeffrey I. Gold PhD,&nbsp;Christopher D. King PhD,&nbsp;Renée El-Gabalawy PhD","doi":"10.1016/j.mcpdig.2026.100393","DOIUrl":"10.1016/j.mcpdig.2026.100393","url":null,"abstract":"<div><h3>Objective</h3><div>To characterize methodological features, intervention characteristics, and outcome trends of medical extended reality (MXR) applications for perioperative patients.</div></div><div><h3>Patients and Methods</h3><div>This scoping review was conducted in accordance with the JBI Manual for Evidence Synthesis. Searches were conducted in January 2025 and updated on August 12, 2025, across MEDLINE, Embase, Scopus, IEEE, Google Scholar, Cochrane Library, arXiv, and ProQuest Dissertations and Theses. Primary studies evaluating immersive MXR interventions in perioperative patients were included.</div></div><div><h3>Results</h3><div>Of 5883 records identified, 190 studies published between 2012 and 2026 were included. Most studies involved adult populations (69.5%), used virtual reality (95.8%), and delivered interventions preoperatively, most often targeting anxiety or pain. Studies were predominantly randomized controlled trials evaluating efficacy (48.4%), with few early developmental studies incorporating user-centered design (2.6%). Early-stage feasibility studies (47.9%) showed substantial methodological and outcome heterogeneity, with patient satisfaction (25.3%) and feasibility (18.4%) infrequently prioritized as primary outcomes. Studies commonly evaluated multiple intervention mechanisms, such as distraction and education, within single designs (35.3%), and some delivered interventions across multiple perioperative phases (10.5%). Few studies included MXR comparator conditions (6.8%).</div></div><div><h3>Conclusion</h3><div>Perioperative MXR shows promising signals of efficacy and feasibility based on reported study outcomes; however, these favorable trends should be interpreted cautiously given possible publication bias and the absence of a formal risk-of-bias assessment. Limited use of user-centered design, inconsistent and nonvalidated measurement of feasibility outcomes, and mechanistic ambiguity may constrain translation. Greater alignment with staged development frameworks and more rigorous feasibility evaluation are needed to support implementation. Preregistration: <span><span>https://osf.io/nkwfy/wiki?wiki=yqanp</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 3","pages":"Article 100393"},"PeriodicalIF":5.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Pelvic Fracture Diagnosis and Outcome Prediction: A Systematic Review and Meta-analysis","authors":"Kevin J. Wang MS ,&nbsp;Aazad Abbas MD ,&nbsp;Geoffrey W. Schemitsch MD ,&nbsp;Matthew Raleigh MD ,&nbsp;Johnathan R. Lex MBChB ,&nbsp;Hilary Felice MD ,&nbsp;Markku Nousiainen MD ,&nbsp;Cari Whyne PhD ,&nbsp;Bheeshma Ravi MD ,&nbsp;Robert Koucheki MD, MEng","doi":"10.1016/j.mcpdig.2026.100367","DOIUrl":"10.1016/j.mcpdig.2026.100367","url":null,"abstract":"<div><h3>Objectives</h3><div>To synthesize the performance of artificial intelligence (AI) applications for detecting pelvic fractures, classifying severity, and predicting clinical outcomes relative to clinicians.</div></div><div><h3>Patients and Methods</h3><div>The study was designed as a systematic review and meta-analysis (PROSPERO CRD420251141768). Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Cochrane CENTRAL were searched for articles published from database inception to September 11, 2025. Studies were included if they evaluated AI models for pelvic ring fractures in adults using pelvic radiographs. Case series, reviews, and abstracts without full data were excluded. Summary level data were independently extracted using a standardized template. Diagnostic metrics were pooled using a random-effects model. Outcomes included pooled sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and accuracy.</div></div><div><h3>Results</h3><div>Fourteen studies were included. Thirteen studies evaluated radiographic fracture detection or classification (n=31,166 radiographs) and one study evaluated outcome prediction. AI demonstrated high pooled performance: accuracy 0.96 (95% CI, 0.91-0.98; <em>I</em>^<em>2</em>=93.3%), AUC 0.94 (95% CI, 0.89-0.97; <em>I</em>^<em>2</em>=97.9%), sensitivity 0.90 (95% CI, 0.84-0.94; <em>τ</em>²=0.42), and specificity 0.93 (95% CI, 0.85-0.97; <em>τ</em>²=1.30). In 3 studies directly comparing AI with clinicians, AI models showed comparable or marginally superior performance. One study on clinical outcomes reported strong performance for predicting hemodynamic instability (AUC 0.92) and mortality (AUC 0.90).</div></div><div><h3>Conclusion</h3><div>AI algorithms show promise as supportive tools for pelvic fracture detection, achieving diagnostic performance comparable to expert clinicians. However, included studies exhibit substantial heterogeneity, selection bias, and limited external validation. Large-scale, prospective validation is necessary before widespread clinical adoption.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 2","pages":"Article 100367"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is More Always Better With Digital Health Interventions? Shifting Engagement From Maximizing Use to Supporting Health","authors":"Michael P. Williams PhD ,&nbsp;Susan A. Murphy PhD ,&nbsp;Ana-Maria Vranceanu PhD","doi":"10.1016/j.mcpdig.2026.100368","DOIUrl":"10.1016/j.mcpdig.2026.100368","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 2","pages":"Article 100368"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Unpredictable: A Data-Driven Machine Learning Model for Emergency Department Waiting Room Surge Status","authors":"Derick Jones MD, MBA, MHI ,&nbsp;Laura Walker MD, MBA ,&nbsp;Lindsey Asher DHA, MS ,&nbsp;Gregory Davis MHA, FACHE ,&nbsp;Brendan Carr MD, MBA ,&nbsp;James Colletti MD","doi":"10.1016/j.mcpdig.2026.100365","DOIUrl":"10.1016/j.mcpdig.2026.100365","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a machine learning algorithm to predict emergency department (ED) waiting room surge status (green, yellow, and red categories) 4, 8, and 12 hours in advance.</div></div><div><h3>Patients and Methods</h3><div>We designed a retrospective cohort with prospective validation conducted between July 1, 2023, and April 30, 2025. A total of 154,956 encounters were analyzed; features included ED operational metrics and timestamps with 72-hour lagging data. Deep neural network and gradient boosted decision tree (XGBoost) models were trained to predict 3 predefined surge categories: green (&lt;15 waiting patients; 48.8% of hours), yellow (15-30 waiting patients; 38.8% of the hours), and red (≥31 waiting patients; 12.3% of the hours).</div></div><div><h3>Results</h3><div>The XGBoost model found strong predictive performance across all forecast horizons. Area under the curve (AUC) curves showed excellent discrimination between green and yellow levels, ranging from 0.87 to 0.91 AUC across all time horizons, demonstrating reliable differentiation between normal and moderate surge conditions. The model showed acceptable discrimination for red levels, with AUCs of 0.76 and 0.77, meeting commonly accepted thresholds for clinical forecasting tools, particularly given the difficulty of predicting rare, high-volume situations. The operational accuracy of 68% to 70% showed strong real-world multiclass operational forecasting over prolonged windows up to 12 hours in advance.</div></div><div><h3>Conclusion</h3><div>Using operational metrics with timestamps, XGBoost models can differentiate between different levels of ED surge states with meaningful accuracy. This ability to forecast risk of high volumes provides a window of opportunity to proactively modify operations.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 2","pages":"Article 100365"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the Hype: The Pragmatic Integration of Intraoperative Artificial Intelligence in Cardiac Surgery","authors":"J. Luti Nonu MD,&nbsp;Julie W. Doberne MD, PhD","doi":"10.1016/j.mcpdig.2026.100369","DOIUrl":"10.1016/j.mcpdig.2026.100369","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 2","pages":"Article 100369"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automotive Health 2.0: Steering Toward Proactive Preventive Care","authors":"Dominik Naumann MD ,&nbsp;Tatjana Amler MSc ,&nbsp;Doreen Schoeppenthau MD ,&nbsp;Sergej Holzmann MSc ,&nbsp;Jörg Preißinger PhD ,&nbsp;Matthias Franz PhD ,&nbsp;Heyo K. Kroemer PhD ,&nbsp;Alexander Meyer MD","doi":"10.1016/j.mcpdig.2025.100334","DOIUrl":"10.1016/j.mcpdig.2025.100334","url":null,"abstract":"<div><div>Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising in daily life. As mobility constitutes one of the most stable and repetitive environments people inhabit, vehicles offer a unique setting for subliminal, continuous health monitoring. This narrative presents the rationale and foundational framework for Automotive Health 2.0, a clinically oriented paradigm that transforms connected vehicles into validated platforms for physiological sensing, data integration, and proactive care delivery. Building on existing in-cabin cameras, radar, and microphones, multimodal algorithms enable unobtrusive estimation of cardiovascular, respiratory, and behavioral parameters during routine driving. Technological innovation lies in combining these signals with artificial intelligence-driven analytics to detect early disease signatures, support dynamic risk assessment, and enable adaptive telemonitoring directly linked to electronic health records. Clinically, this approach distinguishes regulatory-grade monitoring from consumer wellness tools by prioritizing accuracy, reproducibility, and integration with established workflows. Patients gain earlier detection and more equitable access to preventive care; clinicians receive continuous actionable data, and health systems benefit from scalable population-level monitoring. Automotive Health 2.0 positions the vehicle as a novel extension of the health care ecosystem, embedding validated prevention seamlessly into everyday life.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100334"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing Innovation and Ethics: Ambient Listening Artificial Intelligence in Health Care","authors":"Emma O’Neil BA, BS ,&nbsp;Adam Rodman MD, MPH ,&nbsp;Lisa Soleymani Lehmann MD, PhD","doi":"10.1016/j.mcpdig.2026.100341","DOIUrl":"10.1016/j.mcpdig.2026.100341","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking Cardiopulmonary Bypass Management in The Digital Health Era","authors":"Youssef El Dsouki ,&nbsp;Ignazio Condello PhD ,&nbsp;Roberto Lorusso PhD","doi":"10.1016/j.mcpdig.2026.100343","DOIUrl":"10.1016/j.mcpdig.2026.100343","url":null,"abstract":"<div><div>Minimally invasive and robotic cardiac surgery have been developed to reduce surgical trauma, shorten recovery, and improve cosmetic and functional outcomes. However, these approaches often require longer cardiopulmonary bypass (CPB) and aortic cross-clamp times than conventional full sternotomy, and CPB duration remains an independent predictor of postoperative morbidity and mortality, particularly in frail patients with reduced physiological reserve. The resulting less invasive access/prolonged extracorporeal support duration paradox poses a major physiological and clinical challenge. Contemporary evidence from randomized and observational studies reports that while minimally invasive and robotic procedures achieve comparable or improved survival and functional recovery, extended CPB and aortic clamp times can amplify the risk of renal dysfunction, neurological events, and systemic inflammation. Advances in digital health are now transforming intraoperative perfusion management: high-frequency data acquisition, automated oxygen delivery and consumption analytics, and real-time artificial intelligence-driven predictive models enable early detection of perfusion imbalance and metabolic distress. Integration of these data streams within interoperable platforms and patient-specific digital twins may allow dynamic modeling of perfusion adequacy and adaptive control of pump flow, temperature, and hemodynamics. By converting CPB duration from a static procedural metric into a digitally monitored, optimizable variable, precision perfusion could reconcile minimal invasiveness with physiological safety. Future research should validate these digital frameworks in multicenter studies and establish standards for transparency, interoperability, and ethical implementation in real-world cardiac surgery.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100343"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}