Background: This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics on hours free of clinical deterioration events among medical and surgical patients in an acute care cardiology medical-surgical ward. Methods: 10,422 inpatient visits were randomly assigned by cluster to the intervention group of a display of risk trajectories or to a control group of usual medical care. The trial was undertaken on an 85-bed inpatient cardiology and cardiac surgery ward of an academic hospital with a substantial implementation and education plan. This was a passive display with no specific response mandated. The primary analysis compared events of clinical deterioration (death, emergent ICU transfer, emergent endotracheal intubation, cardiac arrest, or emergent surgery) and compared mortality 21 days after admission. Results: Patients with a large spike in risk score had, on average, twice the length of hospital stay (6.8 compared to 3.4 days). There was no change in the primary outcome between groups. Among those who had a clinical event, there were more event-free hours in the intervention/display-on group compared to the standard of care/display-off , but this did not approach statistical significance. 11% of the patients were transferred into or out of display beds, a censoring event removing them from the analysis, thereby undermining aspects of the randomized nature of the study. Conclusion: Predictive analytics monitoring incorporating continuous cardiorespiratory monitoring and displays of risk trajectories coupled with an education plan did not improve patient outcomes or reduce deaths. While necessary to conduct the study, the pragmatic design allowed for significant movement towards intervention/display-on beds for sicker patients. Design considerations in the future must focus on understanding clinicians' interpretation, care processes, and communication practices. ### Competing Interest Statement COI: MC is an employee of Nihon Kohden Digital Health Solutions. LPM and JRM are consultants for Nihon Kohden Digital Health Solutions. JKM owns equity shares of ArteraAI and JRM owns equity shares of Medical Predictive Science Corporation, whose products are not discussed in this unrelated work. The other authors do not express conflicts of interest. ### Clinical Trial NCT04359641 ### Clinical Protocols ### Funding Statement Funding: This study was funded by the Frederick Thomas Advanced Medical Analytics Fund, University of Virginia and AHRQ R01HS028803 (Keim-Malpass/Bourque MPI). The investigators received in-kind support from Nihon Kohden Digital Health Solutions for use and support of the CoMET system. The funders had no direct role/oversight of this published work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Instiutional Review Board of the Unviersity of Virginia gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Incretin-based obesity management medications (OMMs) fill a treatment gap in a stepped-care model between lifestyle change alone and metabolic bariatric surgery, resulting in weight loss of 15% to 20% of body weight. Public interest in and demand for OMMs has recently increased dramatically. Unfortunately, cost and access to OMMs remain a significant barrier for many patients. Although these medications have the potential to produce large weight loss outcomes, many unanswered questions remain regarding informed choice and optimization of obesity care protocols, especially for patients with a body mass index of 35 kg/m2 or higher who may be considering various intervention options such as lifestyle changes, OMMs, endoscopic weight loss procedures, and/or metabolic bariatric surgery. When considering strategies to aid patients in decision making about obesity treatment, several considerations warrant discussion because patients may have unrealistic perceptions about risk vs efficacy and may hold numerous misconceptions about long-term behavior change and outcomes. This article outlines considerations for informed obesity treatment decision making and reviews aspects of obesity treatment specific to OMMs, including adverse effects, patient expectations for treatment outcome, equitable access to care, the impact of weight bias on patient care, the risk of weight recurrence, and the need for long-term multicomponent treatment to achieve weight loss and weight maintenance.

Clinical laboratory tests provide essential biochemical measurements for diagnosis and treatment, but are limited by intermittent and invasive sampling. In contrast, photoplethysmogram (PPG) is a non-invasive, continuously recorded signal in intensive care units (ICUs) that reflects cardiovascular dynamics and can serve as a proxy for latent physiological changes. We propose UNIPHY+Lab, a framework that combines a large-scale PPG foundation model for local waveform encoding with a patient-aware Mamba model for long-range temporal modeling. Our architecture addresses three challenges: (1) capturing extended temporal trends in laboratory values, (2) accounting for patient-specific baseline variation via FiLM-modulated initial states, and (3) performing multi-task estimation for interrelated biomarkers. We evaluate our method on the two ICU datasets for predicting the five key laboratory tests. The results show substantial improvements over the LSTM and carry-forward baselines in MAE, RMSE, and R^2 among most of the estimation targets. This work demonstrates the feasibility of continuous, personalized lab value estimation from routine PPG monitoring, offering a pathway toward non-invasive biochemical surveillance in critical care.

We present UNIPHY+, a unified physiological foundation model (physioFM) framework designed to enable continuous human health and diseases monitoring across care settings using ubiquitously obtainable physiological data. We propose novel strategies for incorporating contextual information during pretraining, fine-tuning, and lightweight model personalization via multi-modal learning, feature fusion-tuning, and knowledge distillation. We advocate testing UNIPHY+ with a broad set of use cases from intensive care to ambulatory monitoring in order to demonstrate that UNIPHY+ can empower generalizable, scalable, and personalized physiological AI to support both clinical decision-making and long-term health monitoring.

High-frequency physiological waveform modality offers deep, real-time insights into patient status. Recently, physiological foundation models based on Photoplethysmography (PPG), such as PPG-GPT, have been shown to predict critical events, including Cardiac Arrest (CA). However, their powerful representation still needs to be leveraged suitably, especially when the downstream data/label is scarce. We offer three orthogonal improvements to improve PPG-only CA systems by using minimal auxiliary information. First, we propose to use time-to-event modeling, either through simple regression to the event onset time or by pursuing fine-grained discrete survival modeling. Second, we encourage the model to learn CA-focused features by making them patient-identity invariant. This is achieved by first training the largest-scale de-identified biometric identification model, referred to as the p-vector, and subsequently using it adversarially to deconfound cues, such as person identity, that may cause overfitting through memorization. Third, we propose regression on the pseudo-lab values generated by pre-trained auxiliary estimator networks. This is crucial since true blood lab measurements, such as lactate, sodium, troponin, and potassium, are collected sparingly. Via zero-shot prediction, the auxiliary networks can enrich cardiac arrest waveform labels and generate pseudo-continuous estimates as targets. Our proposals can independently improve the 24-hour time-averaged AUC from the 0.74 to the 0.78-0.80 range. We primarily improve over longer time horizons with minimal degradation near the event, thus pushing the Early Warning System research. Finally, we pursue multi-task formulation and diagnose it with a high gradient conflict rate among competing losses, which we alleviate via the PCGrad optimization technique.

Non-invasive patient monitoring for tracking and predicting adverse acute health events is an emerging area of research. We pursue in-hospital cardiac arrest (IHCA) prediction using only single-channel finger photoplethysmography (PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator Network (FEAN) leverages powerful representations from pre-trained PPG foundation models (PPG-GPT of size up to 1 Billion) stacked with sequential classification models. We propose two FEAN variants ("1H", "FH") which use the latest one-hour and (max) 24-hour history to make decisions respectively. Our study is the first to present IHCA prediction results in ICU patients using only unimodal (continuous PPG signal) waveform deep representations. With our best model, we obtain an average of 0.79 AUROC over 24 h prediction window before CA event onset with our model peaking performance at 0.82 one hour before CA. We also provide a comprehensive analysis of our model through architectural tuning and PaCMAP visualization of patient health trajectory in latent space.

Despite awareness that lifestyle behaviors—such as a nutritional diet and regular exercise—play a critically important role in achieving health, individuals struggle to achieve these things on their own. The roots of health and wellness coaching (HWC) emerge from multiple fields outside of healthcare. In the 1980s, Thomas Leonard began life coaching as a practice and worked to codify, popularize, and globalize the discipline of coaching outside of the sporting world. All educational approaches focus on conveying information, or perhaps the teaching of skills for managing health conditions. While health and wellness coaches do at times provide information or resources, education is never their dominant function. A successful HWC engagement depends upon trust and privacy. Health coaching can play a pivotal role in the creation of the new narrative. Innovative models that partner education for self-efficacy with health and wellness coaching, like the PACT program for chronic pain, leverage our healthcare dollars and improve outcomes.

Bariatric surgery is increasingly recognized as a safe and effective treatment for obesity in patients with chronic kidney disease (CKD), including stages 4, 5, and 5D (on dialysis). Among the available surgical methods, sleeve gastrectomy (SG) is the most commonly performed weight loss procedure and is mainly done to facilitate kidney transplantation (KT). However, many KT candidates treated with SG remain on the transplant waiting list for months to years, with some never receiving a transplant. Therefore, appropriate candidates for SG must be selected, and post-SG management should address the unique needs of this population, with a focus on sustaining the metabolic benefits of surgery while minimizing potential side effects related to rapid weight loss which may inadvertently lead to muscle and bone catabolism. Multidisciplinary post-SG care in this population may lead to overall better health on the transplant waiting list, resulting in a higher percentage of post-SG patients ultimately receiving KT. To tailor the effective treatment for these patients, clinicians should acknowledge that patients with CKD stage 4-5D have different nutritional needs and are metabolically and psychosocially distinct from the general bariatric surgery population. Sarcopenia is highly prevalent and may be exacerbated by muscle catabolism following SG if not adequately addressed. Blood pressure, glucose, and bone metabolism are all affected by the CKD stage 4-5D, and therefore require distinct diagnostic and management approaches. Long-standing chronic disease, associated comorbidities, and low adherence to medical therapies require ongoing comprehensive psychosocial assessment and support. This paper aims to review and consolidate the existing literature concerning the intersection of CKD stage 4-5D and the consequences of SG. We also suggest future clinical outcome studies examining novel treatment approaches for this medically complex population.

Data linkage errors in prescription drug monitoring programs can affect clinician and pharmacist behavior and patient outcomes, comprehensive clinical evaluation is essential for accurate risk assessment.

Background: Adverse childhood experiences (ACEs) are associated with the development of negative health behaviors and medical illnesses. ACE's association with poor health outcomes has been well documented in the general population; however, this relationship remains less clear in liver transplant (LT) recipients. Objective: The aims of this study were to determine the prevalence of ACE and the influence of ACE on LT outcomes. Methods: A retrospective electronic medical record review of all LT recipients over 11 years at an academic LT center. Demographic, diagnostic, and disease characteristics were extracted and compared for a history of ACE. Associations between a history of ACE and extracted variables were statistically tested using Student's t-test, chi-square tests, or Fisher's exact test, where appropriate. Graft and patient survival were tested using log-rank tests. Results: Of the 1172 LT recipients, 24.1% endorsed a history of ACE. Females (P = 0.017) and recipients with lower levels of education (P < 0.001) had a higher frequency of ACE. Those with a history of ACE had a higher prevalence of hepatitis C virus (P < 0.001) and higher pretransplant body mass index (P < 0.001). Recipients with a history of ACE had higher prevalence of mood (P < 0.001), anxiety (P < 0.001), post traumatic stress disorder (P < 0.001), alcohol use (P < 0.001), and cannabis use (P < 0.001) disorders, as well as higher Patient Health Questionnaire-9 (P < 0.001) and General Anxiety Disorder-7 (P < 0.001) scores pre- and post-transplant. Those with ACE had a higher incidence of recorded relapses to alcohol by 3 years post-transplant (P = 0.027). Mean lab values, graft survival, and patient survival were not significantly different between those with and without a history of ACE except for total bilirubin at 6 months (P = 0.021). Conclusions: One-quarter of LT recipients have experienced ACE. ACE was associated with a history of psychiatric diagnoses, substance use disorders, elevated Patient Health Questionnaire-9 and General Anxiety Disorder-7 scores, and a higher prevalence of relapse to alcohol use after transplant. This population may benefit from increased/improved access to appropriate mental health and substance use services and support in the peri- and post-transplant period.

Optimization of ICH safety guideline studies for inclusion into regulatory submissions is critical for resource conservation, animal use reduction, and efficient drug development. The ICH S7A guidance for Safety Pharmacology (SP) studies adopted in 2001 identified the core battery of studies to evaluate the acute safety of putative pharmaceutical molecules prior to First in Human (FIH) trials. To assess the utility of respiratory studies in predicting clinical AE’s, seven pharmaceutical companies pooled preclinical and clinical respiratory findings. A large database of novel molecules included all relevant data from standard S7A respiratory (n = 459) and FIH studies (n = 309). The data were analyzed with respect to the progression of these molecules, clinical adverse event reporting of these same molecules, and achieved exposures. These S7A respiratory assay findings had no impact on compound progression, and only 12 of 309 drug candidates were ‘positive’ preclinically and reported a respiratory-related AE in clinical trials (i.e. cough, dyspnea, etc.), an overall incidence rate of 3.9%. Contingency tables/statistics support a lack of concordance of these preclinical assays. Overall, our extensive analysis clearly indicated that the preclinical respiratory assay fails to provide any prognostic value for detecting clinically relevant respiratory adverse events.

Background: Buprenorphine initiation in opioid-tolerant patients usually requires decreasing the total opioid intake per day due to its potential for precipitating withdrawal. However, this strategy may not be tolerated in patients who require higher amounts of opioids, such as those with cancer pain.Case Presentation: We utilized a buprenorphine microdosing strategy for a postoperative cancer patient who was previously taking buprenorphine-naloxone for chronic noncancer pain, then initiated on methadone for uncontrolled cancer-related pain. He had a planned cancer resection in the hospital. He subsequently underwent a successful transition from methadone to buprenorphine-naloxone through microdosing in one week with close monitoring in the inpatient setting.Conclusions: Using a microdosing strategy to transition from methadone to buprenorphine-naloxone in a span of days was achieved in this case report. More research regarding the feasibility and tolerability of microinductions is needed, especially in the setting of chronic pain or cancer-related pain.

Evidence-based treatments have been developed for a range of pediatric mental health conditions. These interventions have proven efficacy but require trained pediatric behavioral health specialists for their administration. Unfortunately, the widespread shortage of behavioral health specialists leaves few referral options for primary care providers. As a result, primary care providers are frequently required to support young patients during their lengthy and often fruitless search for specialty treatment. One solution to this treatment-access gap is to draw from the example of integrated behavioral health and adapt brief evidence-based treatments for intra-disciplinary delivery by primary care providers in consultation with mental health providers. This solution has potential to expand access to evidence-based interventions and improve patient outcomes. We outline how an 8-step theory-based process for adapting evidence-based interventions, developed from a scoping review of the wide range of implementation science frameworks, can guide treatment development and implementation for pediatric behavioral health care delivery in the primary care setting, using an example of our innovative treatment adaptation for child and adolescent eating disorders. After reviewing the literature, obtaining input from leaders in eating disorder treatment research, and engaging community stakeholders, we adapted Family-Based Treatment for delivery in primary care. Pilot data suggest that the intervention is feasible to implement in primary care and preliminary findings suggest a large effect on adolescent weight gain. Our experience using this implementation framework provides a model for primary care providers looking to develop intra-disciplinary solutions for other areas where specialty services are insufficient to meet patient needs.

Chapter 14 Privacy in Spectrum Sharing Systems with Applications to Communications and Radar Konstantinos Psounis, Konstantinos Psounis University of Southern California, Los Angeles, CA, USASearch for more papers by this authorMatthew A. Clark, Matthew A. Clark The Aerospace Corporation, El Segundo, CA, USASearch for more papers by this author Konstantinos Psounis, Konstantinos Psounis University of Southern California, Los Angeles, CA, USASearch for more papers by this authorMatthew A. Clark, Matthew A. Clark The Aerospace Corporation, El Segundo, CA, USASearch for more papers by this author Book Editor(s):Kumar Vijay Mishra, Kumar Vijay Mishra United States CCDC Army Research Laboratory, Adelphi, MD, United StatesSearch for more papers by this authorM. R. Bhavani Shankar, M. R. Bhavani Shankar University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg, LuxembourgSearch for more papers by this authorBjörn Ottersten, Björn Ottersten KTH Royal Institute of Technology, Stockholm, SwedenSearch for more papers by this authorA. Lee Swindlehurst, A. Lee Swindlehurst University of California, Irvine, CA United StatesSearch for more papers by this author First published: 06 March 2024 https://doi.org/10.1002/9781119795568.ch14 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary With dramatic growth in demand for radio frequency spectrum by consumer devices, spectrum regulators are turning to spectrum sharing. Radar and communication systems, new and old, have increasingly been operated in shared spectrum environments. With virtually no greenfield spectrum left for new systems, joint radar and communication systems must be designed considering sharing and interference. Effective spectrum sharing inevitably involves information exchange, often raising user privacy concerns. In this chapter, we explore techniques for the design and operation of radar and communication systems in shared spectrum environments, including analytical methods, models, and optimization to achieve performance and user privacy objectives. References P. Atkins . Re: Commercial operations in the 3550-3650 MHz band (GN Docket No. 12-354). National Telecommunications and Information Administration Letter to the FCC , March 2015 . Google Scholar A. Babaei , W. H. Tranter , and T. Bose . A nullspace-based precoder with subspace expansion for radar/communications coexistence . In GLOBECOM, 2013 IEEE , pages 3487 – 3492 . IEEE, 2013 . Google Scholar B. Bahrak , S. Bhattarai , A. Ullah , J. M. J. Park , J. Reed , and D. Gurney . Protecting the primary users' operational privacy in spectrum sharing . In IEEE DySPAN , pages 236 – 247 , April 2014 . https://doi.org/10.1109/DySPAN.2014.6817800 . 10.1109/DySPAN.2014.6817800 Google Scholar K. Bell , T. Corwin , L. Stone , and R. Streit . Bayesian multiple target tracking , 2nd edition . Artech House , 2013 . Google Scholar S. Bhattarai , P. R. Vaka , and J. Park . Thwarting location inference attacks in database-driven spectrum sharing . IEEE Transactions on Cognitive Communications and Networking , 4 ( 2 ): 314 – 327 , 2018 . ISSN 2332-7731. 10.1109/TCCN.2017.2785770 Web of Science®Google Scholar S. Boyd and L. Vandenberghe . Convex optimization . Cambridge University Press , New York, NY, USA , 2004 . ISBN 0521833787. 10.1017/CBO9780511804441 Google Scholar R. Camino , C. Hammerschmidt , and R. State . Generating multi-categorical samples with generative adversarial networks . arXiv preprint arXiv:1807.01202 , 2018 . Google Scholar J. Carroll , G. Sanders , F. Sanders , and R. Sole . Case study: investigation of interference into 5 GHz weather radars from unlicensed national information infrastructure devices . US Dept. of Commerce, NTIA Report TR-12-486 , June 2012 . Google Scholar Y. Chen , J. Zhang , X. Wang , X. Tian , W. Wu , F. Wu , and C. W. Tan . Secrecy capacity scaling of large-scale cognitive networks . MobiHoc , pages 125 – 134 , New York, NY, USA , 2014 . ACM. Google Scholar Q. Cheng , D. N. Nguyen , E. Dutkiewicz , and M. Mueck . Preserving honest/dishonest users' operational privacy with blind interference calculation in spectrum sharing system . IEEE Transactions on Mobile Computing , 19 ( 12 ): 2874 – 2890 , 2020 . https://doi.org/10.1109/TMC.2019.2936377 . 10.1109/TMC.2019.2936377 Google Scholar M. Clark and K. Psounis . Can the privacy of primary networks in shared spectrum be protected? In IEEE INFOCOM , pages 1 – 9 , April 2016 . Google Scholar M. Clark and K. Psounis . Designing sensor networks to protect primary users in spectrum access systems . In 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS) , pages 112 – 119 , February 2017a . Google Scholar M. A. Clark and K. Psounis . Equal interference power allocation for efficient shared spectrum resource scheduling . IEEE Transactions on Wireless Communications , 16 ( 1 ): 58 – 72 , January 2017b . ISSN 1536-1276. https://doi.org/10.1109/TWC.2016.2618376 . 10.1109/TWC.2016.2618376 Google Scholar M. Clark and K. Psounis . Achievable privacy-performance tradeoffs for spectrum sharing with a sensing infrastructure . In 2018 14th Annual Conference on Wireless On-demand Network Systems and Services (WONS) , pages 103 – 110 , February 2018a . Google Scholar M. A. Clark and K. Psounis . Trading utility for privacy in shared spectrum access systems . IEEE/ACM Transactions on Networking , 26 ( 1 ): 259 – 273 , February 2018b . ISSN 1063-6692. https://doi.org/10.1109/TNET.2017.2778260 . 10.1109/TNET.2017.2778260 Web of Science®Google Scholar M. Clark and K. Psounis . Optimizing primary user privacy in spectrum sharing systems . IEEE/ACM Transactions on Networking , 28 ( 2 ): 533 – 546 , 2020 . https://doi.org/10.1109/TNET.2020.2967776 . 10.1109/TNET.2020.2967776 Web of Science®Google Scholar L. Clark , M. Clark , K. Psounis , and P. Kairouz . Privacy utility trades in wireless data via optimization and learning . In Information Theory and Applications Workshop (ITA) , 2019 . Google Scholar T. M. Cover and J. A. Thomas . Elements of information theory . John Wiley & Sons , 2012 . Google Scholar S. Deb , V. Srinivasan , and R. Maheshwari . Dynamic spectrum access in DTV whitespaces: design rules, architecture and algorithms . In MobiCom '09 , pages 1 – 12 , New York, NY, USA , 2009 . https://doi.org/10.1145/1614320.1614322 . 10.1145/1614320.1614322 Google Scholar A. Dimas , M. A. Clark , B. Li , K. Psounis , and A. P. Petropulu . On radar privacy in shared spectrum scenarios . In ICASSP , pages 7790 – 7794 , May 2019 . https://doi.org/10.1109/ICASSP.2019.8682745 . 10.1109/ICASSP.2019.8682745 Google Scholar X. Dong , Y. Gong , J. Ma , and Y. Guo . Protecting operation-time privacy of primary users in downlink cognitive two-tier networks . IEEE Transactions on Vehicular Technology , 67 ( 7 ): 6561 – 6572 , 2018 . ISSN 0018-9545. 10.1109/TVT.2018.2808347 Google Scholar Y. Dou , K. Zeng , H. Li , Y. Yang , B. Gao , K. Ren , and S. Li . -sas: Privacy-preserving centralized dynamic spectrum access system . IEEE Journal on Selected Areas in Communications , 35 ( 1 ): 173 – 187 , 2017 . ISSN 0733-8716. https://doi.org/10.1109/JSAC.2016.2633059 . 10.1109/JSAC.2016.2633059 Google Scholar E. Drocella , J. Richards , R. Sole , F. Najmy , A. Lundy , and P. McKenna . 3.5 GHz exclusion zone analyses and methodology . US Dept. of Commerce, NTIA Report 15-517 , June 2015 . Google Scholar C. Dwork . Differential privacy . In Proceedings of the International Colloquium Automata, Languages and Programming , pages 1 – 12 , 2006 . Google Scholar C. Dwork and A. Smith . Differential privacy for statistics: what we know and what we want to learn . Journal of Privacy and Confidentiality , 1 ( 2 ): 135 – 154 , 2009 . https://doi.org/10.29012/jpc.v1i2.570 . 10.29012/jpc.v1i2.570 Google Scholar Federal Communications Commission . Report and Order and Second Further Notice of Proposed Rulemaking . 15-47, GN Docket No. 12-354, April 2015 . Google Scholar Federal Communications Commission . Order on Reconsideration and Second Report and Order . 16-55, GN Docket No. 12-354, May 2016 . Google Scholar Z. Gao , H. Zhu , S. Li , S. Du , and X. Li . Security and privacy of collaborative spectrum sensing in cognitive radio networks . IEEE Wireless Communications , 19 ( 6 ): 106 – 112 , 2012 . ISSN 1536-1284. https://doi.org/10.1109/MWC.2012.6393525 . 10.1109/MWC.2012.6393525 Web of Science®Google Scholar Z. Gao , H. Zhu , Y. Liu , M. Li , and Z. Cao . Location privacy in database-driven cognitive radio networks: attacks and countermeasures . In 2013 Proceedings IEEE INFOCOM , pages 2751 – 2759 , April 2013 . https://doi.org/10.1109/INFCOM.2013.6567084 . 10.1109/INFCOM.2013.6567084 Google Scholar I. Goodfellow , J. Pouget-Abadie , M. Mirza , B. Xu , D. Warde-Farley , S. Ozair , A. Courville , and Y. Bengio . Generative adversarial nets . In Advances in Neural Information Processing Systems , pages 2672 – 2680 , 2014 . Google Scholar M. Grissa , B. Hamdaoui , and A. A. Yavuza . Location privacy in cognitive radio networks: a survey . IEEE Communication Surveys and Tutorials , 19 ( 3 ): 1726 – 1760 , 2017 . ISSN 1553-877X. https://doi.org/10.1109/COMST.2017.2693965 . 10.1109/COMST.2017.2693965 Web of Science®Google Scholar M. Grissa , A. A. Yavuz , B. Hamdaoui , and C. Tirupathi . Anonymous dynamic spectrum access and sharing mechanisms for the CBRS band . IEEE Access , 9 : 33860 – 33879 , 2021 . https://doi.org/10.1109/ACCESS.2021.3061706 . 10.1109/ACCESS.2021.3061706 Google Scholar Y. Han , E. Ekici , H. Kremo , and O. Altintas . Spectrum sharing methods for the coexistence of multiple RF systems: a survey . Ad Hoc Networks , 53 : 53 – 78 , 2016 . ISSN 1570-8705. 10.1016/j.adhoc.2016.09.009 Web of Science®Google Scholar A. A. Hilli , A. Petropulu , and K. Psounis . MIMO radar privacy protection through gradient enforcement in shared spectrum scenarios . In DySPAN , pages 1 – 5 , 2019 . https://doi.org/10.1109/DySPAN.2019.8935749 . 10.1109/DySPAN.2019.8935749 Google Scholar J. Holdren , et al. Realizing the full potential of government held spectrum to spur economic growth . President's Council of Advisors on Science and Technology Report to the President , 15 – 30 2012 . https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/pcast_spectrum_report_final_july_20_2012.pdf . Google Scholar C. Huang , P. Kairouz , X. Chen , L. Sankar , and R. Rajagopal . Context-aware generative adversarial privacy . CoRR , abs/1710.09549, 2017a . http://arxiv.org/abs/1710.09549 . Google Scholar C. Huang , P. Kairouz , X. Chen , L. Sankar , and R. Rajagopal . Context-aware generative adversarial privacy . Entropy , 19 ( 12 ), 2017b . ISSN 1099-4300. https://doi.org/10.3390/e19120656 . http://www.mdpi.com/1099-4300/19/12/656 . 10.3390/e19120656 Google Scholar X. Jin and Y. Zhang . Privacy-preserving crowdsourced spectrum sensing . IEEE/ACM Transactions on Networking , 26 ( 3 ): 1236 – 1249 , 2018 . ISSN 1063-6692. https://doi.org/10.1109/TNET.2018.2823272 . 10.1109/TNET.2018.2823272 Google Scholar A. Khawar , A. Abdelhadi , and T. C. Clancy . Coexistence analysis between radar and cellular system in LoS channel . IEEE Antennas and Wireless Propagation Letters , 15 : 972 – 975 , 2016 . 10.1109/LAWP.2015.2487368 Web of Science®Google Scholar K. Krishna and M. N. Murty . Genetic K-means algorithm . IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics , 29 ( 3 ): 433 – 439 , 1999 . 10.1109/3477.764879 CASPubMedWeb of Science®Google Scholar B. Li and A. P. Petropulu . Joint transmit designs for coexistence of MIMO wireless communications and sparse sensing radars in clutter . IEEE Transactions on Aerospace and Electronic Systems , 53 ( 6 ): 2846 – 2864 , 2017 . 10.1109/TAES.2017.2717518 Web of Science®Google Scholar B. Li , H. Kumar , and A. P. Petropulu . A joint design approach for spectrum sharing between radar and communication systems . In ICASSP , pages 3306 – 3310 . IEEE, 2016a . Google Scholar B. Li , A. P. Petropulu , and W. Trappe . Optimum co-design for spectrum sharing between matrix completion based MIMO radars and a MIMO communication system . IEEE Transactions on Signal Processing , 64 ( 17 ): 4562 – 4575 , 2016b . 10.1109/TSP.2016.2569479 Web of Science®Google Scholar S. Li , H. Zhu , Z. Gao , X. Guan , Kai Xing , and X. Shen . Location privacy preservation in collaborative spectrum sensing . In 2012 Proceedings IEEE INFOCOM , pages 729 – 737 , March 2012 . https://doi.org/10.1109/INFCOM.2012.6195818 . 10.1109/INFCOM.2012.6195818 Google Scholar J. Liu , C. Zhang , B. Lorenzo , and Y. Fang . DPavatar: A real-time location protection framework for incumbent users in cognitive radio networks . IEEE Transactions on Mobile Computing , 19 ( 3 ): 552 – 565 , 2020 . https://doi.org/10.1109/TMC.2019.2897099 . 10.1109/TMC.2019.2897099 Google Scholar J. Liu , K. V. Mishra , and M. Saquib . Co-designing statistical MIMO radar and in-band full-duplex multi-user MIMO communications , 2021 . Google Scholar J. A. Mahal , A. Khawar , A. Abdelhadi , and T. C. Clancy . Spectral coexistence of MIMO radar and MIMO cellular system . IEEE Transactions on Aerospace and Electronic Systems , 53 ( 2 ): 655 – 668 , 2017 . 10.1109/TAES.2017.2651698 Web of Science®Google Scholar M. Mirza and S. Osindero . Conditional generative adversarial nets . CoRR , abs/1411.1784, 2014 . http://arxiv.org/abs/1411.1784 . Google Scholar A. F. Molisch . Wireless communications , Volume 34 . John Wiley & Sons , 2012 . Google Scholar A. B. Mosbah , T. A. Hall , M. Souryal , and H. Afifi . An analytical model for inference attacks on the incumbent's frequency in spectrum sharing . In IEEE DySPAN , pages 1 – 2 , March 2017 . https://doi.org/10.1109/DySPAN.2017.7920770 . 10.1109/DySPAN.2017.7920770 Google Scholar B. Niu , Q. Li , X. Zhu , G. Cao , and H. Li . Achieving k-anonymity in privacy-aware location-based services . In IEEE INFOCOM , pages 754 – 762 , April 2014 . https://doi.org/10.1109/INFOCOM.2014.6848002 . 10.1109/INFOCOM.2014.6848002 Google Scholar N. Rajkarnikar , J. M. Peha , and A. Aguiar . Location privacy from dummy devices in database-coordinated spectrum sharing . In IEEE DySPAN , pages 1 – 10 , March 2017 . https://doi.org/10.1109/DySPAN.2017.7920796 . 10.1109/DySPAN.2017.7920796 Google Scholar A. Robertson , J. Molnar , and J. Boksiner . Spectrum database poisoning for operational security in policy-based spectrum operations . In IEEE MILCOM , pages 382 – 387 , November 2013 . https://doi.org/10.1109/MILCOM.2013.72 . 10.1109/MILCOM.2013.72 Google Scholar S. Salamatian , A. Zhang , F. du Pin Calmon , S. Bhamidipati , N. Fawaz , B. Kveton , P. Oliveira , and N. Taft . Managing your private and public data: bringing down inference attacks against your privacy . IEEE Journal on Selected Topics in Signal Processing , 9 ( 7 ): 1240 – 1255 , 2015 . https://doi.org/10.1109/JSTSP.2015.2442227 . 10.1109/JSTSP.2015.2442227 Google Scholar R. Saruthirathanaworakun , J. M. Peha , and L. M. Correia . Opportunistic sharing between rotating radar and cellular . IEEE Journal on Selected Areas in Communications , 30 ( 10 ): 1900 – 1910 , 2012 . 10.1109/JSAC.2012.121106 Web of Science®Google Scholar R. Shokri , G. Theodorakopoulos , J. Y. Le Boudec , and J. P. Hubaux . Quantifying location privacy . In 2011 IEEE Symposium on Security and Privacy , pages 247 – 262 , May 2011 . https://doi.org/10.1109/SP.2011.18 . 10.1109/SP.2011.18 Google Scholar S. Sodagari , A. Khawar , T. C. Clancy , and R. McGwier . A projection based approach for radar and telecommunication systems coexistence . In GLOBECOM , pages 5010 – 5014 . IEEE, 2012 . Google Scholar P. R. Vaka , S. Bhattarai , and J. M. Park . Location privacy of non-stationary incumbent systems in spectrum sharing . In GLOBECOM , pages 1 – 6 , December 2016 . https://doi.org/10.1109/GLOCOM.2016.7841962 . 10.1109/GLOCOM.2016.7841962 Google Scholar W. Wang and Q. Zhang . Privacy-preserving collaborative spectrum sensing with multiple service providers . IEEE Transactions on Wireless Communications , 14 ( 2 ): 1011 – 1019 , 2015 . 10.1109/TWC.2014.2363357 Google Scholar W. Wang , Y. Chen , Q. Zhang , and T. Jiang . A software-defined wireless networking enabled spectrum management architecture . IEEE Communications Magazine , 54 ( 1 ): 33 – 39 , 2016 . ISSN 0163-6804. https://doi.org/10.1109/MCOM.2016.7378423 . 10.1109/MCOM.2016.7378423 Web of Science®Google Scholar J. Wang , S. M. Errapotu , Y. Gong , L. Qian , R. Jäntti , M. Pan , and Z. Han . Data-driven optimization based primary users' operational privacy preservation . IEEE Transactions on Cognitive Communications and Networking , 4 ( 2 ): 357 – 367 , 2018 . ISSN 2332-7731. https://doi.org/10.1109/TCCN.2018.2837876 . 10.1109/TCCN.2018.2837876 Google Scholar L. Xing , Q. Ma , J. Gao , and S. Chen . An optimized algorithm for protecting privacy based on coordinates mean value for cognitive radio networks . IEEE Access , 6 : 21971 – 21979 , 2018 . ISSN 2169-3536. https://doi.org/10.1109/ACCESS.2018.2822839 . 10.1109/ACCESS.2018.2822839 Google Scholar Q. Zhao and A. Swami . A survey of dynamic spectrum access: signal processing and networking perspectives . In ICASSP , Volume 4, pages IV – 1349 . IEEE, 2007 . Google Scholar Signal Processing for Joint Radar Communications ReferencesRelatedInformation

The drug Nirmatrelvir/Ritonavir (Paxlovid®) is authorized for outpatient management of COVID-19 and interacts with multiple opioids. Most opioids can be safely administered with Nirmatrelvir/Ritonavir without interrupting therapy. This knowledge is essential for Palliative care clinicians as automated drug – drug alerts may not always provide appropriate clinical guidance.

ABSTRACTVery few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool that was developed prior to COVID-19 and demonstrates model performance during the COVID-19 pandemic. The analytic system (CoMET®, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10,422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns. Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit (ICU), primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.732 to 0.745. The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic. We speculate that some of the model performance’s stability is due to continuous cardiorespiratory monitoring, which should not drift as practices, policies, and patient populations change.Clinical Trial registrationClinicalTrials.govNCT04359641;https://clinicaltrials.gov/ct2/show/NCT04359641.

ObjectiveTo describe the outcomes of kidney transplant (KT) candidates with obesity undergoing sleeve gastrectomy (SG) to meet the criteria for KT.MethodsRetrospective analysis was conducted of electronic medical records of KT candidates with obesity (body mass index >35 kg/m2) who underwent SG in our institution. Weight loss, adverse health events, and the listing and transplant rates were abstracted and compared with the nonsurgical cohort.ResultsThe SG was performed in 54 patients; 50 patients did not have surgery. Baseline demographic characteristics were comparable at the time of evaluation. Mean body mass index ± SD of the SG group was 41.7±3.6 kg/m2 at baseline (vs 41.5±4.3 kg/m2 for nonsurgical controls); at 2 and 12 months after SG, it was 36.4±4.1 kg/m2 and 32.6±4.0 kg/m2 (P<.01 for both). In the median follow-up time of 15.5 months (interquartile range, 6.4 to 23.9 months), SG was followed by active listing (37/54 people), and 20 of 54 received KT during a median follow-up time of 20.9 months (interquartile range, 14.7 to 28.3 months) after SG. In contrast, 14 of 50 patients in the nonsurgical cohort were listed, and 5 received a KT (P<.01). Three patients (5.6%) experienced surgical complications. There was no difference in overall hospitalization rates and adverse health outcomes, but the SG cohort experienced a higher risk of clinically significant functional decline.ConclusionIn KT candidates with obesity, SG appears to be effective, with 37% of patients undergoing KT during the next 18 months (P<.01). Further research is needed to confirm and to improve the safety and efficacy of SG for patients with obesity seeking a KT.

Objective: Semaglutide, a glucagon-like peptide-1 receptor agonist is approved for weight loss and diabetes treatment, but limited literature exists regarding semaglutide use in patients with advanced chronic kidney disease (CKD). Therefore, this project assessed the safety and efficacy of semaglutide among patients with estimated glomerular filtration rate (eGFR) 15-29 mL/min/1.73 m(2) (CKD stage 4), eGFR<15 mL/min/1.73 m(2) (CKD stage 5) or on dialysis. Methods: This is a retrospective electronic medical record based analysis of consecutive patients with advanced CKD (defined as CKD 4 or greater) who were started on semaglutide (injectable or oral). Data was collected between January 2018 and January 2023. Investigators verified CKD diagnosis and manually extracted data. Data were analyzed using Fisher's exact test, paired t test, linear mixed effects models and Wilcoxon signed rank test. Results: Seventy-six patients with CKD 4 or greater who initiated semaglutide were included. Most patients had a history of type 2 diabetes mellitus (96.0%), and most were males (53.9%). The mean age was 66.8 y (SD 11.5) with the mean body mass index was 36.2 (SD 7.5). The initial doses were 3 mg orally and 0.25 mg by injection. Maximum prescribed dose was 1 mg (injectable) in 28 (45.2%) patients and 14 mg (orally) in 2(14.2%) patients. Patients received semaglutide for a median duration of 17.4 (IQR 0.43, 48.8) months. Forty-eight (63.1%) patients reported no adverse effects associated with the therapy. Mean weight decreased from 106.2 (SD 24.2) to 101.3 (SD 27.3) kg (P < .001). Eight patients (16%) with type 2 diabetes mellitus T2DM discontinued insulin after starting semaglutide. Mean hemoglobin A1c (HbA1c) decreased from 8.0% (SD 1.7) to 7.1% (SD 1.3) (P < .001). Adverse effects were the primary reason for semaglutide discontinuation (37.0%), with nausea, vomiting, and abdominal pain being the most common complaints. Conclusions: Based on this retrospective study semaglutide appears to be tolerated by most individuals with CKD 4 or greater despite associated gastrointestinal side effects similar to those observed in patients with better kidney function and leads to an improvement of glycemic control and insulin discontinuation in patients with T2DM. Modest weight loss (approximately 4.6% of the total body weight) was observed on the prescribed doses. Larger prospective randomized studies are needed to comprehensively assess the risks and benefits of semaglutide in patients with CKD 4 or greater and obesity. (c) 2024 AACE. Published by Elsevier Inc. All rights are reserved, including those for text and data mining,