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Summary: Background: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models — logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the ‘long COVID’ label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). Findings: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. Interpretation: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology. Funding: NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.

Summary: Background: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. Methods: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. Findings: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. Funding: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Immune dysregulation is characteristic of the more severe stages of SARS-CoV-2 infection. Understanding the mechanisms by which the immune system contributes to COVID-19 severity may open new avenues to treatment. Here, we report that elevated IL-13 was associated with the need for mechanical ventilation in 2 independent patient cohorts. In addition, patients who acquired COVID-19 while prescribed Dupilumab, a mAb that blocks IL-13 and IL-4 signaling, had less severe disease. In SARS-CoV-2–infected mice, IL-13 neutralization reduced death and disease severity without affecting viral load, demonstrating an immunopathogenic role for this cytokine. Following anti–IL-13 treatment in infected mice, hyaluronan synthase 1 (Has1) was the most downregulated gene, and accumulation of the hyaluronan (HA) polysaccharide was decreased in the lung. In patients with COVID-19, HA was increased in the lungs and plasma. Blockade of the HA receptor, CD44, reduced mortality in infected mice, supporting the importance of HA as a pathogenic mediator. Finally, HA was directly induced in the lungs of mice by administration of IL-13, indicating a new role for IL-13 in lung disease. Understanding the role of IL-13 and HA has important implications for therapy of COVID-19 and, potentially, other pulmonary diseases. IL-13 levels were elevated in patients with severe COVID-19. In a mouse model of the disease, IL-13 neutralization reduced the disease and decreased lung HA deposition. Administration of IL-13–induced HA in the lung. Blockade of the HA receptor CD44 prevented mortality, highlighting a potentially novel mechanism for IL-13–mediated HA synthesis in pulmonary pathology.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease., (© 2024. The Author(s).)

Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID., Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.)., Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events., Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions., Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438., Competing Interests: Declaration of interests Danielle Mowery serves as an unpaid member of the Epic Cosmos Governing Council. Romain Pirracchio received funding from the FDA CERSI grant U01FD005978 and the PCORI grant P0562155 and received a consulting honorarium from Phillips. Martin van der Laan received funding from the NIAID grant 5R01AI074345. Johanna Loomba received contract funding from the NIH RECOVER program. Emily Pfaff received funding from the NIH and PCORI. The views expressed in this manuscript are solely those of the authors and do not necessarily represent those of the National Institutes of Health, the U.S. Department of Health and Human Services or the U.S. government. Qi Long was supported by grants from the NIH., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset., Materials and Methods: Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns., Results: The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification., Conclusion: Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions., Discussion: This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection., Methods: We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan-Meier curves to better understand COVID-19 reinfections., Results: Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer's V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection., Conclusions: In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections., (© 2024. The Author(s).)

Since the outbreak of COVID-19 pandemic in 2020, numerous researches and studies have focused on the long-term effects of COVID infection. The Centers for Disease Control (CDC) implemented an additional code into the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting 'Post COVID-19 condition, unspecified (U09.9)' effective on October 1st 2021, representing that Long COVID is a real illness with potential chronic conditions. The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health records (EHR) data by aggregating and harmonizing EHR data across different clinical organizations in the United States, making it convenient to build up a survival analysis on Long COVID patients and non Long COVID patients among large amounts of COVID positive patients.

Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population., Study Design: Retrospective., Setting: Clinical data in the National COVID Cohort Collaborative database (N3C)., Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period., Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29-2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48-3.78; P < 0.001), delta (OR, 3.03; CI, 2.94-3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90-3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35-9.27; P < 0.001)., Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings., Competing Interests: None declared., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of Otology & Neurotology, Inc.)

The National COVID Cohort Collaborative (N3C) is a public-private-government partnership established during the Coronavirus pandemic to create a centralized data resource called the "N3C data enclave." This resource contains individual-level health data from participating healthcare sites nationwide to support rapid collaborative analytics. N3C has enabled analytics within a cloud-based enclave of data from electronic health records from over 17 million people (with and without COVID-19) in the USA. To achieve this goal of a shared data resource, N3C implemented a shared governance strategy involving stakeholders in decision-making. The approach leveraged best practices in data stewardship and team science to rapidly enable COVID-19-related research at scale while respecting the privacy of data subjects and participating institutions. N3C balanced equitable access to data, team-based scientific productivity, and individual professional recognition - a key incentive for academic researchers. This governance approach makes N3C research sustainable and effective beyond the initial days of the pandemic. N3C demonstrated that shared governance can overcome traditional barriers to data sharing without compromising data security and trust. The governance innovations described herein are a helpful framework for other privacy-preserving data infrastructure programs and provide a working model for effective team science beyond COVID-19., Competing Interests: None of the authors has conflicts of interest related to this work., (© The Author(s) 2023.)

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis., Methods: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC., Results: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls., Conclusions: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Study Objectives: Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC)., Methods: We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities., Results: Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis., Conclusions: Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae., (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified.", Methods: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan., Results: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients., Conclusions: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation., (© 2023. The Author(s).)

Background: The implications of coronavirus disease 2019 (COVID-19) infection on outcomes after invasive therapeutic strategies among patients presenting with acute myocardial infarction (AMI) are not well studied., Hypothesis: To assess the outcomes of COVID-19 patients presenting with AMI undergoing an early invasive treatment strategy., Methods: This study was a cross-sectional, retrospective analysis of the National COVID Cohort Collaborative database including all patients presenting with a recorded diagnosis of AMI (ST-elevation myocardial infarction (MI) and non-ST elevation MI). COVID-19 positive patients with AMI were stratified into one of four groups: (1a) patients who had a coronary angiogram with percutaneous coronary intervention (PCI) within 3 days of their AMI; (1b) PCI within 3 days of AMI with coronary artery bypass graft (CABG) within 30 days; (2a) coronary angiogram without PCI and without CABG within 30 days; and (2b) coronary angiogram with CABG within 30 days. The main outcomes were respiratory failure, cardiogenic shock, prolonged length of stay, rehospitalization, and death., Results: There were 10 506 COVID-19 positive patients with a diagnosis of AMI. COVID-19 positive patients with PCI had 8.2 times higher odds of respiratory failure than COVID-19 negative patients (p = .001). The odds of prolonged length of stay were 1.7 times higher in COVID-19 patients who underwent PCI (p = .024) and 1.9 times higher in patients who underwent coronary angiogram followed by CABG (p = .001)., Conclusion: These data demonstrate that COVID-19 positive patients with AMI undergoing early invasive coronary angiography had worse outcomes than COVID-19 negative patients., (© 2022 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.)

Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations., Materials and Methods: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements., Results: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback., Discussion: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate., Conclusion: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Introduction: We conducted a phase Ib study (NCT02345824) to determine whether ribociclib, an inhibitor of cyclin-dependent kinases 4 and 6 (CDK4/6), penetrates tumor tissue and modulates downstream signaling pathways including retinoblastoma protein (Rb) in patients with recurrent glioblastoma (GBM)., Methods: Study participants received ribociclib (600 mg QD) for 8-21 days before surgical resection of their recurrent GBM. Total and unbound concentrations of ribociclib were measured in samples of tumor tissue, plasma, and cerebrospinal fluid (CSF). We analyzed tumor specimens obtained from the first (initial/pre-study) and second (recurrent/on-study) surgery by immunohistochemistry for Rb status and downstream signaling of CDK4/6 inhibition. Participants with Rb-positive recurrent tumors continued ribociclib treatment on a 21-day-on, 7-day-off schedule after surgery, and were monitored for toxicity and disease progression., Results: Three participants with recurrent Rb-positive GBM participated in this study. Mean unbound (pharmacologically active) ribociclib concentrations in plasma, CSF, MRI-enhancing, MRI-non-enhancing, and tumor core regions were 0.337 μM, 0.632 μM, 1.242 nmol/g, 0.484 nmol/g, and 1.526 nmol/g, respectively, which exceeded the in vitro IC 50 (0.04 μM) for inhibition of CDK4/6 in cell-free assay. Modulation of pharmacodynamic markers of ribociclib CDK 4/6 inhibition in tumor tissues were inconsistent between study participants. No participants experienced serious adverse events, but all experienced early disease progression., Conclusions: This study suggests that ribociclib penetrated recurrent GBM tissue at concentrations predicted to be therapeutically beneficial. Our study was unable to demonstrate tumor pharmacodynamic correlates of drug activity. Although well tolerated, ribociclib monotherapy seemed ineffective for the treatment of recurrent GBM.

Background: Recent advances have enabled delivery of high-intensity focused ultrasound through the intact human cranium with magnetic resonance imaging (MRI) guidance. This preliminary study investigates the use of transcranial MRI-guided focused ultrasound thalamotomy for the treatment of essential tremor., Methods: From February 2011 through December 2011, in an open-label, uncontrolled study, we used transcranial MRI-guided focused ultrasound to target the unilateral ventral intermediate nucleus of the thalamus in 15 patients with severe, medication-refractory essential tremor. We recorded all safety data and measured the effectiveness of tremor suppression using the Clinical Rating Scale for Tremor to calculate the total score (ranging from 0 to 160), hand subscore (primary outcome, ranging from 0 to 32), and disability subscore (ranging from 0 to 32), with higher scores indicating worse tremor. We assessed the patients' perceptions of treatment efficacy with the Quality of Life in Essential Tremor Questionnaire (ranging from 0 to 100%, with higher scores indicating greater perceived disability)., Results: Thermal ablation of the thalamic target occurred in all patients. Adverse effects of the procedure included transient sensory, cerebellar, motor, and speech abnormalities, with persistent paresthesias in four patients. Scores for hand tremor improved from 20.4 at baseline to 5.2 at 12 months (P=0.001). Total tremor scores improved from 54.9 to 24.3 (P=0.001). Disability scores improved from 18.2 to 2.8 (P=0.001). Quality-of-life scores improved from 37% to 11% (P=0.001)., Conclusions: In this pilot study, essential tremor improved in 15 patients treated with MRI-guided focused ultrasound thalamotomy. Large, randomized, controlled trials will be required to assess the procedure's efficacy and safety. (Funded by the Focused Ultrasound Surgery Foundation; ClinicalTrials.gov number, NCT01304758.).