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Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions., (© 2024 John Wiley & Sons Ltd.)

Objective: To estimate the prevalence of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) after infection with SARS-CoV-2 during pregnancy and to characterize associated risk factors., Methods: In a multicenter cohort study (NIH RECOVER [Researching COVID to Enhance Recovery]-Pregnancy Cohort), individuals who were pregnant during their first SARS-CoV-2 infection were enrolled across the United States from December 2021 to September 2023, either within 30 days of their infection or at differential time points thereafter. The primary outcome was PASC , defined as score of 12 or higher based on symptoms and severity as previously published by the NIH RECOVER-Adult Cohort, at the first study visit at least 6 months after the participant's first SARS-CoV-2 infection. Risk factors for PASC were evaluated, including sociodemographic characteristics, clinical characteristics before SARS-CoV-2 infection (baseline comorbidities, trimester of infection, vaccination status), and acute infection severity (classified by need for oxygen therapy). Multivariable logistic regression models were fitted to estimate associations between these characteristics and presence of PASC., Results: Of the 1,502 participants, 61.1% had their first SARS-CoV-2 infection on or after December 1, 2021 (ie, during Omicron variant dominance); 51.4% were fully vaccinated before infection; and 182 (12.1%) were enrolled within 30 days of their acute infection. The prevalence of PASC was 9.3% (95% CI, 7.9-10.9%) measured at a median of 10.3 months (interquartile range 6.1-21.5) after first infection. The most common symptoms among individuals with PASC were postexertional malaise (77.7%), fatigue (76.3%), and gastrointestinal symptoms (61.2%). In a multivariable model, the proportion PASC positive with vs without history of obesity (14.9% vs 7.5%, adjusted odds ratio [aOR] 1.65, 95% CI, 1.12-2.43), depression or anxiety disorder (14.4% vs 6.1%, aOR 2.64, 95% CI, 1.79-3.88) before first infection, economic hardship (self-reported difficulty covering expenses) (12.5% vs 6.9%, aOR 1.57, 95% CI, 1.05-2.34), and treatment with oxygen during acute SARS-CoV-2 infection (18.1% vs 8.7%, aOR 1.86, 95% CI, 1.00-3.44) were associated with increased prevalence of PASC., Conclusion: The prevalence of PASC at a median time of 10.3 months after SARS-CoV-2 infection during pregnancy was 9.3% in the NIH RECOVER-Pregnancy Cohort. The predominant symptoms were postexertional malaise, fatigue, and gastrointestinal symptoms. Several socioeconomic and clinical characteristics were associated with PASC after infection during pregnancy., Clinical Trial Registration: ClinicalTrials.gov , NCT05172024., Competing Interests: Financial Disclosure Torri D. Metz is the site PI for a Pfizer study of Paxlovid in pregnancy and was the site PI for a Pfizer study of COVID-19 vaccination in pregnancy. She has received UpToDate royalties for two topics on trial of labor after cesarean. Carmen J. Beamon disclosed receiving payments from Wellcare of North Carolina. Ann Chang's institution received payment from New York University for her efforts on this study. Kelly S. Gibson disclosed that her institution received funding from the NICHD, NHLBI, and Materna. Rachel Hess received payment from Astellas Pharmaceuticals. M. Camile Hoffman disclosed her institution received payment for her expert testimony for one medicolegal trial from Wheeler, Trigg, and Associates (a defense attorneys firm). Her institution also received payment for a disease state presentation on postpartum depression and zuranolone from SAGE/Biogen. Brenna L. Hughes disclosed receiving payments from UpToDate and Moderna. Stuart Katz disclosed payments for providing expert testimony for Venable LLP. Jennifer Hadlock has received funding (paid to institution) for retrospective studies of COVID-19 from Pfizer, Novartis, Janssen, and Gilead. Grace A. McComsey served as an advisor for Gilead and ViiVGlaxoSmithKline. Patrick Ramsey disclosed receiving royalties from UpToDate. His institution was paid by the Texas Collaborative for Healthy Mothers and Babies (TCHMB)—Texas PQC for part of his efforts. Daniel W. Skupski reports receiving payments from Organon, Inc and Cooper Surgical. Alan T.N. Tita disclosed money paid to his institution from Pfizer for his efforts in this study. Andrea Foulkes disclosed receiving past payments from Round Table, Inc. The other authors did not report any potential conflicts of interest., (Copyright © 2024 by the American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Importance: Most research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment., Objective: To identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC., Design, Setting, and Participants: Multicenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history., Exposure: SARS-CoV-2 infection., Main Outcomes and Measures: PASC and 89 prolonged symptoms across 9 symptom domains., Results: A total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected] and 147 without [referred to as uninfected]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise-related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents., Conclusions and Relevance: This study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges.

Background: Merkel cell carcinoma (MCC) is an aggressive cancer with often poor outcomes. Limited biomarkers exist for predicting clinical outcomes. The Merkel cell polyomavirus (MCPyV) serum antibody test (AMERK) has shown potential for indicating better recurrence-free survival in a single-institution study. The study aimed to evaluate the link between initial AMERK serostatus and survival. Secondary objectives included examining the relationship between initial AMERK titer levels and tumor burden., Methods: A retrospective cohort study across two institutions analyzed patients tested with AMERK within 90 days of MCC diagnosis. Regression models assessed the association of survival outcomes with serostatus, considering various factors. The relationship between AMERK titer and tumor burden indicators was evaluated using ANOVA. Significance testing was exploratory, without a fixed significance level., Results: Of 261 MCC patients tested, 49.4% were initially seropositive (titer ≥75). Multivariable analysis showed that seropositivity improved recurrence, event-free, overall, and MCC-specific survival rates. Strong associations were found between initial AMERK titer and clinical, tumor, and nodal stages, tumor size, and disease extent. Notably, improved survival with seropositivity was observed only in patients with localized disease at initial presentation., Conclusion: Circulating antibodies to MCPyV oncoproteins, as indicated by the AMERK test, are linked with better survival in MCC patients with localized disease at presentation. This could enhance patient risk profiling and treatment personalization. The study's retrospective nature and exploratory analysis are key limitations., Plain Language Summary: Merkel cell carcinoma (MCC) is a potentially aggressive skin cancer, and tools to predict patient outcomes are limited. A blood test called anti-Merkel cell panel (AMERK), which checks for specific antibodies related to this cancer, might give us some clues. In this study, we looked at 261 MCC patients who took the AMERK test within 90 days of diagnosis. We found that patients with an initial positive AMERK result tended to have better outcomes, especially if their cancer was in the early stages. However, it is important to note that this study has limitations, including using retrospective data and exploratory analyses., (© 2024 American Cancer Society.)

An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease (AD) typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as "horizontal" changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent follow-up visits leading to interval-censored outcome information. Moreover, genetic and clinical risk factors are not only high-dimensional, but characterized by underlying grouping structures, such as by function or gene location. Such grouped high-dimensional covariates require shrinkage methods that directly acknowledge this structure to facilitate variable selection and estimation. In this paper, we address these considerations directly by proposing a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. We develop an R package with a Markov chain Monte Carlo sampler for estimation. We present a simulation study examining the performance of this method relative to an ordinary lasso penalty and apply the proposed method to identify groups of predictive genetic and clinical risk factors for AD in the Religious Orders Study and Memory and Aging Project prospective cohort studies of AD and dementia., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Importance: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults., Observations: We describe the protocol for the Pediatric Observational Cohort Study of the NIH's REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science., Conclusions and Relevance: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions., Clinical Trials.gov Identifier: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Brett Anderson reported receiving direct support for work not related to RECOVER work/publications from Genentech and the National Institute of Allergy and Immunology. Walter Dehority reported receiving grant support from Merck and participating in research for the Moderna COVID-19 pediatric vaccine trial and the Pfizer Paxlovid trial. Alex Fiks reported receiving support from NJM insurance and personal consulting fees not related to this paper from Rutgers University and the American Academy of Pediatrics. Ashraf Harahsheh reported serving as a scientific advisory board member unrelated to this paper for OP2 DRUGS. Lawrence Kleinman reported serving as an unpaid member of the Board of Directors for the DARTNet Institute, as a principle investigator at Quality Matters, Inc., and as the Vice Chair for the Borough of Metuchen Board of Health. Dr. Kleinman also reported grant support for work not related to RECOVER work/publications from NIH, HRSA, and the Robert Wood Johnson Foundation. Dr. Kleinman also reported minority individual stock ownership in Apple Computer, Sanofi SA, Experion, GlaxoSmithKline, Magyar Bank, Regeneron Pharmaceuticals, JP Morgan Chase, and Amgen Inc. Torri Metz reported participating as a Principle Investigator in the medical advisory board for the planning of a Pfizer clinical trial of SARS-CoV-2 vaccination in pregnancy. She is also a principle investigator for a Pfizer study evaluating the pharmacokinetics of Paxlovid in pregnant people with COVID-19. Joshua Milner reported serving as a member of the Scientific Advisory Board for Blueprint Medicines, in a capacity unrelated to RECOVER work/publications. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Gross et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia., (© The Author 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: Pregnancy induces unique physiologic changes to the immune response and hormonal changes leading to plausible differences in the risk of developing post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID. Exposure to SARS-CoV-2 during pregnancy may also have long-term ramifications for exposed offspring, and it is critical to evaluate the health outcomes of exposed children. The National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC aims to evaluate the long-term sequelae of SARS-CoV-2 infection in various populations. RECOVER-Pregnancy was designed specifically to address long-term outcomes in maternal-child dyads., Methods: RECOVER-Pregnancy cohort is a combined prospective and retrospective cohort that proposes to enroll 2,300 individuals with a pregnancy during the COVID-19 pandemic and their offspring exposed and unexposed in utero, including single and multiple gestations. Enrollment will occur both in person at 27 sites through the Eunice Kennedy Shriver National Institutes of Health Maternal-Fetal Medicine Units Network and remotely through national recruitment by the study team at the University of California San Francisco (UCSF). Adults with and without SARS-CoV-2 infection during pregnancy are eligible for enrollment in the pregnancy cohort and will follow the protocol for RECOVER-Adult including validated screening tools, laboratory analyses and symptom questionnaires followed by more in-depth phenotyping of PASC on a subset of the overall cohort. Offspring exposed and unexposed in utero to SARS-CoV-2 maternal infection will undergo screening tests for neurodevelopment and other health outcomes at 12, 18, 24, 36 and 48 months of age. Blood specimens will be collected at 24 months of age for SARS-CoV-2 antibody testing, storage and anticipated later analyses proposed by RECOVER and other investigators., Discussion: RECOVER-Pregnancy will address whether having SARS-CoV-2 during pregnancy modifies the risk factors, prevalence, and phenotype of PASC. The pregnancy cohort will also establish whether there are increased risks of adverse long-term outcomes among children exposed in utero., Clinical Trials.gov Identifier: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. Metz reports personal fees from Pfizer for her role as a medical consultant for a SARS-CoV-2 vaccination in pregnancy study, grants from Pfizer for role as a site PI for SARS-CoV-2 vaccination in pregnancy study, grants from Pfizer for role as a site PI for RSV vaccination in pregnancy study, and grants from Gestvision for role as a site PI for a preeclampsia study outside the submitted work. Dr. Horwitz reported serving as a member of the National Academy of Medicine Committee on the Long-Term Health Effects Stemming from COVID-19 and Implications for the Social Security Administration. Dr. Costantine reported receiving grant support for work not related to this paper from Baxter International and Siemens Healthcare and personal consulting fees not related to this paper from Progenity and Siemens Healthcare. These disclosures do not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Metz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: A substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe SARS-CoV-2 outcomes, including intubation and death. However, most existing studies considered each of multiple biomarkers independently and focused analysis on baseline or peak values., Methods: We propose a two-stage analytic strategy combining functional principal component analysis (FPCA) and sparse-group LASSO (SGL) to characterize associations between biomarkers and 30-day mortality rates. Unlike prior reports, our proposed approach leverages: 1) time-varying biomarker trajectories, 2) multiple biomarkers simultaneously, and 3) the pathophysiological grouping of these biomarkers. We apply this method to a retrospective cohort of 12, 941 patients hospitalized at Massachusetts General Hospital or Brigham and Women's Hospital and conduct simulation studies to assess performance., Results: Renal, inflammatory, and cardio-thrombotic biomarkers were associated with 30-day mortality rates among hospitalized SARS-CoV-2 patients. Sex-stratified analysis revealed that hematogolical biomarkers were associated with higher mortality in men while this association was not identified in women. In simulation studies, our proposed method maintained high true positive rates and outperformed alternative approaches using baseline or peak values only with respect to false positive rates., Conclusions: The proposed two-stage approach is a robust strategy for identifying biomarkers that associate with disease severity among SARS-CoV-2-infected individuals. By leveraging information on multiple, grouped biomarkers' longitudinal trajectories, our method offers an important first step in unraveling disease etiology and defining meaningful risk strata., (© 2023. The Author(s).)

Semi-competing risks refer to the time-to-event analysis setting, where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise in a broad range of clinical contexts, including studies of preeclampsia, a condition that may arise during pregnancy and for which delivery is a terminal event. Models that acknowledge semi-competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi-competing risks in high dimensions are lacking. Moreover, in such settings researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility-in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap, we propose a novel penalized estimation framework for frailty-based illness-death multi-state modeling of semi-competing risks. Our approach combines non-convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non-convex optimization, and prove statistical error rate results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record., (© 2022 The International Biometric Society.)

Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis., Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms., Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options., Registration: NCT05172024., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Helen Chu reported consulting for Merck, GSK, Pfizer, Ellume, Janssen, Vindico CME, and the Bill and Melinda Gates Foundation, and receiving research support from Gates Ventures, Ellume, and Sanofi Pasteur. She also serves as a co-investigator on studies funded by Pfizer, Novavax, and GSK. Maged Costantine reported receiving grant support for work not related to RECOVER work/publications from Baxter International and Siemens Healthcare and personal consulting fees not related to this paper from Progenity, Quidel Ortho, and Siemens Healthcare. Kristine Erlandson reported research funding from Gilead Sciences and consulting payments from Gilead Sciences, Merck, and ViiV Pharmaceuticals, all paid to the University of Colorado. Emily Gallagher reported consulting for Novartis, Flare Therapeutics and Seagen. Edward Gardner reported research support (clinical trials) from Gilead Sciences, ViiV Healthcare, and Cepheid. Jason Goldman reported research support from Gilead, Eli Lilly and Regeneron; grants from Gilead, Merck (BARDA); personal fees for consulting from Gilead, Eli Lilly; and non-financial support from Adaptive Biotechnologies and Labcorp/Monogram Biosciences outside the submitted work. Timothy Heinrich reported grant support from Merck Inc. and consulting fees from Roche. Rachel Hess reported serving as Data Safety Monitoring Board member for Astellas Pharmaceuticals unrelated to the current work. Leora Horwitz reported being a member of the National Academy of Medicine Committee on the Long-Term Health Effects Stemming from COVID-19 and Implications for the Social Security Administration. Priscilla Hsue reported receiving honoraria from Gilead and Merck unrelated to study topic, receiving study drug from Regeneron unrelated to study topic, and receiving a research grant from Novartis. Judith James reported OMRF has licensed her IP to Progentec Biosciences, has received grant support from Progentec Biosciences, and serves on Advisory Committees to Glaxo Smith Klein, Merck and Novartis. Arthur Kim reports providing educational materials to Clinical Care Options and UpToDate and serving on a Data Safety Monitoring board for Kintor Pharmaceuticals, Ltd. Bruce Levy reported serving as a consultant for AstraZeneca, Entrinsic Biosciences, Gossamer Bio and Nocion Therapeutics and receiving research support from Amgen, Genentech, GlaxoSmithKline, Pieris Pharmaceuticals, SRA and Sanofi unrelated to the current work. Vincent Marconi reported receiving grants from NIH during the conduct of the study and grants from NIH, Veteran Affairs, and Centers for Disease Control and Prevention; grants, personal fees, nonfinancial support, and other from Lilly and Gilead; grants and personal fees from ViiV; and nonfinancial support from Bayer outside the submitted work. Grace McComsey reported serving as consultant for Merck, Gilead, ViiV, Janssen and have received research support from Pfizer, Vanda, Genentech, Roche, Redhill and Cognivue. Torri Metz reported being a site PI and a participant in the medical advisory board for the planning of a Pfizer clinical trial of SARS-CoV-2 vaccination in pregnancy. She also reported being a site PI for a Pfizer study evaluating the pharmacokinetics of Paxlovid in pregnant people with COVID-19. Janet Mullington reported support for investigator-initiated research by "Open Medicine Foundation and the Patient-Led Research Collaborative" Princess Ogbogu reported research support from Astrazeneca, GSK, Blueprint medical; advisory board for Astrazeneca, GSK, Sanofi, Kalvista; and consulting for Astrazeneca, GSK Sairam Parthasarathy reported research funding to Institution from Sergey Brin foundation of COVID and Long-COVID research. Michael Peluso reported consulting fees from Gilead Sciences and AstraZeneca, and service on data safety monitoring board for American Gene Technologies. Sean Quigley reported service on speaker Board for Servier, Alnylam, Agios; service on advisory board for Recordati, Alexion. Franz Rischard reported research support from NIH/NHLBI, United Therapeutics, Acceleron/Merck, Janssen, Insmed, Aerovate, and Bayer; and consulting/advisory compensation from Acceleron and Bayer. Nadine Rouphael reported being a consultant for ICON and EMMES as a safety consultant for clinical trials; service on the advisory boards for Moderna; funds to institution from Sanofi, Lilly, Merck, Quidel and Pfizer. PJ Utz reported stock ownership of Gilead and PI of biomarker studies for Pfizer STOP-PASC paxlovid trial Juan Wisnivesky received receiving consulting honorarium from Sanofi, Banook, Prospero, PPD and Atea and research grants from Sanofi, Regeneron, Axella, and Arnold Consultants., (Copyright: © 2023 Horwitz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Preeclampsia is a pregnancy complication that contributes substantially to perinatal morbidity and mortality worldwide. Existing approaches to modeling and prediction of preeclampsia typically focus either on predicting preeclampsia risk alone, or on the timing of delivery following a diagnosis of preeclampsia. As such, they are misaligned with typical healthcare interactions during which the 2 events are generally considered simultaneously., Objective: This study aimed to describe the "semicompeting risks" framework as an innovative approach for jointly modeling the risk and timing of preeclampsia and the timing of delivery simultaneously. Through this approach, one can obtain, at any point during the pregnancy, clinically relevant summaries of an individual's predicted outcome trajectories in 4 risk categories: not developing preeclampsia and not having delivered, not developing preeclampsia but having delivered because of other causes, developing preeclampsia but not having delivered, and developing preeclampsia and having delivered., Study Design: To illustrate the semicompeting risks methodology, we presented an example analysis of a pregnancy cohort from the electronic health record of an urban, academic medical center in Boston, Massachusetts (n=9161 pregnancies). We fit an illness-death model with proportional-hazards regression specifications describing 3 hazards for timings of preeclampsia, delivery in the absence of preeclampsia, and delivery following preeclampsia diagnosis., Results: The results indicated nuanced relationships between a variety of risk factors and the timings of preeclampsia diagnosis and delivery, including maternal age, race/ethnicity, parity, body mass index, diabetes mellitus, chronic hypertension, cigarette use, and proteinuria at 20 weeks' gestation. Sample predictions for a diverse set of individuals highlighted differences in projected outcome trajectories with regard to preeclampsia risk and timing, and timing of delivery either before or after preeclampsia diagnosis., Conclusion: The semicompeting risks framework enables characterization of the joint risk and timing of preeclampsia and delivery, providing enhanced, meaningful information regarding clinical decision-making throughout the pregnancy., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: Patient-reported outcome measures (PROMs) are currently the gold standard to evaluate patient physical performance and ability to recover after spine surgery. However, PROMs have significant limitations due to the qualitative and subjective nature of the information reported as well as the impossibility of using this method in a continuous manner. The smartphone global positioning system (GPS) can be used to provide continuous, quantitative, and objective information on patient mobility. The aim of this study was to use daily mobility features derived from the smartphone GPS to characterize the perioperative period of patients undergoing spine surgery and to compare these objective measurements to PROMs, the current gold standard., Methods: Eight daily mobility features were derived from smartphone GPS data in a population of 39 patients undergoing spine surgery for a period of 2 months starting 3weeks before surgery. In parallel, three different PROMs for pain (visual analog scale [VAS]), disability (Oswestry Disability Index [ODI]) and functional status (Patient-Reported Outcomes Measurement Information System [PROMIS]) were serially measured. Segmented linear regression analysis was used to assess trends before and after surgery. The Student paired t-test was used to compare pre- and postoperative PROM scores. Pearson's correlation was calculated between the daily average of each GPS-based mobility feature and the daily average of each PROM score during the recovery period., Results: Smartphone GPS features provided data documenting a reduction in mobility during the immediate postoperative period, followed by a progressive and steady increase with a return to baseline mobility values 1 month after surgery. PROMs measuring pain, physical performance, and disability were significantly different 1 month after surgery compared to the 2 immediate preoperative weeks. The GPS-based features presented moderate to strong linear correlation with pain VAS and PROMIS physical score during the recovery period (Pearson r > 0.7), whereas the ODI and PROMIS mental scores presented a weak correlation (Pearson r approximately 0.4)., Conclusions: Smartphone-derived GPS features were shown to accurately characterize perioperative mobility trends in patients undergoing surgery for spine-related diseases. Features related to time (rather than distance) were better at describing patient physical and performance status. Smartphone GPS has the potential to be used for the development of accurate, noninvasive and personalized tools for patient mobility monitoring after surgery.

Objective: Studies that use patient smartphones to collect ecological momentary assessment and sensor data, an approach frequently referred to as digital phenotyping, have increased in popularity in recent years. There is a lack of formal guidelines for the design of new digital phenotyping studies so that they are powered to detect both population-level longitudinal associations as well as individual-level change points in multivariate time series. In particular, determining the appropriate balance of sample size relative to the targeted duration of follow-up is a challenge., Materials and Methods: We used data from 2 prior smartphone-based digital phenotyping studies to provide reasonable ranges of effect size and parameters. We considered likelihood ratio tests for generalized linear mixed models as well as for change point detection of individual-level multivariate time series., Results: We propose a joint procedure for sequentially calculating first an appropriate length of follow-up and then a necessary minimum sample size required to provide adequate power. In addition, we developed an accompanying accessible sample size and power calculator., Discussion: The 2-parameter problem of identifying both an appropriate sample size and duration of follow-up for a longitudinal study requires the simultaneous consideration of 2 analysis methods during study design., Conclusion: The temporally dense longitudinal data collected by digital phenotyping studies may warrant a variety of applicable analysis choices. Our use of generalized linear mixed models as well as change point detection to guide sample size and study duration calculations provide a tool to effectively power new digital phenotyping studies., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The risk of death or appropriate therapy varies widely among recipients of implantable cardioverter-defibrillators (ICDs). The goals of this study were to develop a risk prediction tool that jointly considers future outcome probabilities of ICD shock and death., Methods and Results: We performed a secondary analysis of patients receiving ICDs as part of the SCD-HeFT trial (Sudden Cardiac Death in Heart Failure Trial). We applied an illness-death regression model to jointly model both ICD shocks and death under the semi-competing risks framework, which predicts for each patient their probability of having received ICD shocks, dying, or both at any given point in time. Among 803 ICD recipients (mean age, 60 years; 23% women) followed for a median of 41.1 months, 430 (53.5%) patients completed the study without dying or receiving an ICD shock, 206 (25.7%) received at least 1 shock but survived, 113 (14.1%) died before experiencing a shock, and 54 (6.7%) received at least 1 shock and subsequently died. Predicted outcome probabilities based on baseline demographic and clinical variables reveal substantial heterogeneity in joint shock and death risks, both between patients at each time point and for each single patient across time. Overall, predictive performance for ICD shock and death individually was adequate, based on area under the curve at 5 years of 0.65 for shocks and of 0.79 for death., Conclusions: Our analysis of outcomes after ICD implantation provides an alternative predictive model for individual risk of death or ICD shocks. If validated, this may provide a useful tool for individualized counseling regarding likely outcomes after device implantation, while also informing the design of further studies to focus the clinical effectiveness and cost-effectiveness of ICD therapy., Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00000609.

Setting: Haiti has the highest burden of tuberculosis (TB) in the Americas, with an estimated prevalence of 254 per 100 000 population. The Haitian Group for the Study of Kaposi's Sarcoma and Opportunistic Infections (Groupe Haïtien d'Etude du Sarcome de Kaposi et des Infections Opportunistes, GHESKIO) conducted active case finding (ACF) for TB at the household level in nine slums in Port-au-Prince., Objective: We report on the prevalence of undiagnosed TB detected through GHESKIO's ACF campaign., Design: From 1 August 2014 to 31 July 2015, we conducted a retrospective cohort analysis using GHESKIO's ACF campaign data. All individuals who reported chronic cough (cough 2 weeks) were tested for TB at GHESKIO, and those aged 10 years were included in the analyses., Results: Of 104 097 individuals screened in the community, 5598 (5%) reported chronic cough and satisfied the study inclusion criteria. A total of 1110 (20%) were diagnosed with active TB disease (prevalence of 1066/100 000). Of the 5472 (98%) patients tested for human immunodeficiency virus (HIV), 528 (10%) were HIV-positive; 143 (3%) patients were diagnosed with both diseases., Conclusion: Household-level screening for cough with TB and HIV testing for symptomatic patients was a high-yield strategy, leading to the detection of a prevalence of undiagnosed disease exceeding national estimates by more than four-fold for TB, and by five-fold for HIV.