Clinical Characteristics of Primary Snoring vs Mild Obstructive Sleep Apnea in Children: Analysis of the Pediatric Adenotonsillectomy for Snoring (PATS) Randomized Clinical Trial.

Importance: It is unknown whether children with primary snoring and children with mild obstructive sleep apnea (OSA) represent populations with substantially different clinical characteristics. Nonetheless, an obstructive apnea-hypopnea index (AHI) of 1 or greater is often used to define OSA and plan for adenotonsillectomy (AT). Objective: To assess whether a combination of clinical characteristics differentiates children with primary snoring from children with mild OSA. Design, Setting, and Participants: Baseline data from the Pediatric Adenotonsillectomy Trial for Snoring (PATS) study, a multicenter, single-blind, randomized clinical trial conducted at 6 academic sleep centers from June 2016 to January 2021, were analyzed. Children aged 3.0 to 12.9 years with polysomnography-diagnosed (AHI <3) mild obstructive sleep-disordered breathing who were considered candidates for AT were included. Data analysis was performed from July 2022 to October 2023. Main Outcomes and Measures: Logistic regression models were fitted to identify which demographic, clinical, and caregiver reports distinguished children with primary snoring (AHI <1; 311 patients [67.8%]) from children with mild OSA (AHI 1-3; 148 patients [32.2%]). Results: A total of 459 children were included. The median (IQR) age was 6.0 (4.0-7.5) years, 230 (50.1%) were female, and 88 (19.2%) had obesity. A total of 121 (26.4%) were Black, 75 (16.4%) were Hispanic, 236 (51.5%) were White, and 26 (5.7%) were other race and ethnicity. Black race (odds ratio [OR], 2.08; 95% CI, 1.32-3.30), obesity (OR, 1.80; 95% CI, 1.12-2.91), and high urinary cotinine levels (>5 µg/L) (OR, 1.88; 95% CI, 1.15-3.06) were associated with greater odds of mild OSA rather than primary snoring. Other demographic characteristics, clinical examination findings, and questionnaire reports did not distinguish between primary snoring and mild OSA. A weighted combination of the statistically significant clinical predictors had limited ability to differentiate children with mild OSA from children with primary snoring. Conclusions and Relevance: In this analysis of baseline data from the PATS randomized clinical trial, primary snoring and mild OSA were difficult to distinguish without polysomnography. Mild OSA vs snoring alone did not identify a clinical group of children who may stand to benefit from AT for obstructive sleep-disordered breathing. Trial Registration: ClinicalTrials.gov Identifier: NCT02562040.

Adenotonsillectomy for Snoring and Mild Sleep Apnea in Children: A Randomized Clinical Trial.

Importance: The utility of adenotonsillectomy in children who have habitual snoring without frequent obstructive breathing events (mild sleep-disordered breathing [SDB]) is unknown. Objectives: To evaluate early adenotonsillectomy compared with watchful waiting and supportive care (watchful waiting) on neurodevelopmental, behavioral, health, and polysomnographic outcomes in children with mild SDB. Design, Setting, and Participants: Randomized clinical trial enrolling 459 children aged 3 to 12.9 years with snoring and an obstructive apnea-hypopnea index (AHI) less than 3 enrolled at 7 US academic sleep centers from June 29, 2016, to February 1, 2021, and followed up for 12 months. Intervention: Participants were randomized 1:1 to either early adenotonsillectomy (n = 231) or watchful waiting (n = 228). Main Outcomes and Measures: The 2 primary outcomes were changes from baseline to 12 months for caregiver-reported Behavior Rating Inventory of Executive Function (BRIEF) Global Executive Composite (GEC) T score, a measure of executive function; and a computerized test of attention, the Go/No-go (GNG) test d-prime signal detection score, reflecting the probability of response to target vs nontarget stimuli. Twenty-two secondary outcomes included 12-month changes in neurodevelopmental, behavioral, quality of life, sleep, and health outcomes. Results: Of the 458 participants in the analyzed sample (231 adenotonsillectomy and 237 watchful waiting; mean age, 6.1 years; 230 female [50%]; 123 Black/African American [26.9%]; 75 Hispanic [16.3%]; median AHI, 0.5 [IQR, 0.2-1.1]), 394 children (86%) completed 12-month follow-up visits. There were no statistically significant differences in change from baseline between the 2 groups in executive function (BRIEF GEC T-scores: -3.1 for adenotonsillectomy vs -1.9 for watchful waiting; difference, -0.96 [95% CI, -2.66 to 0.74]) or attention (GNG d-prime scores: 0.2 for adenotonsillectomy vs 0.1 for watchful waiting; difference, 0.05 [95% CI, -0.18 to 0.27]) at 12 months. Behavioral problems, sleepiness, symptoms, and quality of life each improved more with adenotonsillectomy than with watchful waiting. Adenotonsillectomy was associated with a greater 12-month decline in systolic and diastolic blood pressure percentile levels (difference in changes, -9.02 [97% CI, -15.49 to -2.54] and -6.52 [97% CI, -11.59 to -1.45], respectively) and less progression of the AHI to greater than 3 events/h (1.3% of children in the adenotonsillectomy group compared with 13.2% in the watchful waiting group; difference, -11.2% [97% CI, -17.5% to -4.9%]). Six children (2.7%) experienced a serious adverse event associated with adenotonsillectomy. Conclusions: In children with mild SDB, adenotonsillectomy, compared with watchful waiting, did not significantly improve executive function or attention at 12 months. However, children with adenotonsillectomy had improved secondary outcomes, including behavior, symptoms, and quality of life and decreased blood pressure, at 12-month follow-up. Trial Registration: ClinicalTrials.gov Identifier: NCT02562040.

Psychometric properties of a combined go/no-go and continuous performance task across childhood.

Despite the critical importance of attention for children's self-regulation and mental health, there are few task-based measures of this construct appropriate for use across a wide childhood age range including very young children. Three versions of a combined go/no-go and continuous performance task (GNG/CPT) were created with varying length and timing parameters to maximize their appropriateness for age groups spanning early to middle childhood. As part of the baseline assessment of a clinical trial, 452 children aged 3-12 years (50% male, 50% female; 52% White, non-Hispanic, 27% Black, 16% Hispanic/Latinx; 6% other ethnicity/race) completed the task. Confirmatory factor analysis indicated that all task versions assessed two latent factors, labeled response inhibition and sustained attention. Versions for older children elicited lower overall accuracy while equating levels of inhibitory demand. All versions showed limited floor and ceiling effects, as well as developmental sensitivity. Boys showed higher commission error rates and children from lower income households showed lower performance across multiple task metrics. Task metrics, especially d prime and accuracy summary scores, correlated with parent-reported executive function and externalizing behavior. Task scores show promise as valid and sensitive indicators of inhibition and sustained attention across heterogeneous pediatric age groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Comparison of Caregiver- and Child-Reported Quality of Life in Children With Sleep-Disordered Breathing.

OBJECTIVE: Caregivers frequently report poor quality of life (QOL) in children with sleep-disordered breathing (SDB). Our objective is to assess the correlation between caregiver- and child-reported QOL in children with mild SDB and identify factors associated with differences between caregiver and child report. STUDY DESIGN: Analysis of baseline data from a multi-institutional randomized trial SETTING: Pediatric Adenotonsillectomy Trial for Snoring, where children with mild SDB (obstructive apnea-hypopnea index <3) were randomized to observation or adenotonsillectomy. METHODS: The Pediatric Quality of Life Inventory (PedsQL) assessed baseline global QOL in participating children 5 to 12 years old and their caregivers. Caregiver and child scores were compared. Multivariable regression assessed whether clinical factors were associated with differences between caregiver and child report. RESULTS: PedsQL scores were available for 309 families (mean child age, 7.0 years). The mean caregiver-reported PedsQL score was higher at 75.2 (indicating better QOL) than the mean child-reported score of 67.9 (P < .001). The agreement between caregiver and child total PedsQL scores was poor, with intraclass correlation coefficients of 0.03 (95% CI, -0.09 to 0.15) for children 5 to 7 years old and 0.21 (95% CI, 0.03-0.38) for children 8 to 12 years old. Higher child age and health literacy were associated with closer agreement between caregiver and child report. CONCLUSION: Caregiver- and child-reported global QOL in children with SDB was weakly correlated, more so for young children. In pediatric SDB, child-perceived QOL may be poorer than that reported by caregivers. Further research is needed to assess whether similar trends exist for disease-specific QOL metrics.

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates.

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.

A multistate competing risks framework for preconception prediction of pregnancy outcomes.

BACKGROUND: Preconception pregnancy risk profiles-characterizing the likelihood that a pregnancy attempt results in a full-term birth, preterm birth, clinical pregnancy loss, or failure to conceive-can provide critical information during the early stages of a pregnancy attempt, when obstetricians are best positioned to intervene to improve the chances of successful conception and full-term live birth. Yet the task of constructing and validating risk assessment tools for this earlier intervention window is complicated by several statistical features: the final outcome of the pregnancy attempt is multinomial in nature, and it summarizes the results of two intermediate stages, conception and gestation, whose outcomes are subject to competing risks, measured on different time scales, and governed by different biological processes. In light of this complexity, existing pregnancy risk assessment tools largely focus on predicting a single adverse pregnancy outcome, and make these predictions at some later, post-conception time point. METHODS: We reframe the individual pregnancy attempt as a multistate model comprised of two nested multinomial prediction tasks: one corresponding to conception and the other to the subsequent outcome of that pregnancy. We discuss the estimation of this model in the presence of multiple stages of outcome missingness and then introduce an inverse-probability-weighted Hypervolume Under the Manifold statistic to validate the resulting multivariate risk scores. Finally, we use data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial to illustrate how this multistate competing risks framework might be utilized in practice to construct and validate a preconception pregnancy risk assessment tool. RESULTS: In the EAGeR study population, the resulting risk profiles are able to meaningfully discriminate between the four pregnancy attempt outcomes of interest and represent a significant improvement over classification by random chance. CONCLUSIONS: As illustrated in our analysis of the EAGeR data, our proposed prediction framework expands the pregnancy risk assessment task in two key ways-by considering a broader array of pregnancy outcomes and by providing the predictions at an earlier, preconception intervention window-providing obstetricians and their patients with more information and opportunities to successfully guide pregnancy attempts.

An integrated magneto-electrochemical device for the rapid profiling of tumour extracellular vesicles from blood plasma.

Assays for cancer diagnosis via the analysis of biomarkers on circulating extracellular vesicles (EVs) typically have lengthy sample workups, limited throughput or insufficient sensitivity, or do not use clinically validated biomarkers. Here we report the development and performance of a 96-well assay that integrates the enrichment of EVs by antibody-coated magnetic beads and the electrochemical detection, in less than one hour of total assay time, of EV-bound proteins after enzymatic amplification. By using the assay with a combination of antibodies for clinically relevant tumour biomarkers (EGFR, EpCAM, CD24 and GPA33) of colorectal cancer (CRC), we classified plasma samples from 102 patients with CRC and 40 non-CRC controls with accuracies of more than 96%, prospectively assessed a cohort of 90 patients, for whom the burden of tumour EVs was predictive of five-year disease-free survival, and longitudinally analysed plasma from 11 patients, for whom the EV burden declined after surgery and increased on relapse. Rapid assays for the detection of combinations of tumour biomarkers in plasma EVs may aid cancer detection and patient monitoring.

Estimation of conditional power for cluster-randomized trials with interval-censored endpoints.

Cluster-randomized trials (CRTs) of infectious disease preventions often yield correlated, interval-censored data: dependencies may exist between observations from the same cluster, and event occurrence may be assessed only at intermittent study visits. This data structure must be accounted for when conducting interim monitoring and futility assessment for CRTs. In this article, we propose a flexible framework for conditional power estimation when outcomes are correlated and interval-censored. Under the assumption that the survival times follow a shared frailty model, we first characterize the correspondence between the marginal and cluster-conditional survival functions, and then use this relationship to semiparametrically estimate the cluster-specific survival distributions from the available interim data. We incorporate assumptions about changes to the event process over the remainder of the trial-as well as estimates of the dependency among observations in the same cluster-to extend these survival curves through the end of the study. Based on these projected survival functions, we generate correlated interval-censored observations, and then calculate the conditional power as the proportion of times (across multiple full-data generation steps) that the null hypothesis of no treatment effect is rejected. We evaluate the performance of the proposed method through extensive simulation studies, and illustrate its use on a large cluster-randomized HIV prevention trial.

Quantitative associations between health insurance and stage of melanoma at diagnosis among nonelderly adults in the United States.

BACKGROUND: Health insurance plays a critical role in the accessibility to and quality of health care for patients with melanoma in the United States. Current knowledge regarding the association between insurance status and stage of melanoma is limited because few studies to date have simultaneously controlled for factors known to influence the risk of diagnosis of late-stage melanoma. The current study was conducted to examine the association between health insurance status and stage of melanoma at the time of diagnosis in nonelderly adults, accounting for known risk factors for late-stage diagnosis. METHODS: In this cross-sectional study, the authors analyzed the National Cancer Data Base for cases of invasive melanoma diagnosed between 2004 and 2015 among individuals aged 26 to 64 years. Using the American Joint Committee on Cancer melanoma staging system, early-stage melanoma was defined as stage I or stage II whereas late-stage melanoma was defined as stage III or stage IV. Late-stage diagnosis was the primary outcome compared across 4 insurance types (private, Medicaid, none, and unknown). Adjusted covariates were age, sex, race/ethnicity, educational level, income, year of diagnosis, number of comorbidities, and facility location. Logistic regression was used for univariable and multivariable analyses. RESULTS: Among 177,247 cases, individuals with Medicaid or no health insurance were found to have 3.12 (95% CI, 2.97-3.28) and 2.21 (95% CI, 2.10-2.33) times greater odds, respectively, of being diagnosed with late-stage melanoma compared with individuals with private insurance after adjusting for risk factors in late-stage diagnosis. CONCLUSIONS: Future investigation into insurance disparities in the diagnosis of late-stage melanoma may help to prioritize melanoma screening in populations with nonprivate insurance.

A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures.

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype-phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary p value are a robust approach to different genetic architectures when little a priori knowledge is available about the underlying genetic disease model. To date, however, little consideration has been given to combining gene-based tests of association for the analysis of pedigree data. We propose a flexible framework for combining any number of family-based rare-variant tests of association into a single summary statistic and for assessing the significance of that statistic. We show that this approach maintains type I error and improves the robustness, to different genetic architectures, of the statistical power of family- and gene-based rare-variant tests through application to simulated phenotype data from Genetic Analysis Workshop 19.

A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests.

The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within a pathway). Testing sets of variants such as pathways for association with a disease phenotype reduces multiple testing penalties, may increase power, and allows for straightforward biological interpretation. However, a significant variant-set association test does not indicate precisely which variants contained within that set are causal. Because pathways often contain many variants, it may be helpful to follow-up significant pathway tests by conducting gene-based tests on each gene in that pathway to narrow in on the region of causal variants. In this paper, we propose such a multistep approach for variant-set analysis that can also account for covariates and complex pedigree structure. We demonstrate this approach on simulated phenotypes from Genetic Analysis Workshop 19. We find generally better power for the multistep approach when compared to a more conventional, single-step approach that simply runs gene-based tests of association on each gene across the genome. Further work is necessary to evaluate the multistep approach on different data sets with different characteristics.

A Bayesian Framework for the Classification of Microbial Gene Activity States.

Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences between genes, and failing to provide statistically meaningful confidence estimates. We propose a flexible Bayesian approach to classifying gene activity states based on a Gaussian mixture model. The model integrates genome-wide transcriptomics data from multiple conditions and information about gene co-regulation to provide activity state confidence estimates for each gene in each condition. We compare the performance of our novel method to existing methods on both simulated data and real data from 907 E. coli gene expression arrays, as well as a comparison with experimentally measured flux values in 29 conditions, demonstrating that our method provides more consistent and accurate results than existing methods across a variety of metrics.

Evaluating the impact of genotype errors on rare variant tests of association.

The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an allele segregates in the population. We have recently proposed a general framework for the evaluation and investigation of rare variant tests of association, classifying most rare variant tests into one of two broad categories (length or joint tests). We use this framework to relate factors affecting genotype uncertainty to the power and type I error rate of rare variant tests. We find that non-differential genotype errors (an error process that occurs independent of phenotype) decrease power, with larger decreases for extremely rare variants, and for the common homozygote to heterozygote error. Differential genotype errors (an error process that is associated with phenotype status), lead to inflated type I error rates which are more likely to occur at sites with more common homozygote to heterozygote errors than vice versa. Finally, our work suggests that certain rare variant tests and study designs may be more robust to the inclusion of genotype errors. Further work is needed to directly integrate genotype calling algorithm decisions, study costs and test statistic choices to provide comprehensive design and analysis advice which appropriately accounts for the impact of genotype errors.