High positive predictive value 22q11.2 microdeletion screening by prenatal cell-free DNA testing that incorporates fetal fraction amplification.

OBJECTIVE: 22q11.2 deletion syndrome (DS) is a serious condition with a range of features. The small microdeletion causing 22q11.2DS makes it technically challenging to detect using standard prenatal cfDNA screening. Here, we assess 22q11.2 microdeletion clinical performance by a prenatal cfDNA screen that incorporates fetal fraction (FF) amplification. METHODS: The study cohort consisted of patients who received Prequel (Myriad Genetics, Inc.), a prenatal cfDNA screening that incorporates FF amplification, and met additional eligibility criteria. Pregnancy outcomes were obtained via a routine process for continuous quality improvement. Samples with diagnostic testing results were used to calculate positive predictive value (PPV). RESULTS: 379,428 patients met study eligibility criteria, 76 of whom were screen-positive for a de novo 22q11.2 microdeletion. 22 (29.7%) had diagnostic testing results available, and all 22 cases were confirmed as true positives, for a PPV of 100% (95% CI 84.6%-100%). This performance was based on cases that ranged broadly across FF (5.9%-41.1%, mean 23.0%), body mass index (22.3-44.8, mean 29.9), and gestational age at testing (10.0w-34.6w, median 12.7w). Ultrasound findings in screen-positive pregnancies were consistent with those known to be associated with 22q11.2DS. CONCLUSION: 22q11.2 microdeletion screening that incorporates FF amplification demonstrated high PPV across both general and high-risk population cohorts.

Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study.

BACKGROUND: Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected. OBJECTIVE: This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI. METHODS: This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants' steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care-seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F2) score. RESULTS: An influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89%) and 840 (90.8%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model. CONCLUSIONS: Machine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections.

Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study.

BACKGROUND: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. OBJECTIVE: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non-care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. METHODS: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care-seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. RESULTS: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. CONCLUSIONS: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non-care-seeking populations, which may have future application in health care resource planning. TRIAL REGISTRATION: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800.

An epidemiological, developmental and clinical overview of cannabis use during pregnancy.

The objective of the current narrative literature review is to provide an epidemiological, developmental and clinical overview on cannabis use during pregnancy. Cannabis use in pregnancy poses major health concerns for pregnant mothers and their developing children. Although studies on the short- and long-term consequences of prenatal cannabis exposure are increasing, findings have been inconsistent or difficult to interpret due to methodological issues. Thus, consolidating these findings into clinical recommendations based on the mixed studies in the literature remains a challenge. Synthesizing the available observational studies is also difficult, because some of the published studies have substantial methodological weaknesses. Improving observational studies will be an important step toward understanding the extent to which prenatal exposure to cannabis influences neurodevelopment in the offspring. Therefore, further research on prenatal cannabis exposure and the long-term consequences to offspring health in representative samples are needed to guide and improve clinical care for pregnant women and their children. Future research should also investigate the role of policies on prenatal cannabis use.

Predictors of Seeking Care for Influenza-Like Illness in a Novel Digital Study.

Background: Previous research has estimated that >50% of individuals experiencing influenza-like illness (ILI) do not seek health care. Understanding factors influencing care-seeking behavior for viral respiratory infections may help inform policies to improve access to care and protect public health. We used person-generated health data (PGHD) to identify factors associated with seeking care for ILI. Methods: Two observational studies (FluStudy2020, ISP) were conducted during the United States 2019-2020 influenza season. Participants self-reported ILI symptoms using the online Evidation platform. A log-binomial regression model was used to identify factors associated with seeking care. Results: Of 1667 participants in FluStudy2020 and 47 480 participants in ISP eligible for analysis, 518 (31.1%) and 11 426 (24.1%), respectively, sought health care. Participants were mostly female (92.2% FluStudy2020, 80.6% ISP) and aged 18-49 years (89.6% FluStudy2020, 89.8% ISP). In FluStudy2020, factors associated with seeking care included having health insurance (risk ratio [RR], 2.14; 95% CI, 1.30-3.54), more severe respiratory symptoms (RR, 1.53; 95% CI, 1.37-1.71), and comorbidities (RR, 1.37; 95% CI, 1.20-1.58). In ISP, the strongest predictor of seeking care was high symptom number (RR for 6/7 symptoms, 2.14; 95% CI, 1.93-2.38). Conclusions: Using PGHD, we confirmed low rates of health care-seeking behavior for ILI and show that having health insurance, comorbidities, and a high symptom burden were associated with seeking health care. Reducing barriers in access to care for viral respiratory infections may lead to better disease management and contribute to protecting public health.

Baloxavir vs oseltamivir: reduced utilization and costs in influenza.

OBJECTIVES: To determine whether baloxavir use is associated with lower health care resource utilization (HCRU) and costs for secondary influenza complications post treatment compared with oseltamivir. STUDY DESIGN: Retrospective cohort study. METHODS: Patients filling a prescription for baloxavir or oseltamivir within 48 hours following an influenza-related outpatient visit were identified in the 2018-2019 influenza season from the US Truven MarketScan Research Databases and propensity matched 1:2 (baloxavir:oseltamivir). Outcomes were assessed 15 and 30 days after antiviral treatment and included all-cause, all respiratory-related, and select respiratory-related (influenza, asthma, chronic obstructive pulmonary disease, or infection) HCRU and costs. RESULTS: The study included 5080 baloxavir-treated and 10,160 matched oseltamivir-treated patients. All-cause emergency department (ED) visits and inpatient hospitalizations were lower in baloxavir-treated patients, with a statistically significant difference in the percentage hospitalized at 30 days (0.3% vs 0.5%; P = .04). ED visits for all or select respiratory-related conditions were significantly reduced with baloxavir (P < .01 for all comparisons). Mean per-patient cost savings at day 30 for all-cause, all respiratory-related, and select respiratory-related conditions were $79, $50, and $51, respectively, despite slightly higher prescription costs for baloxavir. In high-risk patients (baloxavir: n = 1958; oseltamivir: n = 3949), the incidence of ED visits was significantly lower for all respiratory-related and select respiratory-related conditions (P < .01); cost savings with baloxavir in the high-risk cohort were substantially greater than in the overall cohort. CONCLUSIONS: Treatment of patients with influenza with single-dose baloxavir was generally associated with lower HCRU and costs post treatment compared with oseltamivir, particularly in high-risk patients.

Severity of COVID-19 and adverse long-term outcomes: a retrospective cohort study based on a US electronic health record database.

OBJECTIVE: To identify potential risk factors for adverse long-term outcomes (LTOs) associated with COVID-19, using a large electronic health record (EHR) database. DESIGN: Retrospective cohort study. Patients with COVID-19 were assigned into subcohorts according to most intensive treatment setting experienced. Newly diagnosed conditions were classified as respiratory, cardiovascular or mental health LTOs at >30-≤90 or >90-≤180 days after COVID-19 diagnosis or hospital discharge. Multivariate regression analysis was performed to identify any association of treatment setting (as a proxy for disease severity) with LTO incidence. SETTING: Optum deidentified COVID-19 EHR dataset drawn from hospitals and clinics across the USA. PARTICIPANTS: Individuals diagnosed with COVID-19 (N=57 748) from 20 February to 4 July 2020. MAIN OUTCOMES: Incidence of new clinical conditions after COVID-19 diagnosis or hospital discharge and the association of treatment setting (as a proxy for disease severity) with their risk of occurrence. RESULTS: Patients were assigned into one of six subcohorts: outpatient (n=22 788), emergency room (ER) with same-day COVID-19 diagnosis (n=11 633), ER with COVID-19 diagnosis≤21 days before ER visit (n=2877), hospitalisation without intensive care unit (ICU; n=16 653), ICU without ventilation (n=1837) and ICU with ventilation (n=1960). Respiratory LTOs were more common than cardiovascular or mental health LTOs across subcohorts and LTO incidence was higher in hospitalised versus non-hospitalised subcohorts. Patients with the most severe disease were at increased risk of respiratory (risk ratio (RR) 1.86, 95% CI 1.56 to 2.21), cardiovascular (RR 2.65, 95% CI 1.49 to 4.43) and mental health outcomes (RR 1.52, 95% CI 1.20 to 1.91) up to 6 months after hospital discharge compared with outpatients. CONCLUSIONS: Patients with severe COVID-19 had increased risk of new clinical conditions up to 6 months after hospital discharge. The extent that treatment setting (eg, ICU) contributed to these conditions is unknown, but strategies to prevent COVID-19 progression may nonetheless minimise their occurrence.

Early prenatal vitamin D concentrations and social-emotional development in infants.

BACKGROUND: Many pregnant women in the United States have suboptimal vitamin D, but the impact on infant development is unclear. Moreover, no pregnancy-specific vitamin D recommendations have been widely accepted. AIMS: Given the ubiquitous expression of vitamin D receptors in the brain, we investigated the association between early prenatal plasma 25-hydroxyvitamin D (25(OH)D) concentrations and children's social and emotional development in the Newborn Epigenetic Study, a prospective study of pregnancies from 2009 to 2011 in Durham, North Carolina. METHODS: We measured 25(OH)D concentrations in first or second trimester plasma samples and categorized 25(OH)D concentrations into quartiles. Covariates were derived from maternal questionnaires. Mothers completed the Infant Toddler Social-Emotional Development Assessment when children were 12-24 months of age. We used multivariable linear regression to evaluate associations between 25(OH)D and specific behavior scores, adjusted for season of blood draw, maternal age, education, parity, smoking, marital status, prepregnancy BMI, and infant gender. We investigated effect-measure modification by race/ethnicity. RESULTS: Of the 218 mother-infant pairs with complete data, Black mothers had much lower 25(OH)D concentrations as compared to White and Hispanic mothers. After adjustment, lower prenatal 25(OH)D was associated with slightly higher (less favorable) Internalizing scores among White children, but lower (more favorable) Internalizing scores among Black and Hispanic children. Lower prenatal 25(OH)D also appears to be associated with higher (less favorable) dysregulation scores, though only among White and Hispanic children. CONCLUSIONS: Though imprecise, preliminary results warrant further investigation regarding a role for prenatal vitamin D on children's early social and emotional development.

Racial and ethnic differences in predictors of vitamin D among pregnant women in south-eastern USA.

Insufficient vitamin D during pregnancy increases risk of adverse outcomes, with known differences by race/ethnicity. We sought to determine whether predictors of vitamin D insufficiency vary by race/ethnicity in an ethnically diverse pregnancy cohort. Plasma 25-hydroxyvitamin D concentrations and patient characteristics were measured at first prenatal visit to prenatal clinics in south-eastern USA between 2009 and 2011 (n 504). Prevalence ratios (PR) and 95 % CI were estimated using multivariable regression to quantify predictors of vitamin D insufficiency, overall and by race/ethnicity. In race/ethnicity-stratified models, season was most associated with vitamin D insufficiency among non-Hispanic white women; PR for winter v. summer were 3·58 (95 % CI 1·64, 7·81) for non-Hispanic white, 1·52 (95 % CI 1·18, 1·95) for Hispanic and 1·14 (95 % CI 0·99, 1·30) for non-Hispanic black women. Although women with darker skin tones are most vulnerable to prenatal vitamin D insufficiency, season may be more strongly associated with insufficiency among women with lighter skin tones.

Broader autism phenotype in parents of children with autism: a systematic review of percentage estimates.

The broader autism phenotype (BAP) is a collection of sub-diagnostic autistic traits more common in families of individuals with autism spectrum disorder (ASD) than in the general population. BAP is a latent construct that can be defined using different domains, measured using multiple instruments, and reported using different techniques. Therefore, estimates of BAP may vary greatly across studies. Our objective was to systematically review studies that reported occurrence of BAP in parents of children with ASD in order to quantify and describe heterogeneity in estimates. We systematically searched PubMed and Scopus using PRISMA guidelines for studies quantifying percentage of parents of children with ASD who had BAP We identified 41 studies that measured BAP in parents of children with ASD. These studies used eight different instruments, four different forms of data collection, and had a wide range of sample sizes (N=4 to N=3299). Percentage with BAP ranged from 2.6% to 80%. BAP was more prevalent in fathers than mothers. Parental BAP may be an important tool for parsing heterogeneity in ASD etiology and for developing parent-mediated ASD interventions. However, the variety in measurement instruments and variability in study samples limits our ability to synthesize estimates. To improve measurement of BAP and increase consistency across studies, universal methods should be accepted and adopted across studies. A more consistent approach to BAP measurement may enable efficient etiologic research that can be meta-analyzed and may allow for a larger evidence base that can be used to account for BAP when developing parent-mediated interventions.

Past-month cannabis use among U.S. individuals from 2002-2015: An age-period-cohort analysis.

BACKGROUND: Cannabis is the most commonly used illicit drug among U.S. adolescents and adults, but little is known about factors that drive trends in cannabis use prevalence. To better understand drivers of these trends, we aimed to estimate age, period, and cohort effects on past-month cannabis use among U.S. individuals age 12 and older from 2002 to 2015. METHODS: We conducted an age-period-cohort analysis on past-month cannabis use among participants ages 12 and older using the National Survey on Drug Use and Health (NSDUH), an annual cross-sectional nationally-representative survey of drug use. Additionally, we examined how age, period, and cohort effects differed across gender. Participants (n = 779,799) self-reported cannabis patterns using a computer-assisted telephone interview (CATI). RESULTS: Past-month cannabis use in this population increased from 6.0% in 2002 to 8.1% in 2015. Distinct age, period, and cohort effects were observed. Compared to participants ages 12-13, participants ages 18-21 (PR: 16.8, 95% CI: 15.6, 18.1) and 22-25 (PR: 13.2, 95% CI: 12.2, 14.4) had dramatically higher prevalence of past-month cannabis use. Compared to participants in 2002, participants in 2014 (PR: 1.2, 95% CI: 1.1, 1.4) and 2014 (PR: 1.2, 95% CI: 1.1, 1.4) had slightly higher prevalence of past-month cannabis use. Compared to the 1940s birth cohort, the 1950s birth cohort (PR: 1.8, 95% CI: 1.5, 2.2) had a higher prevalence of past-month cannabis use. CONCLUSIONS: Past-month cannabis use is prevalent and increasing among U.S. adults. Distinct age, period, and cohort effects are at play, though age effects are strongest.