The Puerto Rican Infant Metagenomic and Epidemiologic Study of Respiratory Outcomes (PRIMERO): Design and Baseline Characteristics for a Birth Cohort Study of Early-life Viral Respiratory Illnesses and Airway Dysfunction in Puerto Rican Children.

Epidemiologic studies demonstrate an association between early-life respiratory illnesses (RIs) and the development of childhood asthma. However, it remains uncertain whether these children are predisposed to both conditions or if early-life RIs induce alterations in airway function, immune responses, or other human biology that contribute to the development of asthma. Puerto Rican children experience a disproportionate burden of early-life RIs and asthma, making them an important population for investigating this complex interplay. PRIMERO, the Puerto Rican Infant Metagenomics and Epidemiologic Study of Respiratory Outcomes , recruited pregnant women and their newborns to investigate how the airways develop in early life among infants exposed to different viral RIs, and will thus provide a critical understanding of childhood asthma development. As the first asthma birth cohort in Puerto Rico, PRIMERO will prospectively follow 2,100 term healthy infants. Collected samples include post-term maternal peripheral blood, infant cord blood, the child's peripheral blood at the year two visit, and the child's nasal airway epithelium, collected using minimally invasive nasal swabs, at birth, during RIs over the first two years of life, and at annual healthy visits until age five. Herein, we describe the study's design, population, recruitment strategy, study visits and procedures, and primary outcomes.

Breathing zone pollutant levels are associated with asthma exacerbations in high-risk children.

Background: Indoor and outdoor air pollution levels are associated with poor asthma outcomes in children. However, few studies have evaluated whether breathing zone pollutant levels associate with asthma outcomes. Objective: Determine breathing zone exposure levels of NO 2 , O 3 , total PM 10 and PM 10 constituents among children with exacerbation-prone asthma, and examine correspondence with in-home and community measurements and associations with outcomes. Methods: We assessed children's personal breathing zone exposures using wearable monitors. Personal exposures were compared to in-home and community measurements and tested for association with lung function, asthma control, and asthma exacerbations. Results: 81 children completed 219 monitoring sessions. Correlations between personal and community levels of PM 10 , NO 2 , and O 3 were poor, whereas personal PM 10 and NO 2 levels correlated with in-home measurements. However, in-home monitoring underdetected brown carbon (Personal:79%, Home:36.8%) and ETS (Personal:83.7%, Home:4.1%) personal exposures, and detected black carbon in participants without these personal exposures (Personal: 26.5%, Home: 96%). Personal exposures were not associated with lung function or asthma control. Children experiencing an asthma exacerbation within 60 days of personal exposure monitoring had 1.98, 2.21 and 2.04 times higher brown carbon (p<0.001), ETS (p=0.007), and endotoxin (p=0.012), respectively. These outcomes were not associated with community or in-home exposure levels. Conclusions: Monitoring pollutant levels in the breathing zone is essential to understand how exposures influence asthma outcomes, as agreement between personal and in-home monitors is limited. Inhaled exposure to PM 10 constituents modifies asthma exacerbation risk, suggesting efforts to limit these exposures among high-risk children may decrease their asthma burden. CLINICAL IMPLICATIONS: In-home and community monitoring of environmental pollutants may underestimate personal exposures. Levels of inhaled exposure to PM 10 constituents appear to strongly influence asthma exacerbation risk. Therefore, efforts should be made to mitigate these exposures. CAPSULE SUMMARY: Leveraging wearable, breathing-zone monitors, we show exposures to inhaled pollutants are poorly proxied by in-home and community monitors, among children with exacerbation-prone asthma. Inhaled exposure to multiple PM 10 constituents is associated with asthma exacerbation risk.

Network Analysis Examining Intrahospital Traffic of Patients With Traumatic Hip Fracture.

INTRODUCTION: Increased intrahospital traffic (IHT) is associated with adverse events and infections in hospitalized patients. Network science has been used to study patient flow in hospitals but not specifically for patients with traumatic injuries. METHODS: This retrospective analysis included 103 patients with traumatic hip fractures admitted to a level I trauma center between April 2021 and September 2021. Associations with IHTs (moves within the hospital) were analyzed using R (4.1.2) as a weighted directed graph. RESULTS: The median (interquartile range) number of moves was 8 (7-9). The network consisted of 16 distinct units and showed mild disassortativity (-0.35), similar to other IHT networks. The floor and intensive care unit (ICU) were central units in the flow of patients, with the highest degree and betweenness. Patients spent a median of 20-28 hours in the ICU, intermediate care unit, or floor. The number of moves per patient was mildly correlated with hospital length of stay (ρ = 0.26, p = .008). Intrahospital traffic volume was higher on weekdays and during daytime hours. Intrahospital traffic volume was highest in patients aged <65 years ( p = .04), but there was no difference in IHT volume by dependent status, complications, or readmissions. CONCLUSIONS: Network science is a useful tool for trauma patients to plan IHT, flow, and staffing.

Age at surgical menopause influences cognitive decline and Alzheimer pathology in older women.

OBJECTIVE: To determine the association between age at surgical menopause and both cognitive decline and Alzheimer disease (AD) pathology in 2 longitudinal cohorts. METHODS: Female subjects from 2 longitudinal studies of cognitive decline (Religious Orders Study and Rush Memory and Aging Project) were included (total n = 1,884). The primary analysis examined the association between age at surgical menopause and decline in a global cognition score. Secondary analyses examined additional outcomes: 1) decline in 5 cognitive subdomains and 2) a global measure of the burden of AD pathology. In exploratory analyses, we examined the effect of hormone replacement therapy (HRT). We adjusted all models for age, education, smoking, and cohort and stratified by surgical vs natural menopause. RESULTS: For the 32% of subjects with surgical menopause, earlier age at menopause was associated with faster decline in global cognition (p = 0.0007), specifically episodic memory (p = 0.0003) and semantic memory (p = 0.002). Earlier age at menopause was also associated with increased AD neuropathology (p = 0.038), in particular neuritic plaques (p = 0.013). HRT use for at least 10 years, when administered within a 5-year perimenopausal window, was associated with decreased decline in global cognition. No associations were seen in women who had natural menopause. CONCLUSIONS: Early age at surgical menopause was associated with cognitive decline and AD neuropathology. Ongoing studies should clarify the potential effect of HRT on this relationship.

Modeling disease severity in multiple sclerosis using electronic health records.

OBJECTIVE: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. METHODS: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). RESULTS: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)). CONCLUSION: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Evaluation of an online platform for multiple sclerosis research: patient description, validation of severity scale, and exploration of BMI effects on disease course.

OBJECTIVES: To assess the potential of an online platform, PatientsLikeMe.com (PLM), for research in multiple sclerosis (MS). An investigation of the role of body mass index (BMI) on MS disease course was conducted to illustrate the utility of the platform. METHODS: First, we compared the demographic characteristics of subjects from PLM and from a regional MS center. Second, we validated PLM's patient-reported outcome measure (MS Rating Scale, MSRS) against standard physician-rated tools. Finally, we analyzed the relation of BMI to the MSRS measure. RESULTS: Compared with 4,039 MS Center patients, the 10,255 PLM members were younger, more educated, and less often male and white. Disease course was more often relapsing remitting, with younger symptom onset and shorter disease duration. Differences were significant because of large sample sizes but small in absolute terms. MSRS scores for 121 MS Center patients revealed acceptable agreement between patient-derived and physician-derived composite scores (weighted kappa = 0.46). The Walking domain showed the highest weighted kappa (0.73) and correlation (rs = 0.86) between patient and physician scores. Additionally, there were good correlations between the patient-reported MSRS composite and walking scores and physician-derived measures: Expanded Disability Status Scale (composite rs = 0.61, walking rs = 0.74), Timed 25 Foot Walk (composite rs = 0.70, walking rs = 0.69), and Ambulation Index (composite rs = 0.81, walking rs = 0.84). Finally, using PLM data, we found a modest correlation between BMI and cross-sectional MSRS (rho = 0.17) and no association between BMI and disease course. CONCLUSIONS: The PLM population is comparable to a clinic population, and its patient-reported MSRS is correlated with existing clinical instruments. Thus, this online platform may provide a venue for MS investigations with unique strengths (frequent data collection, large sample sizes). To illustrate its applicability, we assessed the role of BMI in MS disease course but did not find a clinically meaningful role for BMI in this setting.