The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation.
Abstract Objective To identify risk factors and generate hypotheses for pediatric post-concussion syndrome (PCS) using a phenome-wide association study (PheWAS). Methods A PheWAS (case-control) was conducted following the development and validation of a novel electronic health record-based algorithm that identified PCS cases and controls from an institutional database of >2.8 million patients. Cases were patients ages 5-18 with PCS codes or keywords identified by natural language processing of clinical notes. Controls were patients with mild traumatic brain injury (mTBI) codes only. Patients with moderate or severe brain injury were excluded. All patients used our healthcare system at least three times 180 days before their injury. Exposures included all pre-injury medical diagnoses assigned at least 180 days prior. Results The algorithm identified 274 pediatric PCS cases (156 females) and 1,096 controls that were age and sex matched to cases. Cases and controls both had a mean of >8 years of healthcare system use pre-injury. Of 202 pre-injury medical, four were associated with PCS after controlling for multiple testing: headache disorders (OR=5.3; 95%CI 2.8-10.1; P= 3.8e-7), sleep disorders (OR=3.1; 95%CI 1.8-5.2; P= 2.6e-5), gastritis/duodenitis (OR=3.6, 95%CI 1.8-7.0; P= 2.1e-4), and chronic pharyngitis (OR=3.3; 95%CI 1.8-6.3; P= 2.2e-4). Conclusions These results confirm the strong association of pre-injury headache disorders with PCS and provides evidence for the association of pre-injury sleep disorders with PCS. An association of PCS with prior chronic gastritis/duodenitis and pharyngitis was seen that suggests a role for chronic inflammation in PCS pathophysiology and risk. These factors should be considered during the management of pediatric mTBI cases.
Bicycle helmet legislation has been variably implemented in six of 10 Canadian provinces. The objectives of this study were to determine the association between the comprehensiveness of helmet legislation and both helmet use and bicycle ridership.Analysis of helmet use was based on data from the 2005 Canadian Community Health Survey (CCHS) and included respondents from three Canadian provinces (Saskatchewan, Ontario, and Nova Scotia). Analysis of bicycle use was based on data from the 2000-01, 2003, 2005, and 2007 cycles of the CCHS and included respondents from all provinces. In the time between the 2000-01 and 2007 cycles, two provinces (Prince Edward Island (PEI) and Alberta) implemented helmet legislation.Helmets were reportedly worn by 73.2% (95% CI 69.3% to 77.0%) of respondents in Nova Scotia, where legislation applies to all ages, by 40.6% (95% CI 39.2% to 42.0%) of respondents in Ontario, where legislation applies to those less than 18 years of age, and by 26.9% (95% CI 23.9% to 29.9%) of respondents in Saskatchewan, where no legislation exists. Though legislation applied to youth in both Ontario and Nova Scotia, helmet use was lower among youth in Ontario than among youth in Nova Scotia (46.7% (95% CI 44.1% to 49.4%) vs 77.5% (95% CI 70.9% to 84.1%)). Following the implementation of legislation in PEI and Alberta, recreational and commuting bicycle use remained unchanged among youth and adults.Canadian youth and adults are significantly more likely to wear helmets as the comprehensiveness of helmet legislation increases. Helmet legislation is not associated with changes in ridership.
Perform a phenome-wide association study (PheWAS) to discover predictors of post-concussion syndrome (PCS) in children after sport-related concussion.
Design
Electronic health record-based case-control study.
Setting
Single-institution level-1 academic trauma center in the southeast United States.
Participants
Patients 5–19 years of age were selected from our institution's EHR research database of 583,481 patients. Cases of PCS (>1 symptom for >14 days) were defined using a sequential multi-layered algorithm that leveraged natural language processing within clinical documentation and billing codes. Controls suffered a concussion without PCS. We required patients to have ≥3 separate visits at least 180 days before the index event.
Assessment of Risk Factors
Independent variables consisting of pre-injury diagnoses were captured by phenotype-aggregated ICD-9/10 codes (PheCodes). PheWAS analysis was conducted with codes assigned 180 days prior to the index event.
Outcome Measures
Dependent variable was diagnosis of PCS (binary).
Main Results
There were 274 cases of PCS and 1,096 controls. PPV of our case algorithm was 81%. Of 202 pre-injury diagnoses, PCS was associated with pre-existing headache disorders (OR=5.30,95%CI 2.78–10.09; P=3.85E-7), sleep disorders (OR=3.08,95%CI 1.82–5.20; P=2.60E-5), gastritis/duodenitis (OR=3.57,95%CI 1.82–7.00; P=2.08E-4), or chronic pharyngitis (OR=3.34,95%CI 1.76–6.34; P=2.21E-4).
Conclusions
After successfully creating a computer-based algorithm to identify PCS, our PheWAS confirms the association of headache and sleep disorders with PCS. The association of PCS with prior chronic pharyngitis, gastritis and duodenitis may suggest a role for chronic inflammation as a risk factor for PCS, which represents a new line of study to better understand PCS pathophysiology and risk. This abstract has been published in full manuscript format and has the following citation: BMJ Citation https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389964/
Objective: To identify risk factors and generate hypotheses for pediatric persistent postconcussion symptoms (PPCS). Setting: A regional healthcare system in the Southeastern United States. Participants: An electronic health record–based algorithm was developed and validated to identify PPCS cases and controls from an institutional database of more than 2.8 million patients. PPCS cases ( n = 274) were patients aged 5 to 18 years with PPCS-related diagnostic codes or with PPCS key words identified by natural language processing of clinical notes. Age, sex, and year of index event–matched controls ( n = 1096) were patients with mild traumatic brain injury codes only. Patients with moderate or severe traumatic brain injury were excluded. All patients used our healthcare system at least 3 times 180 days before their injury. Design: Case-control study. Main Measures: The outcome was algorithmic classification of PPCS. Exposures were all preinjury medical diagnoses assigned at least 180 days before the injury. Results: Cases and controls both had a mean of more than 9 years of healthcare system use preinjury. Of 221 preinjury medical diagnoses, headache disorder was associated with PPCS after accounting for multiple testing (odds ratio [OR] = 2.9; 95% confidence interval [CI]: 1.6-5.0; P = 2.1e-4). Six diagnoses were associated with PPCS at a suggestive threshold for statistical significance (false discovery rate P < .10): gastritis/duodenitis (OR = 2.8; 95% CI: 1.6-5.1; P = 5.0e-4), sleep disorders (OR = 2.3; 95% CI: 1.4-3.7; P = 7.4e-4), abdominal pain (OR = 1.6; 95% CI: 1.2-2.2; P = 9.2e-4), chronic sinusitis (OR = 2.8; 95% CI: 1.5-5.2; P = 1.3e-3), congenital anomalies of the skin (OR = 2.9; 95% CI: 1.5-5.5; P = 1.9e-3), and chronic pharyngitis/nasopharyngitis (OR = 2.4; 95% CI: 1.4-4.3; P = 2.5e-3). Conclusions: These results support the strong association of preinjury headache disorders with PPCS. An association of PPCS with prior gastritis/duodenitis, sinusitis, and pharyngitis/nasopharyngitis suggests a role for chronic inflammation in PPCS pathophysiology and risk, although results could equally be attributable to a higher likelihood of somatization among PPCS cases. Identified risk factors should be investigated further and potentially considered during the management of pediatric mild traumatic brain injury cases.
Abstract Introduction Post-concussion syndrome (PCS) is characterized by persistent cognitive, somatic, and emotional symptoms after a mild traumatic brain injury (mTBI). Genetic and other biological variables may contribute to PCS etiology, and the emergence of biobanks linked to electronic health records (EHR) offers new opportunities for research on PCS. We sought to validate the use of EHR data of PCS patients by comparing two diagnostic algorithms. Methods Vanderbilt University Medical Center curates a de-identified database of 2.8 million patient EHR. We developed two EHR-based algorithmic approaches that identified individuals with PCS by: (i) natural language processing (NLP) of narrative text in the EHR combined with structured demographic, diagnostic, and encounter data; or (ii) coded billing and procedure data. The predictive value of each algorithm was assessed, and cases and controls identified by each approach were compared on demographic and medical characteristics. Results First, the NLP algorithm identified 507 cases and 10,857 controls. The positive predictive value (PPV) in the cases was 82% and the negative predictive value in the controls was 78%. Second, the coded algorithm identified 1,142 patients with two or more PCS billing codes and had a PPV of 76%. Comparisons of PCS controls to both case groups recovered known epidemiology of PCS: cases were more likely than controls to be female and to have pre-morbid diagnoses of anxiety, migraine, and PTSD. In contrast, controls and cases were equally likely to have ADHD and learning disabilities, in accordance with the findings of recent systematic reviews of PCS risk factors. Conclusions EHR are a valuable research tool for PCS. Ascertainment based on coded data alone had a predictive value comparable to an NLP algorithm, recovered known PCS risk factors, and maximized the number of included patients.
Abstract Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are typically stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between clinical lab tests and genetics. Clinical lab data, however, are of uneven quality, and previous studies have focused on a small number of lab traits. We present two methods, QualityLab and LabWAS, to clean and analyze EHR labs at scale in a Lab-Wide Association Scan. In a proof of concept analysis focused on blood lipids and coronary artery disease, we found that heritability estimates of QualityLab lipid values were comparable to previous reports; polygenic scores for lipids were strongly associated with the referent lipid in a LabWAS; and a LabWAS of a polygenic score for coronary artery disease recapitulated known heart disease biomarker profiles and identified novel associations. Our methods extend previous EHR-based analysis tools and increase the amount of EHR data usable for discovery.
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