“I Thought I was Going to War”: Experiences of Health-Care Workers During the COVID-19 Pandemic – An Exploration of \u3ci\u3eProjectCOPE\u3c/i\u3e

The COVID-19 pandemic continues to strain health-care systems throughout the world. While early reports compared its impacts to other contemporary disease outbreaks (e.g., SARS and MERS), it quickly became apparent that COVID-19 would dwarf these contemporary crises, escalating to a scale more on par with the 1918 influenza outbreak. This disaster will have unprecedented effects on health-care workers, among whom burnout was already a serious concern. Burnout and linked phenomena moral distress, compassion fatigue, and secondary trauma are associated with increased turnover and intent to leave health-care professions, decreased quality of care delivered to patients, and poor mental and physical health outcomes among health-care workers. ProjectCOPE: Chronicling health-care prOviders’ Pandemic Experiences is a mixed-methods study exploring the perceptions and capturing the stories of a diverse cohort of health-care workers representing more than 21 distinct professions during the COVID-19 pandemic. Chapter 1 includes an extensive review of literature and summary of methodology for this dissertation’s aims. Special attention is paid to two professions, nursing and massage therapy, which are the subject of analysis in Aim 2. Subsequent chapters are formatted as stand-alone manuscripts, each presenting significance, methodology, results, and discussion for one of the aims. Chapter 2 presents Aim 1: “Describe the sample and experiences of ProjectCOPE participants.” In this mixed-methods study of all ProjectCOPE participants, we explore the differences between professions labeled “essential” versus “non-essential”, and lay the foundation for future study of the potential impact of such policies. The study identified four themes: 1) professional identity, 2) intrinsic stressors, 3) extrinsic factors, and 4) coping strategies. Chapter 3 presents Aim 2: “Compare and contrast experiences of nurses and massage therapists during the COVID-19 pandemic.” This study draws on findings from Aim 1, delving into a mixed-methods analysis of the differences and similarities between nurses’ and massage therapists’ experiences of working during the COVID-19 pandemic, specifically burnout and coping strategies. This study found that, despite some differences in experienced burnout as measured by instruments validated in nurses, similar experiences were reported by both professions. As part of ProjectCOPE, we developed a novel approach to meaningfully include medical students in the processing and sorting of data. Chapter 4 covers Aim 3: “Evaluate novel methodology developed for ProjectCOPE.” This novel methodology is called #Evaluation (pronounced “hashtag evaluation”), and builds on medical students’ knowledge and understanding of social media platforms. This chapter demonstrates #Evaluation is a valuable tool for rapid evaluation and assessment, and for teaching qualitative research to students with little-to-no experience. Finally, Chapter 5 provides an executive summary of findings, limitations, and directions for future research. Here, we highlight this dissertations contributions to science, including an inventory of topics for which these chapters represent the first or early exploration

Patient involvement in medical decision-making and pain among elders: physician or patient-driven?

BACKGROUND: Pain is highly prevalent among older adults, but little is known about how patient involvement in medical decision-making may play a role in limiting its occurrence or severity. The purpose of this study was to evaluate whether physician-driven and patient-driven participation in decision-making were associated with the odds of frequent and severe pain. METHODS: A cross-sectional population-based survey of 3,135 persons age 65 and older was conducted in the 108-county region comprising West Texas. The survey included self-reports of frequent pain and, among those with frequent pain, the severity of pain. RESULTS: Findings from multivariate logistic regression analyses showed that higher patient-driven participation in decision-making was associated with lower odds (OR, 0.82; 95% CI, 0.75–0.89) of frequent pain, but was not significantly associated with severe pain. Physician-driven participation was not significantly associated with frequent or severe pain. CONCLUSIONS: The findings suggest that patients may need to initiate involvement in medical decision-making to reduce their chances of experiencing frequent pain. Changes to other modifiable health care characteristics, including access to a personal doctor and health insurance coverage, may be more conducive to limiting the risk of severe pain

Validation of Automated Data Abstraction for SCCM Discovery VIRUS COVID-19 Registry: Practical EHR Export Pathways (VIRUS-PEEP)

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities. OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients. MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen\u27s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson\u27s correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00). MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%. CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

BackgroundThe gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.ObjectiveThis study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.Materials and methodsThis observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen’s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson’s correlation coefficient and Bland–Altman plots. The strength of agreement was defined as almost perfect (0.81–1.00), substantial (0.61–0.80), and moderate (0.41–0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00–0.30), low (0.30–0.50), moderate (0.50–0.70), high (0.70–0.90), and extremely high (0.90–1.00).Measurements and main resultsThe cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.Conclusion and relevanceOur study confirms the feasibility and validity of an automated process to gather data from the EHR

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value OBFC1indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes

Metabolic Syndrome and Acute Respiratory Distress Syndrome in Hospitalized Patients With COVID-19

Importance: Obesity, diabetes, and hypertension are common comorbidities in patients with severe COVID-19, yet little is known about the risk of acute respiratory distress syndrome (ARDS) or death in patients with COVID-19 and metabolic syndrome. Objective: To determine whether metabolic syndrome is associated with an increased risk of ARDS and death from COVID-19. Design, setting, and participants: This multicenter cohort study used data from the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study collected from 181 hospitals across 26 countries from February 15, 2020, to February 18, 2021. Outcomes were compared between patients with metabolic syndrome (defined as ≥3 of the following criteria: obesity, prediabetes or diabetes, hypertension, and dyslipidemia) and a control population without metabolic syndrome. Participants included adult patients hospitalized for COVID-19 during the study period who had a completed discharge status. Data were analyzed from February 22 to October 5, 2021. Exposures: Exposures were SARS-CoV-2 infection, metabolic syndrome, obesity, prediabetes or diabetes, hypertension, and/or dyslipidemia. Main outcomes and measures: The primary outcome was in-hospital mortality. Secondary outcomes included ARDS, intensive care unit (ICU) admission, need for invasive mechanical ventilation, and length of stay (LOS). Results: Among 46 441 patients hospitalized with COVID-19, 29 040 patients (mean [SD] age, 61.2 [17.8] years; 13 059 [45.0%] women and 15713 [54.1%] men; 6797 Black patients [23.4%], 5325 Hispanic patients [18.3%], and 16 507 White patients [57.8%]) met inclusion criteria. A total of 5069 patients (17.5%) with metabolic syndrome were compared with 23 971 control patients (82.5%) without metabolic syndrome. In adjusted analyses, metabolic syndrome was associated with increased risk of ICU admission (adjusted odds ratio [aOR], 1.32 [95% CI, 1.14-1.53]), invasive mechanical ventilation (aOR, 1.45 [95% CI, 1.28-1.65]), ARDS (aOR, 1.36 [95% CI, 1.12-1.66]), and mortality (aOR, 1.19 [95% CI, 1.08-1.31]) and prolonged hospital LOS (median [IQR], 8.0 [4.2-15.8] days vs 6.8 [3.4-13.0] days; P \u3c .001) and ICU LOS (median [IQR], 7.0 [2.8-15.0] days vs 6.4 [2.7-13.0] days; P \u3c .001). Each additional metabolic syndrome criterion was associated with increased risk of ARDS in an additive fashion (1 criterion: 1147 patients with ARDS [10.4%]; P = .83; 2 criteria: 1191 patients with ARDS [15.3%]; P \u3c .001; 3 criteria: 817 patients with ARDS [19.3%]; P \u3c .001; 4 criteria: 203 patients with ARDS [24.3%]; P \u3c .001). Conclusions and relevance: These findings suggest that metabolic syndrome was associated with increased risks of ARDS and death in patients hospitalized with COVID-19. The association with ARDS was cumulative for each metabolic syndrome criteria present

Biallelic sequence and structural variants in RAX2 are a novel cause for autosomal recessive inherited retinal disease.

Purpose RAX2 encodes a homeobox-containing transcription factor, in which four monoallelic pathogenic variants have been described in autosomal dominant cone-dominated retinal disease. Methods Exome sequencing in a European cohort with inherited retinal disease (IRD) (n = 2086) was combined with protein structure modeling of RAX2 missense variants, bioinformatics analysis of deletion breakpoints, haplotyping of RAX2 variant c.335dup, and clinical assessment of biallelic RAX2-positive cases and carrier family members. Results Biallelic RAX2 sequence and structural variants were found in five unrelated European index cases, displaying nonsyndromic autosomal recessive retinitis pigmentosa (ARRP) with an age of onset ranging from childhood to the mid-40s (average mid-30s). Protein structure modeling points to loss of function of the novel recessive missense variants and to a dominant-negative effect of the reported dominant RAX2 alleles. Structural variants were fine-mapped to disentangle their underlying mechanisms. Haplotyping of c.335dup in two cases suggests a common ancestry. Conclusion This study supports a role for RAX2 as a novel disease gene for recessive IRD, broadening the mutation spectrum from sequence to structural variants and revealing a founder effect. The identification of biallelic RAX2 pathogenic variants in five unrelated families shows that RAX2 loss of function may be a nonnegligible cause of IRD in unsolved ARRP cases

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWASs for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally diverse individuals (European, African, Asian, and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole-genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n = 109,122 individuals. We identified 59 sentinel variants (p < 5 × 10−9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated that the independent signals colocalized with cell-type-specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated that our TL polygenic trait scores (PTSs) were associated with an increased risk of cancer-related phenotypes

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution