Case-control Sample Research Articles

Mediation analysis with case-control sampling: Identification and estimation in the presence of a binary mediator.

With reference to a stratified case-control (CC) procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case-control (SCC) data in settings where both the outcome and the mediator are binary. Despite being designed for parametric identification, our strategy is general and can be used also in a nonparametric context. With reference to parametric estimation, we derive the maximum likelihood (ML) estimator and the M-estimator of the joint outcome-mediator parameter vector. We then conduct a simulation study focusing on the main causal mediation quantities (i.e., natural effects) and comparing M- and ML estimation to existing methods, based on weighting. As an illustrative example, we reanalyze a German CC data set in order to investigate whether the effect of reduced immunocompetency on listeriosis onset is mediated by the intake of gastric acidsuppressors.

Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions

We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We offer algorithms for inference on the causal parameters that are aggregated over the true population distribution of the covariates. We show the usefulness of our approach by studying three empirical examples: the benefit of attending private school for entering a prestigious university in Pakistan; the relationship between staying in school and getting involved with drug-trafficking gangs in Brazil; and the link between physicians’ hours and size of the group practice in the United States.

Privacy-preserving analysis of time-to-event data under nested case-control sampling.

Analyses of distributed data networks of rare diseases are constrained by legitimate privacy and ethical concerns. Analytical centers (e.g. research institutions) are thus confronted with the challenging task of obtaining data from recruiting sites that are often unable or unwilling to share personal records of participants. For time-to-event data, recently popularized disclosure techniques with privacy guarantees (e.g., Differentially Private Generative Adversarial Networks) are generally computationally expensive or inaccessible to applied researchers. To perform the widely used Cox proportional hazards regression, we propose an easy-to-implement privacy-preserving data analysis technique by pooling (i.e. aggregating) individual records of covariates at recruiting sites under the nested case-control sampling framework before sharing the pooled nested case-control subcohort. We show that the pooled hazard ratio estimators, under the pooled nested case-control subsamples from the contributing sites, are maximum likelihood estimators and provide consistent estimates of the individual level full cohort HRs. Furthermore, a sampling technique for generating pseudo-event times for individual subjects that constitute the pooled nested case-control subsamples is proposed. Our method is demonstrated using extensive simulations and analysis of the National Lung Screening Trial data. The utility of our proposed approach is compared to the gold standard (full cohort) and synthetic data generated using classification and regression trees. The proposed pooling technique performs to near-optimal levels comparable to full cohort analysis or synthetic data; the efficiency improves in rare event settings when more controls are matched on during nested case-control subcohort sampling.

Haptoglobin genotype is a risk factor for coronary artery disease in prediabetes: A case-control study

ObjectiveCoronary artery disease (CAD) prediction remains inconsistent with many unappreciated risk factors. Haptoglobin genotype determines the haptoglobin protein's effectiveness to bind free hemoglobin and prevent oxidative stress, a contributor to atherosclerosis. The haptoglobin 2-2 genotype increases the prevalence of cardiovascular disease (CVD) approximately five times compared to the 1-1 genotype in individuals with diabetes. The risk is unknown in prediabetes. The purpose of this study was to determine an association between haptoglobin genotype and CAD in prediabetes. MethodsThe researchers used case-control convenience sampling from two cardiovascular disease prevention clinics in Memphis, TN, and Spokane, WA, from January 1, 2016 to March 31, 2020. Participants were ages 35–70, had prediabetes, and free of chronic inflammatory or infectious diseases. Cases had a history of subclinical or clinical CAD, while controls did not have a history of CAD. Differences between cases and controls and among haptoglobin genotypes were analyzed using t-tests and ANOVA for continuous variables and chi-square or Fisher's exact tests for categorical variables. Associations among Hp genotypes and CAD were estimated using logistic regression. ResultsThe sample (N = 178; 72 cases and 106 controls) was 96 % white and 64 % male. Cases had lower total cholesterol (p = 0.0001) and high-sensitivity C-reactive protein (p = 0.021). Except for CAD, haptoglobin genotype was independent of any demographic or clinical variable. Haptoglobin 2-2 genotype had 4.0 times higher odds of CAD than haptoglobin 1-1 (p = 0.01). ConclusionHaptoglobin 2-2 genotype had approximately four times higher odds of having CAD compared to the haptoglobin 1-1 genotype. Cases had more desirable clinical profiles, likely attributable to more aggressive treatment of traditional risk factors than controls. Haptoglobin genotype is a potentially important CAD risk factor in prediabetes (88 million Americans). Further studies are needed for interventions to reduce the oxidative stress associated with the Hp 2-2 genotype and glycosylated hemoglobin and for CAD reduction.

On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint

BackgroundCase-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investigated in the context of a case-cohort study with a time-to-event outcome, it is unclear how this approach performs with a binary outcome.MethodsWe conducted a simulation study to assess and compare the performance of approaches using only MI, only IPW, and a combination of MI and IPW, for handling intended and unintended missingness in the case-cohort setting. We also applied the approaches to a case study.ResultsOur results show that the combined approach is approximately unbiased for estimation of the exposure effect when the sample size is large, and was the least biased with small sample sizes, while MI-only and IPW-only exhibited larger biases in both sample size settings.ConclusionsThese findings suggest that a combined MI/IPW approach should be preferred to handle intended and unintended missing data in case-cohort studies with binary outcomes.

Abstract 14745: Stem Cell Factor Associated With Critical Limb Ischemia in Patients With Peripheral Artery Disease

Introduction: Critical limb ischemia (CLI) and related amputation (AMP) are severe manifestations of peripheral artery disease (PAD). The pathobiology of CLI/AMP is multifactorial including chronic ischemia in the context of large vessel and microvascular disease. Novel biomarkers may identify potential pathways for new therapies to prevent and treat CLI. Hypothesis: Exploration of the proteome will yield insights into new proteins and hence pathways relevant in the pathogenesis and complications of PAD. Methods: A nested case-control study including 50 patients with CLI/AMP and 83 without was assembled from patients with PAD in the TRA2P-TIMI 50 trial for proteomic exploration using the Olink CVDII panel. Candidate markers were identified based on a false discovery rate (FDR) p-value <0.05 and then validated among 7,077 patients with PAD from TRA2P-TIMI 50 and FOURIER trials. Biomarkers were evaluated continuously & by quartile and analyses adjusted for age, sex, diabetes mellitus, and prior peripheral revascularization. Results/Data: A total of 12 protein biomarkers were significantly associated with CLI/AMP in the case-control sample at an FDR of 5% (Fig-Panel A). Three remained significantly associated with CLI/AMP in the validation step, the strongest of which was stem cell factor (SCF), a cytokine expressed by endothelial cells and involved in vasculogenesis. There was a significant inverse association between SCF and CLI/AMP (p-Bonferroni 0.00047). Examined by quartiles and adjusting for baseline differences (Panel B), a graded inverse association between SCF and CLI/AMP was observed (Q4 vs Q1 Adjusted OR 0.52, 95% CI: 0.34, 0.77; p=0.002). Conclusions: A proteomic approach to identifying novel biomarkers associated with CLI/AMP revealed a strong inverse risk relationship with SCF levels. These data suggest that SCF and its effects on vasculogenesis warrant further investigation as a pathway to prevent and treat CLI/AMP in patients with PAD.

Abstract 17022: Transcriptomic Analysis of Calciphylaxis Tissue Reveals Novel Genes Related to Vascular Calcification and Overlap With Coronary Artery Disease

Introduction: Calciphylaxis is a rare, understudied disease of accelerated vascular calcification affecting the skin and soft tissue leading to painful wounds, amputations, and reduced survival. The underlying mechanisms of calciphylaxis remain incompletely understood. Objective: In this study, we aimed to identify altered signaling pathways and novel differentially expressed genes (DEGs) associated with calciphylaxis and to characterize the protein expression of top DEGs in the vasculature. Methods: 6 case and 3 control tissues were obtained from our institution’s calciphylaxis biorepository (NCT03032835). RNA sequencing analysis and pathway analysis were performed using the KEGG database. Immunofluorescent (IF) staining with co-localization analysis was performed on independent case-control samples for the top 9 upregulated DEGs. In vitro functional assessment was performed using human vascular smooth muscle cells (VSMC). Results: 783 DEGs (FDR-adjusted p-value ≤0.05) were identified, 543 of which were upregulated (Fig 1A). 27 altered pathways enriched for calcification, thrombosis, and inflammation were identified. The top 9 upregulated DEGs (adjusted p-values ≤1.2 x10-7) were KYNU, IFI16, KCNJ15, RAB31, FNDC1, HIF1α, DEPP1, NFIL3, and TSP1. On IF, 7 of the top 9 upregulated DEGs exhibited higher protein expression compared to matched controls, some of which are highlighted in Figure 1B. These 7 genes were all upregulated in RNA sequencing analysis of human coronary arteries with atherosclerotic lesions compared to coronaries without lesions. Functional assessment of the top 3 genes in this coronary artery disease analysis (FNDC1, KYNU & KCNJ15) revealed potent effects on calcification, proliferation, and migration of VSMCs. Conclusions: Novel genes identified in this transcriptomic study of calciphylaxis uncovered similarities with coronary artery disease and have identified previously unknown regulators of VSMC calcification.

Association between copy number variations in parkin (PRKN) and schizophrenia and autism spectrum disorder: A case-control study.

The present study aimed to examine the association between copy number variations (CNVs) in parkin (PRKN) and schizophrenia (SCZ) and autism spectrum disorder (ASD) in a large case-control sample. Array comparative genomic hybridization was performed on 3111 cases with SCZ, 1236 cases with ASD, and 2713 controls. We systematically prioritized likely pathogenic CNVs (LP-CNVs) in PRKN and examined their association with SCZ and ASD. In total, 3014 SCZ cases (96.9%), 1205 ASD cases (97.5%), and 2671 controls (98.5%) passed quality control. We found that monoallelic carriers of LP-CNVs in PRKN were common (70/6890, 1.02%) and were not at higher risk of SCZ (p = 0.29) or ASD (p = 0.72). We observed that the distribution pattern of LP-CNVs in the Japanese population was consistent with those in other populations. We also identified a patient diagnosed with SCZ and early-onset Parkinson's disease carrying biallelic pathogenic CNVs in PRKN. The absence of Parkinson's symptoms in 10 other monoallelic carriers of the same pathogenic CNV further reflects the lack of effect of monoallelic pathogenic variants in PRKN in the absence of a second hit. The present findings suggest that monoallelic CNVs in PRKN do not confer a significant risk for SCZ or ASD. However, further studies to investigate the association between biallelic CNVs in PRKN and SCZ and ASD are warranted.

Plasma cell-free DNA methylation analysis for ovarian cancer detection: Analysis of samples from a case-control study and an ovarian cancer screening trial.

Analysis of cell-free DNA methylation (cfDNAme), alone or combined with CA125, could help to detect ovarian cancers earlier and may reduce mortality. We assessed cfDNAme in regions of ZNF154, C2CD4D and WNT6 via targeted bisulfite sequencing in diagnostic and early detection (preceding diagnosis) settings. Diagnostic samples were obtained via prospective blood collection in cell-free DNA tubes in a convenience series of patients with a pelvic mass. Early detection samples were matched case-control samples derived from the UK Familial Ovarian Cancer Screening Study (UKFOCSS). In the diagnostic set (ncases = 27, ncontrols = 41), the specificity of cfDNAme was 97.6% (95% CI: 87.1%-99.9%). High-risk cancers were detected with a sensitivity of 80% (56.3%-94.3%). Combination of cfDNAme and CA125 resulted in a sensitivity of 94.4% (72.7%-99.9%) for high-risk cancers. Despite technical issues in the early detection set (ncases = 29, ncontrols = 29), the specificity of cfDNAme was 100% (88.1%-100.0%). We detected 27.3% (6.0%-61.0%) of high-risk cases with relatively lower genomic DNA (gDNA) contamination. The sensitivity rose to 33.3% (7.5%-70.1%) in samples taken <1 year before diagnosis. We detected ovarian cancer in several patients up to 1 year before diagnosis despite technical limitations associated with archival samples (UKFOCSS). Combined cfDNAme and CA125 assessment may improve ovarian cancer screening in high-risk populations, but future large-scale prospective studies will be required to validate current findings.

Nicotinic Acid-Mediated Modulation of Metastasis-Associated Protein 1 Methylation and Inflammation in Brain Arteriovenous Malformation.

We explored metastasis-associated protein 1 (MTA1) promoter methylation in the development of brain arteriovenous malformation (BAVM). The clinical data of 148 sex- and age-matched BAVMs and controls were collected, and the MTA1 DNA methylation in peripheral white blood cells (WBC) was assessed by bisulfite pyrosequencing. Among them, 18 pairs of case-control samples were used for WBC mRNA detection, 32 pairs were used for WBC MTA1 protein measurement, and 50 pairs were used for plasma inflammatory factor analysis. Lipopolysaccharide (LPS) treatment was used to induce an inflammatory injury cell model of human brain microvascular endothelial cells (BMECS). 5-Aza-2'-deoxycytidine (5-AZA), nicotinic acid (NA), and MTA1 siRNAs were used in functional experiments to examine BMECS behaviors. RT-qPCR, Western blot, and ELISA or cytometric bead arrays were used to measure the expression levels of MTA1, cytokines, and signaling pathway proteins in human blood or BMECS. The degree of MTA1 promoter methylation was reduced in BAVM compared with the control group and was inversely proportional to MTA1 expression. Plasma ApoA concentrations in BAVM patients were significantly lower than those in controls and correlated positively with MTA1 promoter methylation and negatively with MTA1 expression. The expression of cytokine was markedly higher in BAVM than in controls. Cell experiments showed that 5-AZA decreased the methylation level of MTA1 and increased the expression of MTA1 protein. LPS treatment significantly increased cytokine concentrations (p < 0.05). NA and MTA1 silencing could effectively reverse the LPS-mediated increase in IL-6 and TNF-α expression through the NF-κB pathway. Our study indicated that NA may regulate MTA1 expression by affecting promoter DNA methylation, improve vascular inflammation through the NF-κB pathway, and alleviate the pathological development of BAVM.

Identification of southern corn rust resistance QTNs in Chinese summer maize germplasm via multi-locus GWAS and post-GWAS analysis.

Southern corn rust (SCR) caused by Puccinia polysora Underw is a major disease leading to severe yield losses in China Summer Corn Belt. Using six multi-locus GWAS methods, we identified a set of SCR resistance QTNs from a diversity panel of 140 inbred lines collected from China Summer Corn Belt. Thirteen QTNs on chromosomes 1, 2, 4, 5, 6, and 8 were grouped into three types of allele effects and their associations with SCR phenotypes were verified by post-GWAS case-control sampling, allele/haplotype effect analysis. Relative resistance (RRR) and relative susceptibility (RRs) catering to its inbred carrier were estimated from single QTN and QTN-QTN combos and epistatitic effects were estimated for QTN-QTN combos. By transcriptomic annotation, a set of candidate genes were predicted to be involved in transcriptional regulation (S5_145, Zm00001d01613, transcription factor GTE4), phosphorylation (S8_123, Zm00001d010672, Pgk2- phosphoglycerate kinase 2), and temperature stress response (S6_164a/S6_164b, Zm00001d038806, hsp101, and S5_211, Zm00001d017978, cellulase25). The breeding implications of the above findings were discussed.

Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated with Postoperative Delirium.

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case-control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

Genomic risk scores and oral contraceptive-associated ischemic stroke risk: a call for collaboration

BackgroundOral contraceptives (OCs) are generally safe but vascular risk factors increase OC-associated ischemic stroke risk. We performed a case-control study to evaluate whether a genomic risk score for ischemic stroke modifies OC-associated ischemic stroke risk.MethodsThe Genetics of Early-Onset Stroke study includes 332 premenopausal women (136 arterial ischemic stroke cases and 196 controls) with data on estrogen-containing OC use within 30 days before the index event (for cases) or interview (for controls). Using a previously validated genetic risk score (metaGRS) for ischemic stroke based on 19 polygenic risk scores for stroke and stroke-associated risk factors, we stratified our combined case-control sample into tertiles of genomic risk. We evaluated the association between OC use and ischemic stroke within each tertile. We tested if the association between OC use and ischemic stroke depended on the genomic risk of stroke using logistic regression with an OC use × metaGRS interaction term. These analyses were performed with and without adjustment for smoking, hypertension, diabetes, coronary heart disease, and body mass index.ResultsAfter adjustment for vascular risk factors, the odds ratio of OC use was 3.2 (1.7–6.3) overall and increased from the lower, middle, and upper tertile of genomic risk from 1.6 (0.5–5.4) to 2.5 (0.08–8.2) to 13.7 (3.8–67.3) respectively, and a p-value for interaction of 0.001.ConclusionsOur results suggest that genomic profile may modify the OC-associated ischemic stroke risk. Larger studies are warranted to determine whether a genomic risk score could be clinically useful in reducing OC-associated ischemic stroke.

Association of childhood infections and perinatal factors with ankylosing spondylitis: a Swedish nationwide case–control and sibling study

ObjectivesTo identify perinatal and early-life risk factors for ankylosing spondylitis (AS), controlling for family-shared confounding with a sibling comparison design.MethodsIn this nationwide, register-based case–control study, we identified 5612 AS cases...

Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer's Disease.

DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction. Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers. EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points. EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (ps: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOEɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.

Identifying Risk Factors for Appointment No-Shows in a Pediatric Orthopaedic Surgery Clinic

Background: Appointment nonadherence in pediatric orthopaedic clinics negatively affects patient outcomes. While previous studies have examined risk factors for missed appointments, there is a lack of such research in pediatric orthopaedics. This study tests the hypothesis that pediatric orthopaedic patients with greater socioeconomic risk are more likely to miss appointments. Our objective is to identify risk factors contributing to missed appointments.Methods: A retrospective chart review was conducted of all visits in an outpatient pediatric orthopaedic clinic and affiliated Midwest level 1 academic hospital in 2019. Possible covariates with appointment attendance collected included sociodemographic information such as age, gender (male/female), race/ethnicity (non-Hispanic White, Black, Hispanic/Latinx, Other), and insurance (Commercial, Medicaid, Medicaid HMO, Other). The main study outcome was appointment status, defined as either “No-Show” or “Attended/Completed.” Using census data, the Area Deprivation Index (ADI) was determined for a matched case (“No-Show”) control sample to quantify socioeconomic risk. Factors associated with appointment nonadherence were analyzed with a logistic regression model.Results: Out of 10,078 total encounters included in the study, there was a no-show rate of 6.61%. Significant predictors of no-show included race (p<0.001), insurance type (p<0.001), and lag days between appointment scheduling and completion (p<0.001). In a matched case-control sub-study, ADI was positively associated with increased odds of no-show (p<0.001), making this model unique from other studies.Conclusions: This data informs pediatric orthopaedic providers of risk factors for appointment nonadherence in order to individualize patient care plans based on specific socioeconomic needs. Efforts to improve appointment adherence may reduce the rate of poor health outcomes and health disparities in underserved areas. Next steps include qualitative assessments to articulate the experience of families who miss appointments to develop a greater standard for more accessible patient-centered care.Level of Evidence: Retrospective chart review (Level III) Key Concepts•There appears to be a link between pediatric orthopaedic appointment adherence and socioeconomic factors.•The Area Deprivation Index is a useful tool to quantify socioeconomic risk in the clinical setting.•Further research is needed to understand the specific reasons patients miss appointments.

Clearing the hurdles: Revisiting the under-performance hypothesis for women-led VC funded firms

ABSTRACT This study revisits the female underperformance hypothesis by investigating the influence of CEO gender on key performance factors for VC funded firms from 2011–2016. We used a matched case-control sample approach to reduce the confounding influence of industry sector on gender differences for four key performance outcomes – total capital, raised total rounds of funding, successful exit or business failure. Results show gender does not predict funding raised or firm performance, and that key legitimacy markers, including elite education, external endorsement and high-status investors, offset potential gender discounts for women-led firms. Implications are that the female underperformance hypothesis should not be perpetuated as a legitimizing myth or stereotype predicting performance of women entrepreneurs in management studies. In practice, VCs should not rule out investments or expect poor performance based on CEO gender.

Pharmacokinetic serum concentrations of VRC01 correlate with prevention of HIV-1 acquisition.

The phase 2b proof-of-concept Antibody Mediated Prevention (AMP) trials showed that VRC01, an anti-HIV-1 broadly neutralising antibody (bnAb), prevented acquisition of HIV-1 sensitive to VRC01. To inform future study design and dosing regimen selection of candidate bnAbs, we investigated the association of VRC01 serum concentration with HIV-1 acquisition using AMP trial data. The case-control sample included 107 VRC01 recipients who acquired HIV-1 and 82 VRC01 recipients who remained without HIV-1 during the study. We measured VRC01 serum concentrations with a qualified pharmacokinetic (PK) Binding Antibody Multiplex Assay. We employed nonlinear mixed effects PK modelling to estimate daily-grid VRC01 concentrations. Cox regression models were used to assess the association of VRC01 concentration at exposure and baseline body weight, with the hazard of HIV-1 acquisition and prevention efficacy as a function of VRC01 concentration. We also compared fixed dosing vs. body weight-based dosing via simulations. Estimated VRC01 concentrations in VRC01 recipients without HIV-1 were higher than those in VRC01 recipients who acquired HIV-1. Body weight was inversely associated with HIV-1 acquisition among both placebo and VRC01 recipients but did not modify the prevention efficacy of VRC01. VRC01 concentration was inversely correlated with HIV-1 acquisition, and positively correlated with prevention efficacy of VRC01. Simulation studies suggest that fixed dosing may be comparable to weight-based dosing in overall predicted prevention efficacy. These findings suggest that bnAb serum concentration may be a useful marker for dosing regimen selection, and operationally efficient fixed dosing regimens could be considered for future trials of HIV-1 bnAbs. Was provided by the National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIAID) (UM1 AI068614, to the HIV Vaccine Trials Network [HVTN]; UM1 AI068635, to the HVTN Statistical Data and Management Center [SDMC], Fred Hutchinson Cancer Center [FHCC]; 2R37 054165 to the FHCC; UM1 AI068618, to HVTN Laboratory Center, FHCC; UM1 AI068619, to the HPTN Leadership and Operations Center; UM1 AI068613, to the HIV Prevention Trials Network [HPTN] Laboratory Center; UM1 AI068617, to the HPTN SDMC; and P30 AI027757, to the Center for AIDS Research, Duke University (AI P30 AI064518) and University of Washington (P30 AI027757) Centers for AIDS Research; R37AI054165 from NIAID to the FHCC; and OPP1032144 CA-VIMC Bill & Melinda Gates Foundation.

Immune response after two doses of the BNT162b2 COVID-19 vaccine and risk of SARS-CoV-2 breakthrough infection in Tyrol, Austria: an open-label, observational phase 4 trial

Correlates of protection could help to assess the extent to which a person is protected from SARS-CoV-2 infection after vaccination (so-called breakthrough infection). We aimed to clarify associations of antibody and T-cell responses after vaccination against COVID-19 with risk of a SARS-CoV-2 breakthrough infection and whether measurement of these responses enhances risk prediction. We did an open-label, phase 4 trial in two community centres in the Schwaz district of the Federal State of Tyrol, Austria, before the emergence of the omicron (B.1.1.529) variant of SARS-CoV-2. We included individuals (aged ≥16 years) a mean of 35 days (range 27-43) after they had received a second dose of the BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. We quantified associations between immunological parameters and breakthrough infection and assessed whether information on these parameters improves risk discrimination. The study is registered with the European Union Drug Regulating Authorities Clinical Trials Database, 2021-002030-16. 2760 individuals (1682 [60·9%] female, 1078 [39·1%] male, mean age 47·4 years [SD 14·5]) were enrolled into this study between May 15 and May 21, 2021, 712 (25·8%) of whom had a previous SARS-CoV-2 infection. Over a median follow-up of 5·9 months, 68 (2·5%) participants had a breakthrough infection. In models adjusted for age, sex, and previous infection, hazard ratios for breakthrough infection for having twice the immunological parameter level at baseline were 0·72 (95% CI 0·60-0·86) for anti-spike IgG, 0·80 (0·70-0·92) for neutralising antibodies in a surrogate virus neutralisation assay, 0·84 (0·58-1·21) for T-cell response after stimulation with a CD4 peptide pool, and 0·77 (0·54-1·08) for T-cell response after stimulation with a combined CD4 and CD8 peptide pool. For neutralising antibodies measured in a nested case-control sample using a pseudotyped virus neutralisation assay, the corresponding odds ratio was 0·78 (0·62-1·00). Among participants with previous infection, the corresponding hazard ratio was 0·73 (0·61-0·88) for anti-nucleocapsid Ig. Addition of anti-spike IgG information to a model containing information on age and sex improved the C-index by 0·085 (0·027-0·143). In contrast to T-cell response, higher levels of binding and neutralising antibodies were associated with a reduced risk of breakthrough SARS-CoV-2 infection. The assessment of anti-spike IgG enhances the prediction of incident breakthrough SARS-CoV-2 infection and could therefore be a suitable correlate of protection in practice. Our phase 4 trial measured both humoral and cellular immunity and had a 6-month follow-up period; however, the longer-term protection against emerging variants of SARS-CoV-2 remains unclear. None.

Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users

Background: Suicide risk prediction models frequently rely on structured electronic health record (EHR) data, including patient demographics and health care usage variables. Unstructured EHR data, such as clinical notes, may improve predictive accuracy by allowing access to detailed information that does not exist in structured data fields. To assess comparative benefits of including unstructured data, we developed a large case-control dataset matched on a state-of-the-art structured EHR suicide risk algorithm, utilized natural language processing (NLP) to derive a clinical note predictive model, and evaluated to what extent this model provided predictive accuracy over and above existing predictive thresholds.Methods: We developed a matched case-control sample of Veterans Health Administration (VHA) patients in 2017 and 2018. Each case (all patients that died by suicide in that interval, n = 4,584) was matched with 5 controls (patients who remained alive during treatment year) who shared the same suicide risk percentile. All sample EHR notes were selected and abstracted using NLP methods. We applied machine-learning classification algorithms to NLP output to develop predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy overall and for high-risk patients.Results: The best performing NLP-derived models provided 19% overall additional predictive accuracy (AUC = 0.69; 95% CI, 0.67, 0.72) and 6-fold additional risk concentration for patients at the highest risk tier (top 0.1%), relative to the structured EHR model.Conclusions: The NLP-supplemented predictive models provided considerable benefit when compared to conventional structured EHR models. Results support future structured and unstructured EHR risk model integrations.