The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.

We investigate risk factors for severe COVID-19 in persons living with HIV (PWH), including among racialized PWH, using the U.S. population-sampled National COVID Cohort Collaborative (N3C) data released from January 1, 2020 to October 10, 2022. We defined severe COVID-19 as hospitalized with invasive mechanical ventilation, extracorporeal membrane oxygenation, discharge to hospice or death. We used machine learning methods to identify highly ranked, uncorrelated factors predicting severe COVID-19, and used multivariable logistic regression models to assess the associations of these variables with severe COVID-19 in several models, including race-stratified models. There were 3 241 627 individuals with incident COVID-19 cases and 81 549 (2.5%) with severe COVID-19, of which 17 445 incident COVID-19 and 1 020 (5.8%) severe cases were among PWH. The top highly ranked factors of severe COVID-19 were age, congestive heart failure (CHF), dementia, renal disease, sodium concentration, smoking status, and sex. Among PWH, age and sodium concentration were important predictors of COVID-19 severity, and the effect of sodium concentration was more pronounced in Hispanics (aOR 4.11 compared to aOR range: 1.47-1.88 for Black, White, and Other non-Hispanics). Dementia, CHF, and renal disease was associated with higher odds of severe COVID-19 among Black, Hispanic, and Other non-Hispanics PWH, respectively. Our findings suggest that the impact of factors, especially clinical comorbidities, predictive of severe COVID-19 among PWH varies by racialized groups, highlighting a need to account for race and comorbidity burden when assessing the risk of PWH developing severe COVID-19.

Vitamin C Urinary Loss and Deficiency in Human Immunodeficiency Virus (HIV): Cross-sectional Study of Vitamin C Renal Leak in Women With HIV.

BACKGROUND: Reduced plasma vitamin C (vitC) concentrations in human immunodeficiency virus (HIV) may result from abnormal urinary excretion: a renal leak. VitC renal leak indicates underlying nutritional dysregulation independent of diet. We hypothesized that increased renal leak prevalence in HIV would be associated with deficient vitC concentrations. METHODS: We conducted an outpatient cross-sectional study of 96 women (40 HIV [PWH] and 56 without HIV [PWOH]) at the National Institutes of Health and Georgetown University. Renal leak was defined as abnormal urinary vitC excretion at fasting plasma concentrations <43.2µM, 2 SDs below vitC renal threshold in healthy women. To determine the primary outcome of renal leak prevalence, matched urine and plasma samples were collected the morning after overnight fast. Secondary outcomes assessed group differences in mean plasma vitC concentrations and prevalence of vitC deficiency. Exploratory outcomes assessed clinical parameters associated with renal leak. VitC was measured by high-performance liquid chromatography with coulometric electrochemical detection. RESULTS: PWH had significantly higher renal leak prevalence (73%vs14%; OR (odds ratio):16; P<.001), lower mean plasma vitC concentrations (14µMvs50µM; P<.001), and higher prevalence of vitC deficiency (43%vs7%; OR:10; P<.001) compared with PWOH, unchanged by adjustments for confounding factors. Significant predictors of renal leak included antiretroviral therapy (ART), Black race, older age, and metabolic comorbidities but not viral load or CD4 count. When compared with other chronic disease cohorts, PWH had the highest prevalence of renal leak and vitC deficiency (P<.001). CONCLUSIONS: High prevalence of vitC renal leak in HIV was associated with vitC deficiency, ART use, and race/ethnicity differences.

Readmission and Mortality After Hospitalization With Acute Kidney Injury.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) carries high rates of morbidity and mortality. This study quantified various short- and long-term outcomes after hospitalization with AKI. STUDY DESIGN: Retrospective propensity score (PS)-matched cohort study. SETTING & PARTICIPANTS: Optum Clinformatics, a national claims database, was used to identify patients hospitalized with and without an AKI discharge diagnosis between January 2007 and September 2020. EXPOSURE: Among patients with prior continuous enrollment for at least 2years without AKI hospitalization, 471,176 patients hospitalized with AKI were identified and PS-matched to 471,176 patients hospitalized without AKI. OUTCOME(S): All-cause and selected-cause rehospitalizations and mortality 90 and 365 days after index hospitalization. ANALYTICAL APPROACH: After PS matching, rehospitalization and death incidences were estimated using the cumulative incidence function method and compared using Gray's test. The association of AKI hospitalization with each outcome was tested using Cox models for all-cause mortality and, with mortality as competing risk, cause-specific hazard modeling for all-cause and selected-cause rehospitalization. Overall and stratified analyses were performed to evaluate for interaction between an AKI hospitalization and preexisting chronic kidney disease (CKD). RESULTS: After PS matching, AKI was associated with higher rates of rehospitalization for any cause (hazard ratio [HR], 1.62; 95% CI, 1.60-1.65), end-stage renal disease (HR, 6.21; 95% CI, 1.04-36.92), heart failure (HR, 2.81; 95% CI, 2.66, 2.97), sepsis (HR, 2.62; 95% CI, 2.49-2.75), pneumonia (HR, 1.47; 95% CI, 1.37-1.57), myocardial infarction (HR, 1.48; 95% CI, 1.33-1.65), and volume depletion (HR, 1.64; 95% CI, 1.37-1.96) at 90 days after discharge compared with the group without AKI, with similar findings at 365 days. Mortality rate was higher in the group with AKI than in the group without AKI at 90 (HR, 2.66; 95% CI, 2.61-2.72) and 365 days (HR, 2.11; 95% CI, 2.08-2.14). The higher risk of outcomes persisted when participants were stratified by CKD status (P<0.01). LIMITATIONS: Causal associations between AKI and the reported outcomes cannot be inferred. CONCLUSIONS: AKI during hospitalization in patients with and without CKD is associated with increased risk of 90- and 365-day all-cause/selected-cause rehospitalization and death.

Vitamin C Urinary Loss in Fabry Disease: Clinical and Genomic Characteristics of Vitamin C Renal Leak.

BACKGROUND: Reduced plasma vitamin C concentrations in chronic diseases may result from abnormal urinary excretion of vitamin C: a renal leak. We hypothesized that vitamin C renal leak may be associated with disease-mediated renal dysregulation, resulting in aberrant vitamin C renal reabsorption and increased urinary loss. OBJECTIVES: We investigated the prevalence, clinical characteristics, and genomic associations of vitamin C renal leak in Fabry disease, an X-linked lysosomal disease associated with renal tubular dysfunction and low plasma vitamin C concentrations. METHODS: We conducted a non-randomized cross-sectional cohort study of men aged 24-42 y, with Fabry disease (n = 34) and controls without acute or chronic disease (n = 33). To match anticipated plasma vitamin C concentrations, controls were placed on a low-vitamin C diet 3 wk before inpatient admission. To determine the primary outcome of vitamin C renal leak prevalence, subjects were fasted overnight, and matched urine and fasting plasma vitamin C measurements were obtained the following morning. Vitamin C renal leak was defined as presence of urinary vitamin C at plasma concentrations below 38 µM. Exploratory outcomes assessed the association between renal leak and clinical parameters, and genomic associations with renal leak using single nucleotide polymorphisms (SNPs) in the vitamin C transporter SLC23A1. RESULTS: Compared with controls, the Fabry cohort had 16-fold higher odds of renal leak (6% vs. 52%; OR: 16; 95% CI: 3.30, 162; P < 0.001). Renal leak was associated with higher protein creatinine ratio (P < 0.01) and lower hemoglobin (P = 0.002), but not estimated glomerular filtration rate (P = 0.54). Renal leak, but not plasma vitamin C, was associated with a nonsynonymous single nucleotide polymorphism in vitamin C transporter SLC23A1 (OR: 15; 95% CI: 1.6, 777; P = 0.01). CONCLUSIONS: Increased prevalence of renal leak in adult men with Fabry disease may result from dysregulated vitamin C renal physiology and is associated with abnormal clinical outcomes and genomic variation.

Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence.

PURPOSE OF REVIEW: Multiple studies report an increased incidence of diabetes following SARS-CoV-2 infection. Given the potential increased global burden of diabetes, understanding the effect of SARS-CoV-2 in the epidemiology of diabetes is important. Our aim was to review the evidence pertaining to the risk of incident diabetes after COVID-19 infection. RECENT FINDINGS: Incident diabetes risk increased by approximately 60% compared to patients without SARS-CoV-2 infection. Risk also increased compared to non-COVID-19 respiratory infections, suggesting SARS-CoV-2-mediated mechanisms rather than general morbidity after respiratory illness. Evidence is mixed regarding the association between SARS-CoV-2 infection and T1D. SARS-CoV-2 infection is associated with an elevated risk of T2D, but it is unclear whether the incident diabetes is persistent over time or differs in severity over time. SARS-CoV-2 infection is associated with an increased risk of incident diabetes. Future studies should evaluate vaccination, viral variant, and patient- and treatment-related factors that influence risk.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Antagonism of scavenger receptor CD36 by 5A peptide prevents chronic kidney disease progression in mice independent of blood pressure regulation.

Scavenger receptor CD36 participates in lipid metabolism and inflammatory pathways important for cardiovascular disease and chronic kidney disease (CKD). Few pharmacological agents are available to slow the progression of CKD. However, apolipoprotein A-I-mimetic peptide 5A antagonizes CD36 in vitro. To test the efficacy of 5A, and to test the role of CD36 during CKD, we compared wild-type to CD36 knockout mice and wild-type mice treated with 5A, in a progressive CKD model that resembles human disease. Knockout and 5A-treated wild-type mice were protected from CKD progression without changes in blood pressure and had reductions in cardiovascular risk surrogate markers that are associated with CKD. Treatment with 5A did not further protect CD36 knockout mice from CKD progression, implicating CD36 as its main site of action. In a separate model of kidney fibrosis, 5A-treated wild-type mice had less macrophage infiltration and interstitial fibrosis. Peptide 5A exerted anti-inflammatory effects in the kidney and decreased renal expression of inflammasome genes. Thus, CD36 is a new therapeutic target for CKD and its associated cardiovascular risk factors. Peptide 5A may be a promising new agent to slow CKD progression.

Hygiene strategies to prevent methicillin-resistant Staphylococcus aureus skin and soft tissue infections: a cluster-randomized controlled trial among high-risk military trainees.

BACKGROUND: Effective measures are needed to prevent methicillin-resistant Staphylococcus aureus (MRSA) skin and soft tissue infections (SSTIs) in high-risk community settings. The study objective was to evaluate the effect of personal hygiene-based strategies on rates of overall SSTI and MRSA SSTI. METHODS: We conducted a prospective, field-based, cluster-randomized trial in US Army Infantry trainees from May 2010 through January 2012. There were 3 study groups with incrementally increased education and hygiene-based interventions: standard (S), enhanced standard (ES), and chlorhexidine (CHG). The primary endpoints were incidence of overall SSTI and MRSA SSTI. RESULTS: The study included 30 209 trainees constituting 540 platoons (168 S, 192 ES, and 180 CHG). A total of 1203 (4%) participants developed SSTI, 316 (26%) due to MRSA. The overall SSTI rate was 4.15 (95% confidence interval [CI], 3.77-4.58) per 100 person-cycles. SSTI rates by study group were 3.48 (95% CI, 2.87-4.22) for S, 4.18 (95% CI, 3.56-4.90) for ES, and 4.71 (95% CI, 4.03-5.50) for CHG. The MRSA SSTI rate per 100 person-cycles for all groups was 1.10 (95% CI, .91-1.32). MRSA SSTI rates by study group were 1.0 (95% CI, .70-1.42) for S, 1.29 (95% CI, .98-1.71) for ES, and 0.97 (95% CI, .70-1.36) for CHG. CONCLUSIONS: Personal hygiene and education measures, including once-weekly use of chlorhexidine body wash, did not prevent overall SSTI or MRSA SSTI in a high-risk population of military trainees. CLINICAL TRIALS REGISTRATION: NCT01105767.

Epidemiology of Infantile Ureteropelvic Junction Obstruction in the US.

OBJECTIVE: To describe sex- and diagnosis-specific comorbidities, outcomes, and secular trends associated with ureteropelvic junction obstruction (UPJO) in a large, real-world population diagnosed with hydronephrosis in infancy. MATERIALS AND METHODS: We identified all infants ≤1 year old with ≥1 claim in the Optum Clinformatics 2007-2020 nationwide population database and used univariable and multivariable Cox regression analyses to estimate associations of demographic and clinical characteristics of infants with a UPJO diagnosis with surgical status. RESULTS: Of 22,349 infants with hydronephrosis (1.1% of infants; males-1.4%, females-0.7%), 1722 (7.7%; 7.9%-males, 7.2%-females) had UPJO. Follow-up was ≥1 year in 1198 (70%) and ≥3 years in 555 (32%) cases, and UPJO repair was performed in 542 children (31.5%; 32.3%-males, 29.5%-females); 77.7% within 1 year and 97.3% within 3 years. UPJO repair was associated with prior urinary tract infection (UTI) (hazard ratio (HR) 1.41, 95% confidence interval (CI) 1.12-1.76) and South (HR 1.42, 95% CI 1.14-1.78) or Midwest (HR 1.60, 95% CI 1.26-2.04) geographic region but did not change over time. CONCLUSION: This population-based study provides a real-world view of postnatally diagnosed hydronephrosis, focusing on UPJO, for which 522 cases (∼1/3) had ≥3 years continuous coverage. UPJO-associated comorbidities were more common in females, and the frequencies of UPJO-associated surgery and comorbidities were higher than in other studies. Other than UTI, no other associated kidney or urinary tract diagnoses were associated with UPJO repair. We identified unique sex- and diagnosis-specific differences in associated comorbidities and interventions in children diagnosed with UPJO in the first year of life.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Time to Publication for Randomized Clinical Trials Presented as Abstracts at Three Gastroenterology and Hepatology Conferences in 2017.

Background & Aims: Results of randomized clinical trials are often first presented as conference abstracts, but these abstracts may be difficult to find, and trial results included in the abstract may not be followed by subsequent journal publications. In a review of abstracts submitted to eight major medical and surgical conferences in 2017, we identified 237 abstracts reporting primary results of randomized clinical trials accepted for presentation at three major gastroenterology and hepatology conferences. The aims of this new analysis were to determine the publication rate for these abstracts and the proportion of publications that included trial registration numbers in the publication abstract. Methods: Clinical trial registries, PubMed, Europe PMC, and Google Scholar were searched through November 1, 2021, for publications reporting trial results for the selected abstracts. Publications were reviewed to determine if they included a trial registration number and if the registration number was in the abstract. Results: Publications were found for 157 abstracts (66%) within four years of the conference. Publications were found more frequently for the 194 abstracts reporting results of registered trials (144, 74%) than for the 43 abstracts reporting unregistered trials (13, 30%), but only 67% of these 144 publications included the registration number in the publication abstract. Ten unpublished trials had summary results posted on ClinicalTrials.gov. Conclusions: Clinical trial results could be more accessible if all trials were registered, authors included registration numbers in both conference and journal abstracts, and journal editors required the inclusion of registration numbers in publication abstracts for registered clinical trials.

Predictive models of long COVID.

BACKGROUND: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. METHODS: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). FINDINGS: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. INTERPRETATION: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology. FUNDING: NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program.

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies.

Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C.

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative.

BACKGROUND: AKI is associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19); however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. METHODS: Electronic health record data were obtained from 53 health systems in the United States in the National COVID Cohort Collaborative. We selected hospitalized adults diagnosed with COVID-19 between March 6, 2020, and January 6, 2022. AKI was determined with serum creatinine and diagnosis codes. Time was divided into 16-week periods (P1-6) and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. RESULTS: Of a total cohort of 336,473, 129,176 (38%) patients had AKI. Fifty-six thousand three hundred and twenty-two (17%) lacked a diagnosis code but had AKI based on the change in serum creatinine. Similar to patients coded for AKI, these patients had higher mortality compared with those without AKI. The incidence of AKI was highest in P1 (47%; 23,097/48,947), lower in P2 (37%; 12,102/32,513), and relatively stable thereafter. Compared with the Midwest, the Northeast, South, and West had higher adjusted odds of AKI in P1. Subsequently, the South and West regions continued to have the highest relative AKI odds. In multivariable models, AKI defined by either serum creatinine or diagnostic code and the severity of AKI was associated with mortality. CONCLUSIONS: The incidence and distribution of COVID-19-associated AKI changed since the first wave of the pandemic in the United States. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_08_08_CJN0000000000000192.mp3.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.