Neutralizing antibody responses to SARS-CoV-2 Omicron variants: Post six months following two-dose & three-dose vaccination of ChAdOx1 nCoV-19 or BBV152.

BACKGROUND OBJECTIVES: The Omicron sub-lineages are known to have higher infectivity, immune escape and lower virulence. During December 2022 - January 2023 and March - April 2023, India witnessed increased SARS-CoV-2 infections, mostly due to newer Omicron sub-lineages. With this unprecedented rise in cases, we assessed the neutralization potential of individuals vaccinated with ChAdOx1 nCoV (Covishield) and BBV152 (Covaxin) against emerging Omicron sub-lineages. METHODS: Neutralizing antibody responses were measured in the sera collected from individuals six months post-two doses (n=88) of Covishield (n=44) or Covaxin (n=44) and post-three doses (n=102) of Covishield (n=46) or Covaxin (n=56) booster dose against prototype B.1 strain, lineages of Omicron; XBB.1, BQ.1, BA.5.2 and BF.7. RESULTS: The sera of individuals collected six months after the two-dose and the three-dose demonstrated neutralizing activity against all variants. The neutralizing antibody (NAbs) level was highest against the prototype B.1 strain, followed by BA5.2 (5-6 fold lower), BF.7 (11-12 fold lower), BQ.1 (12 fold lower) and XBB.1 (18-22 fold lower). INTERPRETATION CONCLUSIONS: Persistence of NAb responses was comparable in individuals with two- and three-dose groups post six months of vaccination. Among the Omicron sub-variants, XBB.1 showed marked neutralization escape, thus pointing towards an eventual immune escape, which may cause more infections. Further, the correlation of study data with complete clinical profile of the participants along with observations for cell-mediated immunity may provide a clear picture for the sustained protection due to three-dose vaccination as well as hybrid immunity against the newer variants.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

Genome-first recall of healthy individuals by polygenic risk score reveals differences in coronary artery calcium.

Genetic risk for coronary artery disease (CAD) is commonly measured with polygenic risk scores (PRS); yet, the relationship of atherosclerotic burden with PRS in healthy individuals not at high clinical risk for CAD (ie, without a high pooled cohort equations [PCE] score) is unknown. Here, we implemented a novel recall-by-PRS strategy to measure coronary artery calcium (CAC) scores prospectively in 53 healthy individuals with extreme high PRS (median [IQR] PRS = 94% [83-98]) and low PRS (median [IQR] PRS = 3.6% [1.2-10]). The high PRS group was associated with a 2.8-fold greater CAC than the low PRS group, adjusted for age, sex, BMI, smoking, and statin use, and had a 6.7-fold greater proportion of individuals with CAC exceeding 300 HU. These findings reveal that extreme PRS tracks with CAD risk even in those without high clinical risk and demonstrate proof of principle for recall-by-PRS approaches that should be assessed prospectively in larger trials.

High failure rate of ChAdOx1-nCoV19 immunization against asymptomatic infection in healthcare workers during a Delta variant surge.

Immunization is expected to confer protection against infection and severe disease for vaccines while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies of an observational cohort of healthcare workers, we show that during a Severe Acute Respiratory Syndrome -Coronavirus 2 Delta-variant outbreak in Delhi, 25.3% (95% Confidence Interval 16.9-35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare workers were infected within less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, Inter Quartile Range 374) and without (342 U/ml, Inter Quartile Range 497), as was the induction of neutralization activity to wildtype. This was not vaccine failure since vaccine effectiveness estimate based on infection rates in an unvaccinated cohort were about 70% and most infections were asymptomatic. We find that while ChAdOx1-nCoV19 vaccination remains effective in preventing severe infections, it is unlikely to be completely able to block transmission and provide herd immunity.

Coronary Risk Estimation Based on Clinical Data in Electronic Health Records.

BACKGROUND: Clinical features from electronic health records (EHRs) can be used to build a complementary tool to predict coronary artery disease (CAD) susceptibility. OBJECTIVES: The purpose of this study was to determine whether an EHR score can improve CAD prediction and reclassification 1 year before diagnosis, beyond conventional clinical guidelines as determined by the pooled cohort equations (PCE) and a polygenic risk score for CAD. METHODS: We applied a machine learning framework using clinical features from the EHR in a multiethnic, clinical care cohort (BioMe) comprising 555 CAD cases and 6,349 control subjects and in a population-based cohort (UK Biobank) comprising 3,130 CAD cases and 378,344 control subjects for external validation. RESULTS: Compared with the PCE, the EHR score improved CAD prediction by 12% in the BioMe Biobank and by 9% in the UK Biobank. The EHR score reclassified 25.8% and 15.2% individuals in each cohort respectively, compared with the PCE score. We observed larger improvements in the EHR score over the PCE in a subgroup of individuals with low CAD risk, with 20% increased discrimination and 34.4% increased reclassification. In all models, the polygenic risk score for CAD did not improve CAD prediction, compared with the PCE or EHR score. CONCLUSIONS: The EHR score resulted in increased prediction and reclassification for CAD, demonstrating its potential use for population health monitoring of short-term CAD risk in large health systems.

Population-Based Penetrance of Deleterious Clinical Variants.

Importance: Population-based assessment of disease risk associated with gene variants informs clinical decisions and risk stratification approaches. Objective: To evaluate the population-based disease risk of clinical variants in known disease predisposition genes. Design, Setting, and Participants: This cohort study included 72 434 individuals with 37 780 clinical variants who were enrolled in the BioMe Biobank from 2007 onwards with follow-up until December 2020 and the UK Biobank from 2006 to 2010 with follow-up until June 2020. Participants had linked exome and electronic health record data, were older than 20 years, and were of diverse ancestral backgrounds. Exposures: Variants previously reported as pathogenic or predicted to cause a loss of protein function by bioinformatic algorithms (pathogenic/loss-of-function variants). Main Outcomes and Measures: The primary outcome was the disease risk associated with clinical variants. The risk difference (RD) between the prevalence of disease in individuals with a variant allele (penetrance) vs in individuals with a normal allele was measured. Results: Among 72 434 study participants, 43 395 were from the UK Biobank (mean [SD] age, 57 [8.0] years; 24 065 [55%] women; 2948 [7%] non-European) and 29 039 were from the BioMe Biobank (mean [SD] age, 56 [16] years; 17 355 [60%] women; 19 663 [68%] non-European). Of 5360 pathogenic/loss-of-function variants, 4795 (89%) were associated with an RD less than or equal to 0.05. Mean penetrance was 6.9% (95% CI, 6.0%-7.8%) for pathogenic variants and 0.85% (95% CI, 0.76%-0.95%) for benign variants reported in ClinVar (difference, 6.0 [95% CI, 5.6-6.4] percentage points), with a median of 0% for both groups due to large numbers of nonpenetrant variants. Penetrance of pathogenic/loss-of-function variants for late-onset diseases was modified by age: mean penetrance was 10.3% (95% CI, 9.0%-11.6%) in individuals 70 years or older and 8.5% (95% CI, 7.9%-9.1%) in individuals 20 years or older (difference, 1.8 [95% CI, 0.40-3.3] percentage points). Penetrance of pathogenic/loss-of-function variants was heterogeneous even in known disease predisposition genes, including BRCA1 (mean [range], 38% [0%-100%]), BRCA2 (mean [range], 38% [0%-100%]), and PALB2 (mean [range], 26% [0%-100%]). Conclusions and Relevance: In 2 large biobank cohorts, the estimated penetrance of pathogenic/loss-of-function variants was variable but generally low. Further research of population-based penetrance is needed to refine variant interpretation and clinical evaluation of individuals with these variant alleles.

Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants.

Identifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms because they may result in markedly different clinical phenotypes. Here, we generated an extensive database of documented germline GOF and LOF pathogenic variants by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF variants and applied machine learning and statistical analyses to identify discriminative features. We found that GOF variants were enriched in essential genes, for autosomal-dominant inheritance, and in protein binding and interaction domains, whereas LOF variants were enriched in singleton genes, for protein-truncating variants, and in protein core regions. We developed a user-friendly web-based interface that enables the extraction of selected subsets from the GOF/LOF database by a broad set of annotated features and downloading of up-to-date versions. These results improve our understanding of how variants affect gene/protein function and may ultimately guide future treatment options.

Derivation and Validation of Genome-Wide Polygenic Score for Ischemic Heart Failure.

Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF-PRS. Methods and Results Using summary statistics from a recent genome-wide association study for HF, we developed candidate PRSs in the Mount Sinai BioMe CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high-risk cohort who had undergone coronary catheterization. We observed a significant association between HF-PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both BioMe (odds ratio [OR], 1.14 per SD; 95% CI, 1.05-1.24; P=0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01-1.13; P=0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2-fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1-3.7; P=0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.

Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73-0.80) and five breast cancer datasets (C-index 0.68-0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg.

Genetic pleiotropy of ERCC6 loss-of-function and deleterious missense variants links retinal dystrophy, arrhythmia, and immunodeficiency in diverse ancestries.

Biobanks with exomes linked to electronic health records (EHRs) enable the study of genetic pleiotropy between rare variants and seemingly disparate diseases. We performed robust clinical phenotyping of rare, putatively deleterious variants (loss-of-function [LoF] and deleterious missense variants) in ERCC6, a gene implicated in inherited retinal disease. We analyzed 213,084 exomes, along with a targeted set of retinal, cardiac, and immune phenotypes from two large-scale EHR-linked biobanks. In the primary analysis, a burden of deleterious variants in ERCC6 was strongly associated with (1) retinal disorders; (2) cardiac and electrocardiogram perturbations; and (3) immunodeficiency and decreased immunoglobulin levels. Meta-analysis of results from the BioMe Biobank and UK Biobank showed a significant association of deleterious ERCC6 burden with retinal dystrophy (odds ratio [OR] = 2.6, 95% confidence interval [CI]: 1.5-4.6; p = 8.7 × 10-4 ), atypical atrial flutter (OR = 3.5, 95% CI: 1.9-6.5; p = 6.2 × 10-5 ), arrhythmia (OR = 1.5, 95% CI: 1.2-2.0; p = 2.7 × 10-3 ), and lymphocyte immunodeficiency (OR = 3.8, 95% CI: 2.1-6.8; p = 5.0 × 10-6 ). Carriers of ERCC6 LoF variants who lacked a diagnosis of these conditions exhibited increased symptoms, indicating underdiagnosis. These results reveal a unique genetic link among retinal, cardiac, and immune disorders and underscore the value of EHR-linked biobanks in assessing the full clinical profile of carriers of rare variants.

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19.

BACKGROUND AND OBJECTIVES: AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission. RESULTS: A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precision-recall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction. CONCLUSIONS: An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3.

Genome-wide polygenic risk score for retinopathy of type 2 diabetes.

Diabetic retinopathy (DR) is a common consequence in type 2 diabetes (T2D) and a leading cause of blindness in working-age adults. Yet, its genetic predisposition is largely unknown. Here, we examined the polygenic architecture underlying DR by deriving and assessing a genome-wide polygenic risk score (PRS) for DR. We evaluated the PRS in 6079 individuals with T2D of European, Hispanic, African and other ancestries from a large-scale multi-ethnic biobank. Main outcomes were PRS association with DR diagnosis, symptoms and complications, and time to diagnosis, and transferability to non-European ancestries. We observed that PRS was significantly associated with DR. A standard deviation increase in PRS was accompanied by an adjusted odds ratio (OR) of 1.12 [95% confidence interval (CI) 1.04-1.20; P = 0.001] for DR diagnosis. When stratified by ancestry, PRS was associated with the highest OR in European ancestry (OR = 1.22, 95% CI 1.02-1.41; P = 0.049), followed by African (OR = 1.15, 95% CI 1.03-1.28; P = 0.028) and Hispanic ancestries (OR = 1.10, 95% CI 1.00-1.10; P = 0.050). Individuals in the top PRS decile had a 1.8-fold elevated risk for DR versus the bottom decile (P = 0.002). Among individuals without DR diagnosis, the top PRS decile had more DR symptoms than the bottom decile (P = 0.008). The PRS was associated with retinal hemorrhage (OR = 1.44, 95% CI 1.03-2.02; P = 0.03) and earlier DR presentation (10% probability of DR by 4 years in the top PRS decile versus 8 years in the bottom decile). These results establish the significant polygenic underpinnings of DR and indicate the need for more diverse ancestries in biobanks to develop multi-ancestral PRS.

Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury.

BACKGROUND/AIMS: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. METHODS: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. RESULTS: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52-84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67-0.73), followed by MLP 0.59 (0.54-0.64), LR 0.57 (0.52-0.62), SVM 0.51 (0.46-0.56), AdaBoost 0.51 (0.46-0.55), RF 0.44 (0.39-0.48), and XGBoost 0.43 (CI 0.38-0.47). CONCLUSIONS: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19. METHODS AND FINDINGS: We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01-1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02-1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile. CONCLUSIONS: VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI.