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Background Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Acute Kidney Injury (AKI) is among the most common complications of Coronavirus Disease 2019 (COVID-19). Throughout 2020 pandemic, the clinical approach to COVID-19 has progressively improved, but it is unknown how these changes have affected AKI incidence and severity. In this retrospective analysis, we report the trend over time of COVID-19 associated AKI and need of renal replacement therapy in a large health system in New York City, the first COVID-19 epicenter in United States.

More than 800 million people in the world suffer from chronic kidney disease (CKD). Genome-wide association studies (GWAS) have identified hundreds of loci where genetic variants are associated with kidney function; however, causal genes and pathways for CKD remain unknown. Here, we performed integration of kidney function GWAS and human kidney–specific expression quantitative trait analysis and identified that the expression of beta-mannosidase (MANBA) was lower in kidneys of subjects with CKD risk genotype. We also show an increased incidence of renal failure in subjects with rare heterozygous loss-of-function coding variants in MANBA using phenome-wide association analysis of 40,963 subjects with exome sequencing data. MANBA is a lysosomal gene highly expressed in kidney tubule cells. Deep phenotyping revealed structural and functional lysosomal alterations in human kidneys from subjects with CKD risk alleles and mice with genetic deletion of Manba. Manba heterozygous and knockout mice developed more severe kidney fibrosis when subjected to toxic injury induced by cisplatin or folic acid. Manba loss altered multiple pathways, including endocytosis and autophagy. In the absence of Manba, toxic acute tubule injury induced inflammasome activation and fibrosis. Together, these results illustrate the convergence of common noncoding and rare coding variants in MANBA in kidney disease development and demonstrate the role of the endolysosomal system in kidney disease development.

Gain-of-function (GOF) and loss-of-function (LOF) mutations in the same gene may result in markedly different clinical phenotypes and hence require different therapeutic treatments. Identifying the functional consequences of mutations is an important step toward understanding disease mechanisms. While there are numerous computational tools (e.g., CADD, SIFT, PolyPhen-2) to predict the pathogenicity of a variant, there are currently no methods to predict whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity. Here, we investigated various gene- and protein-level features of GOF and LOF mutations. We generated the first extensive database of all currently known germline GOF and LOF pathogenic mutations by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. Machine learning and statistical analyses of gene- and protein-level features associated with GOF and LOF mutations indicated significant differences. For example, GOF mutations were enriched in essential genes, autosomal dominant inheritance, protein binding and interaction domains, whereas LOF mutations were enriched in singleton genes, protein-truncating variants, and protein core regions. These results are consistent with the notion that mutations underlying recessive and dominant disorders have significantly different structural and functional properties. These findings could ultimately improve our understanding of how mutations affect gene/protein function thereby guiding future treatment options.

Background: Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to CT pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods: We use clinical variables, annotated CTPAs, and ECG waveform and morphological parameter data from five hospitals to conduct a retrospective cohort study and develop three models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating tabular clinical data and an embedded representation of the ECG waveform. We benchmark the best model against four clinical scores: Wells’ Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score. Finally, we investigate model robustness through feature sensitivity analyses and assess for demographic subgroup performance parity. Findings: We create a dataset linking 23,793 CTPAs (10·0% PE-positive) and 320,746 ECGs from 21,183 patients for model development and testing. We find that a Fusion model (area under receiver-operating characteristic [AUROC] 0·81 ± 0·01) outperforms both the ECG model (AUROC 0·59 ± 0·01) and EHR model (AUROC 0·65 ± 0·01). On a sample of 100 patients from the test set, the Fusion model has greater specificity (0·18) and performance (AUROC 0·84 ± 0.01) than the all four clinical criteria (AUROC 0·50-0·58, specificity 0·00-0·05). The model also retains superiority over clinical scores in feature sensitivity analyses (AUROC 0·66 to 0·84) and achieves comparable performance across different sex (AUROC 0·81) and racial (AUROC 0·77 to 0·84) subgroups. Interpretation: Integration of electrocardiogram waveforms with traditional clinical variables synergistically increases prediction performance and specificity for PE detection in those who are at least at moderate suspicion for PE. Funding: National Center for Advancing Translational Sciences, National Institutes of Health. Declaration of Interest: All authors declare no competing interests with this study. Ethical Approval: The study was approved by the Mount Sinai Institutional Review Board.

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function., Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät; 19

The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease1. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P

IntroductionThe Coronavirus Disease 2019 (COVID-19) pandemic has resulted in psychological distress in health care workers (HCWs). There is a need to characterize which HCWs are at increased risk of psychological sequela from the pandemic.MethodsHCWs across seven hospitals in New York City were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study App. Participants wore an Apple Watch for the duration of the study measuring HRV throughout the follow up period. Surveys were obtained daily.ResultsThree hundred and sixty-one HCWs were enrolled. Multivariable analysis found New York City COVID-19 case count to be significantly associated with increased longitudinal stress (p=0.008). A non-significant decrease in stress (p=0.23) was observed following COVID-19 diagnosis, though there was a borderline significant increase following the 4-week period after a COVID-19 diagnosis via nasal PCR (p=0.05). Baseline emotional support, baseline quality of life and baseline resilience were associated with decreased longitudinal stress (pConclusionOur findings demonstrate that low resilience, emotional support, and quality of life identify HCWs at risk of high perceived longitudinal stress secondary to the COVID-19 pandemic and have a distinct physiological stress profile. Assessment of HCWs for these features can identify and permit allocation of psychological support to these at-risk individuals as the COVID-19 pandemic and its psychological effects continue in this vulnerable population.

Population-based assessment of disease risk associated with gene variants informs clinical decisions and risk stratification approaches.To evaluate the population-based disease risk of clinical variants in known disease predisposition genes.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.Variants previously reported as pathogenic or predicted to cause a loss of protein function by bioinformatic algorithms (pathogenic/loss-of-function variants).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.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%]).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.

BACKGROUND Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Introduction: The pathogenesis of peripheral artery disease (PAD) and critical limb ischemia (CLI) are poorly understood. Hypothesis: We hypothesize that genetic factors related to abnormalities in coagulation or fibrinolysis, in addition to atherosclerosis, could play an important role in PAD patients and PAD with CLI patients. Methods: A genome-wide association study (GWAS) was performed testing for associations between single-nucleotide variants (SNVs) and PAD case-control (3,190 cases and 463,495 controls) and subgroup analysis of PAD with CLI case-control (142 cases and 3,048 controls) in the UK Biobank cohort. To further validate the results, we selected SNVs with the most significant Cochrane-Armitage trend p values without evidence of strong linkage disequilibrium (r2 > 0.8) for PAD with CLI case-control in the BioMe Biobank. We tested for association using BOLT-LMM with adjustment for age, sex, BMI, and the first ten principal components to control for population structure. The SNV association tests' significance level was set at P < 5x10–8 after Bonferroni correction. Results: 363 SNVs from 81 genetic loci reached the threshold for statistical significance based on a Bonferroni correction (p PLG and CDKN2B ) were independent for PAD with CLI. On pathway analyses, we identify several new loci that implicate thrombotic, inflammation, glycosaminoglycan synthesis, coagulation, and fibrinolytic pathways involved in PAD along with known atherosclerosis pathways (p Conclusions: We show that PLG gene related to coagulation pathways in PAD may play an important role in coagulation-related PAD pathogenesis.

Epidemiological studies suggest that men exhibit a higher mortality rate to COVID-19 than women, yet the underlying biology is largely unknown. Here, we seek to delineate sex differences in the expression of entry genes ACE2 and TMPRSS2, host responses to SARS-CoV-2, and in vitro responses to sex steroid hormone treatment. Using over 220,000 human gene expression profiles covering a wide range of age, tissues, and diseases, we found that male samples show higher expression levels of ACE2 and TMPRSS2, especially in the older group (>60 years) and in the kidney. Analysis of 6,031 COVID-19 patients at Mount Sinai Health System revealed that men have significantly higher creatinine levels, an indicator of impaired kidney function. Further analysis of 782 COVID-19 patient gene expression profiles taken from upper airway and blood suggested men and women present profound expression differences in responses to SARS-CoV-2. Computational deconvolution analysis of these profiles revealed male COVID-19 patients have enriched kidney-specific mesangial cells in blood compared to healthy patients. Finally, we observed selective estrogen receptor modulators, but not other hormone drugs (agonists/antagonists of estrogen, androgen, and progesterone), could reduce SARS-CoV-2 infection in vitro.

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with and observed prior to the clinical identification of infection. We performed an evaluation of this metric collected by wearable devices, to identify and predict Coronavirus disease 2019 (COVID-19) and its related symptoms.MethodsHealth care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study App which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study measuring HRV throughout the follow up period. Survey’s assessing infection and symptom related questions were obtained daily.FindingsUsing a mixed-effect COSINOR model the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), a HRV metric, differed between subjects with and without COVID-19 (p=0.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (p=0.01). Significant changes in the mean MESOR and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19 related symptom compared to all other symptom free days (p=0.01).InterpretationLongitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can identify the diagnosis of COVID-19 and COVID-19 related symptoms. Prior to the diagnosis of COVID-19 by nasal PCR, significant changes in HRV were observed demonstrating its predictive ability to identify COVID-19 infection.FundingSupport was provided by the Ehrenkranz Lab For Human Resilience, the BioMedical Engineering and Imaging Institute, The Hasso Plattner Institute for Digital Health at Mount Sinai, The Mount Sinai Clinical Intelligence Center and The Dr. Henry D. Janowitz Division of Gastroenterology.

BackgroundInterleukin-6 (IL6) signaling is a key inflammatory pathway widely implicated in the pathogenesis of multiple diseases including autoimmune, vascular, and metabolic disorders. While IL6-receptor (IL6R) inhibitors are already in use for the treatment of autoimmune diseases, their repurposing potential and safety profile is still debated.MethodsWe used 7 genetic variants at theIL6Rlocus as proxies for IL6 signaling downregulation and explored their effects on 1,428 clinical outcomes in a phenome-wide association study (PheWAS) using data from the UK Biobank (339,256 unrelated individuals). Significant associations were meta-analyzed with data from the Penn Medicine (10,244 individuals) and BioMe (9,054 individuals) Biobanks for validation. We further investigated associations between genetically downregulated IL6 signaling and 366 biomarkers and endophenotypes of human disease in the UK Biobank and other phenotype-specific consortia. All associations were examined by Mendelian randomization (MR) analyses scaled to the effects of tocilizumab, a monoclonal antibody targeting IL6R.ResultsThe PheWAS-MR analyses showed significant associations with 16 clinical outcomes and 17 biomarkers following correction for multiple comparisons. Genetically downregulated IL6 signaling was associated with a lower risk of several atherosclerotic phenotypes including ischemic heart disease (OR: 0.84, 95%CI: 0.77-0.90) and abdominal aortic aneurysm (OR: 0.44, 95%CI: 0.29-0.67). We further found significant associations with lower risk of type 2 diabetes (OR: 0.80, 95%CI: 0.73-0.88), lower glycated hemoglobin A1c (HbA1c) levels (beta: −0.07, 95%CI: −0.08 to −0.05), and higher HDL-cholesterol levels (beta: 0.04, 95%CI: 0.02-0.06). In accord with clinical trials examining pharmacological IL6 blockade, genetically downregulated IL6 signaling was associated with higher risk of neutropenia and bacterial infections (cellulitis and urinary tract infections) and with higher hemoglobin concentrations. We further found significant associations with higher risk of atopic dermatitis and higher levels of the pro-allergic cytokine interleukin-4.ConclusionsGenetic IL6 signaling downregulation associates with a lower risk of vascular outcomes and a more favorable cardiometabolic profile. These findings further support a repurposing of IL6R blockade for lowering cardiovascular risk while also informing on potential side effects.

Background Patients with pre-existing heart failure (HF) are likely at higher risk for adverse outcomes in coronavirus disease-2019 (COVID-19), but data on this population are sparse. Objectives This study described the clinical profile and associated outcomes among patients with HF hospitalized with COVID-19. Methods This study conducted a retrospective analysis of 6,439 patients admitted for COVID-19 at 1 of 5 Mount Sinai Health System hospitals in New York City between February 27 and June 26, 2020. Clinical characteristics and outcomes (length of stay, need for intensive care unit, mechanical ventilation, and in-hospital mortality) were captured from electronic health records. For patients identified as having a history of HF by International Classification of Diseases-9th and/or 10th Revisions codes, manual chart abstraction informed etiology, functional class, and left ventricular ejection fraction (LVEF). Results Mean age was 63.5 years, and 45% were women. Compared with patients without HF, those with previous HF experienced longer length of stay (8 days vs. 6 days; p, Central Illustration

ObjectiveThe COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive.DesignDemographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive.SettingAll patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system.ParticipantsParticipants over the age of 18 years were included.Primary outcomesWe investigated in-hospital mortality during the study period.ResultsA total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL.ConclusionsIn our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

Poor sleep shows potential causal relationship with risk of developing chronic kidney disease.

Given that gastrointestinal (GI) symptoms are a prominent extrapulmonary manifestation of coronavirus disease 2019 (COVID-19), we investigated the impact of GI infection on disease pathogenesis in three large cohorts of patients in the United States and Europe. Unexpectedly, we observed that GI involvement was associated with a significant reduction in disease severity and mortality, with an accompanying reduction in key inflammatory proteins including IL-6, CXCL8, IL-17A and CCL28 in circulation. In a fourth cohort of COVID-19 patients in which GI biopsies were obtained, we identified severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) within small intestinal enterocytes for the first timein vivobut failed to obtain culturable virus. High dimensional analyses of GI tissues confirmed low levels of cellular inflammation in the GI lamina propria and an active downregulation of key inflammatory genes includingIFNG, CXCL8, CXCL2andIL1Bamong others. These data draw attention to organ-level heterogeneity in disease pathogenesis and highlight the role of the GI tract in attenuating SARS-CoV-2-associated inflammation with related mortality benefit.

Background Thromboembolic disease is common in coronavirus disease-2019 (COVID-19). There is limited evidence on the association of in-hospital anticoagulation (AC) with outcomes and postm...

We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p-8), including 237 attaining a more stringent trans-ancestry threshold (p-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.

BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead.OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging.DESIGN: Individual participant-based meta-analysis.SETTING: 12 research and 21 clinical cohorts.PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein.MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g).RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR.LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample.CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis.PRIMARY FUNDING SOURCE: National Institute of Diabetes and Digestive and Kidney Diseases and National Kidney Foundation.

BACKGROUND Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Background Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Background COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C., Multisystem inflammatory syndrome in children (MIS-C) onsets in COVID-19 patients with manifestations similar to Kawasaki disease (KD). Here the author probe the peripheral blood transcriptome of MIS-C patients to find signatures related to natural killer (NK) cell activation and CD8+ T cell exhaustion that are shared with KD patients.

BACKGROUND COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Does SARS-CoV-2 infection have long-term effects on the kidney? SARS-CoV2 infection and the associated clinical disease, coronavirus disease-19 (COVID-19), have resulted in more than 20 million infections worldwide with an overall mortality rate of 3 9% Initially thought to be a disease of the respiratory system, the clinical syndrome of COVID-19 is actually multifaceted, also affecting the heart, vasculature, and neurological system Acute kidney injury (AKI) is a common complication, affecting up to 40% of patients hospitalized with COVID-19, with 20% of these patients needing acute hemodialysis Along with the shortage of ventilators that has been amply discussed, hospitals suffered from an acute shortage of dialysis machines during case surges Although the epidemiology of in-hospital AKI has been shown before in reports from China, Europe, and the United States of America, Fisher et al delved deeper into the effects of COVID-19 on the kidney They retrospectively evaluated AKI incidence, risk factors, and outcomes for hospitalized adult patients with and without COVID-19 in a large New York City health system They showed increased AKI incidence, greater need for acute dialysis, and higher mortality in those with COVID-19 They also identified several factors that were significantly associated with AKI, regardless of COVID-19 status Critically, they also assessed how many individuals recovered kidney function during hospitalization after an AKI episode Only 42% of patients with COVID-19 and AKI recovered baseline kidney function compared to 70% without COVID-19 Considering the substantial evidence linking in-hospital AKI, and more importantly, non-recovery of kidney function after an acute episode, with long-term mortality and worse kidney function, this may herald the onset of a coming post-COVID kidney disease epidemic This may be worsened by the fact that regular physician visits have decreased during this pandemic, and as a result, many patients with chronic disease may not be monitored regularly Although the research and medical community's focus has been on the acute care of patients with COVID-19, the long-term effects of infection are slowly coming into focus Integrated research and clinical care approaches are urgently needed to better understand and address what is likely to be a growing at-risk population

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

While increased obesity prevalence among persons of African ancestry (AAs) compared to persons of European ancestry (EAs) is linked to social, environmental and behavioral factors, there are no gene variants that are common and significantly associated with obesity in AA populations. We sought to explore the association between ancestry specific renal risk variants in the apolipoprotein L1 (APOL1) gene with obesity related traits in AAs.We conducted a genotype-phenotype association study from 3 electronic medical record linked cohorts (BioMe Biobank, BioVU, nuGENE); randomized controlled trials (genetic testing to understand and address renal disease disparities) and prospective cohort study (Jackson Heart Study). We analyzed association of APOL1 renal risk variants with cross-sectional measures of obesity (average body mass index (BMI), and proportion of overweight and obesity) and with measures of body composition (in Jackson Heart Study).We had data on 11,930 self-reported AA adults. Across cohorts, mean age was from 42 to 49 years and percentage female from 58% to 75.3%. Individuals who have 2 APOL1 risk alleles (14% of AAs) have 30% higher obesity odds compared to others (recessive model adjusted odds ratio 1.30; 95% confidence interval 1.16-1.41; P = 2.75 × 10-6). An additive model better fit the association, in which each allele (47% of AAs) increases obesity odds by 1.13-fold (adjusted odds ratio 1.13; 95% confidence interval 1.07-1.19; P = 3.07 × 10-6) and increases BMI by 0.36 kg/m2 (∼1 kg, for 1.7 m height; P = 2 × 10-4). APOL1 alleles are not associated with refined body composition traits overall but are significantly associated with fat free mass index in women [0.30 kg/m2 increment per allele; P = .03].Thus, renal risk variants in the APOL1 gene, found in nearly half of AAs, are associated with BMI and obesity in an additive manner. These variants could, either on their own or interacting with environmental factors, explain a proportion of ethnic disparities in obesity.

Inflammatory bowel disease (IBD) is a group of chronic diseases, affecting different parts of the gastrointestinal tract, that mainly comprises Crohn’s Disease (CD) and Ulcerative Colitis (UC). Most IBD genomic research to date has involved genome-wide association studies (GWAS) of common genetic variants, mostly in Europeans, resulting in the identification of over 200 risk loci. The incidence of IBD in Ashkenazi Jews (AJ) is particularly high compared to other population groups and rare protein-coding variants are significantly enriched in AJ. These variants are expected to have a larger phenotypic effect and are hypothesized to complement the missing heritability that cannot be fully addressed by GWAS in IBD. Therefore, we genetically identified 4,974 AJs IBD cases and controls from whole exome sequencing (WES) data from the NIDDK IBD Genetics Consortium (IBDGC). We selected credible rare variants with high predicted impact, aggregated them into genes, and performed gene burden and pathway enrichment analyses to identify 7 novel plausible IBD-causing genes:NCF1, CES1, ICAM1, INPP5D, ABCB1, IL33andTLR4. We further perform bulk and single-cell RNA sequencing, demonstrating the likely relatedness of the novel genes to IBD. Importantly, we demonstrate that the rare and high impact genetic architecture of AJ adult IBD displays a significant overlap with very early onset IBD (VEOIBD) genetics. At the variant level, we performed Phenome-wide association studies (PheWAS) in the UK Biobank to replicate risk sites in IBD and reveal shared risk sites with other diseases. Finally, we showed that a polygenic risk score (PRS) has high power to differentiate AJ IBD cases from controls when using rare and high impact variants.

BackgroundThere are limited data regarding the clinical impact of coronavirus disease 2019 (COVID-19) on people living with human immunodeficiency virus (PLWH). In this study, we compared outcomes for PLWH with COVID-19 to a matched comparison group.MethodsWe identified 88 PLWH hospitalized with laboratory-confirmed COVID-19 in our hospital system in New York City between 12 March and 23 April 2020. We collected data on baseline clinical characteristics, laboratory values, HIV status, treatment, and outcomes from this group and matched comparators (1 PLWH to up to 5 patients by age, sex, race/ethnicity, and calendar week of infection). We compared clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these groups, as well as cumulative incidence of death by HIV status.ResultsPatients did not differ significantly by HIV status by age, sex, or race/ethnicity due to the matching algorithm. PLWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PLWH had greater proportions of smoking (P < .001) and comorbid illness than uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (P = .15). Poor outcomes for hospitalized PLWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and 21% died during follow-up (compared with 23% and 20%, respectively). There was similar cumulative incidence of death over time by HIV status (P = .94).ConclusionsWe found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared with a demographically similar patient group.

Multimodal measurements and machine learning provide evidence for personalized dietary advice.

Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 – 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.

ImportanceDiabetic kidney disease (DKD) is the leading cause of kidney failure in the United States and predicting progression is necessary for improving outcomes.ObjectiveTo develop and validate a machine-learned, prognostic risk score (KidneyIntelX™) combining data from electronic health records (EHR) and circulating biomarkers to predict DKD progression.DesignObservational cohort studySettingTwo EHR linked biobanks: Mount Sinai BioMe Biobank and the Penn Medicine Biobank.ParticipantsPatients with prevalent DKD (G3a-G3b with all grades of albuminuria (A1-A3) and G1 & G2 with A2-A3 level albuminuria) and banked plasma.Main outcomes and measuresPlasma biomarkers soluble tumor necrosis factor 1/2 (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) were measured at baseline. Patients were divided into derivation [60%] and validation sets [40%]. The composite primary end point, progressive decline in kidney function, including the following: rapid kidney function decline (RKFD) (estimated glomerular filtration rate (eGFR) decline of ≥5 ml/min/1.73m2/year), ≥40% sustained decline, or kidney failure within 5 years. A machine learning model (random forest) was trained and performance assessed using standard metrics.ResultsIn 1146 patients with DKD the median age was 63, 51% were female, median baseline eGFR was 54 ml/min/1.73 m2, urine albumin to creatinine ratio (uACR) was 61 mg/g, and follow-up was 4.3 years. 241 patients (21%) experienced progressive decline in kidney function. On 10-fold cross validation in the derivation set (n=686), the risk model had an area under the curve (AUC) of 0.77 (95% CI 0.74-0.79). In validation (n=460), the AUC was 0.77 (95% CI 0.76-0.79). By comparison, the AUC for an optimized clinical model was 0.62 (95% CI 0.61-0.63) in derivation and 0.61 (95% CI 0.60-0.63) in validation. Using cutoffs from derivation, KidneyIntelX stratified 46%, 37% and 16.5% of validation cohort into low-, intermediate- and high-risk groups, with a positive predictive value (PPV) of 62% (vs. PPV of 37% for the clinical model and 40% for KDIGO; p < 0.001) in the high-risk group and a negative predictive value (NPV) of 91% in the low-risk group. The net reclassification index for events into high-risk group was 41% (pConclusions and RelevanceA machine learned model combining plasma biomarkers and EHR data improved prediction of progressive decline in kidney function within 5 years over KDIGO and standard clinical models in patients with early DKD.

Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery. Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes(1,2). Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease(3,4), suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns(5-8), the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)(9), 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work(10), confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.

The ability to predict rapid kidney function decline (RKFD) in patients with early stages of type 2 diabetic kidney disease (T2DKD) can improve long-term health outcomes through earlier intervention. Our objective was to develop a robust risk score for patients with early stages of T2DKD. We obtained EHR and plasma from patients with T2DKD (Kidney Disease Improving Global Outcomes [KDIGO] G1-G2, A2-A3 or G3a-G3b, A1-A3) from the Mount Sinai BioMe Biobank and the Penn Medicine Biobank and measured soluble tumor necrosis factor receptor (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) via a validated quantitative electrochemiluminescent assay. A machine learning model was trained using the EHR/biomarker data to predict RKFD, defined as eGFR decline of ≥5 ml/min/year or ≥40% sustained decline in eGFR or kidney failure within 5 years, and performance was compared to a clinical model and KDIGO risk-matrix. A total of 1146 subjects with a mean age of 63, baseline eGFR 54 ml/min, and median UACR 61 mg/g were randomly divided into 60% training (n=686) and 40% validation (n=460) sets. The median follow-up was 4.3 years and 241 (21%) experienced RKFD. The AUC of KidneyIntelX for RFKD was 0.85 (95% CI 0.84-0.86) in training and 0.77 (95% CI 0.76-0.79) in the validation set vs. AUC of 0.67 (95% CI 0.64-0.70) for a clinical model. Using pre-specified cutoffs from the training set, KidneyIntelX stratified 47%, 37% and 16% of the validation set into low-, intermediate- and high-risk strata, with a PPV of 62% (vs. 41% for KDIGO) in the high-risk group (p Disclosure L. Chan: None. G.N. Nadkarni: Advisory Panel; Self; Renalytix AI. Consultant; Self; AstraZeneca, Reata, Renalytix AI. Stock/Shareholder; Self; Renalytix AI. Other Relationship; Self; Renalytix AI. F. Fleming: Board Member; Self; Renalytix AI plc. F.E. Salem: None. B. Murphy: Board Member; Self; RenalytixAI. M.J. Donovan: Consultant; Self; Renalytix AI plc. S. Coca: Advisory Panel; Self; Renalytix AI plc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., CHF Solutions, Relypsa, Inc., Takeda Pharmaceutical Company Limited. Stock/Shareholder; Self; Renalytix AI plc. S.M. Damrauer: Consultant; Spouse/Partner; Rhythm Pharmaceuticals. Research Support; Self; CytoVas LLC, Renalytix AI plc. Funding Renalytix AI plc

The degree of myocardial injury, as reflected by troponin elevation, and associated outcomes among U.S. hospitalized patients with coronavirus disease-2019 (COVID-19) are unknown.The purpose of this study was to describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19.Patients with COVID-19 admitted to 1 of 5 Mount Sinai Health System hospitals in New York City between February 27, 2020, and April 12, 2020, with troponin-I (normal value 0.03 ng/ml) measured within 24 h of admission were included (n = 2,736). Demographics, medical histories, admission laboratory results, and outcomes were captured from the hospitals' electronic health records.The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD), including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. In all, 985 (36%) patients had elevated troponin concentrations. After adjusting for disease severity and relevant clinical factors, even small amounts of myocardial injury (e.g., troponin I0.03 to 0.09 ng/ml; n = 455; 16.6%) were significantly associated with death (adjusted hazard ratio: 1.75; 95% CI: 1.37 to 2.24; p 0.001) while greater amounts (e.g., troponin I0.09 ng/dl; n = 530; 19.4%) were significantly associated with higher risk (adjusted HR: 3.03; 95% CI: 2.42 to 3.80; p 0.001).Myocardial injury is prevalent among patients hospitalized with COVID-19; however, troponin concentrations were generally present at low levels. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation among patients hospitalized with COVID-19 is associated with higher risk of mortality.

ImportancePreliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described.ObjectiveTo provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients.DesignObservational, retrospective study.SettingAdmitted to hospital between February 27 and April 15, 2020.ParticipantsPatients aged ≥18 years with laboratory confirmed COVID-19ExposuresAKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline).Main Outcomes and MeasuresFrequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation.ResultsA total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test.Conclusions and RelevanceAKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.Key PointsQuestionWhat is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19?FindingsIn this observational study of 3,235 hospitalized patients with COVID-19 in New York City, AKI occurred in 46% of patients and 20% of those patients required dialysis. AKI was associated with increased mortality. 44% of patients discharged alive had residual acute kidney disease. A machine learned predictive model using baseline features for dialysis requirement had an AUC Of 0.79.MeaningAKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge.

ImportanceThere is an urgent need to understand patient characteristics of having COVID-19 disease and evaluate markers of critical illness and mortality.ObjectiveTo assess association of clinical features on patient outcomes.Design, Setting, and ParticipantsIn this observational case series, patient-level data were extracted from electronic medical records for 28,336 patients tested for SARS-CoV-2 at the Mount Sinai Health System from 2/24/ to 4/15/2020, including 6,158 laboratory-confirmed cases.ExposuresConfirmed COVID-19 diagnosis by RT-PCR assay from nasal swabs.Main Outcomes and MeasuresEffects of race on positive test rates and mortality were assessed. Among positive cases admitted to the hospital (N = 3,273), effects of patient demographics, hospital site and unit, social behavior, vital signs, lab results, and disease comorbidities on discharge and death were estimated.ResultsHispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to population base rates (ppConclusions and RelevanceWhile race was associated with higher risk of infection, we did not find a racial disparity in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. We identified clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk and evaluate the impact on survival.

The novel coronavirus disease-2019 (COVID-19) has affected nearly every country worldwide. Reports note increased thromboembolic events among hospitalized patients ([1][1],[2][2]), and there are anecdotal observations of improved outcomes with systemic anticoagulation (AC); however, the specific

Multifaceted nonpharmaceutical interventions supported by data restricted spread of COVID-19 in China.

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions.One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.

BackgroundThe coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States.MethodsDemographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive.ResultsA total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL.ConclusionsThis is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes

Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features-tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint-confer a 2.6-fold greater risk of side effects, compared to genes without such features. The presence of eQTL in multiple tissues resulted in more unique phenotypes driven by GWA loci, suggesting that drugs delivered to multiple tissues can induce several side effects. We demonstrate the utility of PheWAS and eQTL data from multiple tissues for informing drug side effect prediction and highlight the need for tissue-specific drug delivery.

suPAR might serve as a biomarker for prediction of acute kidney injury with possible therapeutic implications.

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (P &lt, 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at P = 0.016 and P = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring&rsquo, s total sleep duration and efficiency in relation to the PSQI measure was statistically significant at P = 0.004 and P = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research.

Significant disparities exist in kidney disease, with blacks facing a higher burden of CKD and kidney failure. In addition to socioeconomic/health system factors, these disparities are linked to two sequence variants (G1 and G2) in the apoL1 ( APOL1 ) gene. The high-risk APOL1 genotype (two copies