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Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.

Risk variants in the apolipoprotein L1 (APOL1 [OMIM 603743]) gene on chromosome 22 are common in individuals of West African ancestry and confer increased risk of kidney failure for people with African ancestry and hypertension. Whether disclosing APOL1 genetic testing results to patients of African ancestry and their clinicians affects blood pressure, kidney disease screening, or patient behaviors is unknown.To determine the effects of testing and disclosing APOL1 genetic results to patients of African ancestry with hypertension and their clinicians.This pragmatic randomized clinical trial randomly assigned 2050 adults of African ancestry with hypertension and without existing chronic kidney disease in 2 US health care systems from November 1, 2014, through November 28, 2016; the final date of follow-up was January 16, 2018. Patients were randomly assigned to undergo immediate (intervention) or delayed (waiting list control group) APOL1 testing in a 7:1 ratio. Statistical analysis was performed from May 1, 2018, to July 31, 2020.Patients randomly assigned to the intervention group received APOL1 genetic testing results from trained staff; their clinicians received results through clinical decision support in electronic health records. Waiting list control patients received the results after their 12-month follow-up visit.Coprimary outcomes were the change in 3-month systolic blood pressure and 12-month urine kidney disease screening comparing intervention patients with high-risk APOL1 genotypes and those with low-risk APOL1 genotypes. Secondary outcomes compared these outcomes between intervention group patients with high-risk APOL1 genotypes and controls. Exploratory analyses included psychobehavioral factors.Among 2050 randomly assigned patients (1360 women [66%]; mean [SD] age, 53 [10] years), the baseline mean (SD) systolic blood pressure was significantly higher in patients with high-risk APOL1 genotypes vs those with low-risk APOL1 genotypes and controls (137 [21] vs 134 [19] vs 133 [19] mm Hg; P = .003 for high-risk vs low-risk APOL1 genotypes; P = .001 for high-risk APOL1 genotypes vs controls). At 3 months, the mean (SD) change in systolic blood pressure was significantly greater in patients with high-risk APOL1 genotypes vs those with low-risk APOL1 genotypes (6 [18] vs 3 [18] mm Hg; P = .004) and controls (6 [18] vs 3 [19] mm Hg; P = .01). At 12 months, there was a 12% increase in urine kidney disease testing among patients with high-risk APOL1 genotypes (from 39 of 234 [17%] to 68 of 234 [29%]) vs a 6% increase among those with low-risk APOL1 genotypes (from 278 of 1561 [18%] to 377 of 1561 [24%]; P = .10) and a 7% increase among controls (from 33 of 255 [13%] to 50 of 255 [20%]; P = .01). In response to testing, patients with high-risk APOL1 genotypes reported more changes in lifestyle (a subjective measure that included better dietary and exercise habits; 129 of 218 [59%] vs 547 of 1468 [37%]; P .001) and increased blood pressure medication use (21 of 218 [10%] vs 68 of 1468 [5%]; P = .005) vs those with low-risk APOL1 genotypes; 1631 of 1686 (97%) declared they would get tested again.In this randomized clinical trial, disclosing APOL1 genetic testing results to patients of African ancestry with hypertension and their clinicians was associated with a greater reduction in systolic blood pressure, increased kidney disease screening, and positive self-reported behavior changes in those with high-risk genotypes.ClinicalTrials.gov Identifier: NCT02234063.

OBJECTIVE Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

INTRODUCTIONEarly detection of patients with cognitive impairment may facilitate care for individuals in this population. Natural language processing (NLP) is a potential approach to identifying patients with cognitive impairment from electronic health records (EHR).METHODSWe used three machine learning algorithms (logistic regression, multilayer perceptron, and random forest) using clinical terms extracted by NLP to predict cognitive impairment in a cohort of 199 patients. Cognitive impairment was defined as a mini-mental status exams (MMSE) score RESULTSNLP identified 69 (35%) patients with cognitive impairment and ICD codes identified 44 (22%). Using MMSE as a reference standard, NLP sensitivity was 35%, specificity 66%, precision 41%, and NPV 61%. The random forest method had the best test parameters; sensitivity 95%, specificity 100%, precision 100%, and NPV 97%DISCUSSIONNLP can identify adults with cognitive impairment with moderate test performance that is enhanced with machine learning.

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.

We explored rates of premature births and neonatal intensive care unit (NICU) admissions at the Mount Sinai Hospital after the implementation of COVID-19 lockdown measures (March 16, 2020) and phase one reopening (June 8, 2020), comparing them to those of the same time periods from 2012-2019. Mount Sinai Hospital is in New York City (NYC), an early epicenter of COVID-19 in the United States, which was heavily impacted by the pandemic during the study period. Among 43,963 singleton births, we observed no difference in either outcome after the implementation of lockdown measures when compared to the same trends in prior years (p=0.09-0.35). Of interest, we observed a statistically significant decrease in premature births after NYC phase one reopening compared to those of the same time period in 2012-2019 across all time windows (p=0.0028-0.049), and a statistically significant decrease in NICU admissions over the largest time window (2.75 months) compared to prior years (p=0.0011).

Background Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. Methods This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.

Background Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. Objective To describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge. Design Retrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons. Participants Hospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals. Main Measures Readmission or return to ED following discharge. Results Of 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; Padjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. Conclusions Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions. Electronic supplementary material The online version of this article (10.1007/s11606-020-06120-6) contains supplementary material, which is available to authorized users.

INTRODUCTION A previous study demonstrated that the surface area-normalized standard Kt/V (SAstdKt/V) was better associated with mortality than standard Kt/V (stdKt/V). This study investigates the association of SAstdKt/V and stdKt/V with mortality, anemia, and hypoalbuminemia in a larger patient cohort with a longer follow-up period. METHODS We included adult patients on thrice-weekly hemodialysis in the USRDS database and excluded amputated patients. StdKt/V and SAstdKt/V were calculated from the available single-pool Kt/V. Patients were categorized into five groups according to their stdKt/V and SAstdKt/V

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning’s ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML’s growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear.This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention.A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy.Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor.Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.

PURPOSE OF REVIEW: The universal adoption of electronic health records, improvement in technology, and the availability of continuous monitoring has generated large quantities of healthcare data. Machine learning is increasingly adopted by nephrology researchers to analyze this data in order to improve the care of their patients. RECENT FINDINGS: In this review, we provide a broad overview of the different types of machine learning algorithms currently available and how researchers have applied these methods in nephrology research. Current applications have included prediction of acute kidney disease and chronic kidney disease along with progression of kidney disease. Researchers have demonstrated the ability of machine learning to read kidney biopsy samples, identify patient outcomes from unstructured data, identify subtypes in complex diseases, and discuss the potential benefits on drug discovery. We end with a discussion on the ethics and potential pitfalls of machine learning. SUMMARY: Machine learning provides researchers with the ability to analyze data which was previously inaccessible. While still burgeoning several studies show promising results which will enable researchers to perform larger scale studies and clinicians the ability to provide more personalized care. However, we must ensure that implementation aids providers and does not lead to harm to patients.

BACKGROUND: Pregnancy-related AKI (PR-AKI) is increasing in the United States. PR-AKI is associated with adverse maternal outcomes. Disparities in racial/ethnic differences in PR-AKI by race have not been studied. METHODS: This was a retrospective cohort study using the National Inpatient Sample (NIS) from 2005 to 2015. We identified patients who were admitted for a pregnancy-related diagnosis using the Neomat variable provided by the NIS database that indicates the presence of a maternal or neonatal diagnosis code or procedure code. PR-AKI was identified using ICD codes. Survey logistic regression was used for multivariable analysis adjusting for age, medical comorbidities, socioeconomic factors, and hospital/admission factors. RESULTS: From 48,316,430 maternal hospitalizations, 34,001 (0.07%) were complicated by PR-AKI. Hospitalizations for PR-AKI increased from 3.5/10,000 hospitalizations in 2005 to 11.8/10,000 hospitalizations in 2015 with the largest increase seen in patients aged ≥35 and black patients. PR-AKI was associated with higher odds of miscarriage (adjusted odds ratio [aOR], 1.64; 95% CI, 1.34 to 2.07) and mortality (aOR, 1.53; 95% CI, 1.25 to 1.88). After adjustment for age, medical comorbidities, and socioeconomic factors, blacks were more likely than whites to develop PR-AKI (aOR, 1.17; 95% CI, 1.04 to 1.33). On subgroup analyses in hospitalizations of patients with PR-AKI, blacks and Hispanics were more likely to have preeclampsia/eclampsia compared with whites (aOR, 1.29; 95% CI, 1.01 to 1.65; and aOR, 1.69; 95% CI, 1.23 to 2.31, respectively). Increased odds of mortality in PR-AKI compared with whites were only seen in black patients (aOR, 1.61; 95% CI, 1.02 to 2.55). CONCLUSIONS: The incidence of PR-AKI has increased and the largest increase was seen in older patients and black patients. PR-AKI is associated with miscarriages, adverse discharge from hospital, and mortality. Black and Hispanic patients with PR-AKI were more likely to have adverse outcomes than white patients. Further research is needed to identify factors contributing to these discrepancies.

Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language processing (NLP) can be utilized to identify symptoms from narrative clinical documentation. Here we utilized NLP to identify seven patient symptoms from notes of maintenance hemodialysis patients of the BioMe Biobank and validated our findings using a separate cohort and the MIMIC-III database. NLP performance was compared for symptom detection with International Classification of Diseases (ICD)-9/10 codes and the performance of both methods were validated against manual chart review. From 1034 and 519 hemodialysis patients within BioMe and MIMIC-III databases, respectively, the most frequently identified symptoms by NLP were fatigue, pain, and nausea/vomiting. In BioMe, sensitivity for NLP (0.85 - 0.99) was higher than for ICD codes (0.09 - 0.59) for all symptoms with similar results in the BioMe validation cohort and MIMIC-III. ICD codes were significantly more specific for nausea/vomiting in BioMe and more specific for fatigue, depression, and pain in the MIMIC-III database. A majority of patients in both cohorts had four or more symptoms. Patients with more symptoms identified by NLP, ICD, and chart review had more clinical encounters. NLP had higher specificity in inpatient notes but higher sensitivity in outpatient notes and performed similarly across pain severity subgroups. Thus, NLP had higher sensitivity compared to ICD codes for identification of seven common hemodialysis-related symptoms, with comparable specificity between the two methods. Hence, NLP may be useful for the high-throughput identification of patient-centered outcomes when using electronic health records.

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Objective To determine whether surge conditions were associated with increased mortality. Design Multicenter cohort study. Setting U.S. ICUs participating in STOP-COVID. Patients Consecutive adults with COVID-19 admitted to participating ICUs between March 4 and July 1, 2020. Interventions None Measurements and Main Results The main outcome was 28-day in-hospital mortality. To assess the association between admission to an ICU during a surge period and mortality, we used two different strategies: (1) an inverse probability weighted difference-in-differences model limited to appropriately matched surge and non-surge patients and (2) a meta-regression of 50 multivariable difference-in-differences models (each based on sets of randomly matched surge- and non-surge hospitals). In the first analysis, we considered a single surge period for the cohort (March 23 – May 6). In the second, each surge hospital had its own surge period (which was compared to the same time periods in matched non-surge hospitals). Our cohort consisted of 4342 ICU patients (average age 60.8 [sd 14.8], 63.5% men) in 53 U.S. hospitals. Of these, 13 hospitals encountered surge conditions. In analysis 1, the increase in mortality seen during surge was not statistically significant (odds ratio [95% CI]: 1.30 [0.47-3.58], p = .6). In analysis 2, surge was associated with an increased odds of death (odds ratio 1.39 [95% CI, 1.34-1.43], p Conclusions Admission to an ICU with COVID-19 in a hospital that is experiencing surge conditions may be associated with an increased odds of death. Given the high incidence of COVID-19, such increases would translate into substantial excess mortality.

UNSTRUCTURED N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.

N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice., Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät; 12

IntroductionAKI is a heterogeneous syndrome defined via serum creatinine and urine output criteria. However, these markers are insufficient to capture the biological complexity of AKI and not necessarily inform on future risk of kidney and clinical events.MethodsData from ASSESS-AKI was obtained and analyzed to uncover different clinical and biological signatures within AKI. We utilized a set of unsupervised machine learning algorithms incorporating a comprehensive panel of systemic and organ-specific biomarkers of inflammation, injury, and repair/health integrated into electronic data. Furthermore, the association of these novel biomarker-enriched subphenotypes with kidney and cardiovascular events and death was determined. Clinical and biomarker concentration differences among subphenotypes were evaluated via classic statistics. Kaplan-Meier and cumulative incidence curves were obtained to evaluate longitudinal outcomes.ResultsAmong 1538 patients from ASSESS-AKI, we included 748 AKI patients in the analysis. The median follow-up time was 4.8 years. We discovered 4 subphenotypes via unsupervised learning. Patients with AKI subphenotype 1 (‘injury’ cluster) were older (mean age ± SD): 71.2 ± 9.4 (pConclusionWe discovered four clinically meaningful AKI subphenotypes with statistical differences in biomarker composites that associate with longitudinal risks of adverse clinical events. Our approach is a novel look at the potential mechanisms underlying AKI and the putative role of biomarkers investigation.

Background Fluid management during continuous renal replacement therapy (CRRT) requires accuracy in the prescription of desired patient fluid balance (FBGoal) and precision in the attainable patient fluid balance (FBAchieved). Herein, we examined the association of the gap between prescribed vs. achieved patient fluid balance during CRRT (%FBGap) with hospital mortality in critically ill patients. Methods Cohort study of critically ill adults with acute kidney injury (AKI) requiring CRRT and a prescription of negative fluid balance (mean patient fluid balance goal of negative ≥0.5 liters per day). Fluid management parameters included: 1) NUF (net ultrafiltration rate); 2) FBGoal; 3) FBAchieved; and 4) FBGap (% gap of fluid balance achieved vs. goal), all adjusted by patient’s weight (kg) and duration of CRRT (hours). Results Data from 653 patients (median of 102.2 patient-hours of CRRT) were analyzed. Mean (SD) age was 56.7 (14.6) years and 61.9% were male. Hospital mortality rate was 64%. Despite FBGoal was similar in patients who died vs. survived, survivors achieved greater negative fluid balance during CRRT than non-survivors: median FBAchieved -0.25 [-0.52 to -0.05] vs. 0.06 [-0.26 to 0.62] ml/kg/h, ppGap was higher in patients who died (112.8%, 61.5 to 165.7) vs. survived (64.2%, 30.5 to 91.8), pGap was independently associated with increased risk of hospital mortality: aOR (95% CI) 1.01 (1.01–1.02), pGap and other clinical parameters: aOR 0.96 (0.72–1.28), p = 0.771. Conclusions Higher %FBGap was independently associated with an increased risk of hospital mortality in critically ill adults with AKI on CRRT in whom clinicians prescribed negative fluid balance via CRRT. %FBGap represents a novel quality indicator of CRRT delivery that could assist with operationalizing fluid management interventions during CRRT.

The burden of advanced chronic kidney disease (CKD) falls disproportionately on minorities including African Americans (AAs) and Hispanic Americans (HAs) with admixed ancestry. Even though APOL1 high-risk genotypes increase risk of kidney disease, their penetrance is incomplete, indicating that the modification of APOL1 high risk may be polygenic. For this study, we used three multi-ethnic cohorts with APOL1 high risk genotypes and calculated a multi-ethnic PRS using publicly available summary statistics. We show that CKD risk is significantly modified by a multi-ethnic polygenic risk score. Standardizing population screening for CKD by including APOL1 high-risk genotypes and polygenic risk score may improve risk stratification and outcomes.

Clinical, laboratory, and autopsy findings support an association between coronavirus disease-2019 (COVID-19) and thromboembolic disease. Acute COVID-19 infection is characterized by mononuclear cell reactivity and pan-endothelialitis, contributing to a high incidence of thrombosis in large and small blood vessels, both arterial and venous. Observational studies and randomized trials have investigated whether full-dose anticoagulation may improve outcomes compared with prophylactic dose heparin. Although no benefit for therapeutic heparin has been found in patients who are critically ill hospitalized with COVID-19, some studies support a possible role for therapeutic anticoagulation in patients not yet requiring intensive care unit support. We summarize the pathology, rationale, and current evidence for use of anticoagulation in patients with COVID-19 and describe the main design elements of the ongoing FREEDOM COVID-19 Anticoagulation trial, in which 3,600 hospitalized patients with COVID-19 not requiring intensive care unit level of care are being randomized to prophylactic-dose enoxaparin vs therapeutic-dose enoxaparin vs therapeutic-dose apixaban. (FREEDOM COVID-19 Anticoagulation Strategy [FREEDOM COVID]; NCT04512079).

ImportancePassive and non-invasive identification of SARS-CoV-2 infection remains a challenge. Widespread use of wearable devices represents an opportunity to leverage physiological metrics and fill this knowledge gap.ObjectiveTo determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.DesignA multicenter observational study enrolling health care workers with remote follow-up.SettingSeven hospitals from the Mount Sinai Health System in New York CityParticipantsEligibility criteria included health care workers who were ≥18 years, employees of one of the participating hospitals, with at least an iPhone series 6, and willing to wear an Apple Watch Series 4 or higher. We excluded participants with underlying autoimmune/inflammatory diseases, and medications known to interfere with autonomic function. We enrolled participants between April 29th, 2020, and March 2nd, 2021, and followed them for a median of 73 days (range, 3-253 days). Participants provided patient-reported outcome measures through a custom smartphone application and wore an Apple Watch, collecting heart rate variability and heart rate data, throughout the follow-up period.ExposureParticipants were exposed to SARS-CoV-2 infection over time due to ongoing community spread.Main Outcome and MeasureThe primary outcome was SARS-CoV-2 infection, defined as ±7 days from a self-reported positive SARS-CoV-2 nasal PCR test.ResultsWe enrolled 407 participants with 49 (12%) having a positive SARS-CoV-2 test during follow-up. We examined five machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable 10-CV performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC)=85% (Confidence Interval 83-88%). The model was calibrated to improve sensitivity over specificity, achieving an average sensitivity of 76% (CI ±∼4%) and specificity of 84% (CI ±∼0.4%). The most important predictors included parameters describing the circadian HRV mean (MESOR) and peak-timing (acrophase), and age.Conclusions and RelevanceWe show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV2 infection. Utilizing physiological metrics from wearable devices may improve screening methods and infection tracking.

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 Bio Me 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 Bio Me (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.

Summary ParagraphTwo years into the SARS-CoV-2 pandemic, the post-acute sequelae of infection are compounding the global health crisis. Often debilitating, these sequelae are clinically heterogeneous and of unknown molecular etiology. Here, a transcriptome-wide investigation of this new condition was performed in a large cohort of acutely infected patients followed clinically into the post-acute period. Gene expression signatures of post-acute sequelae were already present in whole blood during the acute phase of infection, with both innate and adaptive immune cells involved. Plasma cells stood out as driving at least two distinct clusters of sequelae, one largely dependent on circulating antibodies against the SARS-CoV-2 spike protein and the other antibody-independent. Altogether, multiple etiologies of post-acute sequelae were found concomitant with SARS-CoV-2 infection, directly linking the emergence of these sequelae with the host response to the virus.

Cardiovascular disease (CVD) is a major cause of mortality among people with diabetic kidney disease (DKD). The pathophysiology is inadequately explained by traditional CVD risk factors. The uremic solutes trimethylamine-N-oxide (TMAO) and asymmetric and symmetric dimethylarginine (ADMA, SDMA) have been linked to CVD in kidney failure with replacement therapy (KFRT), but data are limited in populations with diabetes and less severe kidney disease.Observational cohort.Random subcohort of 555 REGARDS (Reasons for Geographic and Racial Differences in Stroke) study participants with diabetes and estimated glomerular filtration rate (eGFR) 60 mL/min/1.73 mADMA, SDMA, and TMAO assayed by liquid chromatography-mass spectrometry in plasma and urine.Cardiovascular mortality (primary outcome); all-cause mortality and incident KFRT (secondary outcomes).Plasma concentrations and ratios of urine to plasma concentrations of ADMA, SDMA, and TMAO were tested for association with outcomes. Adjusted Cox regression models were fitted and hazard ratios of outcomes calculated per standard deviation and per doubling, and as interquartile comparisons.The mean baseline eGFR was 44 mL/min/1.73 mSingle cohort, restricted to patients with diabetes and eGFR 60 mL/min/1.73 mHigher plasma concentrations and lower ratios of urine to plasma concentrations of uremic solutes were independently associated with cardiovascular and all-cause mortality in DKD. Associations of ratios of urine to plasma concentrations with mortality suggest a connection between renal uremic solute clearance and CVD pathogenesis.

Rationale & Objective Outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19) and acute kidney injury (AKI) are not well understood. The goal of this study was to investigate the survival and kidney outcomes of these patients. Study Design Retrospective cohort study. Setting & Participants Patients (aged ≥18 years) hospitalized with COVID-19 at 13 hospitals in metropolitan New York between March 1, 2020, and April 27, 2020, followed up until hospital discharge. Exposure AKI. Outcomes Primary outcome: in-hospital death. Secondary outcomes: requiring dialysis at discharge, recovery of kidney function. Analytical Approach Univariable and multivariable time-to-event analysis and logistic regression. Results Among 9,657 patients admitted with COVID-19, the AKI incidence rate was 38.4/1,000 patient-days. Incidence rates of in-hospital death among patients without AKI, with AKI not requiring dialysis (AKI stages 1-3), and with AKI receiving dialysis (AKI 3D) were 10.8, 31.1, and 37.5/1,000 patient-days, respectively. Taking those without AKI as the reference group, we observed greater risks for in-hospital death for patients with AKI 1-3 and AKI 3D (HRs of 5.6 [95% CI, 5.0-6.3] and 11.3 [95% CI, 9.6-13.1], respectively). After adjusting for demographics, comorbid conditions, and illness severity, the risk for death remained higher among those with AKI 1-3 (adjusted HR, 3.4 [95% CI, 3.0-3.9]) and AKI 3D (adjusted HR, 6.4 [95% CI, 5.5-7.6]) compared with those without AKI. Among patients with AKI 1-3 who survived, 74.1% achieved kidney recovery by the time of discharge. Among those with AKI 3D who survived, 30.6% remained on dialysis at discharge, and prehospitalization chronic kidney disease was the only independent risk factor associated with needing dialysis at discharge (adjusted OR, 9.3 [95% CI, 2.3-37.8]). Limitations Observational retrospective study, limited to the NY metropolitan area during the peak of the COVID-19 pandemic. Conclusions AKI in hospitalized patients with COVID-19 was associated with significant risk for death., Graphical abstract

Introduction: KidneyIntelX is a composite risk score, incorporating biomarkers and clinical variables for predicting progression of diabetic kidney disease (DKD). The utility of this score in the context of sodium glucose co-transporter 2 inhibitors and how changes in the risk score associate with future kidney outcomes are unknown. Methods: We measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from CANagliflozin cardioVascular Assessment Study (CANVAS) trial participants with baseline DKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2 or urine albumin-to-creatinine ratio [UACR] ≥30 mg/g) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. We assessed the association of baseline and changes in KidneyIntelX with subsequent DKD progression (composite outcome of an eGFR decline of ≥5 mL/min/year [using the 6-week eGFR as the baseline in the canagliflozin group], ≥40% sustained decline in the eGFR, or kidney failure). Results: We included 1,325 CANVAS participants with concurrent DKD and available baseline plasma samples (mean eGFR 65 mL/min/1.73 m2 and median UACR 56 mg/g). During a mean follow-up of 5.6 years, 131 participants (9.9%) experienced the composite kidney outcome. Using risk cutoffs from prior validation studies, KidneyIntelX stratified patients to low- (42%), intermediate- (44%), and high-risk (15%) strata with cumulative incidence for the outcome of 3%, 11%, and 26% (risk ratio 8.4; 95% confidence interval [CI]: 5.0, 14.2) for the high-risk versus low-risk groups. The differences in eGFR slopes for canagliflozin versus placebo were 0.66, 1.52, and 2.16 mL/min/1.73 m2 in low, intermediate, and high KidneyIntelX risk strata, respectively. KidneyIntelX risk scores declined by 5.4% (95% CI: −6.9, −3.9) in the canagliflozin arm at year 1 versus an increase of 6.3% (95% CI: 3.8, 8.7) in the placebo arm (p < 0.001). Changes in the KidneyIntelX score at year 1 were associated with future risk of the composite outcome (odds ratio per 10 unit decrease 0.80; 95% CI: 0.77, 0.83; p < 0.001) after accounting for the treatment arm, without evidence of effect modification by the baseline KidneyIntelX risk stratum or by the treatment arm. Conclusions: KidneyIntelX successfully risk-stratified a large multinational external cohort for progression of DKD, and greater numerical differences in the eGFR slope for canagliflozin versus placebo were observed in those with higher baseline KidneyIntelX scores. Canagliflozin treatment reduced KidneyIntelX risk scores over time and changes in the KidneyIntelX score from baseline to 1 year associated with future risk of DKD progression, independent of the baseline risk score and treatment arm.

Polygenic risk scores (PRS) summarize genetic liability to a disease at the individual level, and the aim is to use them as biomarkers of disease and poor outcomes in real-world clinical practice. To date, few studies have assessed the prognostic value of PRS relative to standards of care. Schizophrenia (SCZ), the archetypal psychotic illness, is an ideal test case for this because the predictive power of the SCZ PRS exceeds that of most other common diseases. Here, we analyzed clinical and genetic data from two multi-ethnic cohorts totaling 8,541 adults with SCZ and related psychotic disorders, to assess whether the SCZ PRS improves the prediction of poor outcomes relative to clinical features captured in a standard psychiatric interview. For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants. The inclusion of polygenic risk scores does not improve the performance of standard-of-care predictive models of disease outcomes in patients with psychosis.

To characterize continuous video electroencephalogram (VEEG) findings of hospitalized COVID-19 patients.We performed a retrospective chart review of patients admitted at three New York City hospitals who underwent VEEG at the peak of the COVID-19 pandemic. Demographics, comorbidities, neuroimaging, VEEG indications and findings, treatment, and outcomes were collected.Of 93 patients monitored, 77% had severe COVID-19 and 40% died. Acute ischemic or hemorrhagic stroke was present in 26% and 15%, respectively. Most common VEEG indications were encephalopathy/coma (60%) and seizure-like movements (38%). Most common VEEG findings were generalized slowing (97%), generalized attenuation (31%), generalized periodic discharges (17%) and generalized sharp waves (15%). Epileptiform abnormalities were present in 43% and seizures in 8% of patients, all of whom had seizure risk factors. Factors associated with an epileptiform VEEG included increasing age (OR 1.07, p = 0.001) and hepatic/renal failure (OR 2.99, p = 0.03).Most COVID-19 patients who underwent VEEG monitoring had severe COVID-19 and over one-third had acute cerebral injury (e.g., stroke, anoxia). Seizures were uncommon. VEEG findings were nonspecific.VEEG findings in this cohort of hospitalized COVID-19 patients were those often seen in critical illness. Seizures were uncommon and occurred in the setting of common seizure risk factors.

Supplemental Digital Content is available in the text., OBJECTIVES: Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019. DESIGN: This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration. SETTING: Sixty-eight U.S. ICUs. PATIENTS: Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao2/Fio2 ratio were the most important predictors in the eXtreme Gradient Boosting model. CONCLUSIONS: eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment.

Aims Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography 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 and results We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning 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 clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (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 also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells’ Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50–0.58, specificity 0.00–0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66–0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77–0.84) subgroups. Conclusion Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.

Diabetic kidney disease (DKD) is considered partially hereditary, but the genetic factors underlying disease remain largely unknown. A key barrier to our understanding stems from its heterogeneity, and likely polygenic etiology. Proteinuric and non-proteinuric DKD are two sub-classes of DKD, defined by high urinary albumin-to-creatinine ratio (UACR) and low creatinine estimated glomerular filtration rate (eGFR). Prior genome-wide association studies (GWAS) have identified multiple loci associated with eGFR and UACR. We aimed to combine summary statistics from previous GWAS’ for eGFR and UACR in one prediction model and associate it with DKD prevalence. We then tested this using genetic data from 18,841 individuals diagnosed with type 2 diabetes in UK Biobank. We computed two genome-wide polygenic risk scores (GPS) aggregating effects of common variants associated with the two measurements, eGFR and UACR. We show that including both GPS’ in a single model confers significant improvement in comparison with the single GPS model generated from GWAS summary statistics for DKD. We also find in replication analysis in 5,389 individuals in the multi-ethnic BioMe Biobank, that although the combined model had consistent direction of association, the lowest performance was in individuals with recent African ancestry. In summary, we show that joint modeling of polygenic associations of eGFR and UACR is more significantly associated with DKD than individual modeling as well as a GPS comprised of only DKD summary statistics and may be used to gain insights into biology and progression. However, efforts should be made to develop and validate polygenic approaches in diverse populations.

Objectives Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred. Materials and Methods We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter. Results We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing “STEM” has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952). Discussion and Conclusion In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.

Federated learning is a technique for training predictive models without sharing patient-level data, thus maintaining data security while allowing inter-institutional collaboration. We used federated learning to predict acute kidney injury within three and seven days of admission, using demographics, comorbidities, vital signs, and laboratory values, in 4029 adults hospitalized with COVID-19 at five sociodemographically diverse New York City hospitals, between March-October 2020. Prediction performance of federated models was generally higher than single-hospital models and was comparable to pooled-data models. In the first use-case in kidney disease, federated learning improved prediction of a common complication of COVID-19, while preserving data privacy.

APOL1 risk alleles are associated with increased cardiovascular and chronic kidney disease (CKD) risk. It is unknown whether knowledge of APOL1 risk status motivates patients and providers to attain recommended blood pressure (BP) targets to reduce cardiovascular disease.Multicenter, pragmatic, randomized controlled clinical trial.6650 individuals with African ancestry and hypertension from 13 health systems.APOL1 genotyping with clinical decision support (CDS) results are returned to participants and providers immediately (intervention) or at 6 months (control). A subset of participants are re-randomized to pharmacogenomic testing for relevant antihypertensive medications (pharmacogenomic sub-study). CDS alerts encourage appropriate CKD screening and antihypertensive agent use.Blood pressure and surveys are assessed at baseline, 3 and 6 months. The primary outcome is change in systolic BP from enrollment to 3 months in individuals with two APOL1 risk alleles. Secondary outcomes include new diagnoses of CKD, systolic blood pressure at 6 months, diastolic BP, and survey results. The pharmacogenomic sub-study will evaluate the relationship of pharmacogenomic genotype and change in systolic BP between baseline and 3 months.To date, the trial has enrolled 3423 participants.The effect of patient and provider knowledge of APOL1 genotype on systolic blood pressure has not been well-studied. GUARDD-US addresses whether blood pressure improves when patients and providers have this information. GUARDD-US provides a CDS framework for primary care and specialty clinics to incorporate APOL1 genetic risk and pharmacogenomic prescribing in the electronic health record.ClinicalTrials.govNCT04191824.

BACKGROUND The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact. OBJECTIVE This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress. METHODS HCWs across 7 hospitals in New York City, NY, 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 to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally. RESULTS A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial. CONCLUSIONS High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

KidneyIntelX is a composite risk score incorporating biomarkers and clinical variables for predicting progression of prevalent diabetic kidney disease (DKD). The change of this score over time in response to SGLT2 inhibitors and how these changes associate with future kidney outcomes is unknown. We measured soluble Tumor Necrosis Factor Receptor 1 (sTNFR-1), soluble Tumor Necrosis Factor Receptor 2 (sTNFR-2), and Kidney Injury Molecule (KIM-1) on banked samples from CANVAS trial participants with baseline DKD (eGFR In conclusion, CANA treatment reduced KidneyIntelX risk scores. Changes in the KidneyIntelX score from baseline to 1 year predict future risk of DKD progression whether they resulted from kidney protection afforded by SGLT2i therapy, or decline in kidney function caused by disease progression. Disclosure D. W. Lam: None. G. N. Nadkarni: Advisory Panel; Self; Renalytix AI plc., Consultant; Self; AstraZeneca, Renalytix AI plc., Variant Bio, Research Support; Self; Renalytix AI plc., Stock/Shareholder; Self; Renalytix AI plc. B. Neal: Advisory Panel; Self; Abbott, Janssen Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc., Roche Pharma, Servier Laboratories, Research Support; Self; Janssen Pharmaceuticals, Inc., Merck Schering Plough, Roche Pharma, Servier Laboratories. K. W. Mahaffey: Consultant; Self; Abbott, Amgen Inc., Anthos, AstraZeneca, Baim Institute for Clinical Research, Bayer Healthcare Pharmaceuticals Inc., Boehringer Ingelheim Pharmaceuticals, Inc., CSL Behring, Elsevier, Inova, Intermountain Health, Johnson & Johnson, Medscape, Mount Sinai, Mundipharma International, MyoKardia, National Institutes of Health, Novartis Pharmaceuticals Corporation, Novo Nordisk, Otsuka America Pharmaceutical, Inc., Portola Pharmaceuticals, Inc., Regeneron Pharmaceuticals Inc., SmartMedics, Theravance Biopharma, Research Support; Self; Afferent, AHA, Amgen Inc., Apple, AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Cardiva Medical, Inc, Eidos, Ferring Pharmaceuticals Inc., Gilead Sciences, Inc., Google, Johnson & Johnson, Luitpold Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., National Institutes of Health, Novartis Pharmaceuticals Corporation, Sanifit, Sanofi, St. Jude Medical. N. Rosenthal: Employee; Self; Janssen Research & Development, LLC. M. K. Hansen: Employee; Self; Janssen Pharmaceuticals, Inc. H. L. Heerspink: Consultant; Self; AbbVie Inc., Astellas Pharma Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Chinook, CSL Behring, Fresenius Medical Care, Gilead Sciences, Inc., Janssen Research & Development, LLC, Merck & Co., Inc., Mitsubishi Corporation Life Sciences Limited, Mundipharma International, Novo Nordisk, Retrophin, Inc., Research Support; Self; AbbVie Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Janssen Research & Development, LLC. S. Coca: Advisory Panel; Self; Akebia Therapeutics, Inc., Bayer AG, Boehringer Ingelheim International GmbH, Consultant; Self; CHF Solutions, Relypsa Inc., Renalytix AI plc., Takeda Pharmaceutical Co., Research Support; Self; inRegen, XORTX Therapeutics Inc., Stock/Shareholder; Self; Renalytix AI plc.

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus, 2. actionability, 3. context-sensitive triggers, 4. workflow integration, 5. feasibility, 6. interpretability, 7. portability, and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS.

Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear.Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability.In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models.A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%).There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

BackgroundRapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECG) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, while ones to estimate left ventricular (LV) function are restricted to quantification of very low LV function only.ObjectivesThis study sought to develop deep learning models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.MethodsA multi-center study was conducted with data from five New York City hospitals; four for internal testing and one serving as external validation. We created novel DL models to classify Left Ventricular Ejection Fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation.ResultsWe obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used Natural Language Processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients.For LVEF classification in internal testing, Area Under Curve (AUC) at detection of LVEF50% was 0.94 (95% CI:0.94-0.94), 0.82 (0.81-0.83), and 0.89 (0.89-0.89) respectively. For external validation, these results were 0.94 (0.94-0.95), 0.73 (0.72-0.74) and 0.87 (0.87-0.88). For regression, the mean absolute error was 5.84% (5.82-5.85) for internal testing, and 6.14% (6.13-6.16) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (0.84-0.84) in both internal testing and external validation.ConclusionsDL on ECG data can be utilized to create inexpensive screening, diagnostic, and predictive tools for both LV/RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography, and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.

This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only.A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation.We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% LVEF ≤50%, and LVEF50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation.DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.