Search

Background: Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown., Methods: This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment., Results: A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003)., Conclusion: The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: S.G.C. has salary support from NIH grants R01DK115562, UO1DK106962, R01HL085757, R01DK112258, R01DK115562, R01DK126477 and UH3DK114920. SGC reports personal income and equity and stock options from Renalytix and pulse Data; he also reports personal income from Axon Therapeutics, Bayer, Boehringer-Ingelheim, CHF Solutions, ProKidney, Vifor, and Takeda. G.N.N. has received consulting fees from AstraZeneca, Reata, BioVie, and GLG Consulting; has received financial compensation as a scientific board member and advisor to RenalytixAI; and owns equity in RenalytixAI and Pensieve Health as a cofounder. D.M.C has received financial compensation for consulting or service on clinical trials comitess from Eli Lilly/Boehringer Ingelheim, Janssen,Astra Zeneca, Allena Pharmaceuticals, Fresenius, Amgen, Gilead, Novo Nordisk, GSK, Medtronic, Merck, Amgen and CSL Behring and receives research funding from Medtronic for clinical trial support, Gilead, NovoNordisk, and Amgen, as well as expert witness fees related to proton pump inhibitors. He also serves as an editor for CJASN. L.C. has received consulting fees from Vifor Pharma, honorarium from Fresenius Medical Care, and is supported in part by K23DK124645. All remaining authors have declared no conflicts of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials, (Copyright: © 2024 Takkavatakarn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., (Copyright © 2021 by the American Society of Nephrology.)

G1/G2 variants in the Apolipoprotein L1 (APOL1) gene are associated with end-stage renal disease (ESRD) in people with African ancestry. Plasma biomarkers may have utility for risk stratification in APOL1 high-risk individuals of African ancestry. To evaluate this, we measured tumor necrosis factor receptor 1/2 (TNFR1/2) and kidney injury molecule-1 (KIM1) in baseline plasma specimens from individuals of African ancestry with high-risk APOL1 genotype. Biomarker association with a composite renal outcome of ESRD or 40% sustained decline in estimated glomerular filtration rate (eGFR) was then determined and then assessed as improvement in area under curve. Among the 498 participants, the median age was 56 years, 67.7% were female, and the baseline eGFR was 83.3 ml/min/1.73 m 2 with 80 reaching outcome over 5.9 years. TNFR1, TNFR2, and KIM1 at enrollment were independently associated with renal outcome continuously (adjusted hazard ratio 2.0 [95% confidence interval 1.3-3.1]; 1.5 [1.2-1.9]; and 1.6 [1.3-1.9] per doubling in levels, respectively) or by tertiles. The area under the curve significantly improved from 0.75 with the clinical model to 0.79 with the biomarker-enhanced model. The event rate was 40% with all 3 biomarkers elevated (adjusted odds ratio of 5.3 (2.3-12.0) vs. 17% (adjusted odds ratio 1.8 [0.9-3.6] with 1 or 2 elevated and 7% with no biomarkers elevated. Thus, plasma concentrations of TNFR1, TNFR2, and KIM1 are independently associated with renal outcome and improve discrimination or reclassification of African ancestry individuals with a high-risk APOL1 genotype and preserve renal function. Elevation of all markers had higher risk of outcome and can assist with better clinical prediction and improved clinical trial efficiency by enriching event rates., (Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)