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    Covid-19 serology in nephrology health care workers

    Authors: Thomas Reiter; Sahra Pajenda; Ludwig Wagner; Martina Gaggl; Johanna Atamaniuk; Barbara Holzer; Irene Zimpernik; Daniela Gerges; Katharina Mayer; Christof Aigner; Robert Strassl; Sonja Jansen-Skoupy; Manuela Födinger; Gere Sunder-Plassmann; Alice Schmidt

    doi:10.1101/2020.07.21.20136218 Date: 2020-07-26 Source: medRxiv

    Background: Chronic kidney disease HP Chronic kidney disease MESHD patients show a high mortality in case of a SARS-CoV-2 infection MESHD. Thus, to be informed on Nephrology personnel's sero-status might be crucial for patient protection. However, limited information exists about the presence of SARS-CoV-2 antibodies SERO in asymptomatic TRANS individuals. Methods: We examined the seroprevalence SERO of SARS-CoV-2 IgG and IgM antibodies SERO among health care workers of a tertiary care kidney center during the peak phase of the Covid-19 crisis in Austria using an orthogonal test strategy and a total of 12 commercial nucleocapsid protein or spike glycoprotein based assays as well as Western blotting and a neutralization assay. Results: At baseline 60 of 235 study participants (25.5%, 95% CI: 20.4-31.5) were judged to be borderline positive or positive for IgM or IgG using a high sensitivity SERO/low specificity threshold in one test system. Follow-up analysis after about two weeks revealed IgG positivity in 12 (5.1%, 95% CI: 2.9-8.8) and IgM positivity in six (2.6%, 95% CI: 1.1-5.6) in at least one assay. 2.1% (95% CI: 0.8-5.0) of health care workers showed IgG nucleocapsid antibodies SERO in at least two assays. By contrast, positive controls with proven Covid-19 showed antibody SERO positivity among almost all test systems. Moreover, serum samples SERO obtained from health care workers did not show SARS-CoV-2 neutralizing capacity, in contrast to positive controls. Conclusions: Using a broad spectrum of antibody tests SERO the present study revealed inconsistent results for SARS-CoV-2 seroprevalence SERO among asymptomatic TRANS individuals, while this was not the case among Covid-19 patients.

    Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

    Authors: Jiandong Zhou; Gary Tse; Sharen Lee; Tong Liu; William KK Wu; zhidong cao; Dajun Zeng; Ian CK Wong; Qingpeng Zhang; Bernard MY Cheung

    doi:10.1101/2020.06.30.20143651 Date: 2020-07-02 Source: medRxiv

    Background: The coronavirus disease MESHD 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. Methods: Consecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. Results: This study included 1043 patients (median age TRANS 35 (IQR: 32-37; 54% male TRANS). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU MESHD patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age TRANS, male TRANS sex, prior coronary artery disease MESHD, respiratory diseases MESHD, diabetes MESHD, hypertension HP hypertension MESHD and chronic kidney disease HP chronic kidney disease MESHD, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum SERO sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male TRANS, 2) older age TRANS, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance SERO of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. Conclusions: A machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

    Exploiting an Early Warning Nomogram for Predicting the Risk of ICU Admission in COVID-19 patients: A Multi-Center Study in China

    Authors: Yiwu Zhou; Yanqi He; Huan Yang; He Yu; Ting Wang; Zhu Chen; Rong Yao; Zongan Liang

    doi:10.21203/rs.3.rs-36964/v1 Date: 2020-06-19 Source: ResearchSquare

    Background Novel corona virus disease 2019 (COVID-19) is an urgent event in the worldwide. We aimed to develop and validate a practical model for early identifying and predicting which patients will be admitted to intensive care unit (ICU) based on a multi-center cohort in China. Methods Data from 1087 patients of laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28 2020 in Sichuan and Wuhan. Patients were randomly divided into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis were employed for the development account. The performance SERO of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. The nomogram was further assessed in a different cohort as external validation. Results The individualized prediction nomogram included 6 predictors, including age TRANS, respiratory rate, systolic blood SERO pressure, smoking status, fever HP fever MESHD and chronic kidney disease HP chronic kidney disease MESHD. The model showed high discrimination ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for prediction the risk of ICU admission. Decision curve analysis showed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission even in community health center. This model can be conveniently used to facilitate predicting the individual risk for ICU admission of COVID-19 patients and optimizing use of limited resources. 

    Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

    Authors: Yiwu Zhou; Yanqi He; Huan Yang; He Yu; Ting Wang; Zhu Chen; Rong Yao; Zongan Liang

    doi:10.21203/rs.3.rs-36964/v2 Date: 2020-06-19 Source: ResearchSquare

    Background Novel coronavirus disease MESHD 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyses were used to develop the nomogram. The performance SERO of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.Results The individualized prediction nomogram included 6 predictors: age TRANS, respiratory rate, systolic blood SERO pressure, smoking status, fever HP fever MESHD, and chronic kidney disease HP chronic kidney disease MESHD. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

    Acute Kidney Injury HP Acute Kidney Injury MESHD in Hospitalized Patients with COVID MESHD-19

    Authors: Lili Chan; Kumardeep Chaudhary; Aparna Saha; Kinsuk Chauhan; Akhil Vaid; Mukta Baweja; Kirk Campbell; Nicholas Chun; Miriam Chung; Priya Deshpande; Samira S Farouk; Lewis Kaufman; Tonia Kim; Holly Koncicki; Vijay Lapsia; Staci Leisman; Emily Lu; Kristin Meliambro; Madhav C Menon; Joshua L Rein; Shuchita Sharma; Joji Tokita; Jaime Uribarri; Joseph A Vassalotti; Jonathan Winston; Kusum S Mathews; Shan Zhao; Ishan Paranjpe; Sulaiman Somani; Felix Richter; Ron Do; Riccardo Miotto; Anuradha Lala; Arash Kia; Prem Timsina; Li Li; Matteo Danieletto; Eddye Golden; Patricia Glowe; Micol Zweig; Manbir Singh; Robert Freeman; Rong Chen; Eric Nestler; Jagat Narula; Allan C Just; Carol Horowitz; Judith Aberg; Ruth J.F. Loos; Judy Cho; Zahi Fayad; Carlos Cordon-Cardo; Eric Schadt; Matthew A Levin; David L Reich; Valentin Fuster; Barbara Murphy; John Cijiang He; Alexander W Charney; Erwin P Bottinger; Benjamin S Glicksberg; Steven G Coca; Girish N Nadkarni

    doi:10.1101/2020.05.04.20090944 Date: 2020-05-08 Source: medRxiv

    Importance: Preliminary reports indicate that acute kidney injury HP acute kidney injury MESHD ( AKI MESHD) is common in coronavirus disease MESHD ( COVID MESHD)-19 patients and is associated with worse outcomes. AKI MESHD in hospitalized COVID MESHD-19 patients in the United States is not well-described. Objective: To provide information about frequency, outcomes and recovery associated with AKI MESHD and dialysis in hospitalized COVID MESHD-19 patients. Design: Observational, retrospective study. Setting: Admitted to hospital between February 27 and April 15, 2020. Participants: Patients aged TRANS [≥]18 years with laboratory confirmed COVID MESHD-19 Exposures: AKI MESHD (peak serum SERO creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI MESHD and dialysis requirement, AKI MESHD recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. Results: A total of 3,235 hospitalized patients were diagnosed with COVID MESHD-19. AKI MESHD occurred in 1406 (46%) patients overall and 280 (20%) with AKI MESHD required renal replacement therapy. The incidence of AKI MESHD (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 MESHD 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 MESHD were chronic kidney disease HP chronic kidney disease MESHD, systolic blood SERO pressure, and potassium at baseline. In-hospital mortality in patients with AKI MESHD was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI MESHD 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 MESHD 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 Relevance: AKI MESHD is common in patients hospitalized with COVID MESHD-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 SERO for dialysis prediction and could be used for resource allocation.

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Human Phenotype
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