COVID-19 in CKD patients: Report from India

Pawar, Nikita; Tiwari, V.; Gupta, Anurag; Bhargava, Vinant; Malik, Manish; Gupta, Ashwani; Bhalla, Anil Kumar; Rana, Devinder Singh

doi:10.4103/ijn.ijn_460_20

Indian J Nephrol

2021

DOI: 10.4103/ijn.ijn_460_20

|View full text |Cite

COVID-19 in CKD patients: Report from India

Nikita Pawar¹,

V. Tiwari²,

Anurag Gupta³

et al.

Abstract: Background: COVID-19 is a novel acute infection that is mainly manifested as acute respiratory disease. Information on coronavirus disease-2019 (COVID-19) in CKD patients who are not on dialysis is very limited. We are reporting a single-center observational study on the effect of COVID-19 in CKD patients. Methods: A single-center retrospective study with consecutive patients who had eGFR <60 mL/min/1.73 m 2 (CKD-EPI) admitted with COVID-19, f… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Robust Chronic Kidney Disease Classifier Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

Clinical support systems are affected by the issue of high variance in terms of chronic disorder prognosis. This uncertainty is one of the principal causes for the demise of large populations around the world suffering from some fatal diseases such as chronic kidney disease (CKD). Due to this reason, the diagnosis of this disease is of great concern for healthcare systems. In such a case, machine learning can be used as an effective tool to reduce the randomness in clinical decision making. Conventional methods for the detection of chronic kidney disease are not always accurate because of their high degree of dependency on several sets of biological attributes. Machine learning is the process of training a machine using a vast collection of historical data for the purpose of intelligent classification. This work aims at developing a machine-learning model that can use a publicly available data to forecast the occurrence of chronic kidney disease. A set of data preprocessing steps were performed on this dataset in order to construct a generic model. This set of steps includes the appropriate imputation of missing data points, along with the balancing of data using the SMOTE algorithm and the scaling of the features. A statistical technique, namely, the chi-squared test, is used for the extraction of the least-required set of adequate and highly correlated features to the output. For the model training, a stack of supervised-learning techniques is used for the development of a robust machine-learning model. Out of all the applied learning techniques, support vector machine (SVM) and random forest (RF) achieved the lowest false-negative rates and test accuracy, equal to 99.33% and 98.67%, respectively. However, SVM achieved better results than RF did when validated with 10-fold cross-validation.

show abstract

A Robust Chronic Kidney Disease Classifier Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

show abstract

Mortality of cases with chronic kidney disease and its risk factors admitted to hospital with COVID-19 in Almaty city, Kazakhstan

Garazhayeva,

Gaipov,

Almagul

2024

Salud, Ciencia y Tecnología

View full text Add to dashboard Cite

Aim: to determine the rate of mortality of cases with chronic kidney disease and its risk factors admitted to hospital with COVID-19 in Almaty City, Kazakhstan. Methods: Patients with coronavirus infections who were hospitalized at a hospital in the Kazakhstani Almaty region from June 2020 until June 2022 are included in the retrospective analysis. The Unified National Electronic Healthcare System (UNEHS) provided the database extraction. Individuals were considered eligible if they had been admitted to the hospital with the primary diagnoses of U07.1 (COVID-19, detected virus) and U07.2. Conclusion: In Almaty, Kazakhstan, hospitalization outcomes for coronavirus patients with and without chronic kidney disease were assessed in this study. Analysis was done on the impact of comorbidities and sociodemographic characteristics on mortality. While there are many ways to prevent and manage chronic kidney disease (CKD), coexisting medical diseases, particularly viruses that cause pandemics, might complicate matters. Thus, to prevent unplanned infectious disease outbreaks, a thorough disease management plan must be established

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

COVID-19 in CKD patients: Report from India

Cited by 2 publications

References 32 publications

A Robust Chronic Kidney Disease Classifier Using Machine Learning

A Robust Chronic Kidney Disease Classifier Using Machine Learning

Mortality of cases with chronic kidney disease and its risk factors admitted to hospital with COVID-19 in Almaty city, Kazakhstan

Contact Info

Product

Resources

About