Comparative Study of Bun/Albumin Ratio (BAR) with D Dimer/Albumin Ratio (DAR), A Better Prognostic Indicator for Outcomes of Hospitalized COVID-19 Patients

Introduction: On December 31, 2019, China reported cases of pneumonia of unknown etiology in the city of Wuhan, Hubei Province of China. With further investigations, the Chinese health authorities, on 7th January 2020 reported the agent as the novel Coronavirus, 2019-nCOV. Initially, Wuhan and later the entire Hubei province was brought under stringent lockdown. Material(s) and Method(s): This retrospective record analysis study involving laboratory investigations was carried out in a single center in the months of June and July 2022. The ethical clearance for this single-centre study was obtained from the Institutional Ethics Committee (IEC). This study included 112 patients, of ages more than or equal to 18 years, who were confirmed cases of COVID-19 with at least one reverse transcriptase polymerase chain reaction test positive and admitted for inpatient treatment for a minimum of 8 days or longer in the wards or ICU between May 2020 to March 2022. Result(s): A total of 112 patients who had a positive RT PCR test were identified and included in the study after excluding patients who had sought discharge against medical advice, who had been referred to other hospitals and patients with a history of chronic renal failure. The mean age of patients included was 60.25 + 15.66. Among these patients 76 (67.9%) were male and 36 (32.1%) were female. Of the 112 patients, 47 patients (42%) survived of which 21(32.3%) were male, 15(31.9%) were female and 65 patients (58%) did not survive, of which 44(67.7%) were male and 21(32.3%) were female. Conclusion(s): Through this study, we can see that all the parameters considered ie. Serum Albumin, Serum Blood urea nitrogen (BUN), D dimer, BUN/Albumin ratio (BAR) and D dimer/Albumin ratio (DAR) are very solid indicators of predicting the outcome of admitted COVID-19 patients.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

A Meta-analysis of Machine Learning for the Diagnosis of Covid-19 Disease

Coronavirus 2019 has wreaked havoc on people's lives all across the globe. The number of positive cases is increasing, and the Asian country is now one of the most severely impacted. This article examines machine learning models that are more accurate at predicting covid. Based on the data from China, regression-based, decision tree-based, naive Bayes, and random forest-based models were developed and verified on a sample from India. A data-driven strategy with better precision, such as the one used here, is beneficial for the government and public to respond in a proactive manner. This study reveals that the suggested framework has superior capabilities in detecting COVID-19. © 2022 IEEE.

Machine Learning - Based Diagnosis of Covid-19 using Clinical Data

Coronavirus (COVID-19) is a worldwide pandemic caused by SARS Coronavirus 2. (SARS-CoV-2). The COVID-19 epidemic has put global healthcare systems in jeopardy. This study's purpose is to develop and evaluate an automated COVID-19 infection detection system using machine learning and chest x-ray images. Early diagnosis and treatment may help avert major illness and even death. It is presently the most favoured and accurate approach for COVID-19 diagnosis. X-ray imaging of the chest may be used instead of the rRT-PCR test to look for early COVID-19 symptoms. A new machine learning (ML)-based analytical framework for automated COVID-19 diagnosis is created utilizing chest X-ray pictures of likely patients. The proposed framework for COVID-19 disease diagnosis using X-ray images has a 99 percent accuracy for Covid and a 92 percent accuracy for Non-covid in two-class categorization. The investigation suggests the COVID-19 detection framework is better. © 2022 IEEE.