COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: A lesson from Northern India

Reddy, D. Himanshu; Atam, Virendra; Raina, Priyanka; Khan, Feroz; Pandey, Saurabh; Malhotra, Hardeep Singh; Usman, Kauser; Chaudhary, Shyam Chand; Sonkar, Satyendra Kumar; Sawlani, Kamal Kumar; Gupta, Kamlesh Kumar; Patel, M.L.; Himanshu, D.; Kumar, Ajay M. V.; Verma, Sudhir Kr; Gautam, Medhavi; Gupta, Harish; Kumar, Satish; Baghchandanani, Deepak; Yadav, Ambuj; Lamba, Monika; Kumar, Amit; Suhail,; Bajaj, Darshan Kumar; Singh, Abhishek; Mahendra, Mayank; Kumar, Gaurav; Kumar, Narendra; Ojha, Bal Krishna; Verma, Rajeev; Verma, Deepti; Kumar, Vinod; Suresh, S.; Gupta, Shivam; Hashim, Mohammad; Verma, Kuldeep Chand; Bhardwaj, Akriti; Chaudhary, Anurag; Chaudhan, Himanshu; Kaustubh,; Dubey, Kinjalk; Kumar, Naveen; Rituraj,; Kumar, J. Ravi; Srivastav, Somesh; Singh, Shiv Paratap; Kumari, Sunita; Srivastave, Sudham; Verma, Jyoti; Hussain, Mohmmad Ahmad; Siddiqui, Ammar Sabir; Rizvi, Azher; Pancholi, Chitranshu; Sharma, Deepak; Verma, Deepak Kumar; Zothansanga, David; Singh, Kuldeep; Singh, Prashant Kumar; Kumar, Rahul; Bharti, Vipin Raj; Ansari, Shahnawaz Ali; Kumar, Vivek; Kallani, Monika; Bharti, Harish; Singh, Ankita; Majumdar, Avirup; Verma, Neeraj; Mishra, Mayank; Gupta, Pankaj; Shivhare, Shubhanshu; Kotwal, Mudit; Mahar, Prashant; Mall, Praduman; Parmar, Krishnapal Singh; Kumar, Guddoo

doi:10.1016/j.cegh.2022.101044

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Uncommon comorbidities included tuberculosis (pulmonary and extra-pulmonary), leukemia, and myasthenia gravis, suspected cases of liver and brain tumor. A recent literature from Northern India also reported uncommon comorbidities in form of tuberculosis 13(28.3%) with predominant cases of extra-pulmonary, acute leukemia 1(2.2%) and myasthenia gravis 2(4.4%) [8]. The frequency of uncommon coexisting illnesses in our study is in accordance with Indian data.…”

Section: Discussionsupporting

confidence: 90%

“…The factors associate to mortality among COVID-19 patient has predominantly included older age, male sex, and current smoker and other factors influencing mortality included-COPD, cardiovascular disease, diabetes, hypertension, obesity, cancer, acute kidney injury and increase and increased d dimer [9]. Now, tuberculosis has been established as risk factor for severe COVID-19 disease [8,10].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Clinico-Epidemiological Profile and Hospital Outcomes of Covid-19 Patients During All Three Waves, an Experience From a Rural Tertiary Care Center, Eastern Uttar Pradesh, India

SHUKLA,

GOYAL,

RAI

et al. 2024

Int J Curr Pharm Sci

Self Cite

View full text Add to dashboard Cite

Objective: In this study, an attempt has been made to report the clinical profile, exposure characteristics and outcomes of the COVID-19 patients admitted to our rural tertiary care facility. Methods: It is a single-centric retrospective observational study and includes data from January 2020 to March 2022. The data were retrieved from patient files. We have recorded patient demography, clinical and laboratory parameters and outcome of the patients. Patients were categorized based on disease severity according to WHO guidelines. Appropriate statistical analysis was applied and p value<0.05 was considered as significant. Results: A total of 2339 patients were included in this study. Total males were 67%, mean age group was 45.43±18.48 y (0 to 101 y), with majority belonging to 46-60 y age group (27.2%). Total comorbid patients were 29.3% with 9.4% accounting for more than one comorbidities. Most common comorbidity noted was diabetes mellitus 15.8% followed by hypertension 13.4%. Total mortality was noted to be 16.2%. Fever was the most common symptom (92%) followed by cough (51%) and myalgia (30%). Disease severity and outcome was significantly correlated with advancing age and the presence of underlying comorbidities. Conclusion: Rural population had comparable proportion of comorbidities as well as mortality. Common risk factors for severe disease in rural setting were similar to urban setting and old age and more than one comorbidities. Country-wide rural data should be collected for a better understanding of COVID-19 disease in rural and remote population.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Clinico-Epidemiological Profile and Hospital Outcomes of Covid-19 Patients During All Three Waves, an Experience From a Rural Tertiary Care Center, Eastern Uttar Pradesh, India

SHUKLA,

GOYAL,

RAI

et al. 2024

Int J Curr Pharm Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…Las gráficas utilizadas para este tipo de variables más comunes son gráficos de barras, que hacen representaciones visuales de datos a través de dos ejes, las categorías discretas en este estudio son las 24 provincias del país y las magnitudes numéricas representadas con los contagios y defunciones (Reddy et al, 2022), los gráficos de dispersión muestran la correlación y tendencia existente entre dos variables de estudio, con el propósito de detectar si fue un comportamiento constante, creciente o decreciente. Los mapas interactivos o dinámicos son representaciones cartográficas generadas de manera digital y considerada como una técnica gráfica que facilita y resume datos estadísticos (Pilotto et al, 2022).…”

Section: Metodologíaunclassified

Análisis gráfico descriptivo de la pandemia Covid-19 en Ecuador

Ávila¹,

Pilacuán²,

Valenzuela³

2022

Rev. Ing. Cs. Apl. en Ind.

View full text Add to dashboard Cite

El presente documento muestra el informe acerca de la propagación del virus COVID-19 en Ecuador, durante los meses más críticos de la pandemia. El análisis se realizó aplicando técnicas de estadística descriptiva a través de gráficos generados en software libre RStudio. El estudio estadístico aplicó una base de datos que muestra los registros diarios de contagios y defunciones por provincia, durante 187 días. Los tipos de gráficos empleados como diagramas de barras y dispersión dinámicos se encuentran en función de los distintos días de estudio. Además, un mapa geográfico de todo el país, donde se aprecia el nivel de afectación en cada zona. De esta manera se pudo evidenciar que las provincias más afectadas eran Guayas y Pichincha, mismas que son consideradas las más pobladas del Ecuador.

show abstract

“…Studies regarding this disease and its repercussions have not ceased since it emerged, which has led to various publications focused on who the people at risk of developing severe COVID-19 are [1,2], the consequences seen in people who were infected with this disease, and the prediction of future cases in order to formulate measures to mitigate the increase in cases [3]. The possibility of developing severe COVID-19 depending on whether a person has other illnesses or is a pregnant woman has been studied [4,5], and the use of drugs and their effects have also been widely studied [1,6]. There is a significant number of models that have been successfully applied to the prediction Axioms 2022, 11, 410. https://doi.org/10.3390/axioms11080410 https://www.mdpi.com/journal/axioms and detection of COVID-19 cases.…”

Section: Introductionmentioning

confidence: 99%

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Melín

Sánchez

Castro

et al. 2022

Axioms

View full text Add to dashboard Cite

In this work, information on COVID-19 confirmed cases is utilized as a dataset to perform time series predictions. We propose the design of ensemble neural networks (ENNs) and type-3 fuzzy inference systems (FISs) for predicting COVID-19 data. The answers for each ENN module are combined using weights provided by the type-3 FIS, in which the ENN is also designed using the firefly algorithm (FA) optimization technique. The proposed method, called ENNT3FL-FA, is applied to the COVID-19 data for confirmed cases from 12 countries. The COVID-19 data have shown to be a complex time series due to unstable behavior in certain periods of time. For example, it is unknown when a new wave will exist and how it will affect each country due to the increase in cases due to many factors. The proposed method seeks mainly to find the number of modules of the ENN and the best possible parameters, such as lower scale and lower lag of the type-3 FIS. Each module of the ENN produces an individual prediction. Each prediction error is an input for the type-3 FIS; moreover, outputs provide a weight for each prediction, and then the final prediction can be calculated. The type-3 fuzzy weighted average (FWA) integration method is compared with the type-2 FWA to verify its ability to predict future confirmed cases by using two data periods. The achieved results show how the proposed method allows better results for the real prediction of 20 future days for most of the countries used in this study, especially when the number of data points increases. In countries such as Germany, India, Italy, Mexico, Poland, Spain, the United Kingdom, and the United States of America, on average, the proposed ENNT3FL-FA achieves a better performance for the prediction of future days for both data points. The proposed method proves to be more stable with complex time series to predict future information such as the one utilized in this study. Intelligence techniques and their combination in the proposed method are recommended for time series with many data points.

show abstract

COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: A lesson from Northern India

Cited by 3 publications

References 19 publications

Clinico-Epidemiological Profile and Hospital Outcomes of Covid-19 Patients During All Three Waves, an Experience From a Rural Tertiary Care Center, Eastern Uttar Pradesh, India

Clinico-Epidemiological Profile and Hospital Outcomes of Covid-19 Patients During All Three Waves, an Experience From a Rural Tertiary Care Center, Eastern Uttar Pradesh, India

Análisis gráfico descriptivo de la pandemia Covid-19 en Ecuador

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Contact Info

Product

Resources

About