Dataset Information

Acute respiratory distress syndrome among patients with severe COVID-19 admitted to treatment center of Wollega University Referral Hospital, Western Ethiopia.

ABSTRACT:

Background

Acute respiratory distress syndrome is a life-threatening condition that has a significant effect on the occurrence of morbidity and mortality among patients with severe Coronavirus disease 2019 (COVID-19). To the best of researchers' knowledge, there is no Study on ARDS of COVID-19 in Ethiopia. Therefore, this study aimed to identify the prevalence of ARDS and associated factors among severe COVID-19 patients at Wollega University Referral Hospital.

Methods

An institution-based retrospective cross-sectional study was conducted from September 20, 2020, to June 10, 2021. Real-Time Reverse transcription-polymerase Chain Reaction (rRT-PCR) test was used to test Patients for COVID-19. Epi-data version 3.2 was used for data entry, and the final data analysis was through STATA version 14. After checking the assumption P-value<0.25 in the bivariable analysis was used to select a candidate variable for multi-variable analysis, and a p-value of <0.05 was used to declare statistical significance.

Results

In this study, the prevalence of ARDS was 32%. Almost all the patients had the clinical feature of cough (93.7%), followed by shortness of breath (79.9%), fever (77.7%), and headache (67%). Age older than 65 years (AOR = 3.35, 95%CI = 1.31, 8.55), male gender (AOR = 5.63, 95%CI = 2.15, 14.77), and low oxygen saturation level (AOR = 4.60, 95%CI = 1.15, 18.35) were the independent predictors of ARDS among severe COVID-19 patients.

Conclusion

The prevalence of ARDS among patients with severe COVID-19 was high in the study area. Therefore, elders and patients with critical conditions (low oxygen saturation) better to get special attention during COVID-19 case management to enhance good care and monitoring of the patients.

SUBMITTER: Tolossa T

PROVIDER: S-EPMC9202843 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction Currently, COVID-19 contributes to mortality and morbidity in developed as well as in developing countries since December 2019. However, there is scarcity of evidence regarding the incidence and predictors of death among patients admitted with COVID-19 in developing country including Ethiopia, where the numbers of deaths are under-reported. Hence, this study aimed to assess the incidence and predictors of death among patients admitted with COVID-19 in Wollega University Referral Hospital (WURH), western Ethiopia. Methods An institution based retrospective cohort study design was conducted among 318 patients admitted with COVID-19 in WURH treatment center. Patients who were tested positive for COVID-19 by using rRT-PCR test and admitted with the diagnosis of severe COVID-19 cases from September 30, 2020 to June 10, 2021 were a source population. Epidata version 3.2 was used for data entry, and STATA version 14 for analysis. A Cox proportional hazard regression analysis was used to determine factors associated with mortality from COVID-19. Multivariable Cox regression model with 95% CI and Adjusted Hazard Ratio (AHR) was used to identify a significant predictor of mortality from COVID-19 at p-value < 0.05. Results A total of 318 patients were included in final analysis with mean age of 44 (SD±16.7) years and about two third (67.9%) were males. More than half (55.7%) of patients had no comorbidity on admission. The majority, 259 (81.45%) of patients recovered from COVID-19 and 267 (84%) of patients were censored at the end of follow up. The incidence rate of mortality was 14.1 per/1000 (95%CI: 10.7, 18.5) person days observation. Age ≥ 59 years (AHR: 5.76, 95%CI: 2.58, 12.84), low oxygen saturation (AHR: 2.34, 95% CI: (2.34, 4.17), and delayed presentation (AHR: 5.60, 95%CI: 2.97, 10.56) were independent predictors of mortality among COVID-19 patients. Conclusion The mortality rate of COVID-19 pandemic was high in the study area, and most of death was happened during the first 10 days. Being old age, low oxygen saturation and delayed presentation were factors which predict mortality due to COVID-19. Hence, strengthening the health care delivery system to satisfy the need of the patients should get due attention to reduce the incidence of mortality from COVID-19 cases.

Project description:IntroductionDespite its alarming spread throughout the world, no effective drug and vaccine is discovered for COVID-19 so far. According to WHO, the recovery time from COVID-19 was estimated to be 2 weeks for patients with mild infection, and 3 to 6 weeks for those with serious illnesses. A studies regarding the median recovery time and its predictors are limited globally and specifically in Ethiopia. Therefore, the aim of this study was to estimate the median time to recovery from COVID-19 and its predictors among COVID-19 cases admitted to WURH, Western Ethiopian.MethodsThis was a hospital-based retrospective cohort study conducted among 263 adult patients admitted with COVID-19 in WURH treatment center from March 29, 2020 through September 30, 2020. Epidata version 3.2 was used for data entry, and STATA version 14 for analysis. A Cox proportional hazard regression model was fitted to determine factors associated with recovery time. A variable with P-value ≤ 0.25 at bivariable Cox regression analysis were selected for multivariable Cox proportional model. Multivariable Cox regression model with 95% CI and Adjusted Hazard Ratio (AHR) was used to identify a significant predictor of time to recovery from COVID-19 at P-value < 0.05.ResultsThe mean age of patient was 36.8 (SD± 10.68) years. At the end of follow up, two hundred twenty seven observations were developed an event (recovered) with median time to recovery of 18 days with IQR of 10-27 days. The overall incidence rate of recovery was of 4.38 per 100 (95% CI: 3.84, 4.99) person-days observations. Being older age (AHR = 1.59, 95% CI: 1.02, 2.49), presence of fever on admission (AHR = 1.78, 95% CI: 1.21, 2.62), and comorbidity (AHR = 0.56, 95% CI, 0.34, 0.90) were found to have statistically significant association with recovery time.Conclusion and recommendationsIn general, the median recovery time of patients with COVID-19 cases was long, and factors such as older age group, presence of fever, and comorbidity was an independent predictors of delayed recovery from COVID-19. Intervention to further reduce recovery time at treatment center has to focus on patients those shows symptoms and with comorbidities.

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Project description:BackgroundOne of the major factors contributing to newborn morbidity and mortality across the globe is respiratory distress. In resource-constrained developing nations like Ethiopia, it is a significant issue. Depending on the quality of the care provided, the incidence and time to recovery may differ amongst medical facilities. However, Ethiopia still lacks appropriate data on the incidence and time to recovery from respiratory distress.ObjectiveThe aim of the study was to assess the incidence, time to recovery, and predictors among neonates admitted with respiratory distress in the neonatal intensive care unit at the University of Gondar Comprehensive Specialized Hospital.MethodsAn institution-based retrospective follow-up study design was conducted among 452 neonates with respiratory distress. Data were collected using a data extraction checklist from the medical registry. The extracted data were entered into EPI INFO version 7.2.1.0 and then exported to STATA version 14 for analysis. The median time to recovery, the Kaplan Meier curve, and the log-rank test was computed. Both bi-variable and multivariable Cox regression models were applied to analyze the data. p-value ≤ 0.05 was considered statistically significant.ResultsOf all respiratory distressed neonate,311 were recovered. The overall incidence rate of neonates admitted with from respiratory distress was 11.5 per 100-neonate day (95% CI: 10.30-12. 87) with 2,703-person day observation and the median time to recovery from respiratory distress was 7 days with (IQR = 3-13 days). Predictors of time to recovery from respiratory distress were very low birth weight (AHR = 0.17, 95% CI: 0.08-0.41), low birth weight (AHR = 0.50, 95% CI: 0.31-0.81), very preterm (AHR = 0.42,95% CI:0.20-0.89), sepsis (AHR = 0.50 95% CI: 0.38-0.65), hypothermia (AHR = 0.61, 95% CI: 0.39-0.81), and Apgar scores less than seven at first (AHR = 0.35, 95% CI: 0.15-0.79) and fifth minute (AHR = 0.45, 95% CI: 0.20-0.97).ConclusionThe incidence and time to recovery in this study were discreetly acceptable as compared to previous study. The aforementioned predictors could be used to identify neonates with respiratory distress who are at risk of developing a long-term illness and guide prompt referral to hospitals. This will also provide clinicians with prognostic information, as longer recovery times have economic and social implications in resource limited countries like Ethiopia.

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