ABSTRACT
Background: Already at the time of hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on following variables which can simply be measured at hospital admission: gender, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results: Cause-specific hazard regression models show that these baseline variables are associated with both hazards, the death as well as the discharge hazard. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC=0.872 [CI 95%: 0.835-0.910]). Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.
Subject(s)
COVID-19 , HypertensionABSTRACT
The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered as primary, and impaired pulmonary function and psychotic disorders were considered as main secondary outcomes. Notably, In the last two follow-ups, each patient attends the hospital to complete the Patient Health Questionnaire-9 (PHQ-9) and the Depression Anxiety Stress Scales (DASS-21). Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, a total of 490 patients with complete information (39% female; the average age of 57{+/-}15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The following comorbidities were significantly different in the positive and negative PCEP groups: acute kidney disease (p=0.008) and diabetes (p=0.026). For signs and symptoms, fatigue (p=0.020) and sore throat (p=0.001) were significantly different. This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.
Subject(s)
COVID-19ABSTRACT
Introduction Diagnosis of COVID-19 is based on clinical manifestation, history of exposure, positive findings on chest CT and laboratory tests. It has been shown that inflammation plays a role in pathogenesis of COVID-19. Method We used the necessary transformations to convert the median and IQR to mean and SD Random-effect model using Der Simonian, and Laird methods was used if heterogeneity between studies was significant, the homogeneity among studies was assessed with I2 Statistic, values above 50%, and for the chi-square test, P-values <0.1 was supposed statistically significant Results Twelve studies were included in the analysis that all of which were conducted in China in the year 2020. The result of combining 12 articles with 772 participants showed that the pooled estimate of the mean of lymphocyte with 95% CI was (Mean: 1.01; 95% CI (0.76-1.26); p-value<0.001). About WBC the pooled result of 9 studies with 402 participants was (Mean: 5.11; 95% CI (3.90-6.32); p-value<0.001) Also the pooled mean estimate of 9 studies with 513 patients for the ratio of Neutrophil/lymphocyte was (Mean: 3.62; 95% CI (1.48-5.77); p-value=0.001). The pooled mean from the combination of 7 studies with 521 patients on CRP was (Mean: 28.75; 95% CI (8.04-49.46). Conclusion Inflammatory Markers increase in patients with Covid-19, which can be a good indicator to find patients.
Subject(s)
COVID-19 , InflammationABSTRACT
Background: Imagery techniques have been used as essential parts of diagnostic workup for patients suspected for 2019-nCoV infection, Multiple studies have reported the features of chest computed tomography (CT) scans among a number of 2019-nCoV patients. Method: Study Identification was carried out in databases (PubMed, Embase and Cochrane Library) to identify published studies examining the diagnosis, the 2019 novel coronavirus (2019-nCoV). Heterogeneity among reported prevalence was assessed by computing p-values of Cochrane Q-test and I2-statics. The pooled prevalence of treatment failure was carried out with a fixed effects meta-analysis model, generating the pooled 95% confidence interval. A random-effect model was used to pool the results since this model could incorporate the heterogeneity of the studies and therefore proved a more generalized result. Results: According to the combined results of meta-analysis, the total 55% of corona patients were males. The mean age of the patients was 41.31 (34.14, 48.47). Two prevalent clinical symptoms between patients were fever, cough with prevalence of 85%, and 62%, respectively. Either Ground Glass Opacity GGO or consolidation was seen in 86% but 14% had NO GGO or consolidation. The other rare CT symptoms were pericardial effusion, and pleural effusion with 4, 5, 7% prevalence, respectively. The most prevalent event was Either GGO or consolidation in 85% of patients. Conclusion: The most CT-scan abnormality is Either Ground Glass Opacity GGO or consolidation however in few patients none of them might be observed, so trusting in just CT findings will lead to miss some patients.