Severity of Disease and COVID-19 Complications During Hospital Stay: A Prospective Cohort Study.

BACKGROUND: COVID-19, with its high transmission and mortality rates and unknown outcomes, has become a major concern in the world. Among people with COVID-19, severe cases can quickly progress to serious complications, and even death. So, the present study aimed to examine the relationship between the severity of the disease and the outcome in patients afflicted by COVID-19 during hospitalization. METHODS: A total of 653 patients with COVID-19 aged 18 years or older were included from Khorshid hospital in Isfahan, Iran and followed for a mean of 22.72 days (median 23.50; range 1-47). Severe COVID-19 was defined by respiration rate≥30 times/min, oxygen saturation level≤88% in the resting position, and pulse rate≥130/min. The primary outcome was mortality. The secondary outcomes included need for mechanical ventilation and intensive care unit (ICU) admission. RESULTS: During 4233 person-days of follow-up, 49 (7.5%) deaths, 27 (4.1%) invasive ventilation and 89 (13.6%) ICU admissions in hospital were reported. After adjustment for potential confounders, severity of the disease was positively associated with risk of mortality, invasive ventilation and ICU admissions (hazard ratio [HR]: 5.99; 95% CI: 2.85, 12.59; P<0.001, HR: 7.09; 95% CI: 3.24, 15.52; P<0.001 and HR: 4.88; 95% CI: 2.98, 7.98; P<0.001, respectively). In addition, greater age (HR=1.04; 95% CI=1.02-1.07; P=0.002), chronic kidney disease (HR=3.05; 95% CI=1.35, 6.90; P=0.008), blood urea nitrogen (BUN) (HR=1.04; 95% CI=1.03-1.05; P<0.001) and creatinine (HR=1.44; 95% CI=1.26-1.65; P<0.001) were probably significant risk factors for mortality in severe COVID-19 patients. CONCLUSION: More intensive therapy and special monitoring should be implemented for patients with older age, hypertension and kidney disease who are infected with COVID-19 to prevent rapid worsening.

Association of systemic complications with mortality in coronavirus disease of 2019: A cohort study on intensive care unit patients.

Background: Since the beginning of the coronavirus disease of 2019 (COVID-19) pandemic, concerns raised by the growing number of deaths worldwide. Acute respiratory distress syndrome (ARDS) and extrapulmonary complications can correlate with prognosis in COVID-19 patients. This study evaluated the association of systemic complications with mortality in severely affected COVID-19 patients. Materials and Methods: This retrospective study was done on 51 intensive care unit (ICU)-admitted COVID-19 adult patients who were admitted to the ICU ward of Khorshid hospital, affiliated with Isfahan University of Medical Sciences. Only the patients who had a definite hospitalization outcome (dead vs. survivors) were included in the study. Daily clinical and paraclinical records were used to diagnose in-hospital complications in these patients. Results: The sample was comprised of 37 males (72.5%) and 14 females (27.4%). The median age of patients was 63 years (Min: 20, Max: 84), with the mortality rate of 47.1%. In total, 70.6% of patients had at least one coexisting disorder. Chronic kidney disease was associated with the worse outcome (29.16% of dead patients against 3.70 of survived ones). Mechanical ventilation was used in 58.8% of patients. Patients who had received invasive ventilation were more likely to die (87.50% of dead patients against 7.40 of survivors), Complications including sepsis and secondary infections (odds ratio: 8.05, confidence interval: 2.11-30.63) was the strongest predictors of mortality. Conclusion: Complications including sepsis and secondary infections can increase the risk of death in ICU-admitted COVID-19 patients. Therefore, it is substantial that the physicians consider preventing or controlling these complications.

The value of thoracic computed tomography scan comparing to reverse transcription-polymerase chain reaction for the diagnosis of COVID-19.

BACKGROUND: Novel coronavirus disease of 2019 (COVID-19) is the current pandemic causing massive morbidity and mortality worldwide. The gold standard diagnostic method in use is reverse transcription-polymerase chain reaction (RT-PCR) which cannot be solely relied upon. Computed tomography (CT) scan is a method currently used for diagnosis of lung disease and can play a substantial role if proved helpful in COVID-19 diagnosis. We conducted this study to evaluate the diagnostic value of CT scan compared to RT-PCR in the diagnosis of COVID-19. MATERIALS AND METHODS: We recruited 291 hospitalized patients suspicious of COVID-19 according to typical clinical findings during February-March 2020. The patients underwent CT-scan and RT-PCR procedures on the day of hospital admission. CT scans were reported by two radiologists as typical, indeterminate, negative, and atypical. Statistical indices were calculated twice: once considering "typical" and "indeterminate" categories as positive and the other time counting "typical" results as positive. RESULTS: The CT reports were classified as typical (64.95%), indeterminate (10.31%), atypical (11%), and negative (13.75%). Considering "typical" and "intermediate" as positive, sensitivity and specificity were 85.3% and 38.8%, respectively, and using the second assumption, the mentioned indices were 75.9% and 50.4%, respectively. CONCLUSION: According to our study, CT results do not create enough diagnostic benefit and could result in incorrect confidence if negative. Since widely available, CT integration in the clinical process may be helpful in screening of suspected patients in epidemics. Yet, suspected patients should be isolated till confirmed by (multiple) PCRs.

Chest Computed Tomography Severity Score to Predict Adverse Outcomes of Patients with COVID-19.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia. MATERIALS AND METHODS: A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves. RESULTS: The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS. Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 - 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities. CONCLUSION: CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient's outcome.

Risk factors associated with intensive care unit (ICU) admission and in-hospital death among adults hospitalized with COVID-19: a two-center retrospective observational study in tertiary care hospitals.

BACKGROUND: The COVID-19 pandemic is straining the health care systems worldwide. Therefore, health systems should make strategic shifts to ensure that limited resources provide the highest benefit for COVID-19 patients. OBJECTIVE: This study aimed to describe the risk factors associated with poor in-hospital outcomes to help clinicians make better patient care decisions. MATERIAL AND METHODS: This retrospective observational study enrolled 176 laboratory-confirmed COVID-19 patients. Demographic characteristics, clinical data, lymphocyte count, CT imaging findings on admission, and clinical outcomes were collected and compared. Two radiologists evaluated the distribution and CT features of the lesions and also scored the extent of lung involvement. The receiver operating characteristic (ROC) curve was used to determine the optimum cutoff point for possible effective variables on patients' outcomes. Multivariable logistic regression models were used to determine the risk factors associated with ICU admission and in-hospital death. RESULT: Thirty-eight (21.5%) patients were either died or admitted to ICU from a total of 176 enrolled ones. The mean age of the patients was 57.5 ± 16.1 years (males: 61%). The best cutoff point for predicting poor outcomes based on age, CT score, and O2 saturation was 60 years (sensitivity: 71%, specificity: 62%), 10.5 (sensitivity: 73%, specificity: 58%), and 90.5% (sensitivity: 73%, specificity: 59%), respectively. CT score cutoff point was rounded to 11 since this score contains only integer numbers. Multivariable-adjusted regression models revealed that ages of ≥ 60 years, CT score of ≥ 11, and O2 saturation of ≤ 90.5% were associated with higher worse outcomes among study population (odds ratio (OR): 3.62, 95%CI: 1.35-9.67, P = 0.019; OR: 4.38, 95%CI: 1.69-11.35, P = 0.002; and OR: 2.78, 95%CI: 1.03-7.47, P = 0.042, respectively). CONCLUSION: The findings indicate that older age, higher CT score, and lower O2 saturation could be categorized as predictors of poor outcome among COVID-19-infected patients. Other studies are required to prove these associations.

A one-year hospital-based prospective COVID-19 open-cohort in the Eastern Mediterranean region: The Khorshid COVID Cohort (KCC) study.

The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising. Thus, 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 patient recruitment phase finished at the end of August 2020, and the follow-up continues by the end of August 2021. 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 primary, and impaired pulmonary function and psychotic disorders were considered main secondary outcomes. Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, 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 Cox Proportional-Hazards Model for PCEP indicated the following significant risk factors: Oxygen saturation < 80% (HR = 6.3; [CI 95%: 2.5,15.5]), lymphopenia (HR = 3.5; [CI 95%: 2.2,5.5]), Oxygen saturation 80%-90% (HR = 2.5; [CI 95%: 1.1,5.8]), and thrombocytopenia (HR = 1.6; [CI 95%: 1.1,2.5]). This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.