Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study.

BACKGROUND: Already at 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 the following variables, which can simply be measured at hospital admission: sex, 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 death, and discharge hazards. 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.

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.

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.

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.

Age-Dependent Clinical Features and Prognosis of COVID-19 Patients.

Background: The clinical and paraclinical symptoms of COVID-19 differ across age groups. This study investigated the differences between these parameters and their outcomes in young, middle-aged, and elderly patients admitted to a COVID-19 referral center. Materials and Methods: This retrospective study encompassed patients with COVID-19 hospitalized at Khorshid Hospital (Isfahan, Iran) during February 23 to April 30, 2020. The patients' predisposing conditions, clinical and paraclinical findings, and outcomes were compared among three young, middle-aged, and elderly groups. Results: Of the 1185 hospitalized patients with suspected COVID-19, 1065 were discharged or died at the end of the study. Among these 1065 patients, 654 patients with the mean age of 57.7 years had positive PCR results or typical CT scans and were included in the study, of whom 77 (11.8%), 353 (54%), and 234 (34.2%) patients were assigned into the young, middle-aged, and elderly groups, respectively. There was no statistically significant difference among the three groups regarding the prevalence of clinical symptoms. Moreover, CRP, ESR, WBC, BUN, Cr, and lymphocytes were higher in the elderly group. The ground-glass opacity (GGO) (24.1%), GGO-consolidation (27.4%), and consolidation (10.3%) were the most common CT scan findings in the young, middle-aged, and elderly groups, respectively. Fifty-three patients (8.1%) died, and the mortality rates were 10.36%, 7.27%, and 3.8% in the elderly, middle-aged, and young groups, respectively. Conclusion: COVID 19 symptoms do not depend on age; however, paraclinical findings differ across young, middle-aged, and elderly patients.

Risk Factors for the Mortality in Hospitalized Patients with COVID-19: A Brief Report.

The cumulative rate of death of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has necessitated better recognizing the risk factors of the disease and the COVID-19-induced mortality. This cross-sectional study aimed to determine the potential risk factors that predict COVID-19-related mortality concentrating on the initial recorded laboratory tests. We extracted admission's medical records of a total of 136 deaths related to COVID-19 and 272 discharged adult inpatients (≥18 years old) related to four referral centers from February 24th to April 12th, 2020, in Isfahan, Iran, to figure out the relationship between the laboratory findings and mortality beyond demographic and clinical findings. We applied the independent sample t test and a chichi square test with SPSS software to compare the differences between the survivor and non-survivor patients. A P value of less than 0.05 was considered significant. Our results showed that greater length of hospitalization (P≤0.001), pre-existing chronic obstructive pulmonary disease (P≤0.001), high pulse rate, hypoxia (P≤0.001), and high computed tomography scan score (P<0.001), in addition to high values of some laboratory parameters, increase the risk of mortality. Moreover, high neutrophil/lymphocyte ratio (OR, 1.890; 95% CI, 1.074-3.325, P=0.027), increased creatinine levels (OR, 15.488; 95% CI, 0.801-299.479, P=0.07), and elevated potassium levels (OR, 13.400; 95% CI, 1.084-165.618, P=0.043) independently predicted in-hospital death related to COVID-19 infection. These results emphasized the potential role of impaired laboratory parameters for the prognosis of fatal outcomes in adult inpatients.

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.