Development and Validation of CoV19-OM Intensive Care Unit Score: An Early ICU Identification for Laboratory-Confirmed SARS-CoV-2 Patients from a Retrospective Cohort Study in Oman

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection

Background

The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness.

Methods

A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed.

Results

The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^⁹/L, white blood cell > 10*10^⁹/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit.

Conclusion

The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

Development of Clinical Risk Scores for Detection of COVID-19 in Suspected Patients During a Local Outbreak in China: A Retrospective Cohort Study

Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks.

Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests.

Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10).

Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted.

Epidemiological Characteristics of Hospitalized Patients with Moderate versus Severe COVID-19 Infection: A Retrospective Cohort Single Centre Study

COVID-19 has a devastating impact worldwide. Recognizing factors that cause its progression is important for the utilization of appropriate resources and improving clinical outcomes. In this study, we aimed to identify the epidemiological and clinical characteristics of patients who were hospitalized with moderate versus severe COVID-19 illness. A single-center, retrospective cohort study was conducted between 3 March and 9 September 2020. Following the CDC guidelines, a two-category variable for COVID-19 severity (moderate versus severe) based on length of stay, need for intensive care or mechanical ventilation and mortality was developed. Data including demographic, clinical characteristics, laboratory parameters, therapeutic interventions and clinical outcomes were assessed using descriptive and inferential analysis. A total of 1002 patients were included, the majority were male (n = 646, 64.5%), Omani citizen (n = 770, 76.8%) and with an average age of 54.2 years. At the bivariate level, patients classified as severe were older (Mean = 55.2, SD = 16) than the moderate patients (Mean = 51.5, SD = 15.8). Diabetes mellitus was the only significant comorbidity potential factor that was more prevalent in severe patients than moderate (n = 321, 46.6%; versus n = 178, 42.4%; p < 0.001). Under the laboratory factors; total white cell count (WBC), C-reactive protein (CRP), Lactate dehydrogenase (LDH), D-dimer and corrected calcium were significant. All selected clinical characteristics and therapeutics were significant. At the multivariate level, under demographic factors, only nationality was significant and no significant comorbidity was identified. Three clinical factors were identified, including; sepsis, Acute respiratory disease syndrome (ARDS) and requirement of non-invasive ventilation (NIV). CRP and steroids were also identified under laboratory and therapeutic factors, respectively. Overall, our study identified only five factors from a total of eighteen proposed due to their significant values (p < 0.05) from the bivariate analysis. There are noticeable differences in levels of COVID-19 severity among nationalities. All the selected clinical and therapeutic factors were significant, implying that they should be a key priority when assessing severity in hospitalized COVID-19 patients. An elevated level of CRP may be a valuable early marker in predicting the progression in non-severe patients with COVID-19. Early recognition and intervention of these factors could ease the management of hospitalized COVID-19 patients and reduce case fatalities as well medical expenditure.