Call, chosen, HA2T2, ANDC: validation of four severity scores in COVID-19 patients

Can the Early Warning Score (ANDC) Predict Tocilizumab Efficacy in Patients with COVID-19 Cytokine Storm?

Background

The aim of this study is to assess the effectiveness of the Early Warning Score [ANDC (age (A), neutrophil-to-lymphocyte ratio (NLR (N)), D-dimer (D), and CRP (C)] in predicting the treatment response in patients receiving tocilizumab for Coronavirus Disease 2019 (COVID-19)-related cytokine storm.

Methods

A retrospective review of medical records was conducted for patients treated with tocili- zumab for a cytokine storm related to COVID-19 between April 1, 2020, and April 1, 2021. Patient demographics, clinical characteristics, and laboratory parameters within 24 hours before tocilizumab were recorded. 1.14 × (age - 20) (years) + 1.63 × NLR + 5.00 × D-dimer (mg/L) + 0.14 × C-reactive protein (CRP) (mg/L) was used as the formula for the ANDC score. The study population was divided into 2 groups: those who died within 28 days of receiving tocilizumab and those who recovered. A comparative analysis was conducted.

Results

Within 28 days of tocilizumab treatment, 59 (35.32%) patients died. In comparison with living patients, deceased patients exhibited considerably higher levels of interleukin (IL)-6, lactate dehydro- genase (LDH), ANDC score, and CRP (P < .05). Lactate dehydrogenase was an independent predictor of response to tocilizumab treatment (P < .001) in a multivariate logistic regression analysis. In patients who did not receive steroid therapy before tocilizumab treatment, the ANDC score had the highest area under the curve (AUC). The optimal cut-off value was determined to be 92.56, with a sensitivity of 91.67% and a specificity of 60.61% (P < .001). In patients receiving steroids before tocilizumab, LDH had the highest AUC. The optimal cut-off value was 484.5 U/L (P < .001).

Conclusion

Lactate dehydrogenase was identified as an independent predictor of response to tocili- zumab treatment. The ANDC score showed the highest AUC value in steroid-naïve patients before tocilizumab, whereas LDH showed the highest AUC value in patients receiving steroids before tocili- zumab. Both the ANDC score and LDH levels show potential as valuable tools to guide treatment decisions.

Head-to-head comparison of six warning scores to predict mortality and clinical impairment in COVID-19 patients in emergency department

The aim was to evaluate the ability of six risk scores (4C, CURB65, SEIMC, mCHOSEN, QuickCSI, and NEWS2) to predict the outcome of patients with COVID-19 during the sixth pandemic wave in Spain. A retrospective observational study was performed to review the electronic medical records in patients ≥ 18 years of age who consulted consecutively in an emergency department with COVID-19 diagnosis throughout 2 months during the sixth pandemic wave. Clinical-epidemiological variables, comorbidities, and their respective outcomes, such as 30-day in-hospital mortality and clinical deterioration risk (a combined outcome considering: mechanical ventilation, intensive care unit admission, and/or 30-day in-hospital mortality), were calculated. The area under the curve for each risk score was calculated, and the resulting curves were compared by the Delong test, concluding with a decision curve analysis. A total of 626 patients (median age 79 years; 49.8% female) fulfilled the inclusion criteria. Two hundred and ninety-three patients (46.8%) had two or more comorbidities. Clinical deterioration risk criteria were present in 10.1% (63 cases), with a 30-day in-hospital mortality rate of 6.2% (39 cases). Comparison of the results showed that score 4C presented the best results for both outcome variables, with areas under the curve for mortality and clinical deterioration risk of 0.931 (95% CI 0.904-0.957) and 0.871 (95% CI 0.833-0.910) (both p < 0.001). The 4C Mortality Score proved to be the best score for predicting mortality or clinical deterioration risk among patients with COVID-19 attended in the emergency department in the following 30 days.

Leucocyte volume, conductivity, and scatter at presentation in COVID-19 patients

Background

In COVID-19 patients, besides changes in leucocyte count, morphological abnormalities of circulating blood cells have been reported.

Aim

This study aims to investigate the relationship between the morphological and functional properties of leucocytes and the severity of the disease in COVID-19 patients.

Materials and Methods

Blood samples were collected from COVID-19 patients (n = 130) at the time of admission. The patients were stratified according to the comorbidity, age, LDH, lymhocyte count score as mild, moderate, and severe. Complete blood count and the cell population data were analyzed by the Volume, conductivity, scatter (VCS) technology on Beckman Coulter LH-780 hematology analyzer. Kruskal-Wal'lis test was used to assess the differences between the groups with subsequent Bonferroni correction.

Results

Neutrophil count was increased, and lymphocyte count was decreased in severe patients compared to mild patients. The increase in the percent of neutrophils and the neutrophil/lymphocyte ratio in the severe patient group was significant in comparison to both the moderate and the mild group. The dispersion of the neutrophil volume and conductivity showed significant changes depending on the severity of the disease. The lymphocyte volume, lymphocyte-volume-SD and lymphocyte-conductivity as well as the monocyte-volume and monocyte-volume-SD were significantly increased in severe patients in comparison to mild patients. The increase of lymphocyte and monocyte volume in severe patients was also significant in comparison to moderate patients.

Conclusions

COVID-19 infection leads to important changes in cell population data of leucocytes. The volumetric changes in lymphocytes and monocytes are related to the severity of the disease.

Multivariate indicators of disease severity in COVID-19

The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying such divergent disease outcomes. Multivariate modeling was used here to define the most distinctive features that separate COVID-19 from healthy controls and severe from moderate disease. Using discriminant analysis and binary logistic regression models we could distinguish between severe disease, moderate disease, and control with rates of correct classifications ranging from 71 to 100%. The distinction of severe and moderate disease was most reliant on the depletion of natural killer cells and activated class-switched memory B cells, increased frequency of neutrophils, and decreased expression of the activation marker HLA-DR on monocytes in patients with severe disease. An increased frequency of activated class-switched memory B cells and activated neutrophils was seen in moderate compared to severe disease and control. Our results suggest that natural killer cells, activated class-switched memory B cells, and activated neutrophils are important for protection against severe disease. We show that binary logistic regression was superior to discriminant analysis by attaining higher rates of correct classification based on immune profiles. We discuss the utility of these multivariate techniques in biomedical sciences, contrast their mathematical basis and limitations, and propose strategies to overcome such limitations.

Factors Associated with ICU Admission in Patients with COVID-19: The GOL2DS Score

Background and Objectives: The COVID-19 pandemic has been shaking lives around the world for nearly two years. The discovery of highly effective vaccines has not been able to stop the transmission of the virus. SARS-CoV-2 shows completely different clinical manifestations. A large percentage (about 40%) of admitted patients require treatment in an intensive care unit (ICU). This study investigates the factors associated with admission of COVID-19 patients to the ICU and whether it is possible to obtain a score that can help the emergency physician to select the hospital ward. Materials and Methods: We retrospectively recorded 313 consecutive patients who were presented to the emergency department (ED) of our hospital and had a diagnosis of COVID-19 confirmed by polymerase chain reaction (PCR) on an oropharyngeal swab. We used multiple logistic regression to evaluate demographic, clinical, and laboratory data statistically associated with ICU admission. These variables were used to create a prognostic score for ICU admission. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver-operating characteristic curve (ROC) of the score for predicting ICU admission during hospitalization were calculated. Results: Of the variables evaluated, only blood type A (p = 0.003), PaO2/FiO2 (p = 0.002), LDH (p = 0.004), lactate (p = 0.03), dyspnea (p = 0.03) and SpO2 (p = 0.0228) were significantly associated with ICU admission after adjusting for sex, age and comorbidity using multiple logistic regression analysis. We used these variables to create a prognostic score called GOL2DS (group A, PaO2/FiO2, LDH, lactate and dyspnea, and SpO2), which had high accuracy in predicting ICU admission (AUROC 0.830 [95% CI, 0.791-0.892). Conclusions: In our single-center experience, the GOL2DS score could be useful in identifying patients at high risk for ICU admission.