The COVID-GRAM Tool for Patients Hospitalized With COVID-19 in Europe

Clinical characteristics and severity markers in hospitalized COVID-19 patients from western Mexico: a comparative analysis of Delta and Omicron variants

Introduction

COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus notable for its rapid mutation rate, which has led to the emergence of various variants such as Delta and Omicron, each with potentially different levels of transmissibility and virulence. Therefore, this study aims to compare clinical charactheristics and markers associated with the severity of COVID-19 in hospitalized patients from western Mexico who were infected with the Delta and Omicron variants of SARS-CoV-2.

Methods

This cross-sectional study involved 66 patients hospitalized for COVID-19, diagnosed by RT-qPCR. SARS-CoV-2 variants were identified through whole genome sequencing using the COVIDseq platform from Illumina. Upon admission, patients underwent a clinical history assessment, blood gas analysis, and blood biometry. Additionally, several tests and markers were measured, including the percentage of neutralizing antibodies, erythrocyte sedimentation rate (ESR), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNFα), D-dimer, lactate dehydrogenase (LDH), C-reactive protein (CRP), and ferritin.

Results and discussion

Patients hospitalized with the Omicron were found to be older, compared to those infected with the Delta (64 vs. 54 years, p = 0.006). Additionally, a higher proportion of male patients were observed in the Omicron compared to the Delta (p = 0.029). Both Omicron and Delta variants were associated with lymphopenia, although the lymphocyte count was lower in Omicron (0.9 vs. 0.56 10x3/L; p = 0.007). The COVID-GRAM scale indicated a high risk for severe disease in both groups, but the score was higher in Omicron compared to Delta (157 vs. 128 points; p = 0.0004). Patients infected with Omicron exhibited a lower percentage of neutralizing antibodies than those with Delta (35.99 vs. 81%; p < 0.05), regardless of their vaccination status. Among the markers assessed, globular ESR was found to be lower in Omicron compared to Delta (30.5 vs. 41.5 mm/h; p = 0.001), while ferritin levels were higher in patients infected with the Omicron (1,359 vs. 960.6 μg/L; p = 0.007). In patients with severe COVID-19, markers such as lymphopenia, neutralizing antibody levels, ferritin, and COVID-GRAM scores are elevated in the Omicron variant, while only the leukocyte count and ESR for the Delta variant.

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality

Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 (COVID-19) indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO2/FiO2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P=0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Modeling mortality risk in patients with severe COVID-19 from Mexico

Background

Severe acute respiratory syndrome caused by a coronavirus (SARS-CoV-2) is responsible for the COVID-19 disease pandemic that began in Wuhan, China, in December 2019. Since then, nearly seven million deaths have occurred worldwide due to COVID-19. Mexicans are especially vulnerable to the COVID-19 pandemic as Mexico has nearly the worst observed case-fatality ratio (4.5%). As Mexican Latinos represent a vulnerable population, this study aimed to determine significant predictors of mortality in Mexicans with COVID-19 who were admitted to a large acute care hospital.

Methods

In this observational, cross-sectional study, 247 adult patients participated. These patients were consecutively admitted to a third-level referral center in Yucatan, Mexico, from March 1st, 2020, to August 31st, 2020, with COVID-19-related symptoms. Lasso logistic and binary logistic regression were used to identify clinical predictors of death.

Results

After a hospital stay of about eight days, 146 (60%) patients were discharged; however, 40% died by the twelfth day (on average) after hospital admission. Out of 22 possible predictors, five crucial predictors of death were found, ranked by the most to least important: (1) needing to be placed on a mechanical ventilator, (2) reduced platelet concentration at admission, (3) increased derived neutrophil to lymphocyte ratio, (4) increased age, and (5) reduced pulse oximetry saturation at admission. The model revealed that these five variables shared ~83% variance in outcome.

Conclusion

Of the 247 Mexican Latinos patients admitted with COVID-19, 40% died 12  days after admission. The patients' need for mechanical ventilation (due to severe illness) was the most important predictor of mortality, as it increased the odds of death by nearly 200-fold.

Influence of indoor environmental conditions on airborne transmission and lifetime of sneeze droplets in a confined space: a way to reduce COVID-19 spread

Effects of indoor temperature (T∞) and relative humidity (RH∞) on the airborne transmission of sneeze droplets in a confined space were studied over the T∞ range of 15-30 °C and RH∞ of 22-62%. In addition, a theoretical evaporation model was used to estimate the droplet lifetime based on experimental data. The results showed that the body mass index (BMI) of the participants played an important role in the sneezing jet velocity, while the impact of the BMI and gender of participants was insignificant on the size distribution of droplets. At a critical relative humidity RH∞,crit of 46%, the sneezing jet velocity and droplet lifetime were roughly independent of T∞. At RH∞ < RH∞,crit, the sneezing jet velocity decreased by increasing T∞ from 15 to 30 °C, while its trend was reversed at RH∞ > RH∞,crit. The maximum spreading distance of aerosols increased by decreasing the RH∞ and increasing T∞, while the droplet lifetime increased by decreasing T∞ at RH∞ > RH∞,crit. The mean diameter of aerosolized droplets was less affected by T∞ than the large droplets at RH∞ < RH∞,crit, while the mean diameter and number fraction of aerosols were more influenced by RH∞ than the T∞ in the range of 46% ≤ RH∞ ≤ 62%. In summary, this study suggests suitable indoor environmental conditions by considering the transmission rate and lifetime of respiratory droplets to reduce the spread of COVID-19.

Early carbohydrate antigen 125 as a mortality predictor in hospitalized patients with coronavirus disease 2019

Background

Carbohydrate antigen 125 (CA125) is an indicator of inflammation, immune response, and impaired cardiac function. The aim was to investigate whether CA125 behaves as a biomarker of severity and poor clinical outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19).

Methods

Serum CA125 [Elecsys CA125 II assay-(Roche Diagnostics GmbH)] was measured in stored biobank samples from COVID-19 hospitalized patients between 01 March 2020 and 17 October 2021. Multiple logistic regression models were built to explore the association between CA125 and clinical outcomes [in-hospital all-cause mortality, need for invasive mechanical ventilation (IMV), or non-invasive respiratory support (non-IRS)], estimating odds ratios (ORs; 95% CI). The gradient of risk of CA125 was evaluated by fractional polynomials.

Results

A total of 691 patients were included, median age of 63 years (50–76), men (57.2%), with high comorbidity. At admission, 85.8% had pneumonia. Median CA125 was 10.33 U/ml (7.48–15.50). The in-hospital mortality rate was 7.2%. After adjusting for confounding factors, CA125 ≥ 15.5 U/ml (75th percentile) showed an increased risk of death [OR 2.85(1.21–6.71)], as age ≥ 65 years, diabetes, and immunosuppression. Furthermore, CA125 as a continuous variable was positive and significantly associated with the risk of death after multivariate adjustment. The mean hospital stay of the patients with CA125 ≥ 15.5 U/ml was longer than the rest of the study population.

Conclusion

CA125 in the first 72 h of hospital admission seems a useful biomarker of mortality in hospitalized patients with moderate–severe COVID-19. If our findings are confirmed, the wide availability of this biomarker would make easy its widespread implementation in clinical practice.

Independent predictors of in-hospital mortality and the need for intensive care in hospitalized non-critical COVID-19 patients: a prospective cohort study

One of the most helpful strategies to deal with ongoing coronavirus pandemics is to use some prudence when treating patients infected with SARS-CoV-2. We aimed to evaluate the clinical, demographic, and laboratory parameters that might have predictive value for in-hospital mortality and the need for intensive care and build a model based on them. This study was a prospective, observational, single-center study including non-critical patients admitted to COVID-19 wards. Besides classical clinic-demographic features, basic laboratory parameters obtained on admission were tested, and then new models for each outcome were developed built on the most significant variables. Receiver operating characteristics (ROC) analyses were performed by calculating each model's probability. A total of 368 non-critical hospitalized patients were recruited, the need for ICU care was observed in 70 patients (19%). The total number of patients who died in either ICU or wards was 39 (10.6%). The first two models (based on clinical features and demographics) were developed to predict ICU and death, respectively; older age, male sex, active cancer, and low baseline saturation were noted to be independent predictors. The area under the curve values of the first two models were noted 0.878 and 0.882 (p < .001; confidence interval [CI] 95% [0.837-0.919], p < .001; CI 95% [0.844-0.922]). Following two models, the third and fourth were based on laboratory parameters with clinic-demographic features. Initial lower sodium and lower albumin levels were determined as independent factors in predicting the need for ICU care; higher blood urea nitrogen and lower albumin were independent factors in predicting in-hospital mortality. The area under the curve values of the third and fourth model was noted 0.938 and 0.929, respectively (p < .001; CI 95% [0.912-0.965], p < .001; CI 95% [0.895-962]). By integrating the widely available blood tests results with simple clinic demographic data, non-critical patients can be stratified according to their risk level. Such stratification is essential to filter the patients' non-critical underlying diseases and conditions that can obfuscate the physician's predictive capacity.

COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation

Background

An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions.

Research objective

To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to acute physiology and chronic health evaluation (APACHE IVa) and sequential organ failure assessment (SOFA).

Methods

A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. Area under the receiver operating curve (AUROC) was calculated for C-TIME, APACHE IVa and SOFA.

Results

The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO₂/FiO₂ ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72–0.79), vs 0.67 (0.64–0.71) and 0.59 (0.55–0.62) for APACHE and SOFA, respectively (Chi² P<0.0001).

Conclusions

C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.

Development of COVID-19 severity assessment score in adults presenting with COVID-19 to the emergency department

BACKGROUND

Critically-ill Covid-19 patients require extensive resources which can overburden a healthcare system already under strain due to a pandemic. A good disease severity prediction score can help allocate resources to where they are needed most.

OBJECTIVES

We developed a Covid-19 Severity Assessment Score (CoSAS) to predict those patients likely to suffer from mortalities within 28 days of hospital admission. We also compared this score to Quick Sequential Organ Failure Assessment (qSOFA) in adults.

METHODS

CoSAS includes the following 10 components: Age, gender, Clinical Frailty Score, number of comorbidities, Ferritin level, D-dimer level, neutrophil/lymphocyte ratio, C-reactive Protein levels, systolic blood pressure and oxygen saturation. Our study was a single center study with data collected via chart review and phone calls. 309 patients were included in the study.

RESULTS

CoSAS proved to be a good score to predict Covid-19 mortality with an Area under the Curve (AUC) of 0.78. It also proved better than qSOFA (AUC of 0.70). More studies are needed to externally validate CoSAS.

CONCLUSION

CoSAS is an accurate score to predict Covid-19 mortality in the Pakistani population.

Assessment of factors affecting the probability of hospitalization of COVID-19 patients with concomitant pathology and development of a prognostic model based on them

Introduction. Currently, a significant number of patients with COVID-19 require inpatient treatment. At the same time, predictors of hospitalization are still stable, including in persons with concomitant pathology. Aim. Assessment of factors affecting the probability of hospitalization of COVID-19 patients with concomitant pathology and the development of a prognostic model based on them. Materials and methods. An observational retrospective cohort study of 74 314 patients with COVID-19 with various comorbidities was carried out in the period from March to November 2020 in the Russian Federation. Results. Based on 16 factors, including age, gender, place of diagnosis, fever, rhinitis, loss of taste, shortness of breath, concomitant diseases of the cardiovascular, bronchopulmonary system, oncological, endocrine diseases in patients included in the study, a prognostic model was developed. The need for inpatient treatment of patients with COVID-19 and comorbidities was determined. Conclusion. The constructed predictive model has demonstrated sufficient efficiency to assess the likelihood of hospitalization of patients with COVID-19 by medical specialists.

Derivation and validation of a risk score for admission to the Intensive Care Unit in patients with COVID-19

BACKGROUND

This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHODS

We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (N = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the β coefficients of the regression model, we developed a score (0-100 points) associated with ICU admission.

RESULTS

The mean age of the patients was 67 years; 57% were men. A total of 1420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%.

CONCLUSION

A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision.

[Derivation and validation of a risk score for admission to the Intensive Care Unit in patients with COVID-19]

BACKGROUND

This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHODS

We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (n = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the β coefficients of the regression model, we developed a score (0 to 100 points) associated with ICU admission.

RESULTS

The mean age of the patients was 67 years; 57% were men. A total of 1,420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%.

CONCLUSION

A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision.

Clinical Management of COVID-19 in Cancer Patients with the STAT3 Inhibitor Silibinin

COVID-19 pathophysiology is caused by a cascade of respiratory and multiorgan failures arising, at least in part, from the SARS-CoV-2-driven dysregulation of the master transcriptional factor STAT3. Pharmacological correction of STAT3 over-stimulation, which is at the root of acute respiratory distress syndrome (ARDS) and coagulopathy/thrombosis events, should be considered for treatment of severe COVID-19. In this perspective, we first review the current body of knowledge on the role of STAT3 in the pathogenesis of severe COVID-19. We then exemplify the potential clinical value of treating COVID-19 disease with STAT3 inhibitors by presenting the outcomes of two hospitalized patients with active cancer and COVID-19 receiving oral Legalon®-a nutraceutical containing the naturally occurring STAT3 inhibitor silibinin. Both patients, which were recruited to the clinical trial SIL-COVID19 (EudraCT number: 2020-001794-77) had SARS-CoV-2 bilateral interstitial pneumonia and a high COVID-GRAM score, and showed systemic proinflammatory responses in terms of lymphocytopenia and hypoalbuminemia. Both patients were predicted to be at high risk of critical COVID-19 illness in terms of intensive care unit admission, invasive ventilation, or death. In addition to physician's choice of best available therapy or supportive care, patients received 1050 mg/day Legalon® for 10 days without side-effects. Silibinin-treated cancer/COVID-19+ patients required only minimal oxygen support (2-4 L/min) during the episode, exhibited a sharp decline of the STAT3-regulated C-reactive protein, and demonstrated complete resolution of the pulmonary lesions. These findings might inspire future research to advance our knowledge and improve silibinin-based clinical interventions aimed to target STAT3-driven COVID-19 pathophysiology.