COVID-19 Severity Index: A predictive score for hospitalized patients

Microfluidic Immunoarray for Point-of-Care Detection of Cytokines in COVID-19 Patients

The "cytokine storm" often induced in COVID-19 patients contributes to the onset of "acute respiratory distress syndrome" (ARDS) accompanied by lung infection and damage, multiorgan failure, and even death. This large increase in pro-inflammatory cytokines in blood may be related to severity. Rapid, on-demand cytokine analyses can thus be critical to inform treatment plans and improve survival rates. Here, we report a sensitive, low-cost, semiautomated 3D-printed microfluidic immunoarray to detect 2 cytokines and CRP simultaneously in a single 10 μL serum sample in 25 min. Accuracy was validated by analyzing 80 COVID-19 patient serum samples, with results well correlated to a commercial Meso Scale protein immunoassay. Capture antibodies immobilized in detection microwells in a flat well plate-type flow chamber facilitate the immunoassay, with a programmable syringe pump automatically delivering reagents. Chemiluminescence signals were captured in a dark box with a CCD camera integrated for 30 s. This system was optimized to detect inflammation biomarkers IL-6, IFN-γ, and CRP simultaneously in blood serum. Ultralow limits of detection (LODs) of 0.79 fg/mL for IL-6, 4.2 fg/mL for CRP, and 2.7 fg/mL for IFN-γ with dynamic ranges of up to 100 pg/mL were achieved. ROC statistical analyses showed a relatively good diagnostic value related to the samples assigned WHO COVID-19 scores for disease severity, with the best results for IL-6 and CRP. Monitoring these biomarkers for coronavirus severity may allow prediction of disease severity as a basis for critical treatment decisions and better survival rates.

Clinical and Laboratory Data Up on Hospital Admission are Predictors of New-Onset Atrial Fibrillation in Patients Hospitalized Due to COVID-19 Pneumonia

BACKGROUND

New-onset atrial fibrillation (NOAF) occurs in patients hospitalized due to COVID-19. It is still unknown whether clinical and laboratory data assessed upon hospital admission have predictive value for NOAF.

OBJECTIVES

To analyze, upon hospital admission, variables with predictive potential for the occurrence of NOAF in patients with COVID-19 pneumonia.

METHODS

Observational, retrospective, case-control study. Electronic medical reports of consecutive patients, 60 years of age or older, hospitalized due to COVID-19 pneumonia between March 1st and July 15th, 2020, were reviewed. Non-paired Student or chi-squared tests compared variables. A Cox proportional hazard model was employed to identify independent predictors of NOAF. P value < 0.05 was considered statistically significant.

RESULTS

Among 667 patients hospitalized due to COVID-19, 201 (30.1%) fulfilled the inclusion criteria. NOAF was documented in 29 patients (14.4%), composing group 1. Group 2 was composed of 162 patients without NOAF. Ten patients were excluded due to the AF rhythm upon hospital admission. In groups 1 and 2, there were differences in overall in-hospital survival rate (24.1 % vs. 67.9%; p<0.001), length of stay in ICU (11.1 ± 10.5 days vs. 4.9 ± 7.5 days; p=0.004) and need for mechanical ventilation rate (82.9% vs. 32.7%; p<0.001). In the Cox model, age > 71 y/o (HR=6.8; p<0.001), total leukocyte count ≤ 7,720 cels.μL-¹ (HR=6.6; p<0.001), serum [Na+] ≤ 137 mEq.L-¹ (HR=5.0; p=0.001), SAPS3 score > 55 (HR=5.6; p=0.002), and disorientation (HR=2.5; p=0.04) on admission were independent predictors of NOAF.

CONCLUSION

NOAF is a common arrhythmia in elderly hospitalized patients with COVID-19 pneumonia. Clinical and laboratory parameters evaluated on admission have a predictive value for the occurrence of NOAF during hospitalization.

Cardiopulmonary Response in Post-COVID-19 Individuals: A Cross-Sectional Study Comparing the Londrina Activities of Daily Living Protocol, 6-Minute Walk Test, and Glittre Activities of Daily Living Test

This study addresses the imperative need for reliable assessment protocols in guiding rehabilitation interventions for individuals post-COVID-19, considering the enduring physiological effects of the virus. A cohort of 40 post-COVID-19 individuals underwent assessments using the Londrina ADL protocol, Glittre ADL test, and the 6-minute walk test (6MWT). Physiological parameters were recorded during and after each test, including heart rate, respiratory rate, and oxygen saturation. The post hoc comparisons between the pre-test and post-test cardiopulmonary response of the three tests showed significant differences, except diastolic blood pressure (6MWT vs. Londrina ADL protocol), heart rate (6MWT vs. Londrina ADL protocol), respiratory rate (6MWT vs. Londrina ADL protocol), blood oxygen level (SpO2) (6MWT vs. Londrina ADL protocol), dyspnea (Londrina ADL protocol vs. Glittre ADL test), and fatigue (Londrina ADL protocol vs. Glittre ADL test). The Londrina ADL protocol demonstrated cardio-pulmonary responses comparable to the Glittre ADL test, as well as the 6MWT, emphasizing its effectiveness in evaluating walking-related outcomes. The study concludes that the Londrina ADL protocol is a robust and practical tool for the routine clinical testing of daily living activities in post-COVID-19 individuals. While the 6MWT remains valuable for assessing walking-related outcomes, a combined approach employing the Londrina ADL protocol and 6MWT offers a comprehensive strategy for evaluating multifaceted functional capacities in this population.

COVID-19: The Development and Validation of a New Mortality Risk Score

Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods: A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results: Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions: In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

The National Early Warning Score 2 with Age and Body Mass Index (NEWS2 Plus) to Determine Patients with Severe COVID-19 Pneumonia

(1) Background: Early identification of severe coronavirus disease 2019 (COVID-19) pneumonia at the initial phase of hospitalization is very crucial. To address this, we validated and updated the National Early Warning Score 2 (NEWS2) for this purpose. (2) Methods: We conducted a study on adult patients with COVID-19 infection in Chiang Mai, Thailand, between May 2021 and October 2021. (3) Results: From a total of 725 COVID-19 adult patients, 350 (48.3%) patients suffered severe COVID-19 pneumonia. In determining severe COVID-19 pneumonia, NEWS2 and NEWS2 + Age + BMI (NEWS2 Plus) showed the C-statistic values of 0.798 (95% CI, 0.767-0.830) and 0.821 (95% CI, 0.791-0.850), respectively. The C-statistic values of NEWS2 Plus were significantly improved compared to those of NEWS2 alone (p = 0.012). Utilizing a cut-off point of five, NEWS2 Plus exhibited better sensitivity and negative predictive value than the traditional NEWS2, with values of 99.7% vs. 83.7% and 98.9% vs. 80.7%, respectively. (4) Conclusions: The incorporation of age and BMI into the traditional NEWS2 score enhanced the efficacy of determining severe COVID-19 pneumonia. Physicians can rely on NEWS2 Plus (NEWS2 + Age + BMI) as a more effective decision-making tool for triaging COVID-19 patients during early hospitalization.

The role of IL-6, ferritin, and coagulopathy in Covid-19 clinical progression

Background In COVID-19, the release of pro-inflammatory mediators in the cytokine storm, primarily interleukin-6 (IL-6), has been hypothesized to induce pulmonary intravascular coagulation. However, the relationship between IL-6 and coagulopathy remains unclear in COVID-19 progression. We aimed to investigate the correlation of IL-6 with D-dimer, fibrinogen, prothrombin time (PT), and ferritin. Furthermore, we also analyzed the effect of those parameters on the worsening of COVID-19 patients. Methods A prospective cohort study was conducted in moderate and severe COVID-19 patients from June 2020 to January 2021. A serial evaluation of IL-6, D-dimer, fibrinogen, ferritin, and PT was performed and correlated with the patient's condition at admission and on the 14th day. The outcomes (improvement, worsening, or discharged patients) were recorded during the study. Results Of 374 patients, 73 study subjects (61 severe and 12 moderate COVID-19) were included in this study. A total of 35 out of 61 severe and one out of 12 moderate illness subjects had experienced worsening. Spearman-rank correlation of IL-6 with with ferritin, D-dimer, fibrinogen, and PT was 0.08 ( p=0.5), -0.13 ( p=0.27), 0.01 ( p=0.91), and 0.03 ( p=0.77), respectively. In ROC analysis, D-dimer (74,77%) and IL-6 (71,32%) were the highest among other variables (>60%). Conclusions In COVID-19 patients, there was a correlation between elevated IL-6 and D-dimer levels with disease deterioration. There was no correlation between elevated IL-6 levels with ferritin, D-dimer, fibrinogen, and PT levels. Therefore, changes in IL-6 and D-dimer can predict worsening in moderate and severe COVID-19 patients.

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity

Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662-0.807) and 0.734 (95% CI: 0.688-0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830-0.835), precision of 0.764 (95% CI: 0.757-0.771), recall of 0.747 (95% CI: 0.741-0.753), and F1 score of 0.747 (95% CI: 0.741-0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156-3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management.

Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID-19 patients

We used an integrated ensemble learning method to build a stable prediction model for severity in COVID-19 patients, which was validated in multicenter cohorts.

Utility of Presepsin and Interferon-λ3 for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients

We explored the utility of novel biomarkers, presepsin and interferon-λ3 (IFN-λ3), for predicting disease severity and clinical outcomes in hospitalized Coronavirus (COVID-19) patients. In a total of 55 patients (non-critical, n = 16; critical, n = 39), presepsin and IFN-λ3 were compared with sequential organ failure assessment (SOFA) scores and age. Disease severity and clinical outcomes (in-hospital mortality, intensive care unit admission, ventilator use, and kidney replacement therapy) were analyzed using receiver operating characteristic (ROC) curves. In-hospital mortality was also analyzed using the Kaplan-Meier method with hazard ratios (HR). SOFA scores, age, presepsin, and IFN-λ3 predicted disease severity comparably (area under the curve [AUC], 0.67-0.73). SOFA score and IFN-λ3 predicted clinical outcomes comparably (AUC, 0.68-0.88 and 0.66-0.74, respectively). Presepsin predicted in-hospital mortality (AUC = 0.74). The combination of presepsin and IFN-λ3 showed a higher mortality risk than SOFA score or age (HR [95% confidence interval, CI], 6.7 [1.8-24.1]; 3.6 [1.1-12.1]; 2.8 [0.8-9.6], respectively) and mortality rate further increased when presepsin and IFN-λ3 were added to SOFA scores or age (8.5 [6.8-24.6], 4.2 [0.9-20.6], respectively). In the elderly (≥65 years), in-hospital mortality rate was significantly higher when both presepsin and IFN-λ3 levels increased than when either one or no biomarker level increased (88.9% vs. 14.3%, p < 0.001). Presepsin and IFN-λ3 predicted disease severity and clinical outcomes in hospitalized COVID-19 patients. Both biomarkers, whether alone or added to the clinical assessment, could be useful for managing COVID-19 patients, especially the elderly.

Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods

Background

Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority.

Method and Material

The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records.

Results

The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model.

Conclusion

Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.

Cardiorespiratory Response to Six-Minute Step Test in Post COVID-19 Patients-A Cross Sectional Study

BACKGROUND AND PURPOSE

New coronavirus disease 2019 (COVID-19) can cause persistent symptoms and physical weakness that can lead to a limitation in activities of daily living (ADL). There is a lack of evidence about the performance in the six-minute step test (6MST) of post-COVID-19 patients and healthy subjects. The aim of this study is to investigate the cardiorespiratory response induced by the 6MST in post-COVID-19 patients and compare it with the response of the six-minute walk test (6MWT).

METHODS

This cross-sectional study was conducted on 34 post-COVID-19 patients and 33 healthy subjects. The assessment was performed at one month from a non-severe SARS-CoV-2 infection. Both groups were assessed by using the 6MST, 6MWT, and the pulmonary function test (PFT). Post COVID functional status (PCFS) scale was used for the post-COVID-19 group to assess functional status. Physiological responses; heart rate (HR), respiratory rate (RR), oxygen saturation (SpO₂), blood pressure (BP), and Borg scale for fatigue and dyspnea were recorded before and after the 6MST and 6MWT.

RESULTS

the performance of the post-COVID-19 group was worse than the healthy group in both tests. In 6MWT, the distance walked by the post-COVID-19 group (423 ± 7) was 94 m less than the healthy group, and the number of climbed steps in the 6MST (121 ± 4) was 34 steps less than the healthy group. Both results were statistically significant (p < 0.001). There was a moderate positive correlation between the 6MST and 6MWT in walked distance versus steps number (r = 0.5, p < 0.001). In addition, there was a moderate correlation between the two tests in the post (HR, RR, SpO₂, systolic blood pressure SBP, diastolic blood pressure DBP, dyspnea, and fatigue) with p < 0.001.

CONCLUSIONS

Six-minute step tests produced similar cardiorespiratory responses when compared to a 6MWT. The 6MST can be used as an assessment tool for COVID-19 patients to evaluate their functional capacity and ADL.

COVID-19 severity scale for claims data research

OBJECTIVE

To create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data.

DATA SOURCE

Secondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020.

STUDY DESIGN

The World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality.

DATA COLLECTION/EXTRACTION METHODS

The strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC).

PRINCIPAL FINDINGS

A total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p < 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19.

CONCLUSION

A standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19.

Multicenter validation of Early Warning Scores for detection of clinical deterioration in COVID-19 hospitalized patients

OBJECTIVE

Investigate the predictive value of NEWS2, NEWS-C, and COVID-19 Severity Index for predicting intensive care unit (ICU) transfer in the next 24h.

DESIGN

Retrospective multicenter study.

SETTING

Two third-level hospitals in Argentina.

PATIENTS

All adult patients with confirmed COVID-19, admitted on general wards, excluding patients with non-intubated orders.

INTERVENTIONS

Patients were divided between those who were admitted to ICU and non-admitted. We calculated the three scores for each day of hospitalization.

VARIABLES

We evaluate the calibration and discrimination of the three scores for the outcome ICU admission within 24, 48h, and at hospital admission.

RESULTS

We evaluate 13,768 days of hospitalizations on general medical wards of 1318 patients. Among these, 126 (9.5%) were transferred to ICU. The AUROC of NEWS2 was 0.73 (95%CI 0.68-0.78) 24h before ICU admission, and 0.52 (95%CI 0.47-0.57) at hospital admission. The AUROC of NEWS-C was 0.73 (95%CI 0.68-0.78) and 0.52 (95%CI 0.47-0.57) respectively, and the AUROC of COVID-19 Severity Index was 0.80 (95%CI 0.77-0.84) and 0.61 (95%CI 0.58-0.66) respectively. COVID-19 Severity Index presented better calibration than NEWS2 and NEWS-C.

CONCLUSION

COVID-19 Severity index has better calibration and discrimination than NEWS2 and NEWS-C to predict ICU transfer during hospitalization.

Multicenter validation of Early Warning Scores for detection of clinical deterioration in COVID-19 hospitalized patients

Objective

Investigate the predictive value of NEWS2, NEWS-C, and COVID-19 Severity Index for predicting intensive care unit (ICU) transfer in the next 24 h.

Design

Retrospective multicenter study.

Setting

Two third-level hospitals in Argentina.

Patients

All adult patients with confirmed COVID-19, admitted on general wards, excluding patients with non-intubated orders.

Interventions

Patients were divided between those who were admitted to ICU and non-admitted. We calculated the three scores for each day of hospitalization.

Variables

We evaluate the calibration and discrimination of the three scores for the outcome ICU admission within 24, 48 h, and at hospital admission.

Results

We evaluate 13,768 days of hospitalizations on general medical wards of 1318 patients. Among these, 126 (9.5%) were transferred to ICU. The AUROC of NEWS2 was 0.73 (95%CI 0.68-0.78) 24 h before ICU admission, and 0.52 (95%CI 0.47-0.57) at hospital admission. The AUROC of NEWS-C was 0.73 (95%CI 0.68-0.78) and 0.52 (95%CI 0.47-0.57) respectively, and the AUROC of COVID-19 Severity Index was 0.80 (95%CI 0.77-0.84) and 0.61 (95%CI 0.58-0.66) respectively. COVID-19 Severity Index presented better calibration than NEWS2 and NEWS-C.

Conclusion

COVID-19 Severity index has better calibration and discrimination than NEWS2 and NEWS-C to predict ICU transfer during hospitalization.

Intricacies Affiliated With Post-COVID Vaccine Complications in Makkah Province, Saudi Arabia

Introduction COVID-19 vaccine side effects have a fundamental role in public confidence in the vaccine and its uptake process. Thus far, evidence on vaccine safety has been exclusively obtained from manufacturer-sponsored studies; therefore, this study is designed to assess post-COVID-19 vaccine complications in Makkah province, Saudi Arabia. Method A cross-sectional study included 840 subjects conducted from August to November 2022 to collect data about COVID-19 vaccine side effects. A validated questionnaire was used with 21 multiple-choice items covering demographic data, COVID-19 vaccination type, side effects, and medication used to relieve side effects. The online Raosoft sample size calculator (Raosoft Inc., Seattle, Washington) was utilized for sample size calculation. The Statistical Package for the Social Sciences version 22.0 (IBM Inc., Armonk, New York) was utilized to carry out descriptive statistics. The Shapiro-Wilk test was used to evaluate normal data distribution. Significance of categorized data made by the Pearson's Chi-Squared test and of the vaccination side effects experienced in general wellbeing on a scale of 1-10 by the Kruskal-Wallis test. Result The survey found that most participants in the 18-26 age group were from Jeddah, women, and Saudis. Only 7.1% of participants had comorbidities alone, and 63.1% of participants had previously had COVID-19. The vaccine types used in this study were primarily Pfizer (83.3%), AstraZeneca (9.5%), Moderna (3.6%), and combination vaccines (3.6%). The majority of the participants received vaccination up to the third dose. No side effects were reported by 9.5% of participants, while mild and severe side effects were reported by 90.5% and 23.8% of participants, respectively. Mild side effects included injection site pain, redness, tenderness, or itching (34.5%), fatigue (22.6%), low-grade fever, chills, diarrhea, headache (17.9%), and myalgia (14.3%). Conclusion The majority of the side effects of COVID-19 vaccination were minor reactions (90.5%), but 23.8% were found to be serious side effects, most of which lasted one to three days. More independent studies are needed to investigate gender differences, COVID-19 vaccine efficacy, and the prevalence of side effects in other populations conducted by academic institutions. Additional independent research on vaccine safety is urgently needed to increase public confidence in vaccines and to better understand risk factors for vaccine side effects.

The Predictive Score for Patients Hospitalized With COVID-19 in Resource-Limited Settings

Background and aims

The second wave of coronavirus disease 2019 (COVID-19) has been devastating in India and many developing countries. The mortality reported has been 40% higher than in the first wave, overwhelming the nation’s health infrastructure. Despite a better understanding of the disease and established treatment protocols including steroids and heparin, the second wave was disastrous. Subsequent waves have the potential to further cripple healthcare deliveries, also affecting non-COVID-19 care across many developing economies. It is then important to identify and triage high-risk patients to best use the limited resources. Routine tests such as neutrophil and monocyte counts have been identified but have not been successfully validated uniformly, and their utility is still being understood in COVID-19.

Various predictive models that are available require online resources and calculators and additionally await validation across all populations. These, although useful, might not be available or accessible across all institutions. It is then important to identify easy-to-use scores that utilize tests done routinely.

In identifying with this goal, we did a retrospective review of the institutional database to identify potential predictors of intensive care unit (ICU) admission and mortality in patients hospitalized during the second wave who accessed healthcare at our academic setup.

Results

Three predictors of mortality and four predictors of ICU admission were identified. Absolute neutrophil count was a common predictor of both ICU admission and mortality but with two separate cut points. An absolute neutrophil count of >4,200 predicted need for ICU admission (odds ratio (OR): 3.1 (95% confidence interval (CI): 2.0, 4.8)), and >7,200 predicted mortality (adjusted OR: 4.2 (95% CI: 1.9, 9.4)). We observed that a blood urea level greater than 45 was predictive of needing ICU care (adjusted OR: 8.0 (95% CI: 3.7, 17.6)). In our dataset, serum ferritin of >500 was predictive of ICU admission (adjusted OR: 2.7 (95% CI: 1.2, 5.9)). We noted a right shift of partial pressure (p50 is the oxygen tension at which hemoglobin is 50% saturated) (p50c) in SARS-CoV-2 as a predictor of ICU care (OR: 2.6 (95% CI: 1.7, 3.9)) when partial pressure is >26.5. In our analysis, a serum protein of less than 7 g/dL (OR: 2.8 (95% CI: 1.7, 4.4)) was a predictive variable for ICU admission. An LDH value of >675 was predictive of severity with a need for ICU admission (OR: 9.2 (95% CI: 5.4, 15.5)) in our series. We then assigned a score to each of the predictive variables based on the adjusted odds ratio.

Conclusion

We identified a set of easy-to-use predictive variables and scores to recognize the subset of patients hospitalized with COVID-19 with the highest risk of death or clinical worsening requiring ICU care.

Sex and gender differences in community-acquired pneumonia

Awareness of the influence of sex ands gender on the natural history of several diseases is increasing. Community-acquired pneumonia (CAP) is the most common acute respiratory disease, and it is associated with both morbidity and mortality across all age groups. Although a role for sex- and gender-based differences in the development and associated complications of CAP has been postulated, there is currently high uncertainty on the actual contribution of these factors in the epidemiology and clinical course of CAP. More evidence has been produced on the topic during the last decades, and sex- and gender-based differences have also been extensively studied in COVID-19 patients since the beginning of the SARS-CoV-2 pandemic. This review aims to provide an extensive outlook of the role of sex and gender in the epidemiology, pathogenesis, treatment, and outcomes of patients with CAP, and on the future research scenarios, with also a specific focus on COVID-19.

Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity

The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.

MuLBSTA skorunun SARS-CoV-2 pnömonili hospitalize hastalarda kritik klinik sonuçları öngörmedeki prediktif değerinin incelenmesi

Giriş:MuLBSTA (Multilobar infiltrasyon, Lenfositopeni, Bakteriyel koenfeksiyon, Sigara öyküsü, hiperTansiyon ve Yaş> 65) skoru, viral pnömonisi olan hastaları beklenen mortaliteye göre sınıflandırmak için kullanılan bir klinik tahmin kuralıdır. Hastanede yatan Sars-Cov-2 hastalarında kötü klinik sonuçlar için MuLBSTA'nın prediktif performansını PSI, CURB-65 ve qSOFA ile karşılaştırdık. Metot:Bu çalışma 11 Mart 2020 ile 31 Mayıs 2020 tarihleri arasında üçüncü basamak bir üniversite hastanesinde yatan Sars-Cov-2'li hastalar üzerinde geriye dönük yapıldı. SARS-Cov-2 testi pozitif çıkan 900 hastadan 271'i çalışmaya dahil edildi. Tüm hastalarda 30 günlük mortalite, YBÜ ihtiyacı, mekanik ventilasyon gereksinimi ve ARDS gelişimini değerlendirmek için MuLBSTA, PSI, CURB65 ve qSOFA skoru kullanıldı. 30 günlük mortalite için prognostik faktörler de analiz edildi. Bulgular:Hastanede yatan 271 hastanın 150'si (%55.3) erkekti. Ortalama yaş 54.2 ± 15.4 yıldı. 30 günlük ölüm oranı %10,7 idi. Çalışmaya dahil edilen hastalardan; 39 hasta (%14,3) yoğun bakıma yatırıldı, 32 hasta (%11,8) mekanik ventilatör desteği aldı ve 23 hasta (%8,4) ARDS tanısı aldı. Mortaliteyi tahmin etmede MuLBSTA, PSI, CURB-65 ve qSOFA skorlarının alıcı işletim karakteristik eğrisi altında kalan alan(AUROC) değerleri sırasıyla 0.877 (%95 CI 0.832 0.914), 0.853 (%95 CI 0.806-0.893), 0.769 (95% CI 0,714-0,817) ve 0,769 (95% CI 0,715-0,818). MuLBSTA puanı, diğer tahmin puanlarına kıyasla daha yüksek bir AUROC değeri gösterdi. MuLBSTA ve PSI skorları, YBÜ ihtiyacı, mekanik ventilasyon gereksinimive ARDS gelişimi olan hastaları belirlemede CURB-65 ve qSOFA skorlarından daha iyi performans gösterdi. Sonuç:MuLBSTA skoru, hastanede yatan Sars-Cov-2 hastalarında kötü klinik sonuçları tahmin etmek için etkili bir araçtır. Kullanımını doğrulamak için daha fazla çalışmaya ihtiyaç vardır.

A multiparametric score for assessing the individual risk of severe Covid-19 among patients with Multiple Sclerosis

Background

Many risk factors for the development of severe forms of Covid-19 have been identified, some applying to the general population and others specific to Multiple Sclerosis (MS) patients. However, a score for quantifying the individual risk of severe Covid-19 in patients with MS is not available. The aim of this study was to construct such score and to evaluate its performance.

Methods

Data on patients with MS infected with Covid-19 in Italy, Turkey and South America were extracted from the Musc-19 platform. After imputation of missing values, data were separated into training data set (70%) and validation data set (30%). Univariable logistic regression models were performed in the training dataset to identify the main risk factors to be included in the multivariable logistic regression analyses. To select the most relevant variables we applied three different approaches: (1) multivariable stepwise, (2) Lasso regression, (3) Bayesian model averaging. Three scores were defined as the linear combination of the coefficients estimated in the models multiplied by the corresponding value of the variables and higher scores were associated to higher risk of severe Covid-19 course. The performances of the three scores were compared in the validation dataset based on the area under the ROC curve (AUC) and an optimal cut-off was calculated in the training dataset for the score with the best performance. The probability of showing a severe Covid-19 course was calculated based on the score with the best performance.

Results

3852 patients were included in the study (2696 in the training dataset and 1156 in the validation data set). 17% of the patients required hospitalization and risk factors for severe Covid-19 course were older age, male sex, living in Turkey or South America instead of living in Italy, presence of comorbidities, progressive MS, longer disease duration, higher Expanded Disability Status Scale, Methylprednisolone use and anti-CD20 treatment. The score with the best performance was the one derived using the Lasso selection approach (AUC= 0.72) and it was built with the following variables: age, sex, country, BMI, presence of comorbidities, EDSS, methylprednisolone use, treatment. An excel spreadsheet to calculate the score and the probability of severe Covid-19 is available at the following link: https://osf.io/ac47u/?view_only=691814d57b564a34b3596e4fcdcf8580.

Conclusions

The originality of this study consists in building a useful tool to quantify the individual risk for Covid-19 severity based on patient's characteristics. Due to the modest predictive ability and to the need of external validation, this tool is not ready for being fully used in clinical practice to make important decisions or interventions. However, it can be used as an additional instrument to identify high-risk patients and persuade them to take important measures to prevent Covid-19 infection (i.e. getting vaccinated against Covid-19, adhering to social distancing, and using of personal protection equipment).

Performance features and mortality prediction of the 4C Score early in COVID-19 infection: a retrospective study in Saudi Arabia

The ISARIC4C consortium developed and internally validated the 4C Score for prediction of mortality only in hospitalized patients. We aimed to assess the validity of the 4C Score in mortality prediction of patients with COVID-19 who had been home isolated or hospitalized.This retrospective cross-sectional study was performed after the first wave of COVID-19. Data of all PCR-positive COVID-19 patients who had been discharged, hospitalized, or died were retrospectively analyzed. Patients were classified into four risk groups according to the 4C Mortality Score. A total of (506) patients were classified as follows: low (57.1%), intermediate (27.9%), high (13%), and very high (2%) risk groups. Clinical, radiological, and laboratory data were significantly more severe in the high and very high-risk groups compared with other groups (p<0.001 for all). Mortality rate was correctly estimated by the model with 71% sensitivity, 88.6% specificity, and area under the curve of 0.9. The mortality rate was underestimated among the very high-risk group (66.2% vs 90%). The odds of mortality were significantly greater in the presence of hypoxia (OR 2.6, 95% CI 1.5 to 4.6, p<0.001) and high respiratory rate (OR 5.3, 95% CI 1.6 to 17.9, p<0.007), C reactive protein (CRP) (OR 3.5, 95% CI 1.8 to 6.8, p<0.001), and blood urea nitrogen (BUN) (OR 1.9, 95% CI 1.3 to 3.1, p<0.002). Other components of the model had non-significant predictions. In conclusion, the 4C Mortality Score has good sensitivity and specificity in early risk stratification and mortality prediction of patient with COVID-19. Within the model, only hypoxia, tachypnea, high BUN, and CRP were the independent mortality predictors with the possibility of overlooking other important predictors.

Evaluation of Admission Chest X-Ray Findings in Patients With Respiratory Infection During the COVID-19 Pandemic

AIMS

To evaluate the prevalence of X-ray findings in hospitalized patients requiring hospitalization with suspected Coronavirus disease 2019 (COVID-19) infection and potential differences in the laboratory values and clinical outcomes related to the presence of abnormal chest X-ray (CXR) findings.

METHODS

 A total of 117 patients suspected of COVID-19 pneumonia and hospitalized with symptoms of lower respiratory tract disease were included in this study. Patients were divided into subgroups according to COVID-19 diagnosis and statistical comparisons were made according to CXR findings.

RESULTS

 In our cohort, CXR abnormalities were more common in patients with confirmed COVID-19 diagnosis and were associated with increased mortality. Patients with abnormal chest X-rays had a significantly lower PaO₂/FiO₂ ratio both in the COVID-19 and non-COVID-19 groups.

CONCLUSION

CXR is a routine examination in all patients with symptoms of lower respiratory tract disease and its findings relate to in-hospital mortality and PaO₂/FiO₂ ratio. Thus, it can be a significant measure of disease severity, especially in resource restrained settings and emergency situations such as the COVID-19 pandemic.

Evaluation of four novel prognostic scores on admission for COVID-19 mortality. An experience from a Mediterranean tertiary center

AIM

To assess the performance of four novel prognostic scores on admission in predicting in-hospital mortality in patients with confirmed SARS-CoV-2 infection and compare it to NEWS2 and respiratory SOFA score.

METHODS

A total of 85 adult patients admitted to a tertiary hospital in Western Greece with positive SARS-CoV-2 PCR test, were enrolled and divided into the non-survivor (n = 10) and survivor (n = 75) groups. Receiver Operating Characteristic (ROC) analysis was conducted to determine the predictive effect of the COVID-19 Mortality Score, COVID-19 Severity Index, 4 C Mortality Score and COVID-IRS NLR. Subsequently, they were compared to the respiratory component of the SOFA score and NEWS2.

RESULTS

ROC curve analysis showed that the COVID-19 Mortality Score (score ≥4) had the highest combination of sensitivity and specificity values for predicting in-hospital mortality (Sensitivity = 0.8, Specificity = 0.853). The Area Under Curve (AUC) for predicting in hospital mortality for the COVID-19 Mortality Score, COVID-19 Severity Index, 4 C Mortality Score and COVID-IRS NLR were 0.846, 0.815, 0.789 and 0.787, respectively. Comparison between the AUC of the four novel COVID-19 scores, respiratory SOFA and NEWS2 showed no significant differences.

CONCLUSION

All four novel prognostic scores had acceptable to excellent AUC values for predicting in hospital mortality. Out of the four novel prognostic scores for patients with COVID-19, the COVID-19 mortality score showed the best results in our cohort. Its prognostic ability was superior to that of the NEWS2 and respiratory SOFA score.

Evaluation of the Risk of Clinical Deterioration among Inpatients with COVID-19

This study aims to assess the risk of severe forms of COVID-19, based on clinical, laboratory, and imaging markers in patients initially admitted to the ward. This is a retrospective observational study, with data from electronic medical records of inpatients, with laboratory confirmation of COVID-19, between March and September 2020, in a hospital from Juiz de Fora-MG, Brazil. Participants (n = 74) were separated into two groups by clinical evolution: those who remained in the ward and those who progressed to the ICU. Mann-Whitney U test was taken for continuous variables and the chi-square test or Fisher's exact test for categorical variables. Comparing the proposed groups, lower values of lymphocytes (p = <0.001) and increases in serum creatinine (p = 0.009), LDH (p = 0.057), troponin (p = 0.018), IL-6 (p = 0.053), complement C4 (p = 0.040), and CRP (p = 0.053) showed significant differences or statistical tendency for clinical deterioration. The average age of the groups was 47.9 ± 16.5 and 66.5 ± 7.3 years (p = 0.001). Hypertension (p = 0.064), heart disease (p = 0.048), and COPD (p = 0.039) were more linked to ICU admission, as well as the presence of tachypnea on admission (p = 0.051). Ground-glass involvement >25% of the lung parenchyma or pleural effusion on chest CT showed association with evolution to ICU (p = 0.027), as well as bilateral opacifications (p = 0.030) when compared to unilateral ones. Laboratory, clinical, and imaging markers may have significant relation with worse outcomes and the need for intensive treatment, being helpful as predictive factors.