Brescia-COVID Respiratory Severity Scale (BRCSS) and Quick SOFA (qSOFA) score are most useful in showing severity in COVID-19 patients

Is 1 Day of Focused Training in Echocardiographic Assessment Using Subxiphoid-Only (EASy) Examination Enough? A Tertiary Hospital Response to the COVID-19 Crisis and the Use of the EASy Examination to Support Unit-Wide Image Acquisition

OBJECTIVES

We assessed the efficacy of 1-day training in echocardiography assessment using subxiphoid-only (EASy) followed by supervised image interpretation and decision-making during patient rounds as a novel approach to scaling up the use of point-of-care ultrasound (POCUS) in critically ill patients.

DESIGN

Retrospective analysis of medical records and EASy examination images.

SETTING

Tertiary care academic hospital.

PATIENTS

A total of 14 adults (> 18 yr old) with COVID-19-associated respiratory failure under the care of Albany Medical Center's surge response team from April 6-17, 2020 who received at least one EASy examination.

INTERVENTIONS

Residents (previously novice sonographers) were trained in EASy examination using 1 day of didactic and hands-on training, followed by independent image acquisition and supervised image interpretation, identification of hemodynamic patterns, and clinical decision-making facilitated by an echocardiography-certified physician during daily rounds.

MEASUREMENTS AND MAIN RESULTS

We recorded the quality of resident-obtained EASy images, scanning time, and frequency with which the supervising physician had to repeat the examination or obtain additional images. A total of 63 EASy examinations were performed; average scanning time was 4.3 minutes. Resident-obtained images were sufficient for clinical decision-making on 55 occasions (87%), in the remaining 8 (13%) the supervising physician obtained further images.

CONCLUSIONS

EASy examination is an efficient, valuable tool under conditions of scarce resources. The educational model of 1-day training followed by supervised image interpretation and decision-making allows rapid expansion of the pool of sonographers and implementation of bedside echocardiography into routine ICU patient management.

The effect of limited healthcare access on poor outcomes among hospitalized COVID-19 patients in Honduras: A single center cohort study

Background

The COVID-19 pandemic has had a severe impact on the Latin American subcontinent, particularly in areas with limited hospital resources and a restricted Intensive Care Unit (ICU) capacity. This study aimed to provide a comprehensive description of the clinical characteristics, outcomes, and factors associated with survival of COVID-19 hospitalized patients in Honduras.

Research question

What were the characteristics and outcomes of COVID-19 patients in a large referral center in Honduras?

Study design and methods

This study employed a retrospective cohort design conducted in a single center in San Pedro Sula, Honduras, between October 2020 to March 2021. All hospitalized cases of confirmed COVID-19 during this timeframe were included in the analysis. Univariable and multivariable survival analysis were performed using Kaplan-Meier curves and Cox proportional hazards model aiming to identify factors associated with decreased 30 day in-hospital survival, using a priori-selected factors.

Results

A total of 929 confirmed cases were identified in this cohort, with males accounting for 55.4 % of cases. The case fatality rate among the hospitalized patients was found to be 50.1 % corresponding to 466 deaths. Patients with comorbidities such as hypertension, diabetes, obesity, chronic kidney disease, chronic obstructive pulmonary disease and cardiovascular disease had a higher likelihood of mortality. Additionally, non-survivors had a significantly longer time from illness onset to hospital admission compared to survivors (8.2 days vs 4.7 days). Among the cohort, 306 patients (32.9 %) met criteria for ICU admission. However, due to limited capacity, only 60 patients (19·6 %) were admitted to the ICU. Importantly, patients that were unable to receive level-appropriate care had lower likelihood of survival compared to those who received level-appropriate care (hazard ratio: 1.84).

Interpretation

This study represents, the largest investigation of in-hospital COVID-19 cases in Honduras and Central America. The findings highlight a substantial case fatality rate among hospitalized patients. In this study, patients who couldn't receive level-appropriate care (ICU admission) had a significantly lower likelihood of survival when compared to those who did. These results underscore the significant impact of healthcare access during the pandemic, particularly in low- and middle-income countries.

Hematological and Biochemical Laboratory Parameters in COVID-19 Patients: A Retrospective Modeling Study of Severity and Mortality Predictors

Background

It is well known that laboratory markers could help in identifying risk factors of severe illness and predicting outcomes of diseases. Here, we performed a retrospective modeling study of severity and mortality predictors of hematological and biochemical laboratory parameters in Iranian COVID-19 patients.

Methods

Data were obtained retrospectively from medical records of 564 confirmed Iranian COVID-19 cases. According to the disease severity, the patients were categorized into two groups (severe or nonsevere), and based on the outcome of the disease, patients were divided into two groups (recovered or deceased). Demographic and laboratory data were compared between groups, and statistical analyses were performed to define predictors of disease severity and mortality in the patients.

Results

The study identified a panel of hematological and biochemical markers associated with the severe outcome of COVID-19 and constructed different predictive models for severity and mortality. The disease severity and mortality rate were significantly higher in elderly inpatients, whereas gender was not a determining factor of the clinical outcome. Age-adjusted white blood cells (WBC), platelet cells (PLT), neutrophil-to-lymphocyte ratio (NLR), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), erythrocyte sedimentation rate (ESR), mean corpuscular hemoglobin (MCHC), blood urea nitrogen (BUN), and creatinine (Cr) also showed high accuracy in predicting severe cases at the time of hospitalization, and logistic regression analysis suggested grouped hematological parameters (age, WBC, NLR, PLT, HGB, and international normalized ratio (INR)) and biochemical markers (age, BUN, and lactate dehydrogenase (LDH)) as the best models of combined laboratory predictors for severity and mortality.

Conclusion

The findings suggest that a panel of several routine laboratory parameters recorded on admission could be helpful for clinicians to predict and evaluate the risk of disease severity and mortality in COVID-19 patients.

Predictive performance of SOFA (Sequential Organ Failure Assessment) and qSOFA (quick Sequential Organ Failure Assessment) for in-hospital mortality in ICU patients with COVID-19 of referral center in the north of Iran a retrospective study

Introduction

Patients diagnosed with Coronavirus disease 2019 exhibit varied clinical outcomes, with a reported mortality rate exceeding 30% in those requiring admission to the ICU. The objective of this study was to assess the predictive capacity of Sequential Organ Failure Assessment (SOFA) and quick Sequential Organ Failure Assessment (qSOFA) scores in determining mortality risk among severe COVID-19 patients.

Method and materials

This retrospective study was performed by analyzing the data of patients with COVID-19 who were hospitalized in the ICUs. Data collection of the parameters required to calculate the SOFA and qSOFA Scores were extracted from patient's medical records. All data analysis was performed using SPSS V.25. Significance level considered as P less than 0.05.

Findings

In this study, 258 patients were included. The results showed that the subjects ranged in age from 21 to 98 years with a mean and SD of 62.7±15.6. Of all patients, 127 (49.2%) were female and the rest were male. The mortality rate was 102 (39.5%). The underlying disease of diabetes mellitus with an odds ratio of 1.81 (CI=1.02-3.22) had a significant effect on mortality. In addition, a significant correlation was obtained between admission duration and SOFA score (r=0.147, P=0.018). The SOFA had a very high accuracy of 0.941 and at the cut-off point less than 5 had a sensitivity and specificity of 91.2% and 82.7%. In addition, qSOFA had high accuracy (0.914) and a sensitivity and specificity of 87.3% and 91.7% at the optimal cutting point of greater than 1.

Conclusion

The findings of present study illustrated that deceased COVID-19 patients admitted to the ICU had higher scores on both SOFA and qSOFA scales than surviving patients. Also, both scales have high sensitivity and specificity for anticipating of mortality in these patients. The underlying diabetes mellitus was associated with an increase in patient mortality.

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.

Serial SOFA-score trends in ICU-admitted COVID-19 patients as predictor of 28-day mortality: A prospective cohort study

Background and Aim

The efficacy of Sequential Organ Failure Assessment (SOFA) score as predictor of clinical outcomes among ICU-admitted COVID-19 patients is still controversial. We aimed to assess whether SOFA-score in different time intervals could predict 28-day mortality compared with other well-acknowledged risk factors of COVID-19 mortality.

Methods

This observational prospective cohort was conducted on 1057 patients from March 2020 to March 2022 at Masih Daneshvari Hospital, Iran. The univariate and multivariate Cox proportional analysis were performed to assess the hazards of SOFA-score models. Receiver operating characteristic (ROC) curves were designed to estimate the predictive values.

Results

Mean SOFA-score during first 96 h (HR: 3.82 [CI: 2.75-5.31]), highest SOFA-score (HR: 2.70 [CI: 1.93-3.78]), and initial SOFA-score (HR: 1.65 [CI: 1.30-2.11]) had strongest association with 28-day mortality (p < .0001). In contrast, SOFA scores at 48 and 96 h as well as Δ-SOFA: 48-0 h and Δ-SOFA: 96-0 h did not show significant correlations. Among them, merely mean SOFA-score (HR: 2.28 [CI: 2.21-3.51]; p < .001) remained as independent prognosticator on multivariate regression analysis; though having less odds of predicting value compared with age (HR: 3.81 [CI: 1.98-5.21]), hypertension (HR: 3.11 [CI: 1.26-3.81]), coronary artery disease [CAD] (HR: 2.82 [CI: 1.51-4.8]), and diabetes mellitus (HR: 2.45 [CI: 1.36-2.99]). The area under ROC (AUROC) for mean SOFA-score (0.77) and highest SOFA-score (0.71) were larger than other SOFA intervals. Calculating the first 96 h of SOFA trends, it was obtained that fatality rate was <12.3% if the score dropped, between 28.8% and 46.29% if the score remained unchanged, and >50.45% if the score increased.

Conclusion

To predict the 28-day mortality among ICU-admitted COVID-19 patients, mean SOFA upon first 96 h of ICU stay is reliable; while having inadequate accuracy comparing with well-acknowledged COVID-19 mortality predictors (age, diabetes mellitus, hypertension, CAD). Notably, increased SOFA levels in the course of first 96 h of ICU-admission, prognosticate at least 50% fatality regardless of initial SOFA score.

The Profile and All-Cause In-Hospital Mortality Dynamics of St-Segment Elevation Myocardial Infarction Patients during the Two Years of the COVID-19 Pandemic

During the coronavirus pandemic 2019 (COVID-19), some studies showed differences in the profile of subjects presenting with acute coronary syndromes as well as in overall mortality due to the delay of presentation and other complications. The purpose of this study was to compare the profile and outcomes, with emphasis on all-cause in-hospital mortality, of ST-elevation myocardial infarction (STEMI) subjects presenting to the emergency department during the pandemic period compared with a control group from the previous year, 2019. The study enrolled 2011 STEMI cases, which were divided into two groups-pre-pandemic (2019-2020) and pandemic period (2020-2022). Hospital admissions for a STEMI diagnosis sharply decreased during the COVID-19 period by 30.26% during the first year and 25.4% in the second year. This trend was paralleled by a significant increase in all-cause in-hospital mortality: 11.5% in the pandemic period versus 8.1% in the previous year. There was a significant association between SARS-CoV-2 positivity and all-cause in-hospital mortality, but no correlation was found between COVID-19 diagnosis and the type of revascularization. However, the profile of subjects presenting with STEMI did not change over time during the pandemic; their demographic and comorbid characteristics remained similar.

"Underneath the visible" - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department

Objectives

Patient risk stratification is the cornerstone of COVID-19 disease management; that has impacted health systems globally. We evaluated the performance of the Brescia-COVID Respiratory Severity Scale (BCRSS), CALL (Co-morbid, age, Lymphocyte and Lactate dehydrogenase) Score, and World Health Organization (WHO) guidelines in Emergency department (ED) on arrival, as predictors of outcomes; Intensive care unit (ICU) admission and in-hospital mortality.

Methods

A two-month retrospective chart review of 88 adult patients with confirmed COVID-19 pneumonia; requiring emergency management was conducted at ED, Indus Hospital and Health Network (IHHN), Karachi, Pakistan, (April 1 to May 31, 2020). The sensitivity, specificity, receiver operator characteristic curve (ROC) and area under the curve (AUC) for the scores were obtained to assess their predictive capability for outcomes.

Results

The in-hospital mortality rate was 48.9 % with 59.1 % ICU admissions and with a mean age at presentation of 56 ± 13 years. Receiver operator curve for BCRSS depicted good predicting capability for in hospital mortality [AUC 0.81(95% CI 0.71-0.91)] and ICU admission [AUC 0.73(95%CI 0.62-0.83)] amongst all models of risk assessment.

Conclusion

BCRSS depicted better prediction of in-hospital mortality and ICU admission. Prospective studies using this tool are needed to assess its utility in predicting high-risk patients and guide treatment escalation in LMIC's.

Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis

Background and objectives

We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19.

Methods

A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test.

Results

Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04.

Conclusion

ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality.

Comparison of CURB-65 Pneumonia Severity Score, Quick COVID-19 Severity Index, and Brescia-COVID Respiratory Severity Scale in Emergently Hospitalized COVID-19 Patients with Pneumonia

Objective

This study aimed to assess the performance of the CURB-65, the quick COVID-19 severity index (qCSI), and the Brescia-COVID respiratory severity scale (BCRSS) scores in predicting ICU (intensive care unit) hospitalization and in-hospital mortality in emergently hospitalized patients with COVID-19 pneumonia.

Materials and Methods

We retrospectively reviewed the emergently hospitalized 258 patients with COVID-19 pneumonia consecutively. The required sample size was calculated to compare the areas under the two ROC (receiver operating characteristic) curves (AUC) using the MedCalc 20.0 program (MedCalc Software Ltd., Ostend, Belgium). In addition, we actualized ROC analyses of the CURB-65, the qCSI, and the BCRSS scores and compared the ROC curves of these three scores.

Results

The median age of the patients was 73, and 63.6% (n=164) were male. Of 258 patients, 29.5% (n=76) were hospitalized in the intensive care unit (ICU), and 15.9% (n=41) died. The CURB-65 and the qCSI scores predicted ICU admission at a moderate level (p≤0.001; AUC values were 0.743 and 0.723, respectively). However, the predictive effect of the BCRSS score for ICU admission was lower (p≤0.001; AUC value was 0.667). The CURB-65 predicted in-hospital mortality at a moderate level ( p≤0.001; AUC value was 0.762). However, the predictive effect of the qCSI and the BCRSS scores for in-hospital mortality were lower ( p≤0.001 and p=0.012, respectively; AUC values were 0.655 and 0.612, respectively).

Conclusion

The CURB-65 score predicted ICU hospitalization and in-hospital mortality better than the qCSI and the BCRSS scores. Also, the qCSI score predicted ICU admission better than the BCRSS score.The predictive effect of the BCRSS score was the lowest. We recommend future studies to evaluate the value and utility of COVID-19 risk classification models.

The Usefulness of the C2HEST Risk Score in Predicting Clinical Outcomes among Hospitalized Subjects with COVID-19 and Coronary Artery Disease

Background: Even though coronary artery disease (CAD) is considered an independent risk factor of an unfavorable outcome of SARS-CoV-2-infection, the clinical course of COVID-19 in subjects with CAD is heterogeneous, ranging from clinically asymptomatic to fatal cases. Since the individual C₂HEST components are similar to the COVID-19 risk factors, we evaluated its predictive value in CAD subjects. Materials and Methods: In total, 2183 patients hospitalized due to confirmed COVID-19 were enrolled onto this study consecutively. Based on past medical history, subjects were assigned to one of two of the study arms (CAD vs. non-CAD) and allocated to different risk strata, based on the C₂HEST score. Results: The CAD cohort included 228 subjects, while the non-CAD cohort consisted of 1956 patients. In-hospital, 3-month and 6-month mortality was highest in the high-risk C₂HEST stratum in the CAD cohort, reaching 43.06%, 56.25% and 65.89%, respectively, whereas in the non-CAD cohort in the high-risk stratum, it reached: 26.92%, 50.77% and 64.55%. Significant differences in mortality between the C₂HEST stratum in the CAD arm were observed in post hoc analysis only for medium- vs. high-risk strata. The C₂HEST score in the CAD cohort could predict hypovolemic shock, pneumonia and acute heart failure during hospitalization, whereas in the non-CAD cohort, it could predict cardiovascular events (myocardial injury, acute heart failure, myocardial infract, carcinogenic shock), pneumonia, acute liver dysfunction and renal injury as well as bleedings. Conclusions: The C₂HEST score is a simple, easy-to-apply tool which might be useful in risk stratification, preferably in non-CAD subjects admitted to hospital due to COVID-19.

Brixia and qSOFA Scores, Coagulation Factors and Blood Values in Spring versus Autumn 2021 Infection in Pregnant Critical COVID-19 Patients: A Preliminary Study

(1) Background: From the recent variants of concern of the SARS-CoV-2 virus, in which the delta variant generated more negative outcomes than the alpha, we hypothesized that lung involvement, clinical condition deterioration and blood alterations were also more severe in autumn infection, when the delta variant dominated (compared with spring infections, when the alpha variant dominated), in severely infected pregnant patients. (2) Methods: In a prospective study, all pregnant patients admitted to the ICU of the Elena Doamna Obstetrics and Gynecology Hospital with a critical form of COVID-19 infection—spring group (n = 11) and autumn group (n = 7)—between 1 January 2021 and 1 December 2021 were included. Brixia scores were calculated for every patient: A score, upon admittance; H score, the highest score throughout hospitalization; and E score, at the end of hospitalization. For each day of Brixia A, H or E score, the qSOFA (quick sepsis-related organ failure assessment) score was calculated, and the blood values were also considered. (3) Results: Brixia E score, C-reactive protein, GGT and LDH were much higher, while neutrophil count was much lower in autumn compared with spring critical-form pregnant patients. (4) Conclusions: the autumn infection generated more dramatic alterations than the spring infection in pregnant patients with critical forms of COVID-19. Larger studies with more numerous participants are required to confirm these results.

Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79–0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81–0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Predictive Value of SOFA and qSOFA for In-Hospital Mortality in COVID-19 Patients: A Single-Center Study in Romania

Two years after the outbreak of the COVID-19 pandemic, the disease continues to claim victims worldwide. Assessing the disease’s severity on admission may be useful in reducing mortality among patients with COVID-19. The present study was designed to assess the prognostic value of SOFA and qSOFA scoring systems for in-hospital mortality among patients with COVID-19. The study included 133 patients with COVID-19 proven by reverse transcriptase polymerase chain reaction (RT-PCR) admitted to the Municipal Emergency Clinical Hospital of Timisoara, Romania between 1 October 2020 and 15 March 2021. Data on clinical features and laboratory findings on admission were collected from electronic medical records and used to compute SOFA and qSOFA. Mean SOFA and qSOFA values were higher in the non-survivor group compared to survivors (3.5 vs. 1 for SOFA and 2 vs. 1 for qSOFA, respectively). Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were performed to determine the discrimination accuracy, both risk scores being excellent predictors of in-hospital mortality, with ROC–AUC values of 0.800 for SOFA and 0.794 for qSOFA. The regression analysis showed that for every one-point increase in SOFA score, mortality risk increased by 1.82 and for every one-point increase in qSOFA score, mortality risk increased by 5.23. In addition, patients with SOFA and qSOFA above the cut-off values have an increased risk of mortality with ORs of 7.46 and 11.3, respectively. In conclusion, SOFA and qSOFA are excellent predictors of in-hospital mortality among COVID-19 patients. These scores determined at admission could help physicians identify those patients at high risk of severe COVID-19.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

BACKGROUND AND AIMS

The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients.

METHODS

Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis.

RESULTS

Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4⁺ T, and CD8⁺ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4⁺ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot.

CONCLUSIONS

XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.