External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals

Acute COVID-19 severity markers predict post-COVID new-onset psychiatric disorders: A 2-year cohort study of 34,489 patients

New-onset psychiatric disorders are frequent after COVID-19. We aim to determine whether acute COVID-19 severity markers can predict post-COVID new-onset psychiatric disorders. We conducted an electronic health records (EHR) cohort study of patients hospitalized for COVID-19 and without any known history of psychiatric disorders. Patients were included between January 2020 and September 2022 in one of the 36 university hospitals of the Assistance Publique - Hôpitaux de Paris. Acute COVID-19 clinical and biological severity markers were recorded during hospitalization for COVID-19. Psychiatric ICD-10 diagnoses were recorded up to 2 years and 9 months after hospitalization for COVID-19. Predictors of post-COVID new-onset psychiatric disorders were identified based on Cox regression models and sensitivity analyses. Predictive scores were built and tested in age- and sex-stratified populations. A total 34,489 patients hospitalized for COVID-19 were included; 3717 patients (10.8%) had at least one post-COVID new-onset psychiatric disorder. Hospital stay >7 days (HR = 1.72, 95%CI [1.59-1.86], p < 0.001), acute delirium (HR = 1.49, 95%CI [1.28-1.74], p < 0.001), elevated monocyte count (HR = 1.14, 95%CI [1.06-1.23], p < 0.001) and elevated plasma CRP (HR = 0.92, 95%CI [0.86-0.99], p = 0.04) independently predicted post-COVID new-onset psychiatric disorders. Sensitivity analyses confirmed hospital stay >7 days, acute delirium, and elevated monocyte count as predictors. Predictive scores based on these variables had good 12-month positive predictive values, up to 7.5 times more accurate than random in women < 65 years. In conclusion, hospital stay >7 days, acute delirium, and elevated monocyte count during acute COVID-19 predict post-COVID new-onset psychiatric disorders.

Impact of COVID-19 on urgent gastrointestinal surgery outcomes: increased mortality in 2020

BACKGROUND

The COVID-19 pandemic significantly disrupted healthcare systems. In France, non-urgent procedures were postponed, leading to a 52% decrease in elective surgical activity in public hospitals in Paris during the first wave. We aimed to assess the impact on gastro-intestinal emergency surgeries of health strategies implemented during this pandemic.

METHODS

This multicenter retrospective cohort study enrolled patients from sixteen public hospitals over five periods: March and April, 2018, and 2019 (pre-pandemic), 2020 (first wave), 2021 (third wave), and 2022 (post-pandemic). All adult patients requiring urgent gastrointestinal surgery admitted through the Emergency Department were included. Statistical tests were performed with the chi-square test, ANOVA test, Student test, Kruskall Wallis or Fisher exact test. Univariate and multivariate logistic regression were performed to investigate the relationship between mortality at day 90 and the primary data recorded.

RESULTS

2692 patients' stay were included: 54% male, median age 48 [32;68], 12% ICU admission rate, median Charlson score 2 [0;5], and 6% mortality rate at day 90. The number of abdominal emergency cases decreased during the first wave (- 37% in 2020 compared to 2019). In the multivariate regression model, ICU admission, Charlson comorbidity score, and surgery in 2020 were independently associated with mortality at day 90 (as hospital length of stay, to a lower extent).

CONCLUSION

Undergoing emergency surgery during the first lockdown was an independent mortality risk factor, independent of the COVID-19 infectious status. Whatever major healthcare issue is ongoing, all efforts should be made to maintain healthcare access to all, including urgent surgical procedures.

TRIAL REGISTRATION

Not applicable.

External Validation of the 4C (Coronavirus Clinical Characterization Consortium) Mortality Score in a Teaching Hospital in Brazil

Background The 4C (Coronavirus Clinical Characterization Consortium) Mortality Score has demonstrated good discrimination in COVID-19 but has not been widely validated in Brazil. The 4C Mortality Score is a clinical tool developed during the COVID-19 pandemic to predict in-hospital mortality for patients admitted with COVID-19. It was derived from a large dataset of hospitalized patients in the United Kingdom and provides a simple yet effective way to stratify patients based on their risk of death. Objective This study aimed to determine the accuracy of the 4C Mortality Score in patients admitted with COVID-19 in a university teaching hospital. Methods The study was observational, longitudinal, and retrospective, conducted in a 180-bed university teaching hospital in Rio de Janeiro, Brazil. We included all patients admitted with COVID-19 and followed them until discharge. The 4C Mortality Score was calculated based on age, sex, Charlson index, respiratory rate, peripheral oxygen saturation (room air), Glasgow Coma Scale, serum urea, and C-reactive protein (CRP) level. The primary outcome was mortality. Results We included 208 participants, with a median age of 63 years. Among them, 111 (53%) were male; 52 (25%) had cardiovascular disease, and 83 (39%) had cancer. Mortality was 39.9%. Independent predictors of mortality were age, hemoglobin, CRP, mechanical ventilation, and the need for vasopressors. The 4C Mortality Score's area under the receiver operating characteristic curve (AUC-ROC) was 89.9%. Conclusion The 4C Mortality Score demonstrated excellent discrimination in a teaching hospital population.

External Validation of the Predictive Accuracy of Clinical and Immunological Scores in COVID-19 Outcomes in a Retrospective Cohort Study

BACKGROUND AND OBJECTIVES

The COVID-19 pandemic has necessitated the development of reliable prognostic tools to predict patient outcomes and guide clinical decisions. This study evaluates the predictive utility of several clinical scores-PAINT, ISARIC4C, CHIS, COVID-GRAM, SOFA, and CURB-65-for in-hospital mortality among COVID-19 patients, comparing their effectiveness at admission and seven days post-symptom onset.

METHODS

In this retrospective cohort study conducted at the Clinical Emergency Hospital Pius Brînzeu in Timișoara, adult patients hospitalized with confirmed SARS-CoV-2 infection were included. The study was approved by the Local Ethics Committee, adhering to GDPR and other regulatory standards. Prognostic scores were calculated using patient data at admission and Day 7. Statistical analyses included ROC curves, Kaplan-Meier survival analysis, and multivariate Cox regression.

RESULTS

The study comprised 269 patients, with a notable distinction in outcomes between survivors and non-survivors. Non-survivors were older (mean age 62.12 years) and exhibited higher comorbidity rates, such as diabetes (55.56% vs. 31.06%) and cardiovascular diseases (48.15% vs. 29.81%). Prognostic scores were significantly higher among non-survivors at both time points, with PAINT and ISARIC4C showing particularly strong predictive performances. The AUROC for PAINT increased from 0.759 at admission to 0.811 by Day 7, while ISARIC4C demonstrated an AUROC of 0.776 at admission and 0.798 by Day 7. Multivariate Cox regression indicated that a PAINT score above 8.10 by Day 7 was associated with a hazard ratio (HR) of 4.9 (95% CI: 3.12-7.72) for mortality.

CONCLUSIONS

The study confirms the strong predictive value of the PAINT, ISARIC4C, CHIS, COVID-GRAM, SOFA, and CURB-65 scores in determining mortality risk among hospitalized COVID-19 patients. These scores can significantly aid clinicians in early-risk stratification and resource prioritization, potentially enhancing patient management and outcomes in acute care settings.

External validation of two clinical prediction models for mortality in COVID-19 patients (4C and NEWS2), in three centers in Medellín, Colombia: Assessing the impact of vaccination over time

OBJECTIVES

External validation of the 4C and NEWS2 scores for the prediction of in-hospital mortality in COVID-19 patients, and evaluation of its operational performance in two time periods: before and after the start of the vaccination program in Colombia.

METHODS

Retrospective cohort in three high complexity hospitals in the city of Medellín, Colombia, between June 2020 and April 2022.

RESULTS

The areas under the ROC curve (AUC) for the 4C mortality risk score and the NEWS2 were 0.75 (95% CI 0.73-0.78) and 0.68 (95% CI 0.66-0.71), respectively. For the 4C score, the AUC for the first and second periods was 0.77 (95% CI 0.74-0.80) and 0.75 (95% CI 0.71-0.78); whilst for the NEWS2 score, it was 0.68 (95% CI 0.65-0.71) and 0.69 (95% CI 0.64-0.73). The calibration for both scores was adequate, albeit with reduced performance during the second period.

CONCLUSIONS

The 4C mortality risk score proved to be the more adequate predictor of in-hospital mortality in COVID-19 patients in this Latin American population. The operational performance during both time periods remained similar, which shows its utility notwithstanding major changes, including vaccination, as the pandemic evolved.

The Predictive Performance of Risk Scores for the Outcome of COVID-19 in a 2-Year Swiss Cohort

Various scoring systems are available for COVID-19 risk stratification. This study aimed to validate their performance in predicting severe COVID-19 course in a large, heterogeneous Swiss cohort. Scores like the National Early Warning Score (NEWS), CURB-65, 4C mortality score (4C), Spanish Society of Infectious Diseases and Clinical Microbiology score (COVID-SEIMC), and COVID Intubation Risk Score (COVID-IRS) were assessed in patients hospitalized for COVID-19 in 2020 and 2021. Predictive accuracy for severe course (defined as all-cause in-hospital death or invasive mechanical ventilation (IMV)) was evaluated using receiver operating characteristic curves and the area under the curve (AUC). The new 'COVID-COMBI' score, combining parameters from the top two scores, was also validated. This study included 1,051 patients (mean age 65 years, 60% male), with 162 (15%) experiencing severe course. Among the established scores, 4C had the best accuracy for predicting severe course (AUC 0.76), followed by COVID-IRS (AUC 0.72). COVID-COMBI showed significantly higher accuracy than all established scores (AUC 0.79, p = 0.001). For predicting in-hospital death, 4C performed best (AUC 0.83), and, for IMV, COVID-IRS performed best (AUC 0.78). The 4C and COVID-IRS scores were robust predictors of severe COVID-19 course, while the new COVID-COMBI showed significantly improved accuracy but requires further validation.

Whole blood transcriptome signature predicts severe forms of COVID-19: Results from the COVIDeF cohort study

COVID-19 is associated with heterogeneous outcome. Early identification of a severe progression of the disease is essential to properly manage the patients and improve their outcome. Biomarkers reflecting an increased inflammatory response, as well as individual features including advanced age, male gender, and pre-existing comorbidities, are risk factors of severe COVID-19. Yet, these features show limited accuracy for outcome prediction. The aim was to evaluate the prognostic value of whole blood transcriptome at an early stage of the disease. Blood transcriptome of patients with mild pneumonia was profiled. Patients with subsequent severe COVID-19 were compared to those with favourable outcome, and a molecular predictor based on gene expression was built. Unsupervised classification discriminated patients who would later develop a COVID-19-related severe pneumonia. The corresponding gene expression signature reflected the immune response to the viral infection dominated by a prominent type I interferon, with IFI27 among the most over-expressed genes. A 48-genes transcriptome signature predicting the risk of severe COVID-19 was built on a training cohort, then validated on an external independent cohort, showing an accuracy of 81% for predicting severe outcome. These results identify an early transcriptome signature of severe COVID-19 pneumonia, with a possible relevance to improve COVID-19 patient management.

A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores

BACKGROUND

Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19).

METHODS

We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system).

RESULTS

Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far.

CONCLUSIONS

The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future.

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.

The Use of the Oxygenation Stretch Index to Predict Outcomes in Mechanically Ventilated Patients With COVID-19 ARDS

BACKGROUND

In ARDS caused by COVID-19 pneumonia, appropriate adjustment of physiologic parameters based on lung stretch or oxygenation may optimize the ventilatory strategy. This study aims to describe the prognostic performance on 60-d mortality of single and composite respiratory variables in subjects with COVID-19 ARDS who are on mechanical ventilation with a lung-protective strategy, including the oxygenation stretch index combining oxygenation and driving pressure (ΔP).

METHODS

This single-center observational cohort study enrolled 166 subjects on mechanical ventilation and diagnosed with COVID-19 ARDS. We evaluated their clinical and physiologic characteristics. The primary study outcome was 60-d mortality. Prognostic factors were evaluated through receiver operating characteristic analysis, Cox proportional hazards regression model, and Kaplan-Meier survival curves.

RESULTS

Mortality at day 60 was 18.1%, and hospital mortality was 22.9%. Oxygenation, ΔP, and composite variables were tested: oxygenation stretch index ([Formula: see text]/[Formula: see text] divided by ΔP) and ΔP × 4 + breathing frequency (f) (ΔP × 4 + f). At both day 1 and day 2 after inclusion, the oxygenation stretch index had the best area under the receiver operating characteristic curve (oxygenation stretch index on day 1 0.76 (95% CI 0.67-0.84) and on day 2 0.83 (95% CI 0.76-0.91) to predict 60-d mortality, although without significant difference from other indexes. In multivariable Cox regression, ΔP, [Formula: see text]/[Formula: see text], ΔP × 4 + f, and oxygenation stretch index were all associated with 60-d mortality. When dichotomizing the variables, ΔP ≥ 14, [Formula: see text]/[Formula: see text] ≤ 152 mm Hg, ΔP × 4 + f ≥ 80, and oxygenation stretch index < 7.7 showed lower 60-d survival probability. At day 2, after optimization of ventilatory settings, the subjects who persisted with the worse cutoff values for the oxygenation stretch index showed a lower probability of survival at 60 d compared with day 1; this was not the case for other parameters.

CONCLUSIONS

The oxygenation stretch index, which combines [Formula: see text]/[Formula: see text] and ΔP, is associated with mortality and may be useful to predict clinical outcomes in COVID-19 ARDS.

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case-control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6-9 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003-1.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0-2 days: aOR: 1.025, 95% CI: 1.006-1.045, p = 0.010; 3-5 days: aOR: 1.03 95% CI: 1.011-1.051, p = 0.002; 6-9 days: aOR; 1.052, 95% CI: 1.015-1.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them.

The Predictive Value of Risk Factors and Prognostic Scores in Hospitalized COVID-19 Patients

INTRODUCTION

Risk stratification in patients with COVID-19 is a challenging task. Early warning scores (EWSs) are commonly used tools in the initial assessment of critical patients. However, their utility in patients with COVID-19 is still undetermined.

AIM

This study aimed to discover the most valuable predictive model among existing EWSs for ICU admissions and mortality in COVID-19 patients.

MATERIALS AND METHODS

This was a single-center cohort study that included 3608 COVID-19 patients admitted to the University Clinical Hospital Center Bezanijska Kosa, Belgrade, Serbia, between 23 June 2020, and 14 April 2021. Various demographic, laboratory, and clinical data were collected to calculate several EWSs and determine their efficacy. For all 3608 patients, five EWSs were calculated (MEWS, NEWS, NEWS2, REMS, and qSOFA). Model discrimination performance was tested using sensitivity, specificity, and positive and negative predictive values. C statistic, representing the area under the receiver operating characteristic (ROC) curve, was used for the overall assessment of the predictive model.

RESULTS

Among the evaluated prediction scores for 3068 patients with COVID-19, REMS demonstrated the highest diagnostic performance with the sensitivity, PPV, specificity, and NPV of 72.1%, 20.6%, 74.9%, and 96.8%, respectively. In the multivariate logistic regression analysis, aside from REMS, age (p < 0.001), higher CT score (p < 0.001), higher values of urea (p < 0.001), and the presence of bacterial superinfection (p < 0.001) were significant predictors of mortality.

CONCLUSIONS

Among all evaluated EWSs to predict mortality and ICU admission in COVID-19 patients, the REMS score demonstrated the highest efficacy.

External validation of the ISARIC 4C Mortality Score to predict in-hospital mortality among patients with COVID-19 in a Canadian intensive care unit: a single-centre historical cohort study

PURPOSE

With uncertain prognostic utility of existing predictive scoring systems for COVID-19-related illness, the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) 4C Mortality Score was developed by the International Severe Acute Respiratory and Emerging Infection Consortium as a COVID-19 mortality prediction tool. We sought to externally validate this score among critically ill patients admitted to an intensive care unit (ICU) with COVID-19 and compare its discrimination characteristics to that of the Acute Physiology and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA) scores.

METHODS

We enrolled all consecutive patients admitted with COVID-19-associated respiratory failure between 5 March 2020 and 5 March 2022 to our university-affiliated and intensivist-staffed ICU (Jewish General Hospital, Montreal, QC, Canada). After data abstraction, our primary outcome of in-hospital mortality was evaluated with an objective of determining the discriminative properties of the ISARIC 4C Mortality Score, using the area under the curve of a logistic regression model.

RESULTS

A total of 429 patients were included, 102 (23.8%) of whom died in hospital. The receiver operator curve of the ISARIC 4C Mortality Score had an area under the curve of 0.762 (95% confidence interval [CI], 0.717 to 0.811), whereas those of the SOFA and APACHE II scores were 0.705 (95% CI, 0.648 to 0.761) and 0.722 (95% CI, 0.667 to 0.777), respectively.

CONCLUSIONS

The ISARIC 4C Mortality Score is a tool that had a good predictive performance for in-hospital mortality in a cohort of patients with COVID-19 admitted to an ICU for respiratory failure. Our results suggest a good external validity of the 4C score when applied to a more severely ill population.

The Role of APRI, FIB-4, and SAD-60 Scores as Predictors of Mortality in COVID-19 Patients

Objective

Predictors of mortality that indicate disease severity plays an important role in COVID-19 management and treatment decisions. This study aimed to investigate the association between fibrosis-4 (FIB-4) score, aspartate aminotransferase-to-platelet ratio index (APRI), and novel biomarker-based score (SAD-60) with mortality in COVID-19 patients treated in a tertiary hospital.

Materials and Methods

In this single-center retrospective study, patients ≥18 years of age who were admitted to our hospital for COVID-19 between December 1 and 31, 2021, were included. Patients were divided into two groups as deceased and survived. A comparative analysis was applied. Predictive abilities of the FIB-4, APRI, and SAD-60 scores for in-hospital mortality were evaluated.

Results

Of the 453 patients enrolled in the study, 248 (54.6%) were male, and the mean age was 52.2±14.7 years. Mortality was recorded in 39 (8.5%) of the patients. The median values of APRI (0.43 and 0.58; p=0.001), FIB-4 score (1.66 and 2.91; p<0.001), and SAD-60 (2 and 8.25; p<0.001) were higher in deceased patients than in survivors. The optimal cut-off value for predicting mortality in the receiver operating characteristic (ROC) curve analysis was 0.58 for APRI (sensitivity=56.4%, specificity=63.6%); 2.14 for FIB-4 score (sensitivity=79.5%, specificity=68.2%); 4.25 for SAD-60 (sensitivity=90%, specificity=73.8%). In Cox regression analysis with a model that included gender, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD), FIB-4 (hazard ratio [HR]=4.013, 95% confidence interval [CI]=1.643-9.803; p=0.002), and SAD-60 (HR=8.850, 95% CI=1.035-75.696; p=0.046) were independent risk factors for mortality.

Conclusion

SAD-60 and FIB-4 scores are easily applicable and may be used to predict mortality in COVID-19 patients.

Barotrauma during Noninvasive Respiratory Support in COVID-19 Pneumonia Outside ICU: The Ancillary COVIMIX-2 Study

BACKGROUND

Noninvasive respiratory support (NIRS) has been extensively used during the COVID-19 surge for patients with acute respiratory failure. However, little data are available about barotrauma during NIRS in patients treated outside the intensive care unit (ICU) setting.

METHODS

COVIMIX-2 was an ancillary analysis of the previous COVIMIX study, a large multicenter observational work investigating the frequencies of barotrauma (i.e., pneumothorax and pneumomediastinum) in adult patients with COVID-19 interstitial pneumonia. Only patients treated with NIRS outside the ICU were considered. Baseline characteristics, clinical and radiological disease severity, type of ventilatory support used, blood tests and mortality were recorded.

RESULTS

In all, 179 patients were included, 60 of them with barotrauma. They were older and had lower BMI than controls (p < 0.001 and p = 0.045, respectively). Cases had higher respiratory rates and lower PaO₂/FiO₂ (p = 0.009 and p < 0.001). The frequency of barotrauma was 0.3% [0.1-1.3%], with older age being a risk factor for barotrauma (OR 1.06, p = 0.015). Alveolar-arterial gradient (A-a) DO₂ was protective against barotrauma (OR 0.92 [0.87-0.99], p = 0.026). Barotrauma required active treatment, with drainage, in only a minority of cases. The type of NIRS was not explicitly related to the development of barotrauma. Still, an escalation of respiratory support from conventional oxygen therapy, high flow nasal cannula to noninvasive respiratory mask was predictive for in-hospital death (OR 15.51, p = 0.001).

CONCLUSIONS

COVIMIX-2 showed a low frequency for barotrauma, around 0.3%. The type of NIRS used seems not to increase this risk. Patients with barotrauma were older, with more severe systemic disease, and showed increased mortality.

Validation of the qSOFA and CRB-65 in SARS-CoV-2-infected community-acquired pneumonia

Rationale

Prognostic accuracy of the quick sequential organ failure assessment (qSOFA) and CRB-65 (confusion, respiratory rate, blood pressure and age (≥65 years)) risk scores have not been widely evaluated in patients with SARS-CoV-2-positive compared to SARS-CoV-2-negative community-acquired pneumonia (CAP). The aim of the present study was to validate the qSOFA(-65) and CRB-65 scores in a large cohort of SARS-CoV-2-positive and SARS-CoV-2-negative CAP patients.

Methods

We included all cases with CAP hospitalised in 2020 from the German nationwide mandatory quality assurance programme and compared cases with SARS-CoV-2 infection to cases without. We excluded cases with unclear SARS-CoV-2 infection state, transferred to another hospital or on mechanical ventilation during admission. Predefined outcomes were hospital mortality and need for mechanical ventilation.

Results

Among 68 594 SARS-CoV-2-positive patients, hospital mortality (22.7%) and mechanical ventilation (14.9%) were significantly higher when compared to 167 880 SARS-CoV-2-negative patients (15.7% and 9.2%, respectively). All CRB-65 and qSOFA criteria were associated with both outcomes, and age dominated mortality prediction in SARS-CoV-2 (risk ratio >9). Scores including the age criterion had higher area under the curve (AUCs) for mortality in SARS-CoV-2-positive patients (e.g. CRB-65 AUC 0.76) compared to SARS-CoV-2 negative patients (AUC 0.68), and negative predictive value was highest for qSOFA-65=0 (98.2%). Sensitivity for mechanical ventilation prediction was poor with all scores (AUCs 0.59-0.62), and negative predictive values were insufficient (qSOFA-65=0 missed 1490 out of 10 198 patients (∼15%) with mechanical ventilation). Results were similar when excluding frail and palliative patients.

Conclusions

Hospital mortality and mechanical ventilation rates were higher in SARS-CoV-2-positive than SARS-CoV-2-negative CAP. For SARS-CoV-2-positive CAP, the CRB-65 and qSOFA-65 scores showed adequate prediction of mortality but not of mechanical ventilation.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

The three facets of the SARS-CoV-2 pandemic during the first two waves in the northern, central, and southern Italy

BACKGROUND

There is a scarcity of information in literature regarding the clinical differences and comorbidities of patients affected by Coronavirus disease 2019 (COVID-19), which could clarify the different prevalence of the outcomes (composite and only death) between several Italian regions.

OBJECTIVE

This study aimed to assess the heterogeneity of clinical features of patients with COVID-19 upon hospital admission and disease outcomes in the northern, central, and southern Italian regions.

METHODS

An observational cohort multicenter retrospective study including 1210 patients who were admitted for COVID-19 in Infectious diseases, Pulmonology, Endocrinology, Geriatrics and Internal Medicine Units in Italian cities stratified between north (263 patients); center (320 patients); and south (627 patients), during the first and second pandemic waves of SARS-CoV-2 (from February 1, 2020 to January 31, 2021). The data, obtained from clinical charts and collected in a single database, comprehended demographic characteristics, comorbidities, hospital and home pharmacological therapies, oxygen therapy, laboratory values, discharge, death and Intensive care Unit (ICU) transfer. Death or ICU transfer were defined as composite outcomes.

RESULTS

Male patients were more frequent in the northern Italian region than in the central and southern regions. Diabetes mellitus, arterial hypertension, chronic pulmonary and chronic kidney diseases were the comorbidities more frequent in the southern region; cancer, heart failure, stroke and atrial fibrillation were more frequent in the central region. The prevalence of the composite outcome was recorded more frequently in the southern region. Multivariable analysis showed a direct association between the combined event and age, ischemic cardiac disease, and chronic kidney disease, in addition to the geographical area.

CONCLUSIONS

Statistically significant heterogeneity was observed in patients with COVID-19 characteristics at admission and outcomes from northern to southern Italy. The higher frequency of ICU transfer and death in the southern region may depend on the wider hospital admission of frail patients for the availability of more beds since the burden of COVID-19 on the healthcare system was less intense in southern region. In any case, predictive analysis of clinical outcomes should consider that the geographical differences that may reflect clinical differences in patient characteristics, are also related to access to health-care facilities and care modalities. Overall, the present results caution against generalizability of prognostic scores in COVID-19 patients derived from hospital cohorts in different settings.

Development of a Risk Score for AKI onset in COVID-19 Patients: COV-AKI Score

PURPOSE

Acute Kidney Injury (AKI) in COVID-19 patients is associated with increased morbidity and mortality. In the present study, we aimed to develop a prognostic score to predict AKI development in these patients.

MATERIALS AND METHODS

This was a retrospective observational study of 2334 COVID 19 patients admitted to 23 different hospitals in Brazil, between January 10th and August 30rd, 2020. The primary outcome of AKI was defined as any increase in serum creatinine (SCr) by 0.3 mg/dL within 48 h or a change in SCr by ≥ 1.5 times of baseline within 1 week, based on Kidney Disease Improving Global Outcomes (KDIGO) guidelines. All patients aged ≥ 18 y/o admitted with confirmed SARS-COV-2 infection were included. Discrimination of variables was calculated by the Receiver Operator Characteristic Curve (ROC curve) utilizing area under curve. Some continuous variables were categorized through ROC curve. The cutoff points were calculated using the value with the best sensitivity and specificity.

RESULTS

A total of 1131 patients with COVID-19 admitted to the ICU were included. Patients mean age was 52 ± 15,8 y/o., with a prevalence of males 60% (n = 678). The risk of AKI was 33% (n = 376), 78% (n = 293) of which did not require dialysis. Overall mortality was 11% (n = 127), while for AKI patients, mortality rate was 21% (n = 80). Variables selected for the logistic regression model and inclusion in the final prognostic score were the following: age, diabetes, ACEis, ARBs, chronic kidney disease and hypertension.

CONCLUSION

AKI development in COVID 19 patients is accurately predicted by common clinical variables, allowing early interventions to attenuate the impact of AKI in these patients.

The performance of the WHO COVID-19 severity classification, COVID-GRAM, VACO Index, 4C Mortality, and CURB-65 prognostic scores in hospitalized COVID-19 patients: data on 4014 patients from a tertiary center registry

AIM

To evaluate the predictive properties of several common prognostic scores regarding survival outcomes in hospitalized COVID-19 patients.

METHODS

We retrospectively reviewed the medical records of 4014 consecutive COVID-19 patients hospitalized in our tertiary level institution from March 2020 to March 2021. Prognostic properties of the WHO COVID-19 severity classification, COVID-GRAM, Veterans Health Administration COVID-19 (VACO) Index, 4C Mortality Score, and CURB-65 score regarding 30-day mortality, in-hospital mortality, presence of severe or critical disease on admission, need for an intensive care unit treatment, and mechanical ventilation during hospitalization were evaluated.

RESULTS

All of the investigated prognostic scores significantly distinguished between groups of patients with different 30-day mortality. The CURB-65 and 4C Mortality Score had the best prognostic properties for prediction of 30-day mortality (area under the curve [AUC] 0.761 for both) and in-hospital mortality (AUC 0.757 and 0.762, respectively). The 4C Mortality Score and COVID-GRAM best predicted the presence of severe or critical disease (AUC 0.785 and 0.717, respectively). In the multivariate analysis evaluating 30-day mortality, all scores mutually independently provided additional prognostic information, except the VACO Index, whose prognostic properties were redundant.

CONCLUSION

Complex prognostic scores based on many parameters and comorbid conditions did not have better prognostic properties regarding survival outcomes than a simple CURB-65 prognostic score. CURB-65 also provides the largest number of prognostic categories (five), allowing more precise risk stratification than other prognostic scores.

The performance of the WHO COVID-19 severity classification, COVID-GRAM, VACO Index, 4C Mortality, and CURB-65 prognostic scores in hospitalized COVID-19 patients: data on 4014 patients from a tertiary center registry

AIM

To evaluate the predictive properties of several common prognostic scores regarding survival outcomes in hospitalized COVID-19 patients.

METHODS

We retrospectively reviewed the medical records of 4014 consecutive COVID-19 patients hospitalized in our tertiary level institution from March 2020 to March 2021. Prognostic properties of the WHO COVID-19 severity classification, COVID-GRAM, Veterans Health Administration COVID-19 (VACO) Index, 4C Mortality Score, and CURB-65 score regarding 30-day mortality, in-hospital mortality, presence of severe or critical disease on admission, need for an intensive care unit treatment, and mechanical ventilation during hospitalization were evaluated.

RESULTS

All of the investigated prognostic scores significantly distinguished between groups of patients with different 30-day mortality. The CURB-65 and 4C Mortality Score had the best prognostic properties for prediction of 30-day mortality (area under the curve [AUC] 0.761 for both) and in-hospital mortality (AUC 0.757 and 0.762, respectively). The 4C Mortality Score and COVID-GRAM best predicted the presence of severe or critical disease (AUC 0.785 and 0.717, respectively). In the multivariate analysis evaluating 30-day mortality, all scores mutually independently provided additional prognostic information, except the VACO Index, whose prognostic properties were redundant.

CONCLUSION

Complex prognostic scores based on many parameters and comorbid conditions did not have better prognostic properties regarding survival outcomes than a simple CURB-65 prognostic score. CURB-65 also provides the largest number of prognostic categories (five), allowing more precise risk stratification than other prognostic scores.

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Background

The aim of our study was to externally validate the predictive capability of five developed coronavirus disease 2019 (COVID-19)-specific prognostic tools, including the COVID-19 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), Shang COVID severity score, COVID-intubation risk score-neutrophil/lymphocyte ratio (IRS-NLR), inflammation-based score, and ventilation in COVID estimator (VICE) score.

Methods

The medical records of all patients hospitalized for a laboratory-confirmed COVID-19 diagnosis between May 2021 and June 2021 were retrospectively analyzed. Data were extracted within the first 24 h of admission, and five different scores were calculated. The primary and secondary outcomes were 30-day mortality and mechanical ventilation, respectively.

Results

A total of 285 patients were enrolled in our cohort. Sixty-five patients (22.8%) were intubated with ventilator support, and the 30-day mortality rate was 8.8%. The Shang COVID severity score had the highest numerical area under the receiver operator characteristic (AUC-ROC) (AUC 0.836) curve to predict 30-day mortality, followed by the SEIMC score (AUC 0.807) and VICE score (AUC 0.804). For intubation, both the VICE and COVID-IRS-NLR scores had the highest AUC (AUC 0.82) compared to the inflammation-based score (AUC 0.69). The 30-day mortality increased steadily according to higher Shang COVID severity scores and SEIMC scores. The intubation rate exceeded 50% in the patients stratified by higher VICE scores and COVID-IRS-NLR score quintiles.

Conclusion

The discriminative performances of the SEIMC score and Shang COVID severity score are good for predicting the 30-day mortality of hospitalized COVID-19 patients. The COVID-IRS-NLR and VICE showed good performance for predicting invasive mechanical ventilation (IMV).

Severity of Illness Scores and Biomarkers for Prognosis of Patients with Coronavirus Disease 2019

The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.

Quality of Life in COVID-Related ARDS Patients One Year after Intensive Care Discharge (Odissea Study): A Multicenter Observational Study

BACKGROUND

Investigating the health-related quality of life (HRQoL) after intensive care unit (ICU) discharge is necessary to identify possible modifiable risk factors. The primary aim of this study was to investigate the HRQoL in COVID-19 critically ill patients one year after ICU discharge.

METHODS

In this multicenter prospective observational study, COVID-19 patients admitted to nine ICUs from 1 March 2020 to 28 February 2021 in Italy were enrolled. One year after ICU discharge, patients were required to fill in short-form health survey 36 (SF-36) and impact of event-revised (IES-R) questionnaire. A multivariate linear or logistic regression analysis to search for factors associated with a lower HRQoL and post-traumatic stress disorded (PTSD) were carried out, respectively.

RESULTS

Among 1003 patients screened, 343 (median age 63 years [57-70]) were enrolled. Mechanical ventilation lasted for a median of 10 days [2-20]. Physical functioning (PF 85 [60-95]), physical role (PR 75 [0-100]), emotional role (RE 100 [33-100]), bodily pain (BP 77.5 [45-100]), social functioning (SF 75 [50-100]), general health (GH 55 [35-72]), vitality (VT 55 [40-70]), mental health (MH 68 [52-84]) and health change (HC 50 [25-75]) describe the SF-36 items. A median physical component summary (PCS) and mental component summary (MCS) scores were 45.9 (36.5-53.5) and 51.7 (48.8-54.3), respectively, considering 50 as the normal value of the healthy general population. In all, 109 patients (31.8%) tested positive for post-traumatic stress disorder, also reporting a significantly worse HRQoL in all SF-36 domains. The female gender, history of cardiovascular disease, liver disease and length of hospital stay negatively affected the HRQoL. Weight at follow-up was a risk factor for PTSD (OR 1.02, p = 0.03).

CONCLUSIONS

The HRQoL in COVID-19 ARDS (C-ARDS) patients was reduced regarding the PCS, while the median MCS value was slightly above normal. Some risk factors for a lower HRQoL have been identified, the presence of PTSD is one of them. Further research is warranted to better identify the possible factors affecting the HRQoL in C-ARDS.

Prognostic models for COVID-19 needed updating to warrant transportability over time and space

BACKGROUND

Supporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time.

METHODS

We included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit.

RESULTS

Twenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data.

CONCLUSIONS

NOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic.

Impact of upper and lower respiratory symptoms on COVID-19 outcomes: a multicenter retrospective cohort study

BACKGROUND

Respiratory symptoms are associated with coronavirus disease 2019 (COVID-19) outcomes. However, the impacts of upper and lower respiratory symptoms on COVID-19 outcomes in the same population have not been compared. The objective of this study was to characterize upper and lower respiratory symptoms and compare their impacts on outcomes of hospitalized COVID-19 patients.

METHODS

This was a multicenter, retrospective cohort study; the database from the Japan COVID-19 Task Force was used. A total of 3314 COVID-19 patients were included in the study, and the data on respiratory symptoms were collected. The participants were classified according to their respiratory symptoms (Group 1: no respiratory symptoms, Group 2: only upper respiratory symptoms, Group 3: only lower respiratory symptoms, and Group 4: both upper and lower respiratory symptoms). The impacts of upper and lower respiratory symptoms on the clinical outcomes were compared. The primary outcome was the percentage of patients with poor clinical outcomes, including the need for oxygen supplementation via high-flow oxygen therapy, mechanical ventilation, and extracorporeal membrane oxygenation or death.

RESULTS

Of the 3314 COVID-19 patients, 605, 1331, 1229, and 1149 were classified as Group 1, Group 2, Group 3, and Group 4, respectively. In univariate analysis, patients in Group 2 had the best clinical outcomes among all groups (odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.11-0.39), while patients in Group 3 had the worst outcomes (OR: 3.27, 95% CI: 2.43-4.40). Group 3 patients had the highest incidence of pneumonia, other complications due to secondary infections, and thrombosis during the clinical course.

CONCLUSIONS

Upper and lower respiratory tract symptoms had vastly different impacts on the clinical outcomes of COVID-19.

The Impact of Antibiotic Use on Mortality in Patients Hospitalized in a COVID-19 Centre from Romania: A Retrospective Study

Background and Objectives: Considering the significant number of patients worldwide that received empirical antibiotic therapy for COVID-19 infection due to their critical condition and the lack of therapeutical guidelines, we wanted to find out the consequences of antibiotic use in our study population. Materials and Methods: We conducted a retrospective cohort study including symptomatic patients older than 18 years, hospitalized for SARS-CoV-2 between March and December 2020 in the Internal Medicine and Pneumology Departments of Colentina Clinical Hospital. The elected outcome was death, while independent variables were antibiotic therapy and literature-cited parameters associated with mortality in this disease. Results: Out of 198 included patients, 96 (48.48%) patients received antibiotic therapy during hospitalization. Female gender (OR = 2.61, p = 0.04), history of neoplasm (OR = 7.147, p = 0.01), heart failure (OR = 8.62, p = 0.002), and diabetes mellitus (OR = 3.05, p = 0.02) were significantly associated with death in multivariate analysis. Antibiotic treatment showed a higher probability of death both in bivariate (OR = 5.333, p < 0.001) and multivariate analysis adjusted for the aforementioned prognostic factors (OR = 3.55, p = 0.01). Conclusions: After adjusting for confounders, in-hospital antibiotic administration did not improve survival in COVID-19 patients.

External Validation of the COVID-NoLab and COVID-SimpleLab Prognostic Tools

Our objective was to externally validate 2 simple risk scores for mortality among a mostly inpatient population with COVID-19 in Canada (588 patients for COVID-NoLab and 479 patients for COVID-SimpleLab). The mortality rates in the low-, moderate-, and high-risk groups for COVID-NoLab were 1.1%, 9.6%, and 21.2%, respectively. The mortality rates for COVID-SimpleLab were 0.0%, 9.8%, and 20.0%, respectively. These values were similar to those in the original derivation cohort. The 2 simple risk scores, now successfully externally validated, offer clinicians a reliable way to quickly identify low-risk inpatients who could potentially be managed as outpatients in the event of a bed shortage. Both are available online (https://ebell-projects.shinyapps.io/covid_nolab/ and https://ebell-projects.shinyapps.io/COVID-SimpleLab/).

Comparison of liver function test- and inflammation-based prognostic scores for coronavirus disease 2019: a single center study

BACKGROUND

Although several liver- and inflammation-based scores to predict the clinical course of patients with coronavirus disease 2019 (COVID-19) have been evaluated, no direct comparison regarding their predictive ability has been performed.

METHODS

1038 patients (608 males, age 63.5 ± 17 years) hospitalized with documented COVID-19 infection to the non-ICU ward, were included retrospectively. Clinical and laboratory characteristics on admission including evaluation of Fibrosis-4 (FIB-4) score and C-Reactive Protein (CRP) to albumin ratio (CAR) were recorded.

RESULTS

One hundred and twenty-four patients (11.9%) died during hospitalization after 8 (3-72) days. In multivariate analysis, FIB-4 (hazard ratio, 1.11; 95% confidence interval (CI), 1.034-1.19; P = 0.004), was independently associated with mortality, with very good discriminative ability (area under the receiver operating characteristic curve curve, 0.76). The patients with FIB-4 &amp;gt;2.67 (n = 377), compared to those with ≤2.67 (n = 661), had worse survival (log-rank 32.6; P &amp;lt; 0.001). Twenty-four (6.8%) of 352 patients with possible nonalcoholic fatty liver disease (NAFLD) (defined as Hepatic Steatosis Index &amp;gt;36) died during hospitalization. In multivariate analysis, CAR was an independent risk factor (1) for mortality (hazard ratio, 1.014; 95% CI, 1.002-1.025; P = 0.021), (2) the need for high-flow nasal cannula with or without intubation (hazard ratio, 1.016; 95% CI, 1.004-1.027; P = 0.007) and (3) development of acute kidney injury (hazard ratio, 1.017; 95% CI, 1.006-1.028; P = 0.002). In addition, the patients with possible NAFLD and CAR &amp;gt;12 (n = 154), compared to those with CAR ≤12 (n = 198), had worse survival (log-rank 5.1; P = 0.024).

CONCLUSIONS

FIB-4 was an independent factor for mortality with better performance compared to other liver function test- and inflammation-based scores in patients with COVID-19, while CAR was the only score independently associated with the clinical course in COVID-19 patients with possible NAFLD.

The Usefulness of the COVID-GRAM Score in Predicting the Outcomes of Study Population with COVID-19

BACKGROUND

The COVID-GRAM is a clinical risk rating score for predicting the prognosis of hospitalized COVID-19 infected patients.

AIM

Our study aimed to evaluate the use of the COVID-GRAM score in patients with COVID-19 based on the data from the COronavirus in the LOwer Silesia (COLOS) registry.

MATERIAL AND METHODS

The study group (834 patients of Caucasian patients) was retrospectively divided into three arms according to the risk achieved on the COVID-GRAM score calculated at the time of hospital admission (between February 2020 and July 2021): low, medium, and high risk. The Omnibus chi-square test, Fisher test, and Welch ANOVA were used in the statistical analysis. Post-hoc analysis for continuous variables was performed using Tukey's correction with the Games-Howell test. Additionally, the ROC analysis was performed over time using inverse probability of censorship (IPCW) estimation. The GRAM-COVID score was estimated from the time-dependent area under the curve (AUC).

RESULTS

Most patients (65%) had a low risk of complications on the COVID-GRAM scale. There were 113 patients in the high-risk group (13%). In the medium- and high-risk groups, comorbidities occurred statistically significantly more often, e.g., hypertension, diabetes, atrial fibrillation and flutter, heart failure, valvular disease, chronic kidney disease, and obstructive pulmonary disease (COPD), compared to low-risk tier subjects. These individuals were also patients with a higher incidence of neurological and cardiac complications in the past. Low saturation of oxygen values on admission, changes in C-reactive protein, leukocytosis, hyperglycemia, and procalcitonin level were associated with an increased risk of death during hospitalization. The troponin level was an independent mortality factor. A change from low to medium category reduced the overall survival probability by more than 8 times and from low to high by 25 times. The factor with the strongest impact on survival was the absence of other diseases. The medium-risk patient group was more likely to require dialysis during hospitalization. The need for antibiotics was more significant in the high-risk group on the GRAM score.

CONCLUSION

The COVID-GRAM score corresponds well with total mortality. The factor with the strongest impact on survival was the absence of other diseases. The worst prognosis was for patients who were unconscious during admission. Patients with higher COVID-GRAM score were significantly less likely to return to full health during follow-up. There is a continuing need to develop reliable, easy-to-adopt tools for stratifying the course of SARS-CoV-2 infection.

External Validation of Mortality Scores among High-Risk COVID-19 Patients: A Romanian Retrospective Study in the First Pandemic Year

Background: We aimed to externally validate three prognostic scores for COVID-19: the 4C Mortality Score (4CM Score), the COVID-GRAM Critical Illness Risk Score (COVID-GRAM), and COVIDAnalytics. Methods: We evaluated the scores in a retrospective study on adult patients hospitalized with severe/critical COVID-19 (1 March 2020–1 March 2021), in the Teaching Hospital of Infectious Diseases, Cluj-Napoca, Romania. We assessed all the deceased patients matched with two survivors by age, gender, and at least two comorbidities. The areas under the receiver-operating characteristic curves (AUROCs) were computed for in-hospital mortality. Results: Among 780 severe/critical COVID-19 patients, 178 (22.8%) died. We included 474 patients according to the case definition (158 deceased/316 survivors). The median age was 75 years; diabetes mellitus, malignancies, chronic pulmonary diseases, and chronic kidney and moderate/severe liver diseases were associated with higher risks of death. According to the predefined 4CM Score, the mortality rates were 0% (low), 13% (intermediate), 27% (high), and 61% (very high). The AUROC for the 4CM Score was 0.72 (95% CI: 0.67–0.77) for in-hospital mortality, close to COVID-GRAM, with slightly greater discriminatory ability for COVIDAnalytics: 0.76 (95% CI: 0.71–0.80). Conclusion: All the prognostic scores showed close values compared to their validation cohorts, were fairly accurate in predicting mortality, and can be used to prioritize care and resources.

The New SARS-CoV-2 Variants and Their Epidemiological Impact in Mexico

The COVID-19 disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus started its deadly journey into a global pandemic in Wuhan, China, in December 2019, where it was first isolated. Subsequently, multiple variants of the virus have been identified worldwide. In this review, we discuss the overall landscape of the pandemic in Mexico, including its most prevalent variants, their surveillance at a genomic level, and how they impacted the epidemiology of the disease. We also evaluate the heterologous vaccination in Mexico and how it may have influenced group immunity and helped mitigate the pandemic. Finally, we present an integrated view that could help us to understand the pandemic and serve as an example of the situation in Latin America.

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study

Background

The SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection.

Methods and analysis

Consecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses.

Results

A total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62).

Conclusion

Scores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.

External validation of risk scores to predict in-hospital mortality in patients hospitalized due to coronavirus disease 2019

BACKGROUND

The coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Prediction models that accurately estimate mortality risk in hospitalized patients could assist medical staff in treatment and allocating limited resources.

AIMS

To externally validate two promising previously published risk scores that predict in-hospital mortality among hospitalized COVID-19 patients.

METHODS

Two prospective cohorts were available; a cohort of 1028 patients admitted to one of nine hospitals in Lombardy, Italy (the Lombardy cohort) and a cohort of 432 patients admitted to a hospital in Leiden, the Netherlands (the Leiden cohort). The endpoint was in-hospital mortality. All patients were adult and tested COVID-19 PCR-positive. Model discrimination and calibration were assessed.

RESULTS

The C-statistic of the 4C mortality score was good in the Lombardy cohort (0.85, 95CI: 0.82-0.89) and in the Leiden cohort (0.87, 95CI: 0.80-0.94). Model calibration was acceptable in the Lombardy cohort but poor in the Leiden cohort due to the model systematically overpredicting the mortality risk for all patients. The C-statistic of the CURB-65 score was good in the Lombardy cohort (0.80, 95CI: 0.75-0.85) and in the Leiden cohort (0.82, 95CI: 0.76-0.88). The mortality rate in the CURB-65 development cohort was much lower than the mortality rate in the Lombardy cohort. A similar but less pronounced trend was found for patients in the Leiden cohort.

CONCLUSION

Although performances did not differ greatly, the 4C mortality score showed the best performance. However, because of quickly changing circumstances, model recalibration may be necessary before using the 4C mortality score.

Development and Internal Validation of a New Prognostic Model Powered to Predict 28-Day All-Cause Mortality in ICU COVID-19 Patients-The COVID-SOFA Score

Background: The sequential organ failure assessment (SOFA) score has poor discriminative ability for death in severely or critically ill patients with Coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Our aim was to create a new score powered to predict 28-day mortality. Methods: Retrospective, observational, bicentric cohort study including 425 patients with COVID-19 pneumonia, acute respiratory failure and SOFA score ≥ 2 requiring ICU admission for ≥72 h. Factors with independent predictive value for 28-day mortality were identified after stepwise Cox proportional hazards (PH) regression. Based on the regression coefficients, an equation was computed representing the COVID-SOFA score. Discriminative ability was tested using receiver operating characteristic (ROC) analysis, concordance statistics and precision-recall curves. This score was internally validated. Results: Median (Q1−Q3) age for the whole sample was 64 [55−72], with 290 (68.2%) of patients being male. The 28-day mortality was 54.58%. After stepwise Cox PH regression, age, neutrophil-to-lymphocyte ratio (NLR) and SOFA score remained in the final model. The following equation was computed: COVID-SOFA score = 10 × [0.037 × Age + 0.347 × ln(NLR) + 0.16 × SOFA]. Harrell’s C-index for the COVID-SOFA score was higher than the SOFA score alone for 28-day mortality (0.697 [95% CI; 0.662−0.731] versus 0.639 [95% CI: 0.605−0.672]). Subsequently, the prediction error rate was improved up to 16.06%. Area under the ROC (AUROC) was significantly higher for the COVID-SOFA score compared with the SOFA score for 28-day mortality: 0.796 [95% CI: 0.755−0.833] versus 0.699 [95% CI: 0.653−0.742, p < 0.001]. Better predictive value was observed with repeated measurement at 48 h after ICU admission. Conclusions: The COVID-SOFA score is better than the SOFA score alone for 28-day mortality prediction. Improvement in predictive value seen with measurements at 48 h after ICU admission suggests that the COVID-SOFA score can be used in a repetitive manner. External validation is required to support these results.

Antibiotic Prescription and In-Hospital Mortality in COVID-19: A Prospective Multicentre Cohort Study

BACKGROUND

Since the beginning of the COVID-19 pandemic, empiric antibiotics (ATBs) have been prescribed on a large scale in both in- and outpatients. We aimed to assess the impact of antibiotic treatment on the outcomes of hospitalised patients with moderate and severe coronavirus disease 2019 (COVID-19).

METHODS

We conducted a prospective multicentre cohort study in six clinical hospitals, between January 2021 and May 2021.

RESULTS

We included 553 hospitalised COVID-19 patients, of whom 58% (311/553) were prescribed antibiotics, while bacteriological tests were performed in 57% (178/311) of them. Death was the outcome in 48 patients-39 from the ATBs group and 9 from the non-ATBs group. The patients who received antibiotics during hospitalisation had a higher mortality (RR = 3.37, CI 95%: 1.7-6.8), and this association was stronger in the subgroup of patients without reasons for antimicrobial treatment (RR = 6.1, CI 95%: 1.9-19.1), while in the subgroup with reasons for antimicrobial therapy the association was not statistically significant (OR = 2.33, CI 95%: 0.76-7.17). After adjusting for the confounders, receiving antibiotics remained associated with a higher mortality only in the subgroup of patients without criteria for antibiotic prescription (OR = 10.3, CI 95%: 2-52).

CONCLUSIONS

In our study, antibiotic treatment did not decrease the risk of death in the patients with mild and severe COVID-19, but was associated with a higher risk of death in the subgroup of patients without reasons for it.

Risk factors for severe COVID-19 differ by age for hospitalized adults

Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71-0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77-0.86). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalized patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.

Comparison of 5 acute pulmonary embolism mortality risk scores in patients with COVID-19

Objective

Methods

Results

Conclusion

Evaluation and Comparison of the Predictive Value of 4C Mortality Score, NEWS, and CURB-65 in Poor Outcomes in COVID-19 Patients: A Retrospective Study from a Single Center in Romania

To date, the COVID-19 pandemic has caused millions of deaths across the world. Prognostic scores can improve the clinical management of COVID-19 diagnosis and treatment. The objective of this study was to assess the predictive role of 4C Mortality, CURB-65, and NEWS in COVID-19 mortality among the Romanian population. A single-center, retrospective, observational study was conducted on patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-proven COVID-19 admitted to the Municipal Emergency Clinical Hospital of Timisoara, Romania, between 1 October 2020 and 15 March 2021. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were performed to determine the discrimination accuracy of the three scores. The mean values of the risk scores were higher in the non-survivors group (survivors group vs. non-survivors group: 8 vs. 15 (4C Mortality Score); 3 vs. 8.5 (NEWS); 1 vs. 3 (CURB-65)). In terms of mortality risk prediction, the NEWS performed best, with an AUC of 0.86, and the CURB-65 score performed poorly, with an AUC of 0.80. CURB-65, NEWS, and 4C Mortality scores were significant mortality predictors in the analysis, with acceptable calibration. Among the scores assessed in our study, NEWS had the highest performance in predicting in-hospital mortality in COVID-19 patients. Thus, the findings from this study suggest that the use of NEWS may be beneficial to the early identification of high-risk COVID-19 patients and the provision of more aggressive care to reduce mortality associated with COVID-19.

Validation of ISARIC 4C Mortality and Deterioration Scores in a Mixed Vaccination Status Cohort of Hospitalized Coronavirus Disease 2019 (COVID-19) Patients in Singapore

In this cross-sectional study, we studied performance of the International Severe Acute Respiratory and Emerging Infections Consortium mortality and deterioration scores in a cohort of 410 hospitalized patients (51.2% fully vaccinated). area under the receiver operating characteristic curves were 0.778 and 0.764, respectively, comparable to originally published validation cohorts. Subgroup analysis showed equally good performance in vaccinated and partially or unvaccinated patients.