Haemogram indices are as reliable as CURB-65 to assess 30-day mortality in Covid-19 pneumonia.

Background Mortality due to Covid-19 and severe community-acquired pneumonia (CAP) remains high, despite progress in critical care management. We compared the precision of CURB-65 score with monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) in prediction of mortality among patients with Covid-19 and CAP presenting to the emergency department. Methods We retrospectively analysed two cohorts of patients admitted to the emergency department of Canakkale University Hospital, namely (i) Covid-19 patients with severe acute respiratory symptoms presenting between 23 March 2020 and 31 October 2020, and (ii) all patients with CAP either from bacterial or viral infection within the 36 months preceding the Covid-19 pandemic. Mortality was defined as in-hospital death or death occurring within 30 days after discharge. Results The first study group consisted of 324 Covid-19 patients and the second group of 257 CAP patients. The non-survivor Covid-19 group had significantly higher MLR, NLR and PLR values. In univariate analysis, in Covid-19 patients, a 1-unit increase in NLR and PLR was associated with increased mortality, and in multivariate analysis for Covid-19 patients, age and NLR remained significant in the final step of the model. According to this model, we found that in the Covid-19 group an increase in 1-unit in NLR would result in an increase by 5% and 7% in the probability of mortality, respectively. According to pairwise analysis, NLR and PLR are as reliable as CURB-65 in predicting mortality in Covid-19. Conclusions Our study indicates that NLR and PLR may serve as reliable predictive factors as CURB-65 in Covid-19 pneumonia, which could easily be used to triage and manage severe patients in the emergency department.

Point-of-care Lung Ultrasound, Lung CT and NEWS to Predict Adverse Outcomes and Mortality in COVID-19 Associated Pneumonia.

Introduction: The appraisal of disease severity and prediction of adverse outcomes using risk stratification tools at early disease stages is crucial to diminish mortality from coronavirus disease 2019 (COVID-19). While lung ultrasound (LUS) as an imaging technique for the diagnosis of lung diseases has recently gained a leading position, data demonstrating that it can predict adverse outcomes related to COVID-19 is scarce. The main aim of this study is therefore to assess the clinical significance of bedside LUS in COVID-19 patients who presented to the emergency department (ED). Methods: Patients with a confirmed diagnosis of SARS-CoV-2 pneumonia admitted to the ED of our hospital between March 2021 and May 2021 and who underwent a 12-zone LUS and a lung computed tomography scan were included prospectively. Logistic regression and Cox proportional hazard models were used to predict adverse events, which was our primary outcome. The secondary outcome was to discover the association of LUS score and computed tomography severity score (CT-SS) with the composite endpoints. Results: We assessed 234 patients [median age 59.0 (46.8-68.0) years; 59.4% M), including 38 (16.2%) in-hospital deaths for any cause related to COVID-19. Higher LUS score and CT-SS was found to be associated with ICU admission, intubation, and mortality. The LUS score predicted mortality risk within each stratum of NEWS. Pairwise analysis demonstrated that after adjusting a base prediction model with LUS score, significantly higher accuracy was observed in predicting both ICU admission (DBA -0.067, P = .011) and in-hospital mortality (DBA -0.086, P = .017). Conclusion: Lung ultrasound can be a practical prediction tool during the course of COVID-19 and can quantify pulmonary involvement in ED settings. It is a powerful predictor of ICU admission, intubation, and mortality and can be used as an alternative for chest computed tomography while monitoring COVID-19-related adverse outcomes.

The Effectiveness of National Early Warning Score, Quick Sequential Organ Failure Assessment, Charlson Comorbidity Index, and Elixhauser Comorbidity Index Scores in Predicting Mortality Due to COVID-19 in Elderly Patients.

INTRODUCTION: As the mortality rate in coronavirus disease 2019 (COVID-19) patients older than 65 years is considerable, evaluation of in-hospital mortality is crucial. This study aimed to evaluate in-hospital mortality in COVID-19 patients older than 65 years using the National Early Warning Score (NEWS), Quick Sequential Organ Failure Assessment (q-SOFA), Charlson Comorbidity Index (CCI), and Elixhauser Comorbidity Index (ECI). METHODS: This retrospective study included data from 480 patients with confirmed COVID-19 and age over 65 years who were evaluated in a university emergency department in Turkey. Data from eligible but deceased COVID-19 patients was also included. NEWS, q-SOFA, CCI, and ECI scores were retrospectively calculated. All clinical data was accessed from the information management system of the hospital, retrieved, and analyzed. RESULTS: In-hospital mortality was seen in 169 patients (169/480). Low oxygen saturation, high C-reactive protein (CRP) and urea levels, and high q-SOFA and ECI scores helped us identify mortality in high-risk patients. A statistically significant difference was found in mortality estimation between q-SOFA and ECI (p <0.001), respectively. CONCLUSION: Q-SOFA and ECI can be used both easily and practically in the early diagnosis of in-hospital mortality in COVID-19 positive patients over 65 years of age admitted to the emergency department. Low oxygen saturation, high CRP and urea levels, and high q-SOFA and ECI scores are helpful in identifying high-risk patients.

Evaluation of the factors predicting the need for intensive care of patients with COVID-19 aged above 65 years: data from an emergency department in Turkey.

OBJECTIVE: Individuals aged ≥65 years are more susceptible to COVID-19 disease and admission to intensive care is most notable. The scoring systems (national early warning score, quick sequential organ failure assessment, shock index) are recommended for rapid assessment of patients in emergency room conditions. The goal of our study is to evaluate scoring systems in conjunction with predictive factors of need for admission to intensive care of patients ≥65 years old with a diagnosis of COVID-19 who applied to the emergency room. METHODS: Patients were divided into two groups according to evolution in the emergency room, being those who needed or not intensive care. National Early Warning Score, quick sequential organ failure assessment, shock index scores and serum biochemistry, blood count and blood gas values were evaluated from hospital information management system records. RESULTS: Of the patients included in the study, 80.8% were admitted to the ward and 14.5% to the unit of intensive care. Lymphocyte count, base deficit and bicarbonate levels were lower, and the levels of C-reactive protein, lactate, D-dimer, urea and lactate dehydrogenase were higher in patients who needed intensive care. Quick sequential organ failure assessment and shock index were considered significant in the group admitted to the intensive care unit. CONCLUSIONS: We recommend that quick sequential organ failure assessment and shock index be used quickly, practically and easily in predicting the need for intensive care unit in patients aged ≥65 years in emergency department diagnosed with COVID-19.

Prediction of mortality in COVID-19 through combing CT severity score with NEWS, qSOFA, or peripheral perfusion index.

INTRODUCTION: The assessment of disease severity and the prediction of clinical outcomes at early disease stages can contribute to decreased mortality in patients with Coronavirus disease 2019 (COVID-19). This study was conducted to develop and validate a multivariable risk prediction model for mortality with using a combination of computed tomography severity score (CT-SS), national early warning score (NEWS), and quick sequential (sepsis-related) organ failure assessment (qSOFA) in COVID-19 patients. METHODS: We retrospectively collected medical data from 655 adult COVID-19 patients admitted to our hospital between July and November 2020. Data on demographics, clinical characteristics, and laboratory and radiological findings measured as part of standard care at admission were used to calculate NEWS, qSOFA score, CT-SS, peripheral perfusion index (PPI) and shock index (SI). Logistic regression and Cox proportional hazard models were used to predict mortality, which was our primary outcome. The predictive accuracy of distinct scoring systems was evaluated by the receiver-operating characteristic (ROC) curve analysis. RESULTS: The median age was 50.0 years [333 males (50.8%), 322 females (49.2%)]. Higher NEWS and SI was associated with time-to-death within 90-days, whereas higher age, CT-SS and lower PPI were significantly associated with time-to-death within both 14 days and 90 days in the adjusted Cox regression model. The CT-SS predicted different mortality risk levels within each stratum of NEWS and qSOFA and improved the discrimination of mortality prediction models. Combining CT-SS with NEWS score yielded more accurate 14 days (DBA: -0.048, p = 0.002) and 90 days (DBA: -0.066, p < 0.001) mortality prediction. CONCLUSION: Combining severity tools such as CT-SS, NEWS and qSOFA improves the accuracy of predicting mortality in patients with COVID-19. Inclusion of these tools in decision strategies might provide early detection of high-risk groups, avoid delayed medical attention, and improve patient outcomes.