A new simple score to predict mortality of COVID-19 in the emergency department

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is one of the greatest challenges facing global medical research. The availability of a clinical score that can predict mortality risk at the time of diagnosis could be a valuable tool in the hands of emergency physicians to make clinical decisions. Our study is designed to evaluate clinical and laboratory endpoints associated with mortality and to determine a prognostic score based on clinical and laboratory variables. We retrospectively enrolled 367 patients diagnosed with coronavirus disease 19 (COVID-19) in our emergency department (ED). We evaluated their mortality 60 days after diagnosis. Symptoms, demographic data, concomitant diseases, and various laboratory parameters were obtained from all patients. Variables related to death were assessed using multiple logistic regression analysis. From these, we created a score called ANCOC (Age, blood urea Nitrogen, C-reactive protein, Oxygen saturation, Comorbidities). The area under the receiver operating characteristic (ROC) curve was calculated for the ANCOC and for the 4C score. The 4C score has been described and validated in previous works and can predict mortality in COVID-19 patients. We compared the 2 scores and analysed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for 60-day mortality for the ANCOC score. The ANCOC and 4C scores accurately predicted death from COVID-19. There were no differences in accuracy between the scores. An ANCOC score <-1 identified patients who will recover with a PPV and sensitivity of 100%, whereas a score >3 identified patients at high risk of death. The ANCOC score has very high diagnostic accuracy in predicting the risk of death in patients with COVID-19 diagnosed at ED. The ANCOC score has similar accuracy to the 4C score but is easier to calculate. If validated by external cohorts, this score could be an additional tool in the hands of ED physicians to identify COVID-19 patients at high risk of death.Copyright © 2023 The Author(s). Published by MRE Press.

Prognostication of COVID-19 patients using ROX index and CURB-65 score - A retrospective observational study.

Objectives: Coronavirus disease-2019 (COVID-19) disease has overwhelmed the healthcare infrastructure worldwide. The shortage of intensive care unit (ICU) beds leads to longer waiting times and higher mortality for patients. High crowding leads to an increase in mortality, length of hospital stays, and hospital costs for patients. Through an appropriate stratification of patients, rational allocation of the available hospital resources can be accomplished. Various scores for risk stratification of patients have been tried, but for a score to be useful at primary care level, it should be readily available at the bedside and be reproducible. ROX index and CURB-65 are simple bedside scores, requiring minimum equipment, and investigations to calculate. Methods: This retrospective, record-based study included adult patients who presented to the ED from May 1, 2020 to November 30, 2020 with confirmed COVID-19 infection. The patient's clinical and demographic details were obtained from the electronic medical records of the hospital. ROX index and CURB-65 score on ED arrival were calculated and correlated with the need for hospitalization and early (14-day) and late (28-day) mortality. Results: 842 patients were included in the study. The proportion of patients with mild, moderate and severe disease was 46.3%, 14.9%, and 38.8%, respectively. 55% patients required hospitalization. The 14-day mortality was 8.8% and the 28-day mortality was 20.7%. The AUROC of ROX index for predicting hospitalization was 0.924 (p < 0.001), for 14-day mortality was 0.909 (p < 0.001) and for 28-day mortality was 0.933 (p < 0.001). The AUROC of CURB-65 score for predicting hospitalization was 0.845 (p < 0.001), for 14-day mortality was 0.905 (p < 0.001) and for 28-day mortality was 0.902 (p < 0.001). The cut-off of ROX index for predicting hospitalization was ≤18.634 and for 14-day mortality was ≤14.122. Similar cut-off values for the CURB-65 score were ≥1 and ≥2, respectively. Conclusion: ROX index and CURB-65 scores are simple and inexpensive scores that can be efficiently utilised by primary care physicians for appropriate risk stratification of patients with COVID-19 infection.

Overall Survival Rate in Allogeneic Stem Cell Transplanted Patients Requiring Intensive Care Can Be Predicted by the Prognostic Index for Critically Ill Allogeneic Transplantation Patients (PICAT) and the Sequential Organ Failure Assessment (SOFA) Scores.

Background. Allogeneic hematopoietic stem cell transplantation (allo-HCT) recipients requiring intensive care unit (ICU) have high mortality rates. Methods. In the current study, we retrospectively assessed whether the Prognostic Index for Critically Ill Allogeneic Transplantation patients (PICAT) score predicted overall survival in a cohort of 111 consecutive allo-HCT recipients requiring ICU. Results. Survival rates at 30 days and 1 year after ICU admission were 57.7% and 31.5%, respectively, and were significantly associated with PICAT scores (p = 0.036). Specifically, survival at 30-day for low, intermediate, and high PICAT scores was 64.1%, 58.1%, and 31.3%, respectively. At one-year, the figures were 37.5%, 29%, and 12.5%, respectively. In multivariate analyses, high PICAT score (HR = 2.23, p = 0.008) and relapse prior to ICU admission (HR = 2.98, p = 0.0001) predicted higher mortality. We next compared the ability of the PICAT and the Sequential Organ Failure Assessment (SOFA) scores to predict mortality in our patients using c-statistics. C statistics for the PICAT and the SOFA scores were 0.5687 and 0.6777, respectively. Conclusions. This study shows that while the PICAT score is associated with early and late mortality in allo-HCT recipients requiring ICU, it is outperformed by the SOFA score to predict their risk of mortality.

What is the role of prognostic indexes in COVID-19 patients with diabetes mellitus? Data of patients from Turkey.

Aim: We aimed to determine the prognostic performance of the Glasgow Prognostic Score (GPS), systemic immune-inflammation index and early warning score (the 'ANDC' system) in patients with diabetes mellitus who had COVID-19. Patients & methods: Patients were divided into two groups: with and without diabetes mellitus. Results: In the diabetic patient group, the rates of in-hospital mortality, intensive care unit hospitalization and corticosteroid treatment were higher compared with the nondiabetic patient group (p < 0.05). A GPS of 2 was useful for predicting in-hospital mortality in diabetic patients (p < 0.05). The ANDC score was significantly higher in diabetic patients (p < 0.05) and in diabetic patients with mortality and those who needed ICU hospitalization (p < 0.05). Conclusion: The presence of a GPS of 2 at the time of admission and a high ANDC value were associated with poor prognosis in diabetic COVID-19 patients.

Acute Venous Disease Anomalies in Critically Ill COVID-19 Patients.

INTRODUCTION: Other entities besides deep vein thrombosis (DVT) affecting the venous system, such as superficial vein phlebitis (SVP) and superficial vein thrombophlebitis (SVT), receive poor attention in the literature. However, both entities may propagate proximally into the deep venous system and progress to a DVT. To our knowledge, the relevance of other venous findings such as SVP or SVT in coronavirus disease 2019 (COVID-19) patients has not been evaluated. This work aimed to assess the clinical, biochemical, and hematological variables associated with the incidence of acute venous diseases, such as DVT, SVP, and SVT, in a cohort of 74 critically ill COVID-19 patients and their association with mortality. METHODS: Given the high thrombotic risk, all patients underwent venous imaging with bedside ultrasound. Clinical variables were obtained from medical records. Comparisons were made by the chi-square test or Fisher's exact test. We constructed Kaplan-Meier curves and used Cox proportional hazard models to calculate hazard ratios for dichotomized risk factors to identify predictors of mortality. SPSS version 21.0 (IBM Corp., Armonk, NY) was used for statistical analysis. RESULTS:  SVP occurred in 28 patients (37.8%), DVT in 22 patients (29.7%), and 28 patients died (37.8%). Elevated D-dimer was associated with DVT but not with SVP. Neither SVP nor DVT was associated with mortality. After adjusting for age, elevated troponins (OR: 2.4, 95% CI: 1.1-5.4), platelets < 244 cell/mm3 (2.4, 1.1-5.6), and IMPROVE (International Medical Prevention Registry on Venous Thromboembolism) bleeding score > 7 (2.8, 1.3-6.3) were predictors of mortality. CONCLUSIONS: Acute venous findings such as SVP and DVT are highly prevalent and independent of mortality in critically ill COVID-19 patients. These entities are not related, although they may occur synchronically. DVT is frequently presented as an asymptomatic distal bilateral finding associated with elevated D-dimer, decreased ferritin, and higher vasoactive drug use but independent from chronic venous disease. Interestingly, elevated troponins, decreased platelets, and a prognostic value > 7 of the IMPROVE bleeding score were predictors of mortality in this group of critically ill COVID-19 patients.

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.

Can We Predict a Higher Risk of Urothelial Bladder Cancer With a Simple Blood Test?

BACKGROUND/AIM: The COVID-19 pandemic highlighted the need to develop tools prioritizing high risk patients for urgent evaluation. Our objective was to determine whether Glasgow Prognostic Score (GPS), an inflammation-based score, can predict higher grade and stage urothelial bladder cancer in patients with gross hematuria who need urgent evaluation. PATIENTS AND METHODS: We analyzed a database of 129 consecutive patients presenting with gross hematuria. GPS was calculated using pretreatment C-reactive protein (CRP) and albumin levels. Patients with bacteriuria or other known malignancies were excluded. The relationship between GPS and final diagnosis was analyzed with multivariate logistic regression. RESULTS: A total of 101 patients were included in the study and 24 patients were identified without any pathology and 77 with a bladder tumor. Pathology demonstrated 21 with muscle invasive, 18 with high grade non-muscle invasive, and 38 with low grade superficial bladder cancer. Twenty-six of 39 (67%) patients with high grade tumors had a GPS of 1 or 2 compared to only 8 out of 62 (13%) patients with either low grade or negative findings (p<0.0001). Ten of 21 (48%) patients with muscle invasive disease had a GPS of 2 compared to 1 out of 18 (6%) with high grade non muscle invasive tumors (p=0.04). On multivariate analysis, GPS was a strong independent predictor of high grade and stage bladder cancer. CONCLUSION: GPS may serve as a highly accessible predictor of high grade, high stage, and large urothelial bladder tumors at the time of initial evaluation and can help identify patients who need urgent evaluation.

Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study.

Objectives: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information. Design: Multicenter retrospective observational cohort study. Setting: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles. Patients: Coronavirus Disease 2019-confirmed and hospitalized adult patients. Measurements and Main Results: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score. Conclusions: The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.

Risk of Seven-Day Worsening and Death: A New Clinically Derived COVID-19 Score.

This monocentric, retrospective, two-stage observational study aimed to recognize the risk factors for a poor outcome in patients hospitalized with SARS-CoV-2 infection, and to develop and validate a risk score that identifies subjects at risk of worsening, death, or both. The data of patients with SARS-CoV-2 infection during the first wave of the pandemic were collected and analyzed as a derivation cohort. Variables with predictive properties were used to construct a prognostic score, which was tried out on a validation cohort enrolled during the second wave. The derivation cohort included 494 patients; the median age was 62 and the overall fatality rate was 22.3%. In a multivariable analysis, age, oxygen saturation, neutrophil-to-lymphocyte ratio, C-reactive protein and lactate dehydrogenase were independent predictors of death and composed the score. A cutoff value of 3 demonstrated a sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of 93.5%, 68.5%, 47.4% and 97.2% for death, and 84.9%, 84.5%, 79.6% and 87.9% for worsening, respectively. The validation cohort included 415 subjects. The score application showed a Se, Sp, PPV and NPV of 93.4%, 61.6%, 29.5% and 98.1% for death, and 81%, 76.3%, 72.1% and 84.1% for worsening, respectively. We propose a new clinical, easy and reliable score to predict the outcome in hospitalized SARS-CoV-2 patients.

Development and Validation of the Acute PNeumonia Early Assessment Score for Safely Discharging Low-Risk SARS-CoV-2-Infected Patients from the Emergency Department.

A continuous demand for assistance and an overcrowded emergency department (ED) require early and safe discharge of low-risk severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. We developed (n = 128) and validated (n = 330) the acute PNeumonia early assessment (aPNea) score in a tertiary hospital and preliminarily tested the score on an external secondary hospital (n = 97). The score's performance was compared to that of the National Early Warning Score 2 (NEWS2). The composite outcome of either death or oral intubation within 30 days from admission occurred in 101 and 28 patients in the two hospitals, respectively. The area under the receiver operating characteristic (AUROC) curve of the aPNea model was 0.86 (95% confidence interval (CI), 0.78-0.93) and 0.79 (95% CI, 0.73-0.89) for the development and validation cohorts, respectively. The aPNea score discriminated low-risk patients better than NEWS2 at a 10% outcome probability, corresponding to five cut-off points and one cut-off point, respectively. aPNea's cut-off reduced the number of unnecessary hospitalizations without missing outcomes by 27% (95% CI, 9-41) in the validation cohort. NEWS2 was not significant. In the external cohort, aPNea's cut-off had 93% sensitivity (95% CI, 83-102) and a 94% negative predictive value (95% CI, 87-102). In conclusion, the aPNea score appears to be appropriate for discharging low-risk SARS-CoV-2-infected patients from the ED.

A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring.

Purpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition. Patients and Methods: In this single-center cohort study, real-world data collected within the first two waves of the COVID-19 pandemic was used (27.04.2020-03.08.2020 and 01.11.2020-19.01.2021, respectively). The first wave data (1,349 cases) was used as a training set for the score development, while the second wave data (1,453 cases) was used as a validation set. No overlapping cases were presented in the study. For all the available patients' features, we tested their association with an outcome. Significant features were taken for further analysis, and their partial sensitivity, specificity, and promptness were estimated. Sensitivity and specificity were further combined into a feature informativeness index. The developed score was derived as a weighted sum of nine features that showed the best trade-off between informativeness and promptness. Results: Based on the training cohort (median age ± median absolute deviation 58 ± 13.3, females 55.7%), the following resulting score was derived: APTT (4 points), CRP (3 points), D-dimer (4 points), glucose (4 points), hemoglobin (3 points), lymphocytes (3 points), total protein (6 points), urea (5 points), and WBC (4 points). Internal and temporal validation based on the second wave cohort (age 60 ± 14.8, females 51.8%) showed that a sensitivity and a specificity over 90% may be achieved with an expected prediction range of more than 7 days. Moreover, we demonstrated high robustness of the score to the varying peculiarities of the pandemic. Conclusions: An extensive application of the score during the pandemic showed its potential for optimization of patient management as well as improvement of medical staff attentiveness in a high workload stress. The transparent structure of the score, as well as tractable cutoff bounds, simplified its implementation into clinical practice. High cumulative informativeness of the nine score components suggests that these are the indicators that need to be monitored regularly during the follow-up of a patient with COVID-19.

Factors Associated with ICU Admission in Patients with COVID-19: The GOL2DS Score.

Background and Objectives: The COVID-19 pandemic has been shaking lives around the world for nearly two years. The discovery of highly effective vaccines has not been able to stop the transmission of the virus. SARS-CoV-2 shows completely different clinical manifestations. A large percentage (about 40%) of admitted patients require treatment in an intensive care unit (ICU). This study investigates the factors associated with admission of COVID-19 patients to the ICU and whether it is possible to obtain a score that can help the emergency physician to select the hospital ward. Materials and Methods: We retrospectively recorded 313 consecutive patients who were presented to the emergency department (ED) of our hospital and had a diagnosis of COVID-19 confirmed by polymerase chain reaction (PCR) on an oropharyngeal swab. We used multiple logistic regression to evaluate demographic, clinical, and laboratory data statistically associated with ICU admission. These variables were used to create a prognostic score for ICU admission. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver-operating characteristic curve (ROC) of the score for predicting ICU admission during hospitalization were calculated. Results: Of the variables evaluated, only blood type A (p = 0.003), PaO2/FiO2 (p = 0.002), LDH (p = 0.004), lactate (p = 0.03), dyspnea (p = 0.03) and SpO2 (p = 0.0228) were significantly associated with ICU admission after adjusting for sex, age and comorbidity using multiple logistic regression analysis. We used these variables to create a prognostic score called GOL2DS (group A, PaO2/FiO2, LDH, lactate and dyspnea, and SpO2), which had high accuracy in predicting ICU admission (AUROC 0.830 [95% CI, 0.791-0.892). Conclusions: In our single-center experience, the GOL2DS score could be useful in identifying patients at high risk for ICU admission.

A simple prognostic score for COVID-19 hospitalized patients developing deep vein thrombosis.

OBJECTIVE: The aim of our study was to analyze the specificity, accuracy and sensitivity of a simple, easy to calculate, prognostic score for hospitalized COVID19 patients developing deep vein thrombosis. METHODS: From March 1st to April 28th, 942 COVID-19 patients with severe symptoms were admitted to the hospital San Matteo of Pavia-Italy. Thirty two patients (3.4%) developed deep vein thrombosis during hospitalization. In all patients hemostatic and inflammatory parameters were abnormal. A simple prognostic score was developed based on the presence of specific co morbidities and D-dimers levels (quick San Matthew Score-quick SMS). RESULTS: Nine patients died in a condition of multiple organ failure, 23 patients (71.9%) survived and left the hospital in good general conditions. The developed score was based simply on two parameters: 1) presence of four specific co morbidities and 2)systemic levels of D-Dimers. The quick San Matthew Score resulted in a sensitivity, specificity and overall accuracy of more than 90% (94%, 92%,93% respectively) and compared favorably with other scores. The score was prospectively validated in 100 COVID19 patients who developed deep vein thrombosis collected from the literature and prospectively confirmed in our hospital. CONCLUSIONS: The findings of our study underline the importance of an immediate aggressive therapeutic approach for moderate and high-risk patients with COVID19 infection. The quick SMS score may help to identify patients at high risk for mortality and to follow the clinical outcome of the patient. A simple, easy to calculate prognostic score may also facilitate communication among health workers.

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. OBJECTIVE: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia. METHODS: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. RESULTS: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. CONCLUSIONS: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome.

PURPOSE: To evaluate the prognostic role of chest computed tomography (CT), alone or in combination with clinical and laboratory parameters, in COVID-19 patients during the first peak of the pandemic. METHODS: A retrospective single-center study of 301 COVID-19 patients referred to our Emergency Department (ED) from February 25 to March 29, 2020. At presentation, patients underwent chest CT and clinical and laboratory examinations. Outcomes included discharge from the ED after improvement/recovery (positive outcome), or admission to the intensive care unit or death (poor prognosis). A visual quantitative analysis was formed using two scores: the Pulmonary Involvement (PI) score based on the extension of lung involvement, and the Pulmonary Consolidation (PC) score based on lung consolidation. The prognostic value of CT alone or integrated with other parameters was studied by logistic regression and ROC analysis. RESULTS: The impact of the CT PI score [≥15 vs. ≤ 6] on predicting poor prognosis (OR 5.71 95 % CI 1.93-16.92, P = 0.002) was demonstrated; no significant association was found for the PC score. Chest CT had a prognostic role considering the PI score alone (AUC 0.722) and when evaluated with demographic characteristics, comorbidities, and laboratory data (AUC 0.841). We, therefore, developed a nomogram as an easy tool for immediate clinical application. CONCLUSIONS: Visual analysis of CT gives useful information to physicians for prognostic evaluations, even in conditions of COVID-19 emergency. The predictive value is increased by evaluating CT in combination with clinical and laboratory data.

The COVID-19 Worsening Score (COWS)-a predictive bedside tool for critical illness.

OBJECTIVES: To evaluate the accuracy of a new COVID-19 prognostic score based on lung ultrasound (LUS) and previously validated variables in predicting critical illness. METHODS: We conducted a single-center retrospective cohort development and internal validation study of the COVID-19 Worsening Score (COWS), based on a combination of the previously validated COVID-GRAM score (GRAM) variables and LUS. Adult COVID-19 patients admitted to the emergency department (ED) were enrolled. Ten variables previously identified by GRAM, days from symptom onset, LUS findings, and peripheral oxygen saturation/fraction of inspired oxygen (P/F) ratio were analyzed. LUS score as a single predictor was assessed. We evaluated GRAM model's performance, the impact of adding LUS, and then developed a new model based on the most predictive variables. RESULTS: Among 274 COVID-19 patients enrolled, 174 developed critical illness. The GRAM score identified 51 patients at high risk of developing critical illness and 132 at low risk. LUS score over 15 (range 0 to 36) was associated with a higher risk ratio of critical illness (RR, 2.05; 95% confidence interval [CI], 1.52-2.77; area under the curve [AUC], 0.63; 95% CI 0.676-0.634). The newly developed COVID-19 Worsening Score relies on five variables to classify high- and low-risk patients with an overall accuracy of 80% and negative predictive value of 93% (95% CI, 87%-98%). Patients scoring more than 0.183 on COWS showed a RR of developing critical illness of 8.07 (95% CI, 4.97-11.1). CONCLUSIONS: COWS accurately identify patients who are unlikely to need intensive care unit (ICU) admission, preserving resources for the remaining high-risk patients.

The BAS2IC Score: A Useful Tool to Identify Patients at High Risk of Early Progression to Severe Coronavirus Disease 2019.

We developed a score, with easily accessible data (age, sex, body mass index, dyspnea, inflammatory parameters), to predict the risk of rapid progression to severe coronavirus disease 2019. Using a cutoff of >6 points, the negative predictive value was 87%.

A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study.

COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration, and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781) increasing to 0.875 (95% CI 0.806-0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients.

A linear prognostic score based on the ratio of interleukin-6 to interleukin-10 predicts outcomes in COVID-19.

BACKGROUND: Prognostic tools are required to guide clinical decision-making in COVID-19. METHODS: We studied the relationship between the ratio of interleukin (IL)-6 to IL-10 and clinical outcome in 80 patients hospitalized for COVID-19, and created a simple 5-point linear score predictor of clinical outcome, the Dublin-Boston score. Clinical outcome was analysed as a three-level ordinal variable ("Improved", "Unchanged", or "Declined"). For both IL-6:IL-10 ratio and IL-6 alone, we associated clinical outcome with a) baseline biomarker levels, b) change in biomarker level from day 0 to day 2, c) change in biomarker from day 0 to day 4, and d) slope of biomarker change throughout the study. The associations between ordinal clinical outcome and each of the different predictors were performed with proportional odds logistic regression. Associations were run both "unadjusted" and adjusted for age and sex. Nested cross-validation was used to identify the model for incorporation into the Dublin-Boston score. FINDINGS: The 4-day change in IL-6:IL-10 ratio was chosen to derive the Dublin-Boston score. Each 1 point increase in the score was associated with a 5.6 times increased odds for a more severe outcome (OR 5.62, 95% CI -3.22-9.81, P = 1.2 × 10-9). Both the Dublin-Boston score and the 4-day change in IL-6:IL-10 significantly outperformed IL-6 alone in predicting clinical outcome at day 7. INTERPRETATION: The Dublin-Boston score is easily calculated and can be applied to a spectrum of hospitalized COVID-19 patients. More informed prognosis could help determine when to escalate care, institute or remove mechanical ventilation, or drive considerations for therapies. FUNDING: Funding was received from the Elaine Galwey Research Fellowship, American Thoracic Society, National Institutes of Health and the Parker B Francis Research Opportunity Award.

Prognostic factors in patients admitted to an urban teaching hospital with COVID-19 infection.

BACKGROUND: Severe COVID-19 infection results in a systemic inflammatory response (SIRS). This SIRS response shares similarities to the changes observed during the peri-operative period that are recognised to be associated with the development of multiple organ failure. METHODS: Electronic patient records for patients who were admitted to an urban teaching hospital during the initial 7-week period of the COVID-19 pandemic in Glasgow, U.K. (17th March 2020-1st May 2020) were examined for routine clinical, laboratory and clinical outcome data. Age, sex, BMI and documented evidence of COVID-19 infection at time of discharge or death certification were considered minimal criteria for inclusion. RESULTS: Of the 224 patients who fulfilled the criteria for inclusion, 52 (23%) had died at 30-days following admission. COVID-19 related respiratory failure (75%) and multiorgan failure (12%) were the commonest causes of death recorded. Age ≥ 70 years (p < 0.001), past medical history of cognitive impairment (p ≤ 0.001), previous delirium (p < 0.001), clinical frailty score > 3 (p < 0.001), hypertension (p < 0.05), heart failure (p < 0.01), national early warning score (NEWS) > 4 (p < 0.01), positive CXR (p < 0.01), and subsequent positive COVID-19 swab (p ≤ 0.001) were associated with 30-day mortality. CRP > 80 mg/L (p < 0.05), albumin < 35 g/L (p < 0.05), peri-operative Glasgow Prognostic Score (poGPS) (p < 0.05), lymphocytes < 1.5 109/l (p < 0.05), neutrophil lymphocyte ratio (p ≤ 0.001), haematocrit (< 0.40 L/L (male)/ < 0.37 L/L (female)) (p ≤ 0.01), urea > 7.5 mmol/L (p < 0.001), creatinine > 130 mmol/L (p < 0.05) and elevated urea: albumin ratio (< 0.001) were also associated with 30-day mortality. On multivariate analysis, age ≥ 70 years (O.R. 3.9, 95% C.I. 1.4-8.2, p < 0.001), past medical history of heart failure (O.R. 3.3, 95% C.I. 1.2-19.3, p < 0.05), NEWS > 4 (O.R. 2.4, 95% C.I. 1.1-4.4, p < 0.05), positive initial CXR (O.R. 0.4, 95% C.I. 0.2-0.9, p < 0.05) and poGPS (O.R. 2.3, 95% C.I. 1.1-4.4, p < 0.05) remained independently associated with 30-day mortality. Among those patients who tested PCR COVID-19 positive (n = 122), age ≥ 70 years (O.R. 4.7, 95% C.I. 2.0-11.3, p < 0.001), past medical history of heart failure (O.R. 4.4, 95% C.I. 1.2-20.5, p < 0.05) and poGPS (O.R. 2.4, 95% C.I. 1.1-5.1, p < 0.05) remained independently associated with 30-days mortality. CONCLUSION: Age ≥ 70 years and severe systemic inflammation as measured by the peri-operative Glasgow Prognostic Score are independently associated with 30-day mortality among patients admitted to hospital with COVID-19 infection.