Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score

Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments

The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90-0.95) for 4C and 0.903 (95% CI: 086-0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.

Comparative analysis of COVID-19-associated venous thromboembolism outcomes: evolution from 2020 to 2021-2022

Patients with COVID-19 are at an increased risk for venous thromboembolism (VTE). With the advent of vaccinations and novel treatments from 2020 through 2022, the landscape of COVID-19 has evolved. Notably, the effects of such interventions on the outcomes of COVID-19-associated VTE have not been thoroughly examined. Data from the RIETE registry were analyzed to evaluate 90-day VTE-related outcomes (all-cause mortality, major bleeding, and VTE recurrences) in patients with COVID-19-associated VTE. We compared the periods before and after the widespread introduction of COVID-19 vaccines: March to December 2020 (pre-vaccine period) and March 2021 to December 2022 (post-vaccine period). Statistical analysis included mixed-effects parametric survival-time models. Among 1,620 patients with COVID-19-associated VTE, most (74.1%) were identified during 2020 period. The analysis revealed a more than two-fold increase in the risk of death within 90 days (adjusted hazard ratio [HR]: 2.27; 95% confidence interval, CI: 1.18-4.38) and major bleeding (adjusted HR: 2.91; 95%CI: 1.08-7.84) for patients from the 2020 period compared to those from the 2021-2022 period. Inpatient subgroup analysis confirmed the observed mortality differences. The frequency of recurrent VTE was low (1.1 vs. 0.7%, respectively), and did not show significant variation between the two periods. Our research provides a comparative perspective on the clinical outcomes of COVID-19-associated VTE before and after the introduction of vaccines. Our findings reveal a significant decrease in the incidence of 90-day mortality and major bleeding in patients with COVID-19-associated VTE in the 2021-2022 period.

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.

Assessment of the Severity of COVID-19 on the Basis of Examination and Laboratory Diagnostics in Relation to Computed Tomography Imagery of Patients Hospitalised Due to COVID-19-Single-Centre Study

From the moment the SARS-CoV-2 virus was identified in December 2019, the COVID-19 disease spread around the world, causing an increase in hospitalisations and deaths. From the beginning of the pandemic, scientists tried to determine the major cause that led to patient deaths. In this paper, the background to creating a research model was diagnostic problems related to early assessment of the degree of damage to the lungs in patients with COVID-19. The study group comprised patients hospitalised in one of the temporary COVID hospitals. Patients admitted to the hospital had confirmed infection with SARS-CoV-2. At the moment of admittance, arterial blood was taken and the relevant parameters noted. The results of physical examinations, the use of oxygen therapy and later test results were compared with the condition of the patients in later computed tomography images and descriptions. The point of reference for determining the severity of the patient's condition in the computer imagery was set for a mild condition as consisting of a percentage of total lung parenchyma surface area affected no greater than 30%, an average condition of between 30% and 70%, and a severe condition as greater than 70% of the lung parenchyma surface area affected. Patients in a mild clinical condition most frequently had mild lung damage on the CT image, similarly to patients in an average clinical condition. Patients in a serious clinical condition most often had average levels of damage on the CT image. On the basis of the collected data, it can be said that at the moment of admittance, BNP, PE and HCO3- levels, selected due to the form of lung damage, on computed tomography differed from one another in a statistically significant manner (p < 0.05). Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of a physical examination and applied oxygen therapy. Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of BNP, HCO3 and BE parameters obtained from arterial blood.

Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis

Introduction

Although COVID-19 is not currently a public health emergency, it will affect susceptible individuals in the post-COVID-19 era. Hence, the present study aimed to develop a model for Iranian patients to identify at-risk groups based on past medical history (PMHx) and some other factors affecting the death of patients hospitalized with COVID-19.

Methods

A secondary study was conducted with the existing data of hospitalized COVID-19 adult patients in the hospitals covered by Iran University of Medical Sciences. PMHx was extracted from the registered ICD-10 codes. Stepwise logistic regression was used to predict mortality by PMHx and background covariates such as intensive care unit (ICU) admission. Crude population attributable fraction (PAF) as well as crude and adjusted odds ratio (OR) with 95% confidence interval (CI) were reported.

Results

A total of 8879 patients were selected with 19.68% mortality. Infectious and parasitic diseases' history showed the greatest association (OR = 5.72, 95% CI: 4.20, 7.82), while the greatest PAF was for cardiovascular system diseases (20.46%). According to logistic regression modeling, the largest effect, other than ICU admission and age, was for history of infectious and parasitic diseases (OR = 3.089, 95% CI: 2.13, 4.47). A good performance was achieved (area under curve = 0.875).

Conclusion

Considering the prevalence of underlying diseases, many mortality cases of COVID-19 are attributable to the history of cardiovascular disease. Future studies are needed for policy making regarding reduction of COVID-19 mortality in susceptible groups in the post-COVID-19 era.

Development and validation of a prediction model for mortality in critically ill COVID-19 patients

Background

Early prediction of prognosis may help early treatment measures to reduce mortality in critically ill coronavirus disease (COVID-19) patients. The study aimed to develop a mortality prediction model for critically ill COVID-19 patients.

Methods

This retrospective study analyzed the clinical data of critically ill COVID-19 patients in an intensive care unit between April and June 2022. Propensity matching scores were used to reduce the effect of confounding factors. A predictive model was built using logistic regression analysis and visualized using a nomogram. Calibration and receiver operating characteristic (ROC) curves were used to estimate the accuracy and predictive value of the model. Decision curve analysis (DCA) was used to examine the value of the model for clinical interventions.

Results

In total, 137 critically ill COVID-19 patients were enrolled; 84 survived, and 53 died. Univariate and multivariate logistic regression analyses revealed that aspartate aminotransferase (AST), creatinine, and myoglobin levels were independent prognostic factors. We constructed logistic regression prediction models using the seven least absolute shrinkage and selection operator regression-selected variables (hematocrit, red blood cell distribution width-standard deviation, procalcitonin, AST, creatinine, potassium, and myoglobin; Model 1) and three independent factor variables (Model 2). The calibration curves suggested that the actual predictions of the two models were similar to the ideal predictions. The ROC curve indicated that both models had good predictive power, and Model 1 had better predictive power than Model 2. The DCA results suggested that the model intervention was beneficial to patients and patients benefited more from Model 1 than from Model 2.

Conclusion

The predictive model constructed using characteristic variables screened using LASSO regression can accurately predict the prognosis of critically ill COVID-19 patients. This model can assist clinicians in implementing early interventions. External validation by prospective large-sample studies is required.

Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19

Objective

Our aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19).

Methods

Clinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort.

Result

In total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027-2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253-2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042-1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707-3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003-1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001-1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440-0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641-0.923) and 0.814 (95 % CI: 0.702-0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765-0.962) and 0.875 (95 % CI: 0.741-0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value.

Conclusion

In-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.

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.

Clinical Manifestations and Outcomes of Older Patients with COVID-19: A Comprehensive Review

The consequences of coronavirus disease 2019 (COVID-19) are particularly severe in older adults with a disproportionate number of severe and fatal outcomes. Therefore, this integrative review aimed to provide a comprehensive overview of the clinical characteristics, management approaches, and prognosis of older patients diagnosed with COVID-19. Common clinical presentations in older patients include fever, cough, and dyspnea. Additionally, preexisting comorbidities, especially diabetes and pulmonary and cardiovascular diseases, were frequently observed and associated with adverse outcomes. Management strategies varied, however, early diagnosis, vigilant monitoring, and multidisciplinary care were identified as key factors for enhancing patient outcomes. Nonetheless, the prognosis remains guarded for older patients, with increased rates of hospitalization, mechanical ventilation, and mortality. However, timely therapeutic interventions, especially antiviral and supportive treatments, have demonstrated some efficacy in mitigating the severe consequences in this age group. In conclusion, while older adults remain highly susceptible to severe outcomes from COVID-19, early intervention, rigorous monitoring, and comprehensive care can play a pivotal role in improving their clinical outcomes.

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

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

Comparison of eight prehospital early warning scores in life-threatening acute respiratory distress: a prospective, observational, multicentre, ambulance-based, external validation study

BACKGROUND

A myriad of early warning scores (EWSs) exist, yet there is a need to identify the most clinically valid score to be used in prehospital respiratory assessments to estimate short-term and midterm mortality, intensive-care unit admission, and airway management in life-threatening acute respiratory distress.

METHODS

This is a prospective, observational, multicentre, ambulance-based, external validation study performed in 44 ambulance services and four hospitals across three Spanish provinces (ie, Salamanca, Segovia, and Valladolid). We identified adults (ie, those aged 18 years and older) discharged to the emergency department with suspected acute respiratory distress. The primary outcome was 2-day all-cause in-hospital mortality, for all the patients or according to prehospital respiratory conditions, including dyspnoea, chronic obstructive pulmonary disease (COPD), COVID-19, other infections, and other conditions (asthma exacerbation, haemoptysis, and bronchoaspirations). 30-day mortality, intensive-care unit admission, and invasive and non-invasive mechanical ventilation were secondary outcomes. Eight EWSs, namely, the National Early Warning Score 2, the Modified Rapid Emergency Medicine Score, the Rapid Acute Physiology Score, the Quick Sequential Organ Failure Assessment Score, the CURB-65 Severity Score for Community-Acquired Pneumonia, the BAP-65 Score for Acute Exacerbation of COPD, the Quick COVID-19 Severity Index, and the Modified Sequential Organ Failure Assessment (mSOFA), were explored to determine their predictive validity through calibration, clinical net benefit as determined through decision curve analysis, and discrimination analysis (area under the curve of the receiver operating characteristic [AUROC], compared with Delong's test).

FINDINGS

Between Jan 1, 2020, and Nov 31, 2022, 902 patients were enrolled. The global 2-day mortality rate was 87 (10%); in proportion to various respiratory conditions, the rates were 35 (40%) for dyspnoea, nine (10%) for COPD, 13 (15%) for COVID-19, 28 (32%) for other infections, and two (2%) for others conditions. mSOFA showed the best calibration, a higher net benefit, and the best discrimination (AUROC 0·911, 95% CI 0·86-0·95) for predicting 2-day mortality, and its discrimination was statistically significantly more accurate (p<0·0001) compared with the other scores. The performance of mSOFA for predicting 2-day mortality was higher than the other scores when considering the prehospital respiratory conditions, and was also higher for the secondary outcomes, except for non-invasive mechanical ventilation.

INTERPRETATION

Our results showed that mSOFA outperformed other EWSs. The inclusion of mSOFA in prehospital decision making will entail a quick identification of patients in acute respiratory distress at high risk of deterioration, allowing prioritisation of resources and patient care.

FUNDING

Gerencia Regional de Salud, Public Health System of Castilla y León (GRS Spain).

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Development and validation of a diagnostic nomogram model for predicting monoclonal gammopathy of renal significance

In patients with kidney disease, the presence of monoclonal gammopathy necessitates the exploration of potential causal relationships. Therefore, in this study, we aimed to address this concern by developing a nomogram model for the early diagnosis of monoclonal gammopathy of renal significance (MGRS). Univariate and multivariate logistic regression analyses were employed to identify risk factors for MGRS. Verification and evaluation of the nomogram model's differentiation, calibration, and clinical value were conducted using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. The study encompassed 347 patients who underwent kidney biopsy, among whom 116 patients (33.4%) were diagnosed with MGRS and 231 (66.6%) with monoclonal gammopathy of undetermined significance. Monoclonal Ig-related amyloidosis (n = 86) and membranous nephropathy (n = 86) was the most common renal pathological type in each group. Notably, older age, abnormal serum-free light chain ratio, and the absence of microscopic hematuria were identified as independent prognostic factors for MGRS. The areas under the ROC curves for the training and verification sets were 0.848 and 0.880, respectively. In conclusion, the nomogram model demonstrated high accuracy and clinical applicability for diagnosing MGRS, potentially serving as a valuable tool for noninvasive early MGRS diagnosis.

Prediction models for in-hospital deaths of patients with COVID-19 using electronic healthcare data

OBJECTIVE

Many models for predicting various disease prognoses have achieved high performance without laboratory test results. However, whether laboratory test results can improve performance remains unclear. This study aimed to investigate whether laboratory test results improve the model performance for coronavirus disease 2019 (COVID-19).

METHODS

Prediction models were developed using data from the electronic healthcare record database in Japan. Patients aged ≥18 years hospitalized for COVID-19 after February 11, 2020, were included. Their age, sex, comorbidities, laboratory test results, and number of days from February 11, 2020, were collected. We developed a logistic regression, XGBOOST, random forest, and neural network analysis and compared the performance with and without laboratory test results. The performance of predicting in-hospital death was evaluated using the area under the curve (AUC).

RESULTS

Data from 8,288 hospitalized patients (females, 46.5%) were analyzed. The median patient age was 71 years. A total of 6,630 patients were included in the training dataset, and 312 (4.7%) died. In the logistic regression model, the area under the curve was 0.88 (95% confidence interval [CI] = 0.83-0.93) and 0.75 (95% CI = 0.68-0.81) with and without laboratory test results, respectively. The performance was not fundamentally different between the model types, and the laboratory test results improved the performance in all cases. The variables useful for prediction were blood urea nitrogen, albumin, and lactate dehydrogenase.

CONCLUSIONS

Laboratory test results, such as blood urea nitrogen, albumin, and lactate dehydrogenase levels, along with background information, helped estimate the prognosis of patients hospitalized for COVID-19.

Head-to-head comparison of six warning scores to predict mortality and clinical impairment in COVID-19 patients in emergency department

The aim was to evaluate the ability of six risk scores (4C, CURB65, SEIMC, mCHOSEN, QuickCSI, and NEWS2) to predict the outcome of patients with COVID-19 during the sixth pandemic wave in Spain. A retrospective observational study was performed to review the electronic medical records in patients ≥ 18 years of age who consulted consecutively in an emergency department with COVID-19 diagnosis throughout 2 months during the sixth pandemic wave. Clinical-epidemiological variables, comorbidities, and their respective outcomes, such as 30-day in-hospital mortality and clinical deterioration risk (a combined outcome considering: mechanical ventilation, intensive care unit admission, and/or 30-day in-hospital mortality), were calculated. The area under the curve for each risk score was calculated, and the resulting curves were compared by the Delong test, concluding with a decision curve analysis. A total of 626 patients (median age 79 years; 49.8% female) fulfilled the inclusion criteria. Two hundred and ninety-three patients (46.8%) had two or more comorbidities. Clinical deterioration risk criteria were present in 10.1% (63 cases), with a 30-day in-hospital mortality rate of 6.2% (39 cases). Comparison of the results showed that score 4C presented the best results for both outcome variables, with areas under the curve for mortality and clinical deterioration risk of 0.931 (95% CI 0.904-0.957) and 0.871 (95% CI 0.833-0.910) (both p < 0.001). The 4C Mortality Score proved to be the best score for predicting mortality or clinical deterioration risk among patients with COVID-19 attended in the emergency department in the following 30 days.

The Impact of Antibiotic Prophylaxis on a Retrospective Cohort of Hospitalized Patients with COVID-19 Treated with a Combination of Steroids and Tocilizumab

OBJECTIVES

In the context of COVID-19, patients with a severe or critical illness may be more susceptible to developing secondary bacterial infections. This study aims to investigate the relationship between the use of prophylactic antibiotic therapy and the occurrence of bacterial or fungal isolates following the administration of tocilizumab in hospitalized COVID-19 patients who had previously received steroids during the first and second waves of the pandemic in Spain.

METHODS

This retrospective observational study included 70 patients hospitalized with COVID-19 who received tocilizumab and steroids between January and December 2020. Data on demographics, comorbidities, laboratory tests, microbiologic results, treatment, and outcomes were collected from electronic health records. The patients were divided into two groups based on the use of antibiotic prophylaxis, and the incidence of bacterial and fungal colonizations/infections was analyzed.

RESULTS

Among the included patients, 45 patients received antibiotic prophylaxis. No significant clinical differences were observed between the patients based on prophylaxis use regarding the number of clinically diagnosed infections, ICU admissions, or mortality rates. However, the patients who received antibiotic prophylaxis showed a higher incidence of colonization by multidrug-resistant bacteria compared to that of the subgroup that did not receive prophylaxis. The most commonly isolated microorganisms were Candida albicans, Enterococcus faecalis, Staphylococcus aureus, and Staphylococcus epidermidis. Conclusions: In this cohort of hospitalized COVID-19 patients treated with tocilizumab and steroids, the use of antibiotic prophylaxis did not reduce the incidence of secondary bacterial infections. However, it was associated with an increased incidence of colonization by multidrug-resistant bacteria.

Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

External validation of the Oldham composite Covid-19 associated mortality model (OCCAM), a prognostic model for death in patients hospitalised with Covid-19

OBJECTIVE

External validation of the Oldham Composite Covid-19 associated Mortality Model (OCCAM), a prognostic model for Covid-19 mortality in hospitalised patients comprised of age, history of hypertension, current or previous malignancy, admission platelet count < 150 × 103/µL, admission CRP ≥ 100 µg/mL, acute kidney injury (AKI), and radiographic evidence of > 50% total lung field infiltrates.

PATIENTS AND METHODS

Retrospective study assessing discrimination (c-statistic) and calibration of OCCAM for death in hospital or within 30 days of discharge. 300 adults admitted to six district general and teaching hospitals in North West England for treatment of Covid-19 between September 2020 and February 2021 were included.

RESULTS

Two hundred and ninety-seven patients were included in the validation cohort analysis, with a mortality rate of 32.8%. The c-statistic was 0.794 (95% confidence interval 0.742-0.847) vs. 0.805 (95% confidence interval 0.766 - 0.844) in the development cohort. Visual inspection of calibration plots demonstrate excellent calibration across risk groups, with a calibration slope for the external validation cohort of 0.963.

CONCLUSION

The OCCAM model is an effective prognostic tool that can be utilised at the time of initial patient assessment to aid decisions around admission and discharge, use of therapeutics, and shared decision-making with patients. Clinicians should remain aware of the need for ongoing validation of all Covid-19 prognostic models in light of changes in host immunity and emerging variants.

Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

The mortality of COVID-19 patients has left the world devastated. Many scoring systems have been developed to predict the mortality of COVID-19 patients, but several scoring components cannot be carried out in limited health facilities. Herein, the authors attempted to create a new and easy scoring system involving mean arterial pressure (MAP), PF Ratio, or SF ratio-respiration rate (SF Ratio-R), and lymphocyte absolute, which were abbreviated as MPL or MSLR functioning, as a predictive scoring system for mortality within 30 days for COVID-19 patients. Of 132 patients with COVID-19 hospitalized between March and November 2021, we followed up on 96 patients. We present bivariate and multivariate analyses as well as the area under the curve (AUC) and Kaplan-Meier charts. From 96 patients, we obtained an MPL score of 3 points: MAP < 75 mmHg, PF Ratio < 200, and lymphocyte absolute < 1500/µL, whereas the MSLR score was 6 points: MAP < 75 mmHg, SF Ratio < 200, lymphocyte absolute < 1500/µL, and respiration rate 24/min. The MPL cut-off point is 2, while the MSLR is 4. MPL and MSLR have the same sensitivity (79.1%) and specificity (75.5%). The AUC value of MPL vs. MSLR was 0.802 vs. 0.807. The MPL ≥ 2 and MSLR ≥ 4 revealed similar predictions for survival within 30 days (p < 0.05). Conclusion: MPL and MSLR scores are potential predictors of mortality in COVID-19 patients within 30 days in a resource-limited country.

Ivermectin Effect on In-Hospital Mortality and Need for Respiratory Support in COVID-19 Pneumonia: Propensity Score-Matched Retrospective Study

INTRODUCTION

There is negligible evidence on the efficacy of ivermectin for treating COVID-19 pneumonia. This study aimed to assess the efficacy of ivermectin for pre-emptively treating Strongyloides stercoralis hyperinfection syndrome in order to reduce mortality and the need for respiratory support in patients hospitalized for COVID-19.

METHODS

This single-center, observational, retrospective study included patients admitted with COVID-19 pneumonia at Hospital Vega Baja from 23 February 2020 to 14 March 2021. Because strongyloidiasis is endemic to our area, medical criteria support empiric administration of a single, 200 μg/kg dose of ivermectin to prevent Strongyloides hyperinfection syndrome. The outcome was a composite of all-cause in-hospital mortality and the need for respiratory support.

RESULTS

Of 1167 patients in the cohort, 96 received ivermectin. After propensity score matching, we included 192 patients. The composite outcome of in-hospital mortality or need for respiratory support occurred in 41.7% of the control group (40/96) and 34.4% (33/96) of the ivermectin group. Ivermectin was not associated with the outcome of interest (adjusted odds ratio [aOR] 0.77, 95% confidence interval [CI] 0.35, 1.69; p = 0.52). The factors independently associated with this endpoint were oxygen saturation (aOR 0.78, 95% CI 0.68, 0.89, p < 0.001) and C-reactive protein at admission (aOR: 1.09, 95% CI 1.03, 1.16, p < 0.001).

CONCLUSIONS

In hospitalized patients with COVID-19 pneumonia, ivermectin at a single dose for pre-emptively treating Strongyloides stercoralis is not effective in reducing mortality or the need for respiratory support measures.

Time from symptoms onset to remdesivir is associated with the risk of ICU admission: a multicentric analyses

BACKGROUND

Shorter duration of symptoms before remdesivir has been associated with better outcomes. Our goal was to evaluate variables associated with the need of ICU admission in a cohort of hospitalized patients for COVID-19 under remdesivir including the period from symptoms onset to remdesivir.

METHODS

We conducted a retrospective multicentric study analysing all patients admitted with COVID-19 in 9 Spanish hospitals who received treatment with remdesivir in October 2020. The main outcome was the need of ICU admission after 24 h of the first dose of remdesivir.

RESULTS

In our cohort of 497 patients, the median of days from symptom onset to remdesivir was 5 days, and 70 of them (14.1%) were later admitted into ICU. The clinical outcomes associated with ICU admission were days from symptoms onset (5 vs. 6; p = 0.023), clinical signs of severe disease (respiratory rate, neutrophil count, ferritin levels and very-high mortality rate in SEIMC-Score) and the use of corticosteroids and anti-inflammatory drugs before ICU. The only variable significatively associated with risk reduction in the Cox-regression analyses was ≤ 5 days from symptoms onset to RDV (HR: 0.54, CI95%: 0.31-0.92; p = 0.024).

CONCLUSION

For patients admitted to the hospital with COVID-19, the prescription of remdesivir within 5 days from symptoms onset diminishes the need of ICU admission.

Risk factors for clinically significant diffuse parenchymal lung abnormalities persisting after severe COVID-19 pneumonia

Background & objectives

The risk factors for clinically significant diffuse parenchymal lung abnormalities (CS-DPLA) persisting after severe coronavirus disease 2019 (COVID-19) pneumonia remain unclear. The present study was conducted to assess whether COVID-19 severity and other parameters are associated with CS-DPLA.

Methods

The study participants included patients who recovered after acute severe COVID-19 and presented with CS-DPLA at two or six month follow up and control group (without CS-DPLA). Adults volunteers without any acute illness, chronic respiratory illness and without a history of severe COVID-19 were included as healthy controls for the biomarker study. The CS-DPLA was identified as a multidimensional entity involving clinical, radiological and physiological pulmonary abnormalities. The primary exposure was the neutrophil-lymphocyte ratio (NLR). Recorded confounders included age, sex, peak lactate dehydrogenase (LDH), advanced respiratory support (ARS), length of hospital stay (LOS) and others; associations were analyzed using logistic regression. The baseline serum levels of surfactant protein D, cancer antigen 15-3 and transforming growth factor-β (TGF-β) were also compared among cases, controls and healthy volunteers.

Results

We identified 91/160 (56.9%) and 42/144 (29.2%) participants with CS-DPLA at two and six months, respectively. Univariate analyses revealed associations of NLR, peak LDH, ARS and LOS with CS-DPLA at two months and of NLR and LOS at six months. The NLR was not independently associated with CS-DPLA at either visit. Only LOS independently predicted CS-DPLA at two months [adjusted odds ratios (aOR) (95% confidence interval [CI]), 1.16 (1.07-1.25); P<0.001] and six months [aOR (95% CI) and 1.07 (1.01-1.12); P=0.01]. Participants with CS-DPLA at six months had higher baseline serum TGF-β levels than healthy volunteers.

Interpretation and conclusions

Longer hospital stay was observed to be the only independent predictor of CS-DPLA six months after severe COVID-19. Serum TGF-β should be evaluated further as a biomarker.

The clinical practice of palliative sedation in patients dying from COVID-19: a retrospective chart review

BACKGROUND

Clinical experts experienced challenges in the practice of palliative sedation (PS) during the COVID-19 pandemic. Rapid deterioration in patients' situation was observed while the indications for starting PS seemed to differ compared to other terminal patients. It is unclear to which extent clinical trajectories of PS differ for these COVID patients compared to regular clinical practice of PS.

OBJECTIVES

To describe the clinical practice of PS in patients with COVID versus non-COVID patients.

METHODS

A retrospective analysis of data from a Dutch tertiary medical centre was performed. Charts of adult patients who died with PS during hospitalisation between March '20 and January '21 were included.

RESULTS

During the study period, 73 patients received PS and of those 25 (34%) had a COVID infection. Refractory dyspnoea was reported as primary indication for starting PS in 84% of patients with COVID compared to 33% in the other group (p < 0.001). Median duration of PS was significantly shorter in the COVID group (5.8 vs. 17.1 h, p < 0.01). No differences were found for starting dosages, but median hourly dose of midazolam was higher in the COVID group (4.2 mg/hr vs. 2.4 mg/hr, p < 0.001). Time interval between start PS and first medication adjustments seemed to be shorter in COVID patients (1.5 vs. 2.9 h, p = 0.08).

CONCLUSION

PS in COVID patients is characterized by rapid clinical deterioration in all phases of the trajectory. What is manifested by earlier dose adjustments and higher hourly doses of midazolam. Timely evaluation of efficacy is recommended in those patients.

Validation of the COVID-19-12O score for predicting readmissions/revisits in patients with SARS-CoV-2 pneumonia discharged from the emergency department

OBJECTIVE

The COVID-19-12O-score has been validated to determine the risk of respiratory failure in patients hospitalized for COVID-19. Our study aims to assess whether the score is effective in patients with SARS-CoV-2 pneumonia discharged from a hospital emergency department (HED) to predict readmission and revisit.

METHOD

Retrospective cohort of patients with SARS-CoV-2 pneumonia discharged consecutively from an HUS of a tertiary hospital, from January 7 to February 17, 2021, where we applied the COVID-19-12O -score, with a cut-off point of 9 points to define the risk of admission or revisit. The primary outcome variable was revisit with or without hospital readmission after 30 days of discharge from HUS.

RESULTS

We included 77 patients, with a median age of 59 years, 63.6% men and Charlson index of 2. 9.1% had an emergency room revisit and 15.3% had a deferred hospital admission. The relative risk (RR) for emergency journal was 0.46 (0.04-4.62, 95% CI, p=0.452), and the RR for hospital readmission was 6.88 (1.20-39.49, 95% CI, p<0.005).

CONCLUSIONS

The COVID-19-12O -score is effective in determining the risk of hospital readmission in patients discharged from HED with SARS-CoV-2 pneumonia, but is not useful for assessing the risk of revisit.

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).

Smart healthcare: A prospective future medical approach for COVID-19

COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2, (2) patient stratification with distinct short-term outcomes (eg, mild or severe diseases) and long-term outcomes (eg, long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep learning/machine learning capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things, which encompasses wearable devices, smartphone apps, internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.

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.

Protective role of vitamin D status against COVID-19: a mini-review

An outbreak of pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is called COVID-19 and has led to a pandemic worldwide. It is reasonable to investigate and control factors affecting disease severity and mortality. The relation between vitamin D and viral pneumonia has been previously reported. Vitamin D deficiency is common and may increase hospital admission and mortality rate in patients with COVID-19. This mini-review examines the pathways that show the association between vitamin D and COVID-19. On the other hand, it deals with the available evidence related to the relationship between vitamin D deficiency and the effect of vitamin D supplementation on the prevalence, severity, and mortality of COVID-19. Also, we described the pathophysiology of the organs' involvement in COVID-19 and the effect of vitamin D on these outcomes. Vitamin D strengthens the innate and adaptive immune system, modulates immune responses, prevents lung and cardiovascular system damage, and reduces thrombotic events. Vitamin D exerts these effects in several pathways. Vitamin D prevents virus entry and replication by maintaining the integrity of the body's physical barrier. Vitamin D reduces the damage to vital organs and thrombotic events by increasing the level of Angiotensin-converting enzyme 2 (ACE2), nitric oxide, and antioxidants or by reducing inflammatory cytokines and free radicals. Sufficient vitamin D may be reduced morbidity and mortality due to COVID-19. However, this issue should be investigated and confirmed by further research in the future.

[Validation of the COVID-19-12O scale for predicting readmissions/revisits in patients with SARS-CoV-2 pneumonia discharged from the emergency department]

OBJECTIVE

The COVID-19-12O scale has been validated for determining the risk of respiratory failure in patients hospitalized due to COVID-19. This study aims to assess whether the scale is effective for predicting readmissions and revisits in patients with SARS-CoV-2 pneumonia discharged from a hospital emergency department (HED).

METHOD

This work is a retrospective cohort of consecutive patients with SARS-CoV-2 pneumonia discharged from the HED of a tertiary hospital from January 7 to February 17, 2021. The COVID-19-12O scale with a cut-off point of nine points was used to define the risk of admissions or revisits. The primary outcome variable was a revisit with or without hospital readmission after 30 days of discharge from the HED.

RESULTS

Seventy-seven patients were included. The median age was 59 years, 63.6% were men, and the Charlson Comorbidity Index was 2. A total of 9.1% had an emergency room revisit and 15.3% had a deferred hospital admission. The relative risk (RR) for an HED revisit was 0.46 (0.04-4.62, 95% CI p=0.452) and the RR for hospital readmission was 6.88 (1.20-39.49, 95% CI, p<0.005).

CONCLUSIONS

The COVID-19-12O scale is effective in determining the risk of hospital readmission in patients discharged from an HED with SARS-CoV-2 pneumonia, but is not useful for assessing the risk of revisit.

Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.

Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infection

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has prevailed globally since November 2021. The extremely high transmissibility and occult manifestations were notable, but the severity and mortality associated with the Omicron variant and subvariants cannot be ignored, especially for immunocompromised populations. However, no prognostic model for specially predicting the severity of the Omicron variant infection is available yet. In this study, we aim to develop and validate a prognostic model based on immune variables to early recognize potentially severe cases of Omicron variant-infected patients.

Methods

This was a single-center prognostic study involving patients with SARS-CoV-2 Omicron variant infection. Eligible patients were randomly divided into the training and validation cohorts. Variables were collected immediately after admission. Candidate variables were selected by three variable-selecting methods and were used to construct Cox regression as the prognostic model. Discrimination, calibration, and net benefit of the model were evaluated in both training and validation cohorts.

Results

Six hundred eighty-nine of the involved 2,645 patients were eligible, consisting of 630 non-ICU cases and 59 ICU cases. Six predictors were finally selected to establish the prognostic model: age, neutrophils, lymphocytes, procalcitonin, IL-2, and IL-10. For discrimination, concordance indexes in the training and validation cohorts were 0.822 (95% CI: 0.748-0.896) and 0.853 (95% CI: 0.769-0.942). For calibration, predicted probabilities and observed proportions displayed high agreements. In the 21-day decision curve analysis, the threshold probability ranges with positive net benefit were 0~1 and nearly 0~0.75 in the training and validation cohorts, correspondingly.

Conclusions

This model had satisfactory high discrimination, calibration, and net benefit. It can be used to early recognize potentially severe cases of Omicron variant-infected patients so that they can be treated timely and rationally to reduce the severity and mortality of Omicron variant infection.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Predicting COVID-19 prognosis in the ICU remained challenging: external validation in a multinational regional cohort

OBJECTIVES

Many prediction models for coronavirus disease 2019 (COVID-19) have been developed. External validation is mandatory before implementation in the intensive care unit (ICU). We selected and validated prognostic models in the Euregio Intensive Care COVID (EICC) cohort.

STUDY DESIGN AND SETTING

In this multinational cohort study, routine data from COVID-19 patients admitted to ICUs within the Euregio Meuse-Rhine were collected from March to August 2020. COVID-19 models were selected based on model type, predictors, outcomes, and reporting. Furthermore, general ICU scores were assessed. Discrimination was assessed by area under the receiver operating characteristic curves (AUCs) and calibration by calibration-in-the-large and calibration plots. A random-effects meta-analysis was used to pool results.

RESULTS

551 patients were admitted. Mean age was 65.4 ± 11.2 years, 29% were female, and ICU mortality was 36%. Nine out of 238 published models were externally validated. Pooled AUCs were between 0.53 and 0.70 and calibration-in-the-large between -9% and 6%. Calibration plots showed generally poor but, for the 4C Mortality score and Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC) score, moderate calibration.

CONCLUSION

Of the nine prognostic models that were externally validated in the EICC cohort, only two showed reasonable discrimination and moderate calibration. For future pandemics, better models based on routine data are needed to support admission decision-making.

Associations between muscle strength, dyspnea and quality of life in post-COVID-19 patients

OBJECTIVE

Patients with severe coronavirus disease 2019 (COVID-19) develop high muscle weakness. The objective of this study was to analyze the physical fitness of post-COVID-19 patients and its relationship with dyspnea and health-related quality of life (HrQoL).

METHODS

This observational, retrospective, cross-sectional study was conducted between October and November 2021 in the Universidad Europea de Madrid (Spain), with 32 post-COVID-19 patients aged 63.2 (14.1) years. Muscle strength, aerobic capacity, maximal respiratory mouth pressures, dyspnea, and HrQoL were analyzed 6-12 months after discharge for COVID-19. To analyze the relationship between continuous variables, Spearman's correlation test and Pearson's correlation test were performed.

RESULTS

The participants had a mean handgrip strength of 22.1 (9.0) kg and very poor HrQoL. Negative moderate correlations were found between handgrip strength and length of hospital and intensive care unit stay (r=-0.37; p=0.002). In addition, muscle strength was negatively correlated with dyspnea (r=-0.37; p=0.008) and HrQoL, and moderate-large negative correlations were found between dyspnea and HrQoL.

CONCLUSION

Higher handgrip strength was associated with lower COVID-19 severity and less sequelae. Therefore, either the patients with severe COVID-19 suffered greater muscle breakdown, or higher muscle strength acted as a mitigating factor for the disease. It is suggested that post-COVID-19 rehabilitation programs should focus on increasing muscle strength. Also, adequate physical fitness could mitigate the physical and mental post-COVID-19 sequelae.

Construction and Validation of Mortality Risk Nomograph Model for Severe/Critical Patients with COVID-19

Objective: A nomograph model of mortality risk for patients with coronavirus disease 2019 (COVID-19) was established and validated. Methods: We collected the clinical medical records of patients with severe/critical COVID-19 admitted to the eastern campus of Renmin Hospital of Wuhan University from January 2020 to May 2020 and to the north campus of Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, from April 2022 to June 2022. We assigned 254 patients to the former group, which served as the training set, and 113 patients were assigned to the latter group, which served as the validation set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the mortality risk prediction model. Results: The nomogram model was constructed with four risk factors for patient mortality following severe/critical COVID-19 (≥3 basic diseases, APACHE II score, urea nitrogen (Urea), and lactic acid (Lac)) and two protective factors (percentage of lymphocyte (L%) and neutrophil-to-platelets ratio (NPR)). The area under the curve (AUC) of the training set was 0.880 (95% confidence interval (95%CI), 0.837~0.923) and the AUC of the validation set was 0.814 (95%CI, 0.705~0.923). The decision curve analysis (DCA) showed that the nomogram model had high clinical value. Conclusion: The nomogram model for predicting the death risk of patients with severe/critical COVID-19 showed good prediction performance, and may be helpful in making appropriate clinical decisions for high-risk patients.

Factors which impact the length of hospitalisation and death rate of COVID-19 patients based on initial triage using capillary blood gas tests: a single centre study

An important element in the effective treatment of patients infected with the SARS-CoV-2 virus during the pandemic is an effective early triage to determine patient allocation and in-patient therapy. This paper assesses the prognostic value of capillary blood gas tests in predicting extended hospitalisation and death due to COVID-19. This retrospective statistical research is based on a group of 200 patients, hospitalised from 15 October 2020 to 08 March 2021. The study utilised the treatment documentation of these patients hospitalised due to COVID-19 at the Pulmonology and Thoracic Surgery Centre in Bystra (Southern Poland) during this period. The hospital has 50 beds with access to oxygen for COVID-19 patients and a five-bed intensive care unit. On the basis of the obtained results, conclusions were drawn that the need for early oxygen therapy with an oxygen mask and low pH values in capillary blood are significant risk factors for prolonging hospitalisation due to COVID-19. Age, the need for early oxygen mask therapy and low oxygen saturation are important risk factors for death from COVID-19. Capillary blood gas analysis is a simple and effective method of early in-patient segregation of COVID-19 patients.

In-hospital mortality prediction using frailty scale and severity score in elderly patients with severe COVID-19

Background

Elderly patients with coronavirus disease 2019 (COVID-19) have a high disease severity and mortality. However, the use of the frailty scale and severity score to predict in-hospital mortality in the elderly is not well established. Therefore, in this study, we investigated the use of these scores in COVID-19 cases in the elderly.

Methods

This multicenter retrospective study included severe COVID-19 patients admitted to seven hospitals in Korea from February 2020 to February 2021. We evaluated patients’ Acute Physiology and Chronic Health Evaluation (APACHE) II score; confusion, urea nitrogen, respiratory rate, blood pressure, 65 years of age and older (CURB-65) score; modified early warning score (MEWS); Sequential Organ Failure Assessment (SOFA) score; clinical frailty scale (CFS) score; and Charlson comorbidity index (CCI). We evaluated the predictive value using receiver operating characteristic (ROC) curve analysis.

Results

The study included 318 elderly patients with severe COVID-19 of whom 237 (74.5%) were survivors and 81 (25.5%) were non-survivors. The non-survivor group was older and had more comorbidities than the survivor group. The CFS, CCI, APACHE II, SOFA, CURB-65, and MEWS scores were higher in the non-survivor group than in the survivor group. When analyzed using the ROC curve, SOFA score showed the best performance in predicting the prognosis of elderly patients (area under the curve=0.766, P<0.001). CFS and SOFA scores were associated with in-hospital mortality in the multivariate analysis.

Conclusions

The SOFA score is an efficient tool for assessing in-hospital mortality in elderly patients with severe COVID-19.

COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation

Background

An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions.

Research objective

To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to acute physiology and chronic health evaluation (APACHE IVa) and sequential organ failure assessment (SOFA).

Methods

A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. Area under the receiver operating curve (AUROC) was calculated for C-TIME, APACHE IVa and SOFA.

Results

The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO₂/FiO₂ ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72–0.79), vs 0.67 (0.64–0.71) and 0.59 (0.55–0.62) for APACHE and SOFA, respectively (Chi² P<0.0001).

Conclusions

C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.

Overhydration Assessed Using Bioelectrical Impedance Vector Analysis Adversely Affects 90-Day Clinical Outcome among SARS-CoV2 Patients: A New Approach

Background: COVID-19 has taken on pandemic proportions with growing interest in prognostic factors. Overhydration is a risk factor for mortality in several medical conditions with its role in COVID-19, assessed with bioelectrical impedance (BI), gaining research interest. COVID-19 affects hydration status. The aim was to determine the hydration predictive role on 90 d survival COVID-19 and to compare BI assessments with traditional measures of hydration. Methods: We studied 127 consecutive COVID-19 patients. Hydration status was estimated using a 50 kHz phase-sensitive BI and estimated, compared with clinical scores and laboratory markers to predict mortality. Results: Non-surviving COVID-19 patients had significantly higher hydration 85.2% (76.9−89.3) vs. 73.7% (73.2−82.1) and extracellular water/total body water (ECW/TBW) 0.67 (0.59−0.75) vs. 0.54 (0.48−0.61) (p = 0.001, respectively), compared to surviving. Patients in the highest hydration tertile had increased mortality (p = 0.012), Intensive Care Unit (ICU) admission (p = 0.027), COVID-19 SEIMC score (p = 0.003), and inflammation biomarkers [CRP/prealbumin (p = 0.011)]. Multivariate analysis revealed that hydration status was associated with increased mortality. HR was 2.967 (95%CI, 1.459−6.032, p < 0.001) for hydration and 2.528 (95%CI, 1.664−3.843, p < 0.001) for ECW/TBW, which were significantly greater than traditional measures: CRP/prealbumin 3.057(95%CI, 0.906−10.308, p = 0.072) or BUN/creatinine 1.861 (95%CI, 1.375−2.520, p < 0.001). Hydration > 76.15% or ECW/TBW > 0.58 were the cut-off values predicting COVID-19 mortality with 81.3% and 93.8% sensitivity and 64 and 67.6% specificity, respectively. Hydration status offers a sensitive and specific prognostic test at admission, compared to established poor prognosis parameters. Conclusions and Relevance: Overhydration, indicated as high hydration (>76.15%) and ECW/TBW (>0.58), were significant predictors of COVID-19 mortality. These findings suggest that hydration evaluation with 50 kHz phase-sensitive BI measurements should be routinely included in the clinical assessment of COVID-19 patients at hospital admission, to identify increased mortality risk patients and assist medical care.

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.

Caracterización y factores pronóstico de mortalidad en pacientes ingresados en UCI por COVID-19 en un hospital público de referencia en Bogotá, Colombia

Introducción

Objetivo

Materiales y métodos

Resultados

Conclusión

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network

OBJECTIVES

Estimating mortality risk in hospitalised SARS-CoV-2+ patients may help with choosing level of care and discussions with patients. The Coronavirus Clinical Characterisation Consortium Mortality Score (4C Score) is a promising COVID-19 mortality risk model. We examined the association of risk factors with 30-day mortality in hospitalised, full-code SARS-CoV-2+ patients and investigated the discrimination and calibration of the 4C Score. This was a retrospective cohort study of SARS-CoV-2+ hospitalised patients within the RECOVER (REgistry of suspected COVID-19 in EmeRgency care) network.

SETTING

99 emergency departments (EDs) across the USA.

PARTICIPANTS

Patients ≥18 years old, positive for SARS-CoV-2 in the ED, and hospitalised.

PRIMARY OUTCOME

Death within 30 days of the index visit. We performed logistic regression analysis, reporting multivariable risk ratios (MVRRs) and calculated the area under the ROC curve (AUROC) and mean prediction error for the original 4C Score and after dropping the C reactive protein (CRP) component.

RESULTS

Of 6802 hospitalised patients with COVID-19, 1149 (16.9%) died within 30 days. The 30-day mortality was increased with age 80+ years (MVRR=5.79, 95% CI 4.23 to 7.34); male sex (MVRR=1.17, 1.05 to 1.28); and nursing home/assisted living facility residence (MVRR=1.29, 1.1 to 1.48). The 4C Score had comparable discrimination in the RECOVER dataset compared with the original 4C validation dataset (AUROC: RECOVER 0.786 (95% CI 0.773 to 0.799), 4C validation 0.763 (95% CI 0.757 to 0.769). Score-specific mortalities in our sample were lower than in the 4C validation sample (mean prediction error 6.0%). Dropping the CRP component from the 4C Score did not substantially affect discrimination and 4C risk estimates were now close (mean prediction error 0.7%).

CONCLUSIONS

We independently validated 4C Score as predicting risk of 30-day mortality in hospitalised SARS-CoV-2+ patients. We recommend dropping the CRP component of the score and using our recalibrated mortality risk estimates.

Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients

This analysis, using data from the Brazilian kidney transplant (KT) COVID-19 study, seeks to develop a prediction score to assist in COVID-19 risk stratification in KT recipients. In this study, 1379 patients (35 sites) were enrolled, and a machine learning approach was used to fit models in a derivation cohort. A reduced Elastic Net model was selected, and the accuracy to predict the 28-day fatality after the COVID-19 diagnosis, assessed by the area under the ROC curve (AUC-ROC), was confirmed in a validation cohort. The better calibration values were used to build the applicable ImAgeS score. The 28-day fatality rate was 17% (n = 235), which was associated with increasing age, hypertension and cardiovascular disease, higher body mass index, dyspnea, and use of mycophenolate acid or azathioprine. Higher kidney graft function, longer time of symptoms until COVID-19 diagnosis, presence of anosmia or coryza, and use of mTOR inhibitor were associated with reduced risk of death. The coefficients of the best model were used to build the predictive score, which achieved an AUC-ROC of 0.767 (95% CI 0.698-0.834) in the validation cohort. In conclusion, the easily applicable predictive model could assist health care practitioners in identifying non-hospitalized kidney transplant patients that may require more intensive monitoring. Trial registration: ClinicalTrials.gov NCT04494776.

Impact of early interferon-β treatment on the prognosis of patients with COVID-19 in the first wave: A post hoc analysis from a multicenter cohort

BACKGROUND

Interferon-β is an attractive drug for repurposing and use in the treatment of COVID-19, based on its in vitro antiviral activity and the encouraging results from clinical trials. The aim of this study was to analyze the impact of early interferon-β treatment in patients admitted with COVID-19 during the first wave of the pandemic.

METHODS

This post hoc analysis of a COVID-19@Spain multicenter cohort included 3808 consecutive adult patients hospitalized with COVID-19 from 1 January to 17 March 2020. The primary endpoint was 30-day all-cause mortality, and the main exposure of interest was subcutaneous administration of interferon-β, defined as early if started ≤ 3 days from admission. Multivariate logistic and Cox regression analyses were conducted to identify the associations of different variables with receiving early interferon-β therapy and to assess its impact on 30-day mortality. A propensity score was calculated and used to both control for confounders and perform a matched cohort analysis.

RESULTS

Overall, 683 patients (17.9%) received early interferon-β therapy. These patients were more severely ill. Adjusted HR for mortality with early interferon-β was 1.03 (95% CI, 0.82-1.30) in the overall cohort, 0.96 (0.82-1.13) in the PS-matched subcohort, and 0.89 (0.60-1.32) when interferon-β treatment was analyzed as a time-dependent variable.

CONCLUSIONS

In this multicenter cohort of admitted COVID-19 patients, receiving early interferon-β therapy after hospital admission did not show an association with lower mortality. Whether interferon-β might be useful in the earlier stages of the disease or specific subgroups of patients requires further research.

The CCEDRRN COVID-19 Mortality Score to predict death among nonpalliative patients with COVID-19 presenting to emergency departments: a derivation and validation study

BACKGROUND

Predicting mortality from COVID-19 using information available when patients present to the emergency department can inform goals-of-care decisions and assist with ethical allocation of critical care resources. The study objective was to develop and validate a clinical score to predict emergency department and in-hospital mortality among consecutive nonpalliative patients with COVID-19; in this study, we define palliative patients as those who do not want resuscitative measures, such as intubation, intensive care unit care or cardiopulmonary resuscitation.

METHODS

This derivation and validation study used observational cohort data recruited from 46 hospitals in 8 Canadian provinces participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included adult (age ≥ 18 yr) nonpalliative patients with confirmed COVID-19 who presented to the emergency department of a participating site between Mar. 1, 2020, and Jan. 31, 2021. We randomly assigned hospitals to derivation or validation, and prespecified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort and examined its performance in predicting emergency department and in-hospital mortality in a validation cohort.

RESULTS

Of 8761 eligible patients, 618 (7.0%) died. The CCEDRRN COVID-19 Mortality Score included age, sex, type of residence, arrival mode, chest pain, severe liver disease, respiratory rate and level of respiratory support. The area under the curve was 0.92 (95% confidence interval [CI] 0.90-0.93) in derivation and 0.92 (95% CI 0.90-0.93) in validation. The score had excellent calibration. These results suggest that scores of 6 or less would categorize patients as being at low risk for in-hospital death, with a negative predictive value of 99.9%. Patients in the low-risk group had an in-hospital mortality rate of 0.1%. Patients with a score of 15 or higher had an observed mortality rate of 81.0%.

INTERPRETATION

The CCEDRRN COVID-19 Mortality Score is a simple score that can be used for level-of-care discussions with patients and in situations of critical care resource constraints to accurately predict death using variables available on emergency department arrival. The score was derived and validated mostly in unvaccinated patients, and before variants of concern were circulating widely and newer treatment regimens implemented in Canada.

STUDY REGISTRATION

ClinicalTrials.gov, no. NCT04702945.

SARS COV-2 Pandemic. Chilean Air Force experience in the air transport of critical ill patients. The first 100 cases

Objective

Methods

Results

Conclusions

Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements

BACKGROUND

In the event of a sudden shortage of medical resources, a rapid, simple, and accurate prediction model is essential for the 30-day mortality rate of patients with COVID-19.

METHODS

This retrospective study compared the characteristics of the survivals and non-survivals of 278 patients with COVID-19. Logistic regression analysis was performed to obtain the "COVID-19 death risk score" (CDRS) model. Using the area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow goodness-of-fit test, discrimination and calibration were assessed. Internal validation was conducted using a regular bootstrap method.

RESULTS

A total of 63 (22.66%) of 278 included patients died. The logistic regression analysis revealed that high-sensitivity C-reactive protein (hsCRP; odds ratio [OR]=1.018), D-dimer (OR=1.101), and respiratory rate (RR; OR=1.185) were independently associated with 30-day mortality. CDRS was calculated as follows: CDRS=-10.245+(0.022×hsCRP)+(0.172×D-dimer)+(0.203×RR). CDRS had the same predictive effect as the sequential organ failure assessment (SOFA) and "confusion, uremia, respiratory rate, blood pressure, and age over 65 years" (CURB-65) scores, with AUROCs of 0.984 for CDRS, 0.975 for SOFA, and 0.971 for CURB-65, respectively. And CDRS showed good calibration. The AUROC through internal validations was 0.980 (95% confidence interval [CI]: 0.965-0.995). Regarding the clinical value, the decision curve analysis of CDRS showed a net value similar to that of CURB-65 in this cohort.

CONCLUSION

CDRS is a novel, efficient and accurate prediction model for the early identification of COVID-19 patients with poor outcomes. Although it is not as advanced as the other models, CDRS had a similar performance to that of SOFA and CURB-65.

Mid-Regional Pro-Adrenomedullin, Methemoglobin and Carboxyhemoglobin as Prognosis Biomarkers in Critically Ill Patients with COVID-19: An Observational Prospective Study

Mid-regional pro-adrenomedullin (MR-proADM), methemoglobin (MetHb), and carboxyhemoglobin (COHb) levels have been associated with sepsis. In this study, we assessed the role of this potential biomarkers in critically ill COVID-19 patients. Outcomes were mortality and a combined event (mortality, venous or arterial thrombosis, and orotracheal intubation (OTI)) during a 30-day follow-up. A total of 95 consecutive patients were included, 51.6% required OTI, 12.6% patients died, 8.4% developed VTE, and 3.1% developed arterial thrombosis. MetHb and COHb levels were not associated with mortality nor combined event. Higher MR-proADM levels were found in patients with mortality (median of 1.21 [interquartile range-IQR-0.84;2.33] nmol/L vs. 0.76 [IQR 0.60;1.03] nmol/L, p = 0.011) and combined event (median of 0.91 [IQR 0.66;1.39] nmol/L vs. 0.70 [IQR 0.51;0.82] nmol/L, p < 0.001); the positive likelihood ratio (LR+) and negative likelihood ratio (LR−) for mortality were 2.40 and 0.46, respectively. The LR+ and LR− for combined event were 3.16 and 0.63, respectively. MR-proADM ≥1 nmol/L was the optimal cut-off for mortality and combined event prediction. The predictive capacity of MR-proADM showed an area under the ROC curve of 0.73 (95% CI, 0.62–0.81) and 0.72 (95% CI, 0.62–0.81) for mortality and combined event, respectively. In conclusion, elevated on-admission MR-proADM levels were associated with higher risk of 30-day mortality and 30-day poor outcomes in a cohort of critically ill patients with COVID-19.

Lung Ultrasound, Clinical and Analytic Scoring Systems as Prognostic Tools in SARS-CoV-2 Pneumonia: A Validating Cohort

At the moment, several COVID-19 scoring systems have been developed. It is necessary to determine which one better predicts a poor outcome of the disease. We conducted a single-center prospective cohort study to validate four COVID-19 prognosis scores in adult patients with confirmed infection at ward. These are National Early Warning Score (NEWS) 2, Lung Ultrasound Score (LUS), COVID-19 Worsening Score (COWS), and Spanish Society of Infectious Diseases and Clinical Microbiology score (SEIMC Score). Our outcomes were the combined variable “poor outcome” (non-invasive mechanical ventilation, intubation, intensive care unit admission, and death at 28 days) and death at 28 days. Scores were analysed using univariate logistic regression models, receiver operating characteristic curves, and areas under the curve. Eighty-one patients were included, from which 21 had a poor outcome, and 9 died. We found a statistically significant correlation between poor outcome and NEWS2, LUS > 15, and COWS. Death at 28 days was statistically correlated with NEWS2 and SEIMC Score although COWS also performs well. NEWS2, LUS, and COWS accurately predict poor outcome; and NEWS2, SEIMC Score, and COWS are useful for anticipating death at 28 days. Lung ultrasound is a diagnostic tool that should be included in COVID-19 patients evaluation.