Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ​±​ 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization

The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Role of biomarkers in prognostication of moderate and severe COVID-19 cases

Background

COVID-19 pandemic demanded upgrading of laboratory medicine to limit morbidity, disability and mortality from moderate and severe SARS-COV-2 infections.

Objective

To assess among moderate and severe COVID-19 patients, C-reactive protein (CRP), procalcitonin (PCT), ferritin, D-dimer, interleukin 6 (IL-6), lactate dehydrogenase (LDH), total and differential leucocyte count (TLC and DLC), neutrophil-to-lymphocyte ratio (NLR), absolute platelet count (APC), prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalized ratio (INR) to find their interdependence and role in prognosis.

Methods

This open label analytical cross-sectional noninterventional study evaluated array of independent biochemical, haematological and coagulopathy markers, viz. CRP, PCT, ferritin, D-dimer, IL-6, LDH, TLC, DLC, NLR, absolute platelet count, PT, APTT and INR on consecutive 100 patients with diagnosis of moderate and severe COVID-19 from July to August 2021.

Results

In our study, on consecutive designated 100 cases (55 cases moderate and 45 cases severe), more severity were reported as the age progressed; gender difference was not noted. Among independent markers, CRP, PCT, ferritin, D-dimer, IL-6 and LDH had statistically significant relation in comparison with severity of the disease as Chi-square calculated value (P < 0.05). TLC, DLC and APC showed no significant relation in comparison with severity of the disease; NLR had highly significant relation. PT showed significant relation in comparison with severity, though APTT and INR did not show significant relation.

Conclusion

Our research group felt that CRP, PCT, ferritin, D-dimer, IL-6, LDH and NLR should be in included in clinical practice guidelines to prognosticate COVID-19 cases. Furthermore, translational researches are needed at all levels of healthcare to improve validity for practices of primary care physicians.

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

The Evaluation of Laboratory Parameters as Predictors of Disease Severity and Mortality in COVID-19 Patients: A Retrospective Study From a Tertiary Care Hospital in India

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects and alters various laboratory parameters that are predictors of disease severity and mortality, and hence, their prompt identification can aid in patient triaging and resource allocation. Objectives A retrospective study was conducted on 7416 admitted coronavirus disease 2019 (COVID-19) patients from 20 March 2020 to 9 August 2021 to identify crucial laboratory biomarkers as predictors of disease severity and outcome; also, their optimal cutoffs were also calculated. A comparison of laboratory markers between both COVID-19 waves was also performed. Results The majority of patients had mild disease (4295/7416, 57.92%), whereas 1262/7416 (17.02%) had severe disease. The overall fatal outcome was reported in 461 (6.22%) patients. Predictors for mortality were age (>52 years), albumin/globulin (A/G) ratio (≤1.47), chloride (≤101 mmol/L), ferritin (>483.89 ng/mL), lactate dehydrogenase (LDH) (>393 U/L), procalcitonin (>0.10 ng/mL), interleukin-6 (IL-6) (>8.8 pg/mL), fibrinogen (>403 mg/dL), international normalized ratio (INR) (>1.18), and D-dimer (>268 ng/mL). Disease severity predictors were neutrophils (>81%), lymphocyte (≤25.4%), absolute lymphocyte count (ALC) (≤1.38×10³/µL), absolute eosinophil count (AEC) (≤0.03×10³/µL), total bilirubin (TBIL) (≥0.51 mg/dL), A/G ratio (≤1.49), albumin (≤4.2 g/dL), ferritin (≥445.4 mg/dL), LDH (≥479 U/L), IL-6 (≥28.6 pg/mL), C-reactive protein/albumin (CRP/ALB) ratio (≥1.78), D-dimer (≥237 ng/mL), and fibrinogen (≥425 mg/dL). The majority of patients admitted in the second wave were older and had severe disease, increased fatality, and significantly deranged laboratory parameters than first wave patients. Conclusion Our findings suggested that several biomarkers are crucial for both severe disease and mortality in COVID-19 patients. Ferritin, LDH, IL-6, A/G ratio, fibrinogen, and D-dimer are important biomarkers for both severity and mortality, and when combined, they provide valuable information for patient monitoring and triaging. In addition to these, older age, INR, chloride, and procalcitonin are also significant risk factors for mortality. For severe COVID-19, TBIL, CRP/ALB, albumin, neutrophil percentage, lymphocyte percentage, ALC, and AEC are also important biomarkers. According to the study, the majority of the baseline laboratory parameters associated with COVID-19 mortality and severe disease were significantly higher during the second wave, which could be one of the possible causes for the high mortality rate in India during the second wave. So, the combination of all these parameters can be a powerful tool in emergency settings to improve the efficacy of treatment and prevent mortality, and the planning of subsequent waves should be done accordingly.

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

COVID-19 is a respiratory disease caused by the SARS-CoV-2 contagion, severely disrupted the healthcare infrastructure. Various countries have developed COVID-19 vaccines that have effectively prevented the severe symptoms caused by the virus to a certain extent. However, a small section of people continues to perish. Artificial intelligence advances have revolutionized healthcare diagnosis and prognosis infrastructure. In this study, we predict the severity of COVID-19 using heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors. This study extensively reviews various classifier architectures to predict the COVID-19 severity. We built and evaluated multiple pipelines entailing combinations of five state-of-the-art data-balancing techniques (Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic, Borderline SMOTE, SMOTE with Tomek links, and SMOTE with Edited Nearest Neighbor (ENN)) and twelve heterogeneous classifiers such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Xgboost, Extratrees, Adaboost, Light GBM, Catboost, and 1-D Convolution Neural Network. The best-performing pipeline consists of Random Forest trained on Borderline SMOTE balanced data that produced the highest recall of 83%. We deployed Explainable Artificial Intelligence tools such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, ELI5, Qlattice, Anchor, and Feature Importance to demystify complex tree-based ensemble models. These tools provide valuable insights into the significance of critical features in the severity prediction of a COVID-19 patient. It was observed that changes in respiratory rate, blood pressure, lactate, and calcium values were the primary contributors to the increase in severity of a COVID-19 patient. This architecture aims to be an explainable decision-support triaging system for medical professionals in countries lacking advanced medical technology and infrastructure to reduce fatalities.

Anthropometric Measurements and Admission Parameters as Predictors of Acute Respiratory Distress Syndrome in Hospitalized COVID-19 Patients

Aim: We aimed to single out admission predictors of acute respiratory distress syndrome (ARDS) in hospitalized COVID-19 patients and investigate the role of bioelectrical impedance (BIA) measurements in ARDS development. Method: An observational, prospective cohort study was conducted on 407 consecutive COVID-19 patients hospitalized at the University Clinical Center Kragujevac between September 2021 and March 2022. Patients were followed during the hospitalization, and ARDS was observed as a primary endpoint. Body composition was assessed using the BMI, body fat percentage (BF%), and visceral fat (VF) via BIA. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis. Results: Patients with BMI above 30 kg/m², very high BF%, and/or very high VF levels were at a significantly higher risk of developing ARDS compared to nonobese patients (OR: 4.568, 8.892, and 2.448, respectively). In addition, after performing multiple regression analysis, six admission predictors of ARDS were singled out: (1) very high BF (aOR 8.059), (2) SaO₂ < 87.5 (aOR 5.120), (3) IL-6 > 59.75 (aOR 4.089), (4) low lymphocyte count (aOR 2.880), (5) female sex (aOR 2.290), and (6) age < 68.5 (aOR 1.976). Conclusion: Obesity is an important risk factor for the clinical deterioration of hospitalized COVID-19 patients. BF%, assessed through BIA measuring, was the strongest independent predictor of ARDS in hospitalized COVID-19 patients.

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

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

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

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).

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg’s analysis (p = 0.835) and Egger’s analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Clinical Characteristics and Risk Prediction Score in Patients With Mild-to-Moderate Coronavirus Disease 2019 in Japan

BACKGROUND

Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, causing widespread mortality. Many patients with COVID-19 have been treated in homes, hotels, and medium-sized hospitals where doctors were responsible for assessing the need for critical care hospitalization. This study aimed to establish a severity prediction score for critical care triage.

METHOD

We analyzed the data of 368 patients with mild-to-moderate COVID-19 who had been admitted to Fussa Hospital, Japan, from April 2020 to February 2022. We defined a high-oxygen group as requiring ≥4 l/min of oxygen. Multivariable logistic regression was used to construct a risk prediction score, and the best model was selected using a stepwise selection method.

RESULTS

Multivariable analysis showed that older age (≥70 years), elevated creatine kinase (≥127 U/L), C-reactive protein (≥2.19 mg/dL), and ferritin (≥632.7 ng/mL) levels were independent risk factors associated with the high-oxygen group. Each risk factor was assigned a score ranging from 0 to 4, and we referred to the final overall score as the Fussa score. Patients were classified into two groups, namely, high-risk (total risk factors, ≥2) and low-risk (total risk score, <2) groups. The high-risk group had a significantly worse prognosis (low-risk group, undefined vs. high-risk group, undefined; P< 0.0001).

CONCLUSIONS

The Fussa score might help to identify patients with COVID-19 who require critical care hospitalization.

Admission Predictors of Mortality in Hospitalized COVID-19 Patients-A Serbian Cohort Study

Background: Early prediction of COVID-19 patients’ mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission. Methods: An observational, retrospective−prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients’ medical records. Results: The overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001). Conclusions: Parameters available on hospital admission can be beneficial in predicting COVID-19 mortality.

Risk stratification and prognostic value of prothrombin time and activated partial thromboplastin time among COVID-19 patients

Background

COVID-19 is a viral disease caused by a new strain of corona virus. Currently, prognosis and risk stratification of COVID-19 patients is done by the disease’s clinical presentation. Therefore, identifying laboratory biomarkers for disease prognosis and risk stratification of COVID-19 patients is critical for prompt treatment. Therefore, the main objective of this study was to assess the risk stratification and prognostic value of basic coagulation parameters and factors associated with disease severity among COVID-19 patients at the Tibebe Ghion Specialized Hospital, COVID-19 treatment center, Northwest Ethiopia.

Methods

A follow-up study was conducted among conveniently recruited COVID-19 patients attended from March to June 2021. Socio-demographic and clinical data were collected using a structured questionnaire and checklist, respectively. Prothrombin time (PT) and activated partial thromboplastin time (APTT) were analyzed by the HUMACLOT DUE PLUS^® machine. Descriptive statistics were used to summarize the socio-demographic and clinical characteristics of study participants. Kruskal Wallis tests were used to compare the difference between parametric and non-parametric continuous variables, respectively. The area under the receiver operating characteristic curve (AUC) was used to evaluate the value of PT and APTT in the risk stratification and disease prognosis of COVID-19 patients. Ordinal logistic regression was used to identify the factors associated with disease severity and prognosis. A P-value < 0.05 was defined as statistically significant for all results.

Result

Baseline PT at a cut-off value ≥ 16.25 seconds differentiated severe COVID-19 patients from mild and moderate patients (AUC: 0.89, 95% CI: 0.83–0.95). PT also differentiated mild COVID-19 patients from moderate and severe patients at a cut-off value ≤ 15.35 seconds (AUC: 0.90, 95% CI: 0.84–0.96). Moreover, alcohol drinkers were a 3.52 times more likely chance of having severe disease than non-drinkers (95% CI: 1.41–8.81). A one-year increment in age also increased the odds of disease severity by 6% (95% CI: 3–9%). An increment of ≥ 0.65 seconds from the baseline PT predicted poor prognosis (AUC: 0.93, 0.87–0.99).

Conclusions and recommendations

Prolonged baseline PT was observed in severe COVID-19 patients. Prolonged baseline PT was also predicted to worsen prognosis. An increase from the baseline PT was associated with worsen prognosis. Therefore, PT can be used as a risk stratification and prognostic marker in COVID-19 patients.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

Background

Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.

Objective

We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.

Materials and Methods

We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO₂, HCO3-, K⁺, Na⁺, anion gap, C-reactive protein) served as ground truth.

Results

Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.

Conclusion

The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Up-regulated serum levels of interleukin (IL)-17A and IL-22 in Egyptian pediatric patients with COVID-19 and MIS-C: Relation to the disease outcome

Both IL-17A and IL-22 share cellular sources and signaling pathways. They have synergistic action on epithelial cells to stimulate their production of antimicrobial peptides which are protective against infections. However, both interleukins may contribute to ARDS pathology if their production is not controlled. This study aimed to investigate serum levels of IL-17A and IL-22 in relation to the disease outcome in patients with SARS-CoV-2. Serum IL-17A and IL-22 were measured by ELISA in 40 patients with SARS-CoV-2, aged between 2 months and 16 years, (18 had COVID-19 and 22 had multisystem inflammatory syndrome in children "MIS-C") in comparison to 48 age- and sex-matched healthy control children. Patients with COVID-19 and MIS-C had significantly higher serum IL-17A and IL-22 levels than healthy control children (P < 0.001). Increased serum IL-17A and IL-22 levels were found in all patients. Elevated CRP and serum ferritin levels were found in 90% of these patients. Lymphopenia, neutrophilia, neutropenia, thrombocytopenia and elevated ALT, LDH and D-dimer were found in 45%, 42.5 %, 2.5%, 30%, 32.5%, 82.5%, and 65%, respectively of these patients. There were non-significant differences between patients who recovered and those who died or had a residual illness in serum levels of IL-17A, IL-22 and the routine inflammatory markers of COVID-19. In conclusions, serum IL-17A and IL-22 levels were up-regulated in all patients with COVID-19 and MIS-C. Levels of serum IL-17A, IL-22 and the routine inflammatory markers of COVID-19 were not correlated with the disease outcome. Our conclusions are limited by the sample size.

Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Background

The second wave of coronavirus disease 2019 (COVID-19) in South Africa was caused by the Beta variant of severe acute respiratory syndrome coronavirurus-2. This study aimed to explore clinical and biochemical parameters that could predict outcome in patients with COVID-19.

Methods

A prospective study was conducted between 5 November 2020 and 30 April 2021 among patients with confirmed COVID-19 admitted to the intensive care unit (ICU) of a tertiary hospital. The Cox proportional hazards model in Stata 16 was used to assess risk factors associated with survival or death. Factors with P<0.05 were considered significant.

Results

Patients who died were found to have significantly lower median pH (P<0.001), higher median arterial partial pressure of carbon dioxide (P<0.001), higher D-dimer levels (P=0.001), higher troponin T levels (P=0.001), higher N-terminal-prohormone B-type natriuretic peptide levels (P=0.007) and higher C-reactive protein levels (P=0.010) compared with patients who survived. Increased standard bicarbonate (HCO3std) was associated with lower risk of death (hazard ratio 0.96, 95% confidence interval 0.93-0.99).

Conclusions

The mortality of patients with COVID-19 admitted to the ICU was associated with elevated D-dimer and a low HCO3std level. Large studies are warranted to increase the identification of patients at risk of poor prognosis, and to improve the clinical approach.

The study of serum hsCRP, ferritin, IL-6 and plasma D-dimer in COVID-19: a retrospective study

OBJECTIVES

The cut off values for serum high sensitivity C-reactive protein (hsCRP), ferritin, interleukin 6 (IL-6) and plasma D-dimer could be of profound help in detecting COVID-19 patients at risk of adverse outcomes. Therefore, the aim of the present study is to determine the cut off values of the serum hsCRP, ferritin, IL-6 and plasma D-dimer in predicting mortality in COVID-19 patients.

METHODS

Four hundred RT-PCR confirmed cases of COVID-19 were sub divided into two groups based on their outcome during hospitalisation. Group I consisted of survivors and Group II consisted of non-survivors. The survivors were further divided into three sub-groups: mild, moderate and severe based on the severity of infection. The laboratory data of serum hsCRP, ferritin, IL-6 and plasma D-dimer for all these patients was retrieved from the Medical Record Section of the Hospital.

RESULTS

Mean serum hsCRP, ferritin, IL-6 and plasma D-dimer levels were significantly higher in non-survivors as compared to survivors of COVID-19. The levels of these biomarkers correlated with the severity of COVID-19 illness. ROC curve analysis revealed that plasma D-dimer is having a better predictive value as compared to other parameters in predicting mortality in COVID-19.

CONCLUSIONS

The serum hsCRP, ferritin, IL-6 and plasma D-dimer levels could be used in risk stratification of COVID-19 patients. The optimum cut off given by the current study could be considered in predicting adverse outcome in these patients. Amongst the many studied biomarkers, plasma D-dimer might be the best early biomarker to predict mortality in COVID-19 patients.

Ethnicity-Specific Features of COVID-19 Among Arabs, Africans, South Asians, East Asians, and Caucasians in the United Arab Emirates

Background

Dubai (United Arab Emirates; UAE) has a multi-national population which makes it exceptionally interesting study sample because of its unique demographic factors.

Objective

To stratify the risk factors for the multinational society of the UAE.

Methods

A retrospective chart review of 560 patients sequentially admitted to inpatient care with laboratory confirmed COVID-19 was conducted. We studied patients’ demographics, clinical features, laboratory results, disease severity, and outcomes. The parameters were compared across different ethnic groups using tree-based estimators to rank the ethnicity-specific disease features. We trained ML classification algorithms to build a model of ethnic specificity of COVID-19 based on clinical presentation and laboratory findings on admission.

Results

Out of 560 patients, 43.6% were South Asians, 26.4% Middle Easterns, 16.8% East Asians, 10.7% Caucasians, and 2.5% are under others. UAE nationals represented half of the Middle Eastern patients, and 13% of the entire cohort. Hypertension was the most common comorbidity in COVID-19 patients. Subjective complaint of fever and cough were the chief presenting symptoms. Two-thirds of the patients had either a mild disease or were asymptomatic. Only 20% of the entire cohort needed oxygen therapy, and 12% needed ICU admission. Forty patients (~7%) needed invasive ventilation and fifteen patients died (2.7%). We observed differences in disease severity among different ethnic groups. Caucasian or East-Asian COVID-19 patients tended to have a more severe disease despite a lower risk profile. In contrast to this, Middle Eastern COVID-19 patients had a higher risk factor profile, but they did not differ markedly in disease severity from the other ethnic groups. There was no noticeable difference between the Middle Eastern subethnicities—Arabs and Africans—in disease severity (p = 0.81). However, there were disparities in the SOFA score, D-dimer (p = 0.015), fibrinogen (p = 0.007), and background diseases (hypertension, p = 0.003; diabetes and smoking, p = 0.045) between the subethnicities.

Conclusion

We observed variations in disease severity among different ethnic groups. The high accuracy (average AUC = 0.9586) of the ethnicity classification model based on the laboratory and clinical findings suggests the presence of ethnic-specific disease features. Larger studies are needed to explore the role of ethnicity in COVID-19 disease features.

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes.

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings

Background

Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population.

Objective

Assess whether age and sex interact with other risk factors to influence the severity of SARS-CoV-2 infection.

Material and Methods

The study sample included all consecutive patients who satisfied the inclusion criteria and who were treated from 24 February to 1 July 2020 in Dubai Mediclinic Parkview (560 cases) and Al Ain Hospital (605 cases), United Arab Emirates. We compared disease severity estimated from the radiological findings among patients of different age groups and sex. To analyze factors associated with an increased risk of severe disease, we conducted uni- and multivariate regression analyses. Specifically, age, sex, laboratory findings, and personal risk factors were used to predict moderate and severe COVID-19 with conventional machine learning methods.

Results

Need for O₂ supplementation was positively correlated with age. Intensive care was required more often for men of all ages (p < 0.01). Males were more likely to have at least moderate disease severity (p = 0.0083). These findings were aligned with the results of biochemical findings and suggest a direct correlation between older age and male sex with a severe course of the disease. In young males (18–39 years), the percentage of the lung parenchyma covered with consolidation and the density characteristics of lesions were higher than those of other age groups; however, there was no marked sex difference in middle-aged (40–64 years) and older adults (≥65 years). From the univariate analysis, the risk of the non-mild COVID-19 was significantly higher (p < 0.05) in midlife adults and older adults compared to young adults. The multivariate analysis provided similar findings.

Conclusion

Age and sex were important predictors of disease severity in the set of data typically collected on admission. Sexual dissimilarities reduced with age. Age disparities were more pronounced if studied with the clinical markers of disease severity than with the radiological markers. The impact of sex on the clinical markers was more evident than that of age in our study.

SARS-CoV-2 in Egypt: epidemiology, clinical characterization and bioinformatics analysis

COVID-19 is an infectious disease caused by SARS-CoV-2 and has spread globally, resulting in the ongoing coronavirus pandemic. The current study aimed to analyze the clinical and epidemiological features of COVID-19 in Egypt. Oropharyngeal swabs were collected from 197 suspected patients who were admitted to the Army Hospital and confirmation of the positivity was performed by rRT-PCR assay. Whole genomic sequencing was conducted using Illumina iSeq 100® System. The average age of the participants was 48 years, of which 132 (67%) were male. The main clinical symptoms were pneumonia (98%), fever (92%), and dry cough (66%). The results of the laboratory showed that lymphocytopenia (79.2%), decreased levels of haemoglobin (77.7%), increased levels of interleukin 6, C-reactive protein, serum ferritin, and D-dimer (77.2%, 55.3%, 55.3%, and 25.9%, respectively), and leukocytopenia (25.9%) were more common. The CT findings showed that scattered opacities (55.8%) and ground-glass appearance (27.9%) were frequently reported. The recovered validated sequences (n = 144) were submitted to NCBI Virus GenBank. All sequenced viruses have at least 99% identity to Wuhan-Hu-1. All variants were GH clade, B.1 PANGO lineage, and L.GP.YP.HT haplotype. The most predominant subclade was D614G/Q57H/V5F/G823S. Our findings have aided in a deep understanding of COVID-19 evolution and identifying strains with unique mutational patterns in Egypt.

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Isolation and clinical characterization of SARS-CoV-2 from Egyptian patients.

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Whole Genome Sequencing of recovered isolates revealed unique sequences.

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Egyptian SARS-CoV-2 variants in 2020 with at least 99% identity to Wuhan-Hu-1.

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Egyptian SARS-CoV-2 variants were GH clade and L.GP.YP.HT haplotype.

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A unique mutation (D614G/Q57H/V5F/G823S) pattern was predominant among SARS-CoV-2.

Gastrointestinal manifestations in children with COVID-19 infection: Retrospective tertiary center experience

OBJECTIVE

The majority of pediatric severe acute respiratory syndrome coronavirus 2 (COVID-19) cases demonstrate asymptomatic, mild or moderate disease. The main symptoms in children with COVID-19 are respiratory symptoms but some patients develop gastrointestinal symptoms and liver injury. We aim to review gastrointestinal symptoms and liver injury in children with confirmed COVID-19 infection.

METHOD

This is a retrospective case note review of children with positive COVID-19 nasal Polymerase Chain Reaction aged 0-18 years admitted to a tertiary pediatric hospital from March 1st till June 1st 2020.

RESULTS

180 children were identified. Mean age was 5 years (Range: 0.01-17), the majority of patients were school aged (30%). Patients were mainly from East Asia 81 (45%) and Arabs 67 (37%). Gastrointestinal symptoms were encountered in 48 (27%) patients and 8 (4%) patients had only Gastrointestinal symptoms with no associated fever or respiratory symptoms. Liver injury was seen in 57 (32%) patients. Patients with fever and cough were more likely to have gastrointestinal symptoms (P = <0.001 and 0.004 respectively). Fever was more likely to be associated with liver injury (P = 0.021). Children with abdominal pain were more likely to have elevated C-Reactive Protein (P = 0.037). Patients with diarrhea and vomiting were more likely to have elevated procalcitonin (P = 0.034 and 0.002 respectively). Children with Gastrointestinal symptoms were not more likely to be admitted to Pediatric Intensive Care Unit (P = 0.57).

CONCLUSION

COVID-19 infection in children can display gastrointestinal symptoms at initial presentation. Additionally, gastrointestinal symptoms can be the only symptoms patients display. We demonstrated that children with gastrointestinal symptoms and liver injury can develop more severe COVID-19 disease and are more likely to have fever, cough, and raised inflammatory markers. Identifying children with gastrointestinal manifestations needs to be part of the initial screening assessment of children.What is known?• Pediatric COVID-19 cases mostly demonstrate asymptomatic, mild or moderate disease.• The symptoms in children are mainly respiratory but some display gastrointestinal symptoms.• Children with COVID-19 display increased gastrointestinal symptoms when compared to adults.What is new?• Children with COVID-19 displaying gastrointestinal symptoms are more likely to have fever, cough and elevated inflammatory markers.• Children with liver injury are more likely to develop fever.• Children with gastrointestinal involvement in COVID-19 are more likely to demonstrate more severe disease but are not more likely to be admitted to PICU.

Evaluation of biochemical characteristics of 183 COVID-19 patients: A retrospective study

Introduction and aim

Coronavirus disease 2019 (COVID-19), with a high mortality rate, has caught the eyes of researchers worldwide and placed a heavy burden on the health care system. Accordingly, this study aimed to evaluate the values of biochemical parameters on the outcomes of COVID-19 patients in Golestan, Iran.

Materials and methods

This retrospective study was conducted on 183 COVID-19 patients (i.e., 94 males and 89 females) between March and September 2020. The biochemical parameters and demographic data of the patients (including age, sex, urea, creatinine [Cr], lactate dehydrogenase [LDH], and creatine kinase [CK]) were obtained from electrical medical records. According to the outcome of COVID-19, the patients were categorized into two groups (i.e., death [n = 63] and survival [n = 120] groups), and the biochemical parameters and outcomes of COVID-19 were analyzed.

Results

Of the 183 patients, 120 (65.5%) had a non-severe type and recovered from COVID-19, and 63 (34.4%) developed into a critically severe type and died. The mean age of all patients was 56.5 years old. The highest mortality was observed in patients with LDH ≥280. The data obtained by the one-sample t-test showed that there were significantly higher mean values of urea, Cr, CK, and LDH in COVID-19 patients when compared to their reference intervals (P˂0.0001 for all).

Conclusions

Some biochemical parameters are effective in the evaluation of dynamic variations in COVID-19 patients. It can be concluded from the results that biochemical parameters and reinforce LDH may be useful for the evaluation of the COVID-19 outcome.

Correlation of biochemical profile at admission with severity and outcome of COVID-19

Background

COVID-19 was detected in China in December 2019. The rapid dissemination and novelty of the disease resulted in an epidemic. This study aimed to identify biochemical parameters at admission that can be used to categorize severity and outcome of COVID -19 infection.

Materials and Methods

This cross-sectional study was conducted at Allied Hospitals of RMU from April 2020 to July 2020. It included 128 randomly selected confirmed COVID-19 patients. At admission, biochemical profile (total bilirubin, alanine aminotransferases {ALT}, aspartate aminotransferases {AST}, urea, creatinine, uric acid, sodium, potassium, and chloride were correlated with severity and outcome of COVID-19 by employing t-tests and ANOVA where required. Cut-off values to predict disease severity and outcome were calculated using ROC curve.

Results

The study comprised 46.1% non-severe, 29.7% severe, and 24.2% critical COVID-19 patients. 84.4% patients improved and 15.6% expired. Urea was increased in critical disease patients (p < 0.000). Higher ALT (p 0.030) and AST (p 0.004) levels were noted in severe and critical disease. Sodium (p 0.001) and chloride (p 0.026) were decreased in critical disease. Patients who expired had increased urea (p 0.000), ALT (p 0.040) and AST (p 0.002). At admission, urea >42.7 mg (sensitivity of 64.7%, specificity of 87.5%), AST >43.5 IU/L (64% sensitivity, 60% specificity), and sodium <136.9 mmol/L (sensitivity of 70.6%, specificity of 71.2%) predicted critical COVID-19 infection.

Conclusion

At admission, increased urea, AST, and ALT along with decreased sodium can help in identifying COVID-19 patients with severe illness and poor outcome.

The role of C-reactive protein in predicting the severity of COVID-19 disease: A systematic review

Since December 2019, coronavirus diseases-2019 (COVID-19) dispersed into 200 countries and affected more than 70 million people. The clear picture of the SARS-CoV-2 infection is still under investigation. In this review, we evaluated whether C-reactive protein biomarker is able to predict the clinical outcomes or correlated with the severity of COVID-19 disease. The databases MEDLINE, Hinari, Google Scholar, and Google search were used to find potential studies published from COVID-19 epidemic until May 2021. A format prepared in Microsoft Excel spreadsheet was used to extract the appropriate details from each original report. For further review, the extracted data were exported to STATA/MP version 16.0 software. Keywords including "COVID-19," "SARS-CoV-2," and "C-reactive protein," among others were used to search relevant articles. Only studies which reported the average C-reactive protein value and COVID-19 disease stage outcomes were included. Twenty articles were included in the review. All studies found considerably higher level of C-reactive protein in patients with severe COVID-19 as compared to mildly infected patients. This review evidenced that it is still there for a given biomarker to early identify the state of progression in asymptomatic and/or mildly infected individuals into severe disease; the level of C-reactive protein may be used in predicting the likelihood of disease progression. Findings from this review showed level of C-reactive protein is a good biomarker to predict the severity of COVID-19 disease. Although COVID-19 researches are at the early stages, investigation of C-reactive protein levels throughout the disease course may have paramount importance for clinicians in early detection of severe manifestations and subsequently improve the prognosis. However, further large-scale studies are required to confirm these findings.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

Background

Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.

Objective

In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.

Methods

Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.

Results

Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.

Conclusions

Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Laboratory medicine in the COVID-19 era: six lessons for the future

The lockdown due to the coronavirus disease 2019 (COVID-19), a major healthcare challenge, is a worldwide threat to public health, social stability, and economic development. The pandemic has affected all aspects of society, dramatically changing our day-to-day lives and habits. It has also changed clinical practice, including practices of clinical laboratories. After one year, it is time to rethink what has happened, and is still happening, in order to learn lessons for the future of laboratory medicine and its professionals. While examining this issue, I was inspired by Italo Calvino's famous work, "Six memos for the next millennium".But I rearranged the Author's six memos into "Visibility, quickness, exactitude, multiplicity, lightness, consistency".

Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.