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1.
Epidemiol Infect ; 148: e168, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-1537262

ABSTRACT

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/therapy , Logistic Models , Machine Learning , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Adolescent , Adult , Aged , COVID-19 , China/epidemiology , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Prognosis , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment/methods , Young Adult
2.
BMC Infect Dis ; 21(1): 1173, 2021 Nov 22.
Article in English | MEDLINE | ID: covidwho-1528680

ABSTRACT

BACKGROUND: As the COVID-19 pandemic continues, the number of patients admitted to the intensive care unit (ICU) is still increasing. The aim of our article is to estimate which of the conventional ICU mortality risk scores is the most accurate at predicting mortality in COVID-19 patients and to determine how these scores can be used in combination with the 4C Mortality Score. METHODS: This was a retrospective study of critically ill COVID-19 patients treated in tertiary reference COVID-19 hospitals during the year 2020. The 4C Mortality Score was calculated upon admission to the hospital. The Simplified Acute Physiology Score (SAPS) II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sequential Organ Failure Assessment (SOFA) scores were calculated upon admission to the ICU. Patients were divided into two groups: ICU survivors and ICU non-survivors. RESULTS: A total of 249 patients were included in the study, of which 63.1% were male. The average age of all patients was 61.32 ± 13.3 years. The all-cause ICU mortality ratio was 41.4% (n = 103). To determine the accuracy of the ICU mortality risk scores a ROC-AUC analysis was performed. The most accurate scale was the APACHE II, with an AUC value of 0.772 (95% CI 0.714-0.830; p < 0.001). All of the ICU risk scores and 4C Mortality Score were significant mortality predictors in the univariate regression analysis. The multivariate regression analysis was completed to elucidate which of the scores can be used in combination with the independent predictive value. In the final model, the APACHE II and 4C Mortality Score prevailed. For each point increase in the APACHE II, mortality risk increased by 1.155 (OR 1.155, 95% CI 1.085-1.229; p < 0.001), and for each point increase in the 4C Mortality Score, mortality risk increased by 1.191 (OR 1.191, 95% CI 1.086-1.306; p < 0.001), demonstrating the best overall calibration of the model. CONCLUSIONS: The study demonstrated that the APACHE II had the best discrimination of mortality in ICU patients. Both the APACHE II and 4C Mortality Score independently predict mortality risk and can be used concomitantly.


Subject(s)
COVID-19 , Critical Illness , Aged , Hospital Mortality , Humans , Intensive Care Units , Male , Middle Aged , Pandemics , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2
3.
Stud Health Technol Inform ; 287: 149-152, 2021 Nov 18.
Article in English | MEDLINE | ID: covidwho-1525369

ABSTRACT

One serious pandemic can nullify years of efforts to extend life expectancy and reduce disability. The coronavirus pandemic has been a perturbing factor that has provided an opportunity to assess not only the effectiveness of health systems for cardio-vascular diseases (CVD), but also their sustainability. The goal of our research is to analyze the influence of public health factors on the mortality from circulatory diseases using machine learning methods. We analysed a very large dataset that consisted of the information collected from the national registers in Russia. We included data from 2015 to 2021. It included 340 factors that characterize organization of healthcare in Russia. The resulting area under receiver operating characteristic curve (AUC of ROC) of the Random Forest based regression model was 92% with a testing dataset. The models allow for automated retraining as time passes and epidemiological and other situations change. They also allow additional characteristics of regions and health care organizations to be added to existing training datasets depending on the target. The developed models allow the calculation of the probability of the target for 6-12 months with an error of 8%. Moreover, the models allow to calculate scenarios and the value of the target indicator when other indicators of the region change.


Subject(s)
Cardiovascular Diseases , Coronavirus Infections , Cardiovascular Diseases/epidemiology , Delivery of Health Care , Humans , Machine Learning , ROC Curve
4.
Eur Rev Med Pharmacol Sci ; 25(21): 6767-6774, 2021 11.
Article in English | MEDLINE | ID: covidwho-1524864

ABSTRACT

OBJECTIVE: We aimed to test the efficiency of CHA2DS2-VASc, CHA2DS2-VASc-HS, R2CHA2DS2-VASc score systems on the prediction of mortality in the patients with COVID-19. PATIENTS AND METHODS: The data were collected from 508 hospitalized patients with COVID-19. Comorbidity features including coronary artery disease, peripheral arterial disease, congestive heart failure, hypertension, atrial fibrillation, diabetes mellitus, hyperlipidemia, smoking, chronic obstructive pulmonary disease, cerebrovascular event, cancer status, and renal disease were recorded. The patients were divided as surviving group (n=440) and non-survivors (n=68). RESULTS: The in-hospital mortality rate of the patients with COVID-19 was 13.4%. Factors found to be associated with mortality in univariate analysis were CHA2DS2-VASc, CHA2DS2-VASc-HS, R2CHA2DS2-VASc, cancer state, atrial fibrillation, hemoglobin, lymphocyte count, CRP, albumin and ferritin. Model 1 multivariate cox regression analysis revealed CHA2DS2-VASc, hemoglobin, CRP and ferritin levels to be independently associated with mortality. Factors that were found to be independently associated with in-hospital mortality in Model 2 analysis were CHA2DS2-VASc-HS, R2CHA2DS2-VASc, hemoglobin, CRP and ferritin whereas except hemoglobin in Model 3 analysis, the other variables had been the same. Predictive power of R2CHA2DS2-VASc was better than of both CHA2DS2-VASc (p=0.002) and CHA2DS2-VASc-HS (p=0.034) in determining the in-hospital mortality. Patients with higher R2CHA2DS2-VASc (> 3 points), CHA2DS2-VASc-HS (> 3 points) and CHA2DS2-VASc (> 2 points) scores exhibited the highest mortality rate in survival analysis by using Kaplan-Meier and long-rank tests. CONCLUSIONS: CHA2DS2-VASc, CHA2DS2-VASc-HS, and R2CHA2DS2-VASc were found to be independent predictors of mortality in hospitalized COVID-19 patients. The current study revealed that the predictive ability of R2CHA2DS2-VASc was better than the both of CHA2DS2-VASc and CHA2DS2-VASc-HS score.


Subject(s)
COVID-19/mortality , Comorbidity , Severity of Illness Index , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19/pathology , COVID-19/virology , Female , Hemoglobins/analysis , Hospital Mortality , Hospitalization , Humans , Kaplan-Meier Estimate , Lymphocyte Count , Male , Middle Aged , Proportional Hazards Models , ROC Curve , SARS-CoV-2/isolation & purification
5.
Eur Rev Med Pharmacol Sci ; 25(21): 6731-6740, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1524861

ABSTRACT

OBJECTIVE: The aim of the study was to determine the association between platelet indices and disease severity, and outcomes of the patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a secondary hospital. PATIENTS AND METHODS: 722 hospitalized patients who had positive rRT-PCR for SARS-CoV-2 and/or typical findings of COVID-19 at chest computed tomography (CT) were enrolled in this study. Initial platelet count (PLT) and indices, including mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), MPV/PCT, MPV/PLT, PDW/PLT, PDW/PCT on admission and the third day of hospitalization, and their relationship with disease severity and outcomes were evaluated retrospectively. RESULTS: The mean age of the patients was 57.2±15.6 years (range: 16-94) and male/female ratio was 1.22. 81.9% of the patients had moderate and 11.8% had severe disease. 1.8% of the patients had thrombocytopenia at admission. The patients transferred to the intensive care unit (ICU) had significantly lower baseline lymphocyte counts, PLT, PCT, and 3rd day lymphocyte counts when compared with the patients in wards. ICU patients also had higher baseline CRP, LDH, ferritin, MPV/PCT, MPV/PLT, PDW/PLT, PDW/PCT ratios, and 3rd day PDW, CRP, LDH, and ferritin levels than the patients in wards. Mortality was associated with lower baseline lymphocyte counts, PLT, PCT, 3rd day lymphocyte counts and PCT. Higher baseline CRP, LDH, ferritin, MPV/PCT, PDW/PLT, PDW/PCT and 3rd day CRP, LDH, ferritin, procalcitonin, PDW, MPV/PCT, PDW/PLT, and PDW/PCT ratios were also associated with poor prognosis. CONCLUSIONS: Platelet count and ratios were significantly associated with mortality in patients with COVID-19.


Subject(s)
Blood Platelets/cytology , COVID-19/pathology , Adolescent , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19/mortality , COVID-19/virology , Female , Humans , Intensive Care Units , Male , Middle Aged , Platelet Count , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2/isolation & purification , Severity of Illness Index , Survival Analysis , Young Adult
6.
Int J Lab Hematol ; 43 Suppl 1: 137-141, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1526369

ABSTRACT

INTRODUCTION: Eosinopenia has been observed during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. This study evaluated the role of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection. METHODS: Information on 429 patients with confirmed COVID-19, admitted to Apollo Hospitals, Chennai, India between 04 June 2020 to 15 August 2020, was retrospectively collected through electronic records and analysed. RESULTS: 79.25% of the patients included in the study had eosinopenia on admission. The median eosinophil count in COVID-19-positive patients was 0.015 × 109 /L, and in negative patients, it was 0.249 × 109 /L. Eighteen per cent of the positive patients presented with 0 eosinophil count. Eosinopenia for early diagnosis of COVID-19 had a sensitivity of 80.68% and specificity of 100% with an accuracy of 85.24. Role of eosinopenia in prognostication of COVID-19 was found to be insignificant. There was no statistically significant difference between the median eosinophil counts in survivors and nonsurvivors. Eosinophil trends during the course of disease were found to be similar between survivors and nonsurvivors. CONCLUSIONS: Eosinopenia on admission is a reliable and convenient early diagnostic marker for COVID-19 infection, helping in early identification, triaging and isolation of the patients till nucleic acid test results are available. Role of eosinopenia as a prognostic indicator is insignificant.


Subject(s)
COVID-19 Testing/methods , COVID-19/blood , Eosinophils , Leukocyte Count , Leukopenia/etiology , Area Under Curve , Biomarkers , COVID-19/diagnosis , COVID-19/mortality , Eosinophilia/blood , Eosinophilia/etiology , Humans , India , Leukopenia/blood , Prognosis , ROC Curve , Retrospective Studies , Selection Bias , Sensitivity and Specificity , Survival Analysis
7.
Comput Math Methods Med ; 2021: 9269173, 2021.
Article in English | MEDLINE | ID: covidwho-1511543

ABSTRACT

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/classification , COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , COVID-19 Testing/statistics & numerical data , Databases, Factual , Early Diagnosis , False Positive Reactions , Humans , Neural Networks, Computer , Pandemics , ROC Curve , Radiography, Thoracic/statistics & numerical data , Software Design , Tomography, X-Ray Computed/statistics & numerical data
8.
Sci Rep ; 11(1): 7334, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1500696

ABSTRACT

To identify the risk factors of mortality for the coronavirus disease 19 (COVID-19) patients admitted to intensive care units (ICUs) through a retrospective analysis. The demographic, clinical, laboratory, and chest imaging data of patients admitted to the ICU of Huoshenshan Hospital from February 10 to April 10, 2020 were retrospectively analyzed. Student's t-test and Chi-square test were used to compare the continuous and categorical variables, respectively. The logistic regression model was employed to ascertain the risk factors of mortality. This retrospective study involved 123 patients, including 64 dead and 59 survivors. Among them, 57 people were tested for interleukin-6 (IL-6) (20 died and 37 survived). In all included patients, the oxygenation index (PaO2/FiO2) was identified as an independent risk factor (odd ratio [OR] = 0.96, 95% confidence interval [CI]: 0.928-0.994, p = 0.021). The area under the curve (AUC) was 0.895 (95% CI: 0.826-0.943, p < 0.0001). Among the patients tested for IL-6, the PaO2/FiO2 (OR = 0.955, 95%CI: 0.915-0.996, p = 0.032) and IL-6 (OR = 1.013, 95%CI: 1.001-1.025, p = 0.028) were identified as independent risk factors. The AUC was 0.9 (95% CI: 0.791-0.964, p < 0.0001) for IL-6 and 0.865 (95% CI: 0.748-0.941, p < 0.0001) for PaO2/FiO2. PaO2/FiO2 and IL-6 could potentially serve as independent risk factors for predicting death in COVID-19 patients requiring intensive care.


Subject(s)
COVID-19/mortality , Interleukin-6/analysis , Aged , Area Under Curve , COVID-19/pathology , COVID-19/virology , Comorbidity , Female , Humans , Intensive Care Units , Logistic Models , Male , Middle Aged , Oxygen Consumption , ROC Curve , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification
9.
Sci Rep ; 11(1): 21519, 2021 11 02.
Article in English | MEDLINE | ID: covidwho-1500511

ABSTRACT

A high neutrophil to lymphocyte ratio (NLR) is considered an unfavorable prognostic factor in various diseases, including COVID-19. The prognostic value of NLR in other respiratory viral infections, such as Influenza, has not hitherto been extensively studied. We aimed to compare the prognostic value of NLR in COVID-19, Influenza and Respiratory Syncytial Virus infection (RSV). A retrospective cohort of COVID-19, Influenza and RSV patients admitted to the Tel Aviv Medical Center from January 2010 to October 2020 was analyzed. Laboratory, demographic, and clinical parameters were collected. Two way analyses of variance (ANOVA) was used to compare the association between NLR values and poor outcomes among the three groups. ROC curve analyses for each virus was applied to test the discrimination ability of NLR. 722 COVID-19, 2213 influenza and 482 RSV patients were included. Above the age of 50, NLR at admission was significantly lower among COVID-19 patients (P < 0.001). NLR was associated with poor clinical outcome only in the COVID-19 group. ROC curve analysis was performed; the area under curve of poor outcomes for COVID-19 was 0.68, compared with 0.57 and 0.58 for Influenza and RSV respectively. In the COVID-19 group, multivariate logistic regression identified a high NLR (defined as a value above 6.82) to be a prognostic factor for poor clinical outcome, after adjusting for age, sex and Charlson comorbidity score (odds ratio of 2.9, P < 0.001). NLR at admission is lower and has more prognostic value in COVID-19 patients, when compared to Influenza and RSV.


Subject(s)
COVID-19/pathology , Influenza, Human/pathology , Respiratory Syncytial Virus Infections/pathology , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19/immunology , COVID-19/virology , Female , Humans , Influenza, Human/immunology , Lymphocytes/cytology , Lymphocytes/metabolism , Male , Middle Aged , Neutrophils/cytology , Neutrophils/metabolism , Prognosis , ROC Curve , Respiratory Syncytial Virus Infections/immunology , Retrospective Studies , SARS-CoV-2/isolation & purification
10.
Front Cell Infect Microbiol ; 11: 654272, 2021.
Article in English | MEDLINE | ID: covidwho-1497024

ABSTRACT

Introduction: Asymptomatic coronavirus disease 2019 (COVID-19) and moderate COVID-19 may be the most common COVID-19 cases. This study was designed to develop a diagnostic model for patients with asymptomatic and moderate COVID-19 based on demographic, clinical, and laboratory variables. Methods: This retrospective study divided the subjects into 2 groups: asymptomatic COVID-19 (without symptoms, n = 15) and moderate COVID-19 (with symptoms, n = 57). Demographic characteristics, clinical data, routine blood tests, other laboratory tests, and inpatient data were collected and analyzed to compare patients with asymptomatic COVID-19 and moderate COVID-19. Results: Comparison of the asymptomatic COVID-19 group with the moderate COVID-19 group yielded the following results: the patients were younger (P = 0.045); the cluster of differentiation (CD)8+ (cytotoxic) T cell level was higher (P = 0.017); the C-reactive protein (CRP) level was lower (P = 0.001); the white blood cell (WBC, P < 0.001), neutrophil (NEU, P = 0.036), lymphocyte (LYM, P = 0.009), and eosinophil (EOS, P = 0.036) counts were higher; and the serum iron level (P = 0.049) was higher in the asymptomatic COVID-19 group. The multivariate analysis showed that the NEU count (odds ratio [OR] = 2.007, 95% confidence interval (CI): 1.162 - 3.715, P = 0.014) and LYM count (OR = 9.380, 95% CI: 2.382 - 36.934, P = 0.001) were independent factors for the presence of clinical symptoms after COVID-19 infection. The NEU count and LYM count were diagnostic predictors of asymptomatic COVID-19. This diagnostic prediction model showed high discriminatory power, consistency, and net clinical benefits. Conclusions: The proposed model can distinguish asymptomatic COVID-19 from moderate COVID-19, thereby helping clinicians identify and distinguish patients with potential asymptomatic COVID-19 from those with moderate COVID-19.


Subject(s)
COVID-19 , Neutrophils , Humans , Lymphocytes , ROC Curve , Retrospective Studies , SARS-CoV-2
11.
PLoS One ; 16(10): e0259179, 2021.
Article in English | MEDLINE | ID: covidwho-1496531

ABSTRACT

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Algorithms , COVID-19/metabolism , Data Accuracy , Deep Learning , Humans , Neural Networks, Computer , ROC Curve , Radiography/methods , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
12.
Sci Rep ; 11(1): 21136, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1493228

ABSTRACT

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.


Subject(s)
COVID-19/mortality , Machine Learning , Pandemics , SARS-CoV-2 , Aged , Aged, 80 and over , Blood Cell Count , Blood Chemical Analysis , COVID-19/blood , Cohort Studies , Female , Hospital Mortality , Humans , Male , Middle Aged , Models, Statistical , Multivariate Analysis , Oxygen/blood , Pandemics/statistics & numerical data , ROC Curve , Risk Factors , Rome/epidemiology
13.
Sci Rep ; 11(1): 21124, 2021 10 26.
Article in English | MEDLINE | ID: covidwho-1493211

ABSTRACT

Patients with coronavirus disease 2019 (COVID-19) can have increased risk of mortality shortly after intubation. The aim of this study is to develop a model using predictors of early mortality after intubation from COVID-19. A retrospective study of 1945 intubated patients with COVID-19 admitted to 12 Northwell hospitals in the greater New York City area was performed. Logistic regression model using backward selection was applied. This study evaluated predictors of 14-day mortality after intubation for COVID-19 patients. The predictors of mortality within 14 days after intubation included older age, history of chronic kidney disease, lower mean arterial pressure or increased dose of required vasopressors, higher urea nitrogen level, higher ferritin, higher oxygen index, and abnormal pH levels. We developed and externally validated an intubated COVID-19 predictive score (ICOP). The area under the receiver operating characteristic curve was 0.75 (95% CI 0.73-0.78) in the derivation cohort and 0.71 (95% CI 0.67-0.75) in the validation cohort; both were significantly greater than corresponding values for sequential organ failure assessment (SOFA) or CURB-65 scores. The externally validated predictive score may help clinicians estimate early mortality risk after intubation and provide guidance for deciding the most effective patient therapies.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Intubation, Intratracheal/methods , Severity of Illness Index , Adolescent , Adult , Age Factors , Aged , Arterial Pressure , COVID-19/therapy , Female , Ferritins/blood , Humans , Hydrogen-Ion Concentration , Male , Middle Aged , New York , Nitrogen/metabolism , Oxygen/metabolism , Predictive Value of Tests , ROC Curve , Regression Analysis , Reproducibility of Results , Retrospective Studies , Sensitivity and Specificity , Vasoconstrictor Agents/pharmacology , Young Adult
14.
In Vivo ; 35(6): 3305-3313, 2021.
Article in English | MEDLINE | ID: covidwho-1485627

ABSTRACT

BACKGROUND: The study provides a novel prediction model for COVID-19 progression and outcome by the combination of the CD8+: B-cells ratio with neutrophil-to-lymphocyte ratio (NLR). PATIENTS AND METHODS: Immune phenotyping was performed in 120 COVID-19 patients. RESULTS: A decrease in CD8+:B-cell (p<0.0001) and in lymphocyte-to-CRP (LCR) ratio (p<0.0001) was observed in intubated patients versus non-intubated with an increase for CD4+:CD8+ (p<0.01), NLR (p<0.0001) and CRP: Albumin (p<0.001). Receiving operating curve (ROC) analysis predicting requirement for mechanical ventilation revealed the highest AUC for CD8+:B-cells, (AUC=0.795, p<0.001) versus NLR (AUC=0.783, p<0.001), LCR (AUC=0.779, p<0.001), Albumin:CRP (AUC=0.750, p<0.001) and CD4+:CD8+ (AUC=0.779, p<0.001). Combination of the CD8+: B-cell ratio with the NLR increased the AUC (AUC=0.845, p<0.001). The combined ratios correlated with outcome defined as duration of hospital (r=0.435, p<0.001) or ICU stay (r=0.596, p<0.001). CONCLUSION: Combination of the CD8+: B-cell ratio and NLR serves as a useful prognostic tool for COVID-19 patient progression.


Subject(s)
COVID-19 , Neutrophils , B-Lymphocytes , CD8-Positive T-Lymphocytes , Humans , Intubation, Intratracheal , Lymphocytes , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
15.
Ann Med ; 53(1): 1863-1874, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1483235

ABSTRACT

OBJECTIVE: To compare the performance of the Risk-stratification of Emergency Department suspected Sepsis (REDS) score to the SIRS criteria, NEWS2, CURB65, SOFA, MEDS and PIRO scores, to risk-stratify Emergency Department (ED) suspected sepsis patients for mortality. METHOD: A retrospective observational cohort study of prospectively collected data. Adult patients admitted from the ED after receiving intravenous antibiotics for suspected sepsis in the year 2020, were studied. Patients with COVID-19 were excluded. The scores stated above were calculated for each patient. Receiver operator characteristics (ROC) curves were constructed for each score for the primary outcome measure, all-cause in-hospital mortality. The area under the ROC (AUROC) curves and cut-off points were identified by the statistical software. Scores above the cut-off point were deemed high-risk. The test characteristics of the high-risk groups were calculated. Comparisons were based on the AUROC curve and sensitivity for mortality of the high-risk groups. Previously published cut-off points were also studied. Calibration was also studied. RESULTS: Of the 2594 patients studied, 332 (12.8%) died. The AUROC curve for the REDS score 0.73 (95% confidence interval [CI] 0.72-0.75) was significantly greater than the AUROC curve for the SIRS criteria 0.51 (95% CI 0.49-0.53), p < .0001 and the NEWS2 score 0.69 (95% CI 0.67-0.70), p = .005, and similar to all other scores studied. Sensitivity for mortality at the respective cut-off points identified (REDS ≥3, NEWS2 ≥ 8, CURB65 ≥ 3, SOFA ≥3, MEDS ≥10 and PIRO ≥10) was greatest for the REDS score at 80.1% (95% CI 75.4-84.3) and significantly greater than the other scores. The sensitivity for mortality for an increase of two points from baseline in the SOFA score was 63% (95% CI 57.5-68.2). CONCLUSIONS: In this single centre study, the REDS score had either a greater AUROC curve or sensitivity for mortality compared to the comparator scores, at the respective cut-off points identified.KEY MESSAGESThe REDS score is a simple and objective scoring system to risk-stratify for mortality in emergency department (MED) patients with suspected sepsis.The REDS score is better or equivalent to existing scoring systems in its discrimination for mortality.


Subject(s)
Anti-Bacterial Agents/administration & dosage , Emergency Service, Hospital/statistics & numerical data , Intensive Care Units/statistics & numerical data , Sepsis/mortality , Severity of Illness Index , Administration, Intravenous , Aged , Aged, 80 and over , Female , Hospital Mortality , Humans , Male , Middle Aged , Prognosis , Prospective Studies , ROC Curve , Retrospective Studies , Risk Assessment/methods , Sepsis/diagnosis , Sepsis/drug therapy
16.
Elife ; 102021 10 18.
Article in English | MEDLINE | ID: covidwho-1478421

ABSTRACT

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.


Subject(s)
COVID-19/mortality , Hospital Mortality , Machine Learning , Aged , Aged, 80 and over , Algorithms , COVID-19/diagnostic imaging , Emergency Service, Hospital , Female , Hospitals , Humans , Italy/epidemiology , Male , Middle Aged , ROC Curve , Risk Factors , SARS-CoV-2/isolation & purification , X-Rays
17.
Medicine (Baltimore) ; 100(32): e26900, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1475915

ABSTRACT

ABSTRACT: Coronavirus disease 2019 (COVID-19) can lead to serious illness and death, and thus, it is particularly important to predict the severity and prognosis of COVID-19. The Sequential Organ Failure Assessment (SOFA) score has been used to predict the clinical outcomes of patients with multiple organ failure requiring intensive care. Therefore, we retrospectively analyzed the clinical characteristics, risk factors, and relationship between the SOFA score and the prognosis of COVID-19 patients.We retrospectively included all patients ≥18 years old who were diagnosed with COVID-19 in the laboratory continuously admitted to Jingzhou Central Hospital from January 16, 2020 to March 23, 2020. The demographic, clinical manifestations, complications, laboratory results, and clinical outcomes of patients infected with the severe acute respiratory syndrome coronavirus-2 were collected and analyzed. Clinical variables were compared between patients with mild and severe COVID-19. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for severe COVID-19. The Cox proportional hazards model was used to analyze risk factors for hospital-related death. Survival analysis was performed by the Kaplan-Meier method, and survival differences were assessed by the log-rank test. Receiver operating characteristic (ROC) curves of the SOFA score in different situations were drawn, and the area under the ROC curve was calculated.A total of 117 patients with confirmed diagnoses of COVID-19 were retrospectively analyzed, of which 108 patients were discharged and 9 patients died. The median age of the patients was 50.0 years old (interquartile range [IQR], 35.5-62.0). 63 patients had comorbidities, of which hypertension (27.4%) was the most frequent comorbidities, followed by diabetes (8.5%), stroke (4.3%), coronary heart disease (3.4%), and chronic liver disease (3.4%). The most common symptoms upon admission were fever (82.9%) and dry cough (70.1%). Regression analysis showed that high SOFA scores, advanced age, and hypertension were associated with severe COVID-19. The median SOFA score of all patients was 2 (IQR, 1-3). Patients with severe COVID-19 exhibited a significantly higher SOFA score than patients with mild COVID-19 (3 [IQR, 2-4] vs 1 [IQR, 0-1]; P  < .001). The SOFA score can better identify severe COVID-19, with an odds ratio of 5.851 (95% CI: 3.044-11.245; P < .001). The area under the ROC curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score in predicting severe COVID-19 (cutoff value = 2; AUC = 0.908 [95% CI: 0.857-0.960]; sensitivity: 85.20%; specificity: 80.40%) and the risk of death in COVID-19 patients (cutoff value = 5; AUC = 0.995 [95% CI: 0.985-1.000]; sensitivity: 100.00%; specificity: 95.40%). Regarding the 60-day mortality rates of patients in the 2 groups classified by the optimal cutoff value of the SOFA score (5), patients in the high SOFA score group (SOFA score ≥5) had a significantly greater risk of death than those in the low SOFA score group (SOFA score < 5).The SOFA score could be used to evaluate the severity and 60-day mortality of COVID-19. The SOFA score may be an independent risk factor for in-hospital death.


Subject(s)
COVID-19/complications , Organ Dysfunction Scores , Adult , Area Under Curve , COVID-19/epidemiology , COVID-19/mortality , Female , Humans , Logistic Models , Male , Middle Aged , Prognosis , Proportional Hazards Models , ROC Curve , Retrospective Studies , Risk Factors , Severity of Illness Index , Statistics, Nonparametric
18.
EMBO Mol Med ; 13(11): e13714, 2021 11 08.
Article in English | MEDLINE | ID: covidwho-1471196

ABSTRACT

Risk stratification of COVID-19 patients is essential for pandemic management. Changes in the cell fitness marker, hFwe-Lose, can precede the host immune response to infection, potentially making such a biomarker an earlier triage tool. Here, we evaluate whether hFwe-Lose gene expression can outperform conventional methods in predicting outcomes (e.g., death and hospitalization) in COVID-19 patients. We performed a post-mortem examination of infected lung tissue in deceased COVID-19 patients to determine hFwe-Lose's biological role in acute lung injury. We then performed an observational study (n = 283) to evaluate whether hFwe-Lose expression (in nasopharyngeal samples) could accurately predict hospitalization or death in COVID-19 patients. In COVID-19 patients with acute lung injury, hFwe-Lose is highly expressed in the lower respiratory tract and is co-localized to areas of cell death. In patients presenting in the early phase of COVID-19 illness, hFwe-Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8-100% and a negative predictive value of 64.1-93.2%. hFwe-Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93-0.97 in predicting hospitalization/death. Specifically, this is significantly higher than the prognostic value of combining biomarkers (serum ferritin, D-dimer, C-reactive protein, and neutrophil-lymphocyte ratio), patient age and comorbidities (AUROC of 0.67-0.92). The cell fitness marker, hFwe-Lose, accurately predicts outcomes in COVID-19 patients. This finding demonstrates how tissue fitness pathways dictate the response to infection and disease and their utility in managing the current COVID-19 pandemic.


Subject(s)
COVID-19 , Biomarkers , Flowers , Humans , Pandemics , ROC Curve , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
19.
PLoS One ; 16(10): e0257421, 2021.
Article in English | MEDLINE | ID: covidwho-1468157

ABSTRACT

Coronavirus Disease-2019 (COVID-19) quickly surged the whole world and affected people's physical, mental, and social health thereby upsetting their quality of life. Therefore, we aimed to investigate the quality of life (QoL) of COVID-19 positive patients after recovery in Bangladesh. This was a study of adult (aged ≥18 years) COVID-19 individuals from eight divisions of Bangladesh diagnosed and confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) from June 2020 to November 2020. Given a response rate of 60% in a pilot study, a random list of 6400 COVID-19 patients was generated to recruit approximately 3200 patients from eight divisions of Bangladesh and finally a total of 3244 participants could be recruited for the current study. The validated Bangla version of the World Health Organization Quality of Life Brief (WHOQOL-BREF) questionnaire was used to assess the QoL. Data were analyzed by STATA (Version 16.1) and R (Version 4.0.0). All the procedures were conducted following ethical approval and in accordance with the Declaration of Helsinki. The mean scores of QoL were highest for the physical domain (68.25±14.45) followed by social (65.10±15.78), psychological (63.28±15.48), and environmental domain (62.77±13.07). Psychological and physical domain scores among females were significantly lower than the males (p<0.001). The overall quality of life was lower in persons having a chronic disease. Participants over 45 years of age were 52% less likely to enjoy good physical health than the participants aged below 26 years (AOR: 0.48, CI: 0.28-0.82). The quality of life of employed participants was found 1.8 times higher than the unemployed (AOR: 1.80, CI: 1.11-2.91). Those who were admitted to hospitals during infection had a low QoL score in physical, psychological, and socials domains. However, QoL improved in all aspect except the psychological domain for each day passed after the diagnosis. These findings call for a focus on the quality of life of the COVID-19 affected population, with special emphasis given to females, older adults, unemployed, and people with comorbidities.


Subject(s)
COVID-19/psychology , Quality of Life , Adult , Area Under Curve , Bangladesh , COVID-19/pathology , COVID-19/virology , Comorbidity , Female , Humans , Logistic Models , Male , Middle Aged , ROC Curve , SARS-CoV-2/isolation & purification , Smoking , Surveys and Questionnaires
20.
Medicine (Baltimore) ; 100(40): e27422, 2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1462561

ABSTRACT

ABSTRACT: As severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals.In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model.Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient's age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital's COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively.We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Age Factors , Aged , Aged, 80 and over , Body Mass Index , Comorbidity , Continental Population Groups , Creatinine/blood , Female , Health Behavior , Humans , Logistic Models , Male , Michigan/epidemiology , Middle Aged , Oximetry , Prognosis , ROC Curve , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Sex Factors , Socioeconomic Factors
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