Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

Yu, Yanxia; Wang, Ximing; Li, Min; Gu, Liqun; Xie, Zongyu; Gu, Wenhao; Xu, Feng; Bao, Yaxing; Liu, Rongrong; Hu, Su; Hu, Mengjie; Hu, Chunhong

doi:10.1186/s12880-020-00513-z

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…Previous studies have suggested nomograms for predicting the risk of severe COVID-19 and COVID-19-related mortality using diverse laboratory findings, clinical features, and chest computed tomography (CT) findings. 15 16 17 18 19 24 25 In the present study, we considered age, sex, body mass index, symptoms at the time of COVID-19 diagnosis, and underlying diseases for the prediction model as these parameters can be obtained in the early clinical course. This can be very helpful for identifying patients at risk of mortality at the time of COVID-19 diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“… 2 6 8 9 In other studies, laboratory or radiologic findings were included in the factors predicting severity or mortality associated with COVID-19. 15 16 17 18 19 However, data on these factors are available for a limited number of COVID-19 patients in clinical settings and it takes time to obtain the results of laboratory and radiological examinations, thereby delaying the prediction of mortality and severity in patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

et al. 2021

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Background Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. Methods This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. Results Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/ . Conclusion The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

et al. 2021

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“…The independent predictors among CT signs were selected by using Chi-square test (or Fisher exact test), univariate and backward stepwise multivariate logistic regression methods [ 30 – 33 ]. Firstly, the CT signs with P-value less than 0.05 in Chi-square (or Fisher exact test) were retained to be further conducted with univariate and multivariate logistic regression.…”

Section: Methodsmentioning

confidence: 99%

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia

Huang

Zhang

Liu³

et al. 2021

BMC Med Imaging

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Background In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. Methods A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. Results The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). Conclusions CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

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“…9,10 Several studies have reported risk factors for severe COVID-19 such as demographics, symptoms, laboratory, and imaging findings. [5][6][7] Gong et al 5 found For personal use only. that old age, C-reactive protein, and lower albumin are associated with severe COVID-19.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies indicate that Nomograms based on laboratory investigation, CT imaging, or immunological features can predict the progression to severe disease of COVID-19. [5][6][7] However, most of previous studies used only part of these data. Our objective was to establish a clinical nomogram combining clinical and imaging data to improve the accuracy of prediction.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a Clinical Nomogram Model to Predict the Progression of COVID-19 to Severe Disease

Tu¹,

Wang²,

Geng³

et al. 2021

TCRM

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Background Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aims to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely and efficient manner. Methods This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed using the discrimination curve. Results Risk factors, including demographic data, symptoms, laboratory and image findings, were recorded for the 202 patients. Eight of the 53 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. AUC, sensitivity, and specificity were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset. Conclusion We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

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Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

Cited by 18 publications

References 14 publications

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia

Establishment of a Clinical Nomogram Model to Predict the Progression of COVID-19 to Severe Disease

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