scholarly journals A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Treatment Strategies ◽  
Predictive Performance ◽  
Maximum Dimension ◽  
Ki 67 ◽  
Training Cohort ◽  
Index Level

BackgroundThe Ki-67 index is an indicator of proliferation and aggressive behavior in pituitary adenomas (PAs). This study aims to develop and validate a predictive nomogram for forecasting Ki-67 index levels preoperatively in PAs.MethodsA total of 439 patients with PAs underwent PA resection at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020; they were enrolled in this retrospective study and were classified randomly into a training cohort (n = 300) and a validation cohort (n = 139). A range of clinical, radiological, and laboratory characteristics were collected. The Ki-67 index was classified into the low Ki-67 index (<3%) and the high Ki-67 index (≥3%). Least absolute shrinkage and selection operator algorithm and uni- and multivariate logistic regression analyses were applied to identify independent risk factors associated with Ki-67. A nomogram was constructed to visualize these risk factors. The receiver operation characteristic curve and calibration curve were computed to evaluate the predictive performance of the nomogram model.ResultsAge, primary-recurrence subtype, maximum dimension, and prolactin were included in the nomogram model. The areas under the curve (AUCs) of the nomogram model were 0.694 in the training cohort and 0.658 in the validation cohort. A well-fitted calibration curve was also generated for the nomogram model. A subgroup analysis revealed stable predictive performance for the nomogram model. A correlation analysis revealed that age (R = −0.23; p < 0.01), maximum dimension (R = 0.17; p < 0.01), and prolactin (R = 0.16; p < 0.01) were all significantly correlated with the Ki-67 index level.ConclusionsAge, primary-recurrence subtype, maximum dimension, and prolactin are independent predictors for the Ki-67 index level. The current study provides a novel and feasible nomogram, which can further assist neurosurgeons to develop better, more individualized treatment strategies for patients with PAs by predicting the Ki-67 index level preoperatively.

scholarly journals Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cerebrospinal Fluid ◽  
Prediction Model ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Pituitary Surgery ◽  
Cerebrospinal Fluid Leakage ◽  
Training Cohort ◽  
Preoperative Prediction

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

scholarly journals Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19 Pneumonia: A Multicenter Study

2020 ◽  
Author(s):  
Yun Yang ◽  
Xiaofei Zhu ◽  
Jian Huang ◽  
Cui Chen ◽  
Yang Zheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Validation Cohort ◽  
Fatal Outcome ◽  
Treatment Strategies ◽  
Training Cohort ◽  
Factors Associated ◽  
Risk Of Death ◽  
High Risk Factors ◽  
Cox Analysis

Abstract Background & Aims: To develop an effective model of predicting fatal Outcome in the severe coronavirus disease 2019 (COVID-19) patients.Methods: Between February 20, 2020 and April 4, 2020, consecutive COVID-19 patients from three designated hospitals were enrolled in this study. Independent high- risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR 1.184, 95% CI 1.061-1.321), Panting(breathing rate ≥ 30/min) (HR 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR 2.283, 95% CI 1.779-3.267), and IL-6 >10pg/mL (HR 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC 0.900, [95% CI 0.841-0.960], sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC 0.862, [95% CI 0.763-0.961], sensitivity 92.9%, specificity 64.5%); in validation cohort 2 (AUC 0.811, [95% CI 0.698-0.924], sensitivity 77.3%, specificity 73.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients.

scholarly journals Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19: A Multicenter Study

2021 ◽  
Author(s):  
Yun Yang ◽  
Xiaofei Zhu ◽  
Jian Huang ◽  
Cui Chen ◽  
Yang Zheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Validation Cohort ◽  
Fatal Outcome ◽  
Treatment Strategies ◽  
Training Cohort ◽  
Factors Associated ◽  
Risk Of Death ◽  
High Risk Factors ◽  
Cox Analysis

Abstract Background & Aims: To develop an effective model of predicting fatalOutcome in the severe coronavirus disease 2019 (COVID-19) patients.Methods: Between February 20, 2020 and April 4, 2020, consecutive COVID-19 patients from three designated hospitals were enrolled in this study. Independent high- risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR 1.184, 95% CI 1.061-1.321), Panting(breathing rate ≥ 30/min) (HR 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR 2.283, 95% CI 1.779-3.267), and IL-6 >10pg/mL (HR 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC 0.900, [95% CI 0.841-0.960], sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC 0.811, [95% CI 0.763-0.961], sensitivity 77.3%, specificity 73.5); in validation cohort 2 (AUC 0.862, [95% CI 0.698-0.924], sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of interleukin-6 (IL-6) receiving tocilizumab was better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (p = 0.105 for training cohort, p = 0.133 for validation cohort 1, and p = 0.210 for validation cohort 2).Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

scholarly journals MetS Risk Score: A Clear Scoring Model to Predict a 3-Year Risk for Metabolic Syndrome

2018 ◽  
Vol 50 (09) ◽  
pp. 683-689 ◽  
Author(s):  
Tian-Tian Zou ◽  
Yu-Jie Zhou ◽  
Xiao-Dong Zhou ◽  
Wen-Yue Liu ◽  
Sven Van Poucke ◽  
...  
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Risk Score ◽  
Validation Cohort ◽  
Medical Center ◽  
Characteristic Curve ◽  
Model Development ◽  
Predictive Performance ◽  
Risk Scores ◽  
Individual Risk

AbstractAlthough several risk factors for metabolic syndrome (MetS) have been reported, there are few clinical scores that predict its incidence. Therefore, we created and validated a risk score for prediction of 3-year risk for MetS. Three-year follow-up data of 4395 initially MetS-free subjects, enrolled for an annual physical examination from Wenzhou Medical Center were analyzed. Subjects at enrollment were randomly divided into the training and the validation cohort. Univariate and multivariate logistic regression models were employed for model development. The selected variables were assigned an integer or half-integer risk score proportional to the estimated coefficient from the logistic model. Risk scores were tested in a validation cohort. The predictive performance of the model was tested by computing the area under the receiver operating characteristic curve (AUROC). Four independent predictors were chosen to construct the MetS risk score, including BMI (HR=1.906, 95% CI: 1.040–1.155), FPG (HR=1.507, 95% CI: 1.305–1.741), DBP (HR=1.061, 95% CI: 1.002–1.031), HDL-C (HR=0.539, 95% CI: 0.303–0.959). The model was created as –1.5 to 4 points, which demonstrated a considerable discrimination both in the training cohort (AUROC=0.674) and validation cohort (AUROC=0.690). Comparison of the observed with the estimated incidence of MetS revealed satisfactory precision. We developed and validated the MetS risk score with 4 risk factors to predict 3-year risk of MetS, useful for assessing the individual risk for MetS in medical practice.

scholarly journals Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19: A Multicenter Study

2020 ◽  
Author(s):  
Giorgio Bozzini ◽  
Matteo Maltagliati ◽  
Umberto Besana ◽  
Lorenzo Berti ◽  
Alberto Calori ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Validation Cohort ◽  
Fatal Outcome ◽  
Treatment Strategies ◽  
Training Cohort ◽  
Factors Associated ◽  
Risk Of Death ◽  
High Risk Factors ◽  
Cox Analysis

Abstract Background & Aims: To develop an effective model of predicting fatalOutcome in the severe coronavirus disease 2019 (COVID-19) patients.Methods: Between February 20, 2020 and April 4, 2020, consecutive COVID-19 patients from three designated hospitals were enrolled in this study. Independent high- risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR 1.184, 95% CI 1.061-1.321), Panting(breathing rate ≥ 30/min) (HR 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR 2.283, 95% CI 1.779-3.267), and IL-6 >10pg/mL (HR 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC 0.900, [95% CI 0.841-0.960], sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC 0.811, [95% CI 0.763-0.961], sensitivity 77.3%, specificity 73.5); in validation cohort 2 (AUC 0.862, [95% CI 0.698-0.924], sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of interleukin-6 (IL-6) receiving tocilizumab was better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (p = 0.105 for training cohort, p = 0.133 for validation cohort 1, and p = 0.210 for validation cohort 2).Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

scholarly journals Radiomic Analysis of Gd-EOB-DTPA-enhanced MRI Predicts Ki-67 Expression in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Yanfen Fan ◽  
Yixing Yu ◽  
Ximing Wang ◽  
Mengjie Hu ◽  
Chunhong Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Hepatobiliary Phase ◽  
Net Benefit ◽  
Combined Model ◽  
Ki 67 ◽  
Portal Venous Phase ◽  
Enhanced Mri

Abstract Background: Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods: This retrospective study included 151 (training cohort: n=103; validation cohort: n=48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results: The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC=0.873 vs 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30-60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions: A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

scholarly journals Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yanfen Fan ◽  
Yixing Yu ◽  
Ximing Wang ◽  
Mengjie Hu ◽  
Chunhong Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Hepatobiliary Phase ◽  
Net Benefit ◽  
Combined Model ◽  
Ki 67 ◽  
Portal Venous Phase ◽  
Enhanced Mri

Abstract Background Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30–60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

scholarly journals Preoperative and Postoperative Nomograms in Predicting Early Recurrence of Hepatocellular Carcinoma Without Macrovascular Invasion After Curative Resection

2021 ◽  
Author(s):  
Yanfang Zhang ◽  
Liangliang Xu ◽  
Mingqing Xu ◽  
Hong Tang
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Validation Cohort ◽  
Predictive Performance ◽  
Early Recurrence ◽  
Tumor Diameter ◽  
Training Cohort ◽  
Factors Associated ◽  
Tumor Number ◽  
Before And After

Abstract This study aimed to establish pre- and postoperative nomograms in predicting postoperative early recurrence (ER) for hepatocellular carcinoma (HCC) without macrovascular invasion. The patients who underwent curative LR for HCC from January 2012 to December 2016 in our center were divided into training and internal prospective validation cohorts. Nomograms were constructed based on the independent risk factors derived from multivariate logistic regression analyses in training cohort. The predictive performance of nomograms was validated by internal prospective validation cohort. A total of 698 patients fulfilled with eligible criteria. Among them, 265 out of 482 patients (55.0%) in training cohort and 120 out 216 (55.6%) patients in validation cohort developed ER. The preoperative risk factors associated with ER were age, alpha fetoprotein (AFP), tumor diameter, tumor number; the postoperative risk factors associated with ER were age, tumor diameter, tumor number, microvasular invasion (MVI) and differentiation. The pre- and postoperative nomograms based on these factors showed good accuracy with C-indices of 0.712 and 0.850 in training cohort, and 0.754 and 0.857 in validation cohort, respectively. The calibration curves showed optimal agreement between the prediction by the nomograms and actual observation. The area under the receiver operating characteristic curves of pre- and postoperative nomograms were 0.721 and 0.848 in training cohort, and 0.754 and 0.844 in validation cohort, respectively. Present nomograms showed good performance in predicting ER for HCC without macrovascular invasion before and after surgery, which were helpful for doctors in designation of treatments and selection of patients for regularly surveillance or administration of neoadjuvant therapies.

scholarly journals Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19: A Multicenter Study

2020 ◽  
Author(s):  
Yun Yang ◽  
Xiaofei Zhu ◽  
Jian Huang ◽  
Cui Chen ◽  
Yang Zheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Validation Cohort ◽  
Fatal Outcome ◽  
Treatment Strategies ◽  
Training Cohort ◽  
Factors Associated ◽  
Risk Of Death ◽  
High Risk Factors ◽  
Cox Analysis

Abstract Background & Aims: To develop an effective model of predicting fatalOutcome in the severe coronavirus disease 2019 (COVID-19) patients.Methods: Between February 20, 2020 and April 4, 2020, consecutive COVID-19 patients from three designated hospitals were enrolled in this study. Independent high- risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR 1.184, 95% CI 1.061-1.321), Panting(breathing rate ≥ 30/min) (HR 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR 2.283, 95% CI 1.779-3.267), and IL-6 >10pg/mL (HR 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC 0.900, [95% CI 0.841-0.960], sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC 0.811, [95% CI 0.698-0.924], sensitivity 77.3%, specificity 73.5); in validation cohort 2 (AUC 0.862, [95% CI 0.763-0.961], sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of interleukin-6 (IL-6) receiving tocilizumab was better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (p = 0.105 for training cohort, p = 0.133 for validation cohort 1, and p = 0.210 for validation cohort 2).Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

scholarly journals Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19 Pneumonia: A Multicenter Study

2020 ◽  
Author(s):  
Yun Yang ◽  
Xiaofei Zhu ◽  
Jian Huang ◽  
Cui Chen ◽  
Yang Zheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Validation Cohort ◽  
Fatal Outcome ◽  
Treatment Strategies ◽  
Training Cohort ◽  
Factors Associated ◽  
Risk Of Death ◽  
High Risk Factors ◽  
Cox Analysis

Abstract Background & Aims: To develop an effective model of predicting fatal Outcome in the severe coronavirus disease 2019 (COVID-19) patients. Methods: Between February 20, 2020 and April 4, 2020, consecutive COVID-19 patients from three designated hospitals were enrolled in this study. Independent high- risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients. Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR 1.184, 95% CI 1.061-1.321), Panting(breathing rate ≥ 30/min) (HR 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR 2.283, 95% CI 1.779-3.267), and IL-6 >10pg/mL (HR 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC 0.900, [95% CI 0.841-0.960], sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC 0.862, [95% CI 0.763-0.961], sensitivity 92.9%, specificity 64.5%); in validation cohort 2 (AUC 0.811, [95% CI 0.698-0.924], sensitivity 77.3%, specificity 73.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Keywords: Severe COVID-19; Nomogram; Prediction; Survival;

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