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 FXYD6 overexpression in HBV-related hepatocellular carcinoma with cirrhosis

2020 ◽  
Vol 15 (1) ◽  
pp. 259-266
Author(s):  
Xiongfei Chen ◽  
Lishuang Ding ◽  
Deshuai Kong ◽  
Xiulei Zhao ◽  
Lili Liao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Normal Liver ◽  
Early Recurrence ◽  
Tumor Diameter ◽  
Tumor Number ◽  
B Virus ◽  
Expression Rate ◽  
Polymerase Chain ◽  
And Gender ◽  
Liver Tissues

AbstractObjectiveThe aim of this study was to investigate the expression of FXYD domain-containing ion transport regulator 6 (FXYD6) mRNA and protein in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) tissues with cirrhosis, the corresponding paracancerous tissues and the normal liver tissues, and to explore the clinical significance of FXYD6 expression in HBV-related HCC with cirrhosis.MethodsThe FXYD6 mRNA and protein were examined by semi-quantitative reverse transcription polymerase chain reaction and immunohistochemistry, respectively.ResultsThe FXYD6 mRNA in HBV-related HCC tissues was significantly higher than that in the cirrhosis tissues or that in the normal liver tissues. The positive expression rate of FXYD6 protein was statistically higher in HBV-related HCC tissues than that in HBV-related cirrhosis or that in normal liver tissues. There was no significant correlation between the expression of FXYD6 protein and gender, age, histological differentiation, tumor diameter, tumor number, integrity of tumor capsule or not and alpha fetoprotein (AFP) concentration in serum, but the protein expression was associated with microvascular invasion, pathological stage, and early recurrence after operation within 1 year.ConclusionFXYD6 might be involved in hepatocyte carcinogenesis and tumor progression in HBV-related HCC with cirrhosis and indicated a poor prognosis.

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 Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Ying Zhao ◽  
Nan Wang ◽  
Jingjun Wu ◽  
Qinhe Zhang ◽  
Tao Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Therapeutic Response ◽  
Validation Cohort ◽  
Combined Model ◽  
Training Cohort ◽  
Contrast Enhanced ◽  
Three Phase ◽  
Radiological Model

PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC 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 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 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 (&lt;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 &lt; 0.01), maximum dimension (R = 0.17; p &lt; 0.01), and prolactin (R = 0.16; p &lt; 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 Preoperative Risk Stratification for Early Recurrence of HBV-related Hepatocellular Carcinoma After Deceased Donor Liver Transplantation:A Five-Eight Model Development and Validation

2019 ◽  
Author(s):  
Abdulahad Abdulrab Mohammed Al-Ameri ◽  
Xuyong Wei ◽  
Lidan Lin ◽  
Zhou Shao ◽  
Haijun Guo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Model Development ◽  
Deceased Donor ◽  
Early Recurrence ◽  
Individual Risk ◽  
Tumor Diameter ◽  
Tumor Number ◽  
Serum Alpha Fetoprotein ◽  
Transplant Registry ◽  
Development And Validation

Abstract Background: Early recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) is associated with poor surgical outcomes. This study aims to construct a preoperative model to predict individual risk of post-LT HCC recurrence. Methods: Data of 748 adult patients who underwent deceased donor LT for HCC between January 2015, and February 2019 were collected retrospectively from the China Liver Transplant Registry database and randomly divided into training (n=486) and validation(n=262) cohorts. A multivariate analysis was performed and the five-eight model was developed. Results: A total of 748 patients were included in the study; of them, 96% had hepatitis B virus (HBV) and 84 % had cirrhosis. Pre-LT serum alpha-fetoprotein (AFP), tumor number and largest tumor diameter were incorporated to construct the 5-8 model which can stratify patients accurately according to their risk of recurrence into three prognostic subgroups; low-(0-5 points), medium-(6–8 points) and high-risk (>8 points) with 2-year post-LT recurrence rate of (5%,20% and 51%,p<0.001) respectively.The 5-8 model was better than Milan, Hangzhou, and AFP-model for prediction of HCC early recurrence. These findings were confirmed by the results of the validation cohort. Conclusions: The 5-8 model is a simple validated and accurate tool for preoperative stratification of early recurrence of HCC after LT.

scholarly journals Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib

2020 ◽  
Vol 10 ◽  
Author(s):  
Bin-Yan Zhong ◽  
Zhi-Ping Yan ◽  
Jun-Hui Sun ◽  
Lei Zhang ◽  
Zhong-Heng Hou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Transarterial Chemoembolization ◽  
Validation Cohort ◽  
Survival Prediction ◽  
Ann Model ◽  
Training Cohort ◽  
Prognostic Performance ◽  
Independent Risk Factors ◽  
Artificial Neural Network Ann

PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.

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: a multicenter study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yun Yang ◽  
Xiao-Fei Zhu ◽  
Jian Huang ◽  
Cui Chen ◽  
Yang Zheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Interleukin 6 ◽  
Validation Cohort ◽  
Fatal Outcome ◽  
Training Cohort ◽  
Factors Associated ◽  
Risk Of Death ◽  
High Risk Factors ◽  
Arterial Blood

Abstract Background To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. Methods Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO2 was measured using pulse oxygen saturation in room air at resting status. 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 interleukin-6 (IL-6) >  10 pg/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 IL-6 receiving tocilizumab were 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.

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