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 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 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;

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 0.763-0.961], 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

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.

Analysis of Risk Factors Related to Complications of Puncture Biopsy in Lung Cancer Patients Under Image Guidance

2021 ◽  
Vol 11 (1) ◽  
pp. 47-52
Author(s):  
Degang Yin ◽  
Kan Feng ◽  
Biao Yan ◽  
Jiansheng Wang ◽  
Qinming Hou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Complication Rate ◽  
Lung Cancer Patients ◽  
Factors Associated ◽  
Lesion Diameter ◽  
High Risk Factors ◽  
Needle Penetration

To investigate the risk factors of complications in lung cancer patients after CT image-guided percutaneous lung biopsy (PTNB), in this study, 110 patients admitted to Xixi Hospital from January 30, 2017 to June 30, 2019 were selected for PTNB, and the basic characteristic information, lesion diameter, number of needle penetration, depth of needle penetration, physiological results of biopsy, postoperative concurrent symptoms, and success rate of biopsy were recorded. In addition, multivariate Logistic regression model (MLRM) was adopted to explore the correlation between various correlated characters and concurrent symptoms. The results showed that the biopsy pathological results were 53 cases of adenocarcinoma, 31 patients with squamous cell carcinoma, 8 patients with thymic carcinoma, 7 patients with small cell carcinoma and 11 patients with lymph carcinoma, and the success rate of needle biopsy was 100% by comparison with the final diagnosis. Among them, 35 patients developed pneumothorax symptoms postoperatively with a complication rate of 31.82%, 22 patients developed hemoptysis postoperatively with a complication rate of 20%, and 6 patients developed infection with a complication rate of 5.45%. The results of regression analysis showed that pneumothorax and hemoptysis were positively correlated with the number of de needles (P < 0.05), and negatively correlated with lesion diameter (P < 0.05). In addition, pneumothorax was also significantly positively correlated with age (P < 0.05), and infection was significantly positively correlated with the number of puncture needles (P < 0.05). Therefore, the main complications after PTNB are pneumothorax and hemoptysis, the high risk factors associated with pneumothorax include lesion diameter, number of puncture needles and age, the high risk factors associated with hemoptysis include lesion diameter and number of puncture needles, and the risk factors associated with infection are number of puncture needles.

scholarly journals Tumor Characteristics Predicting Perineural Invasion in Cutaneous Squamous Cell Carcinoma Identified by Stepwise Logistic Regression Analysis

2018 ◽  
Author(s):  
Brandon T Beal ◽  
Maulik M Dhandha ◽  
Melinda B Chu ◽  
Vamsi Varra ◽  
Eric S Armbrecht ◽  
...  
Keyword(s):  
Risk Factors ◽  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
High Risk ◽  
Cell Carcinoma ◽  
Perineural Invasion ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Stepwise Logistic Regression ◽  
Factors Associated ◽  
High Risk Factors

Background: Perineural invasion (PNInv) is a significant risk factor for metastasis and death in cutaneous squamous cell carcinoma (cSCC).  Despite this known association, factors contributing to the presence of PNInv are not well characterized.Aims: To determine risk factors associated with the presence of PNInv using the high-risk cSCC criteria developed by the National Comprehensive Cancer Network (NCCN).Methods: After receiving Institutional Review Board approval for this retrospective review, the presence of NCCN high-risk factors for cSCC were recorded for patients treated at a tertiary referral academic medical center, from January 1, 2010 to March 31, 2012. Stepwise logistic regression was used to identify factors associated with the presence of PNInv.Results: PNInv was present in 34 of 507 cSCCs (6.7%). Moderately or poorly differentiated histology (P < .001, OR 6.6 [95% CI, 3.2-13.7]), acantholytic, adenosquamous, or desmoplastic subtype (P =.01, OR 1.8 [95% CI, 0.8-4.2]), and tumors in areas M (≥10mm) and H ( ≥6mm) (P = .05, OR 5.0 [95% CI, 1.2-21.0]) were significantly associated with the presence of PNInv.Conclusions:  This data suggests clinicians should have a higher suspicion and may be able to identify PNInv in high-risk cSCC based on the presence of specific high-risk factors.

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 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.

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