Development and Validation of Nomogram to Predict Very Early Recurrence of Combined Hepatocellular-Cholangiocarcinoma After Hepatic Resection: A Multi-Institutional Study

2021 ◽  
Author(s):  
Yijun Wu ◽  
Hongzhi Liu ◽  
Jianxing Zeng ◽  
Yifan Chen ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Risk Groups ◽  
Early Recurrence ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
High Incidence ◽  
Combined Hepatocellular Cholangiocarcinoma ◽  
Development And Validation

Abstract Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.

scholarly journals Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

2020 ◽  
Author(s):  
Jian Wang ◽  
Zhihua Xu ◽  
Guohua Cheng ◽  
Qiuxiang Hu ◽  
Linyang He ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Chest Ct ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
Selection Operator ◽  
Lasso Method ◽  
Development And Validation ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. Thus, we aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusions We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

scholarly journals CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19

2021 ◽  
Author(s):  
Hengfeng Shi ◽  
Zhihua Xu ◽  
Guohua Cheng ◽  
Hongli Ji ◽  
Linyang He ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Chest Ct ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
Selection Operator ◽  
Lasso Method ◽  
Radiomics Signature ◽  
Good Calibration

Abstract Background: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. We aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19. To develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results: The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P<0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusion: We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

scholarly journals Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2020 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

ABATRACTIMPORTANCEIn the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively.OBJECTIVETo develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19).DESIGNDiagnostic model based on retrospective case series.SETTINGTwo hospitals in Wuhan and Beijing, China.PTRTICIPANTS584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020.METHODSLASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram.RESULTSSix potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p ≤0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale.CONCLUSIONSWe established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

Prognosis viewing for nasopharyngeal carcinoma treated with intensity-modulated radiation therapy: application of nomogram and decision curve analysis

2019 ◽  
Vol 50 (2) ◽  
pp. 159-168
Author(s):  
Zhaodong Fei ◽  
Xiufang Qiu ◽  
Mengying Li ◽  
Chuanben Chen ◽  
Yi Li ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Intensity Modulated Radiation Therapy ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Intensity Modulated ◽  
T Stage ◽  
N Stage

Abstract Objective To view and evaluate the prognosis factors in patients with nasopharyngeal carcinoma (NPC) treated with intensity modulated radiation therapy using nomogram and decision curve analysis (DCA). Methods Based on a primary cohort comprising consecutive patients with newly confirmed NPC (n = 1140) treated between January 2014 and December 2015, we identified independent prognostic factors of overall survival (OS) to establish a nomogram. The model was assessed by bootstrap internal validation and external validation in an independent validation cohort of 460 patients treated between January 2013 and December 2013. The predictive accuracy and discriminative ability were measured by calibration curve, concordance index (C-index) and risk-group stratification. The clinical usefulness was assessed by DCA. Results The nomogram incorporated T-stage, N-stage, age, concurrent chemotherapy and primary tumour volume (PTV). The calibration curve presented good agreement for between the nomogram-predicted OS and the actual measured survival probability in both the primary and validation cohorts. The model showed good discrimination with a C-index of 0.741 in the primary cohort and 0.762 in the validation cohort. The survival curves of different risk-groups were separated clearly. Decision curve analysis demonstrated that the nomogram provided a higher net benefit (NB) across a wider reasonable range of threshold probabilities for predicting OS. Conclusion This study presents a predictive nomogram model with accurate prediction and independent discrimination ability compared with combination of T-stage and N-stage. The results of DCA supported the point that PTV can help improve the prognostic ability of T-stage and should be added to the TNM staging system.

scholarly journals Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

2021 ◽  
Author(s):  
Qing-Bo Zeng ◽  
Long-Ping He ◽  
Nian-Qing Zhang ◽  
Qing-Wei Lin ◽  
Lin-Cui Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Validation Cohort ◽  
International Normalized Ratio ◽  
Curve Analysis ◽  
Individual Risk ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Receiver Operating Characteristic Curves ◽  
Individual Risk Factors

Abstract Background Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work aimed to define a nomogram for reliably predicting mortality. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

scholarly journals Development and validation of a preoperative nomogram for predicting patients with impacted ureteral stone: a retrospective analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chenglu Wang ◽  
Lu Jin ◽  
Xinyang Zhao ◽  
Boxin Xue ◽  
Min Zheng
Keyword(s):  
Calibration Curve ◽  
Ureteral Stone ◽  
Multivariate Logistic Regression Analysis ◽  
Curve Analysis ◽  
Ureteral Stones ◽  
Ureteral Calculus ◽  
Validation Group ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Development And Validation

Abstract Background To develop and validate a practical nomogram for predicting the probability of patients with impacted ureteral stone. Methods Between June 2020 to March 2021, 214 single ureteral stones received ureteroscopy lithotripsy (URSL) were selected in development group. While 82 single ureteral stones received URSL between April 2021 to May 2021 were included in validation group. Independent factors for predicting impacted ureteral stone were screened by univariate and multivariate logistic regression analysis. The relationship between preoperative factors and stone impaction was modeled according to the regression coefficients. Discrimination and calibration were estimated by area under the receiver operating characteristic (AUROC) curve and calibration curve respectively. Clinical usefulness of the nomogram was evaluated by decision curve analysis. Results Age, ipsilateral stone treatment history, hydronephrosis and maximum ureteral wall thickness (UWTmax) at the portion of stone were identified as independent predictors for impacted stone. The AUROC curve of development and validation group were 0.915 and 0.882 respectively. Calibration curve of two groups showed strong concordance between the predicted and actual probabilities. Decision curve analysis showed that the predictive nomogram had a superior net benefit than UWTmax for all examined probabilities. Conclusions We developed and validated an individualized model to predict impacted ureteral stone prior to surgery. Through this prediction model, urologists can select an optimal treatment method and decrease intraoperative and postoperative complications for patients with impacted ureteral calculus.

scholarly journals A nomogram to predict prognosis after surgery for young patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
Xingchen Li ◽  
Xinyu Bi ◽  
Jianjun Zhao ◽  
Zhiyu Li ◽  
Jianguo Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Young Patients ◽  
Risk Groups ◽  
Curve Analysis ◽  
Rank Test ◽  
Cancer Center ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis ◽  
Log Rank Test ◽  
Kaplan Meier

Abstract Background Only few studies have been evaluated the clinical characteristics and prognosis of hepatocellular carcinoma in young patients. The purpose of this study is to identify prognostic factors and develop an efficient and practical nomogram to predict cancer-specific survival in young patients with hepatocellular carcinoma.Methods Four-hundred-and-forty-one young patients with hepatocellular carcinoma who had undergone surgery from 2004-2015 were selected from the Surveillance, Epidemiology, and End Results database. The competing risk model, Lasso and Cox regression were used to screen prognostic factors for cancer-specific survival, and a prognostic nomogram was established using these factors. Thirty-nine young patients with hepatocellular carcinoma from the National Cancer Center, Cancer Hospital, Chinese Academy of Medical Science were used to validate our model. To further evaluate the predictive performance of our model, the concordance index was calculated and the calibration curves were drawn. The clinical usefulness was evaluated by decision curve analysis(DCA). Finally, all patients were grouped by our nomogram. The survival of different risk groups was analyzed using the Kaplan-Meier method, and the differences among survival curves were compared by the log-rank test.Results The median survival times of the Surveillance, Epidemiology, and End Results training group and the external National Cancer Center validation group were 41 and 52 months, respectively. Histological grade, tumor size, Alpha-fetoprotein, T stage, and M stage were selected as independent factors for cancer-specific survival, and a prognostic nomogram was established. The concordance indices of the training and external validation groups were 0.76 (95% CI, 0.72 to 0.80) and 0.92 (se=0.085), respectively. The calibration plots showed good agreement. Decision curve analysis revealed that our nomogram resulted in a better clinical net benefit than the AJCC 7th edition and Barcelona Clinic Liver Cancer staging systems. Patients were divided into two risk groups according to the cut-off value of 125 of the total points from our nomogram. Kaplan-Meier plots for cancer-specific survival were performed using the log-rank test, the p-value of which was <0.001.Conclusions The practical nomogram resulted in a more-accurate prognostic prediction for young hepatocellular carcinoma patients after curative liver resection.

scholarly journals Development and Validation of a Nomogram Incorporating Colloid Osmotic Pressure for Predicting Mortality in Critically Ill Neurological Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Bo Lv ◽  
Linhui Hu ◽  
Heng Fang ◽  
Dayong Sun ◽  
Yating Hou ◽  
...  
Keyword(s):  
Osmotic Pressure ◽  
Validation Cohort ◽  
External Validation ◽  
Tertiary Hospital ◽  
Colloid Osmotic Pressure ◽  
Curve Analysis ◽  
Apache Ii Score ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Neurological Patients

Backgrounds: The plasma colloid osmotic pressure (COP) values for predicting mortality are not well-estimated. A user-friendly nomogram could predict mortality by incorporating clinical factors and scoring systems to facilitate physicians modify decision-making when caring for patients with serious neurological conditions.Methods: Patients were prospectively recruited from March 2017 to September 2018 from a tertiary hospital to establish the development cohort for the internal test of the nomogram, while patients recruited from October 2018 to June 2019 from another tertiary hospital prospectively constituted the validation cohort for the external validation of the nomogram. A multivariate logistic regression analysis was performed in the development cohort using a backward stepwise method to determine the best-fit model for the nomogram. The nomogram was subsequently validated in an independent external validation cohort for discrimination and calibration. A decision-curve analysis was also performed to evaluate the net benefit of the insertion decision using the nomogram.Results: A total of 280 patients were enrolled in the development cohort, of whom 42 (15.0%) died, whereas 237 patients were enrolled in the validation cohort, of which 43 (18.1%) died. COP, neurological pathogenesis and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were predictors in the prediction nomogram. The derived cohort demonstrated good discriminative ability, and the area under the receiver operating characteristic curve (AUC) was 0.895 [95% confidence interval (CI), 0.840–0.951], showing good correction ability. The application of this nomogram to the validation cohort also provided good discrimination, with an AUC of 0.934 (95% CI, 0.892–0.976) and good calibration. The decision-curve analysis of this nomogram showed a better net benefit.Conclusions : A prediction nomogram incorporating COP, neurological pathogenesis and APACHE II score could be convenient in predicting mortality for critically ill neurological patients.

scholarly journals Development and Validation of a Nomogram for Predicting Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Wenqiang Guan ◽  
Kang Xie ◽  
Yixin Fan ◽  
Stefan Lin ◽  
Rui Huang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Temporal Lobe ◽  
Validation Cohort ◽  
External Validation ◽  
Multivariable Logistic Regression Analysis ◽  
Maximum Point ◽  
Good Discrimination ◽  
Internal Validation ◽  
Dose Volume Histograms ◽  
Radiation Induced

BackgroundThe purpose was to develop and validate a nomogram for prediction on radiation-induced temporal lobe injury (TLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThe prediction model was developed based on a primary cohort that consisted of 194 patients. The data was gathered from January 2008 to December 2010. Clinical factors associated with TLI and dose–volume histograms for 388 evaluable temporal lobes were analyzed. Multivariable logistic regression analysis was used to develop the predicting model, which was conducted by R software. The performance of the nomogram was assessed with calibration and discrimination. An external validation cohort contained 197 patients from January 2011 to December 2013.ResultsAmong the 391 patients, 77 patients had TLI. Prognostic factors contained in the nomogram were Dmax (the maximum point dose) of temporal lobe, D1cc (the maximum dose delivered to a volume of 1 ml), T stage, and neutrophil-to-lymphocyte ratios (NLRs). The Internal validation showed good discrimination, with a C-index of 0.847 [95%CI 0.800 to 0.893], and good calibration. Application of the nomogram in the external validation cohort still obtained good discrimination (C-index, 0.811 [95% CI, 0.751 to 0.870]) and acceptable calibration.ConclusionsThis study developed and validated a nomogram, which may be conveniently applied for the individualized prediction of TLI.

Development And Validation Of Models To Predict Cesarean Delivery Among Low-Risk Nulliparous Women At Term: A Retrospective Study In China

2020 ◽  
Author(s):  
Fangcan Sun ◽  
Bing Han ◽  
Fangfang Wu ◽  
Qianqian Shen ◽  
Minhong Shen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Cesarean Delivery ◽  
Prediction Models ◽  
External Validation ◽  
Curve Analysis ◽  
Low Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Nulliparous Women

Abstract Background: Cesarean delivery after failure of trial of labor is associated with adverse maternal and perinatal outcomes. A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict cesarean delivery for low-risk nulliparous women in Chinese population.Methods: This retrospective study analyzed the low-risk nulliparous women with singleton cephalic full-term fetus delivered in two medical centers. After the clinical data of the women who delivered at the tertiary referral center (n=6 551) was collected and was used univariate and multivariable logistic regression analysis, the prediction model was fitted. We performed external validation using data from nulliparous who delivered from another hospital(secondary referral center, n=7 657). A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance. Results: The cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6 551) and 7.82% (599/7 657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. Because sex of the fetuses were unknown until they born(China's Fertility Policy), we established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The Hosmer-Lemeshow goodness-of-fit test showed that these two models fitted well. Decision curve analysis demonstrated that the models were clinically useful. And internal validation using Bootstrap method showed that these prediction models perform well. On the external validation set, the AUC were 0.775 and 0.775, respectively. The calibration plots for the probability of cesarean showed a good correlation. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions: Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.

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