scholarly journals A New Prognostic Algorithm Predicting HCC Recurrence in Patients With Barcelona Clinic Liver Cancer Stage B Who Received PA-TACE

2021 ◽  
Vol 11 ◽  
Author(s):  
Shuyang Hu ◽  
Wei Gan ◽  
Liang Qiao ◽  
Cheng Ye ◽  
Demin Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Cancer ◽  
Evaluation System ◽  
Validation Cohort ◽  
Barcelona Clinic Liver Cancer ◽  
Time Dependent ◽  
Training Cohort ◽  
Individual Probability ◽  
Risk Factors For Recurrence ◽  
Independent Risk Factors

BackgroundPostoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) is effective in preventing the recurrence of hepatocellular carcinoma (HCC) in patients treated with surgery. However, there is a lack of reports studying the risk factors associated with recurrence in HCC patients who received PA-TACE. In this study, we identified the independent risk factors for recurrence of HCC patients who received PA-TACE. We also developed a novel, effective, and valid nomogram to predict the individual probability of recurrence, 1, 3, and 5 years after PA-TACE.MethodsA retrospective study was performed to identify the independent risk factors for recurrence of HCC in a group of 502 patients diagnosed in stage B based on the Barcelona Clinic Liver Cancer (BCLC) evaluation system for HCC that underwent curative resections. Then, subgroup analysis was performed for 184 patients who received PA-TACE, who were included in the training cohort. The other 147 HCC patients were included in a validation cohort. A recurrence-free survival (RFS)-predicting nomogram was constructed, and results were assessed using calibration and decision curves and a time-dependent AUC diagram.ResultsPA-TACE was shown to be a significant independent prognostic value for patients with BCLC stage B [p < 0.001, hazard ratio (HR) = 0.508, 95% CI = 0.375–0.689 for OS, p = 0.002; HR = 0.670, 95%CI = 0.517–0.868 for RFS]. Alpha fetoprotein (AFP), tumor number, tumor size, microvascular invasion (MVI), and differentiation were considered as independent risk factors for RFS in the training cohort, and these were further confirmed in the validation cohort. Next, a nomogram was constructed to predict RFS. The C-index for RFS in the nomogram was 0.721 (95% CI = 0.718–0.724), which was higher than SNACOR, HAP, and CHIP scores (0.587, 0.573, and 0.607, respectively). Calibration and decision curve analyses and a time-dependent AUC diagram were used. Our nomogram showed stronger performance than these other nomograms in both the training and validation cohorts.ConclusionsHCC patients diagnosed as stage B according to BCLC may benefit from PA-TACE after surgery. The RFS nomogram presented here provides an accurate and reliable prognostic model to monitor recurrence. Patients with a high recurrence score based on the nomogram should receive additional high-end imaging exams and shorter timeframes in between follow-up visits.

scholarly journals ANVCFV Score System: Assessment for Probability of New Vertebral Compression Fractures after Percutaneous Vertebroplasty in Patients with Vertebral Compression Fractures

2015 ◽  
Vol 6;18 (6;11) ◽  
pp. E1047-E1057
Author(s):  
Gao-Jun Teng
Keyword(s):  
Risk Factors ◽  
Cox Regression ◽  
Validation Cohort ◽  
Percutaneous Vertebroplasty ◽  
Vertebral Compression Fractures ◽  
Score System ◽  
Training Cohort ◽  
Compression Fractures ◽  
Independent Risk Factors ◽  
Risk Score System

Background: Percutaneous vertebroplasty (PVP) is widely used for the treatment of painful vertebral compression fractures (VCFs). However, new VCFs occur frequently after PVP. Objectives: We aim to establish an objective risk score system to assess the possibility of new vertebral fractures in patients with VCFs undergoing PVP. Study Design: This study was a retrospective study, and it was approved by the Institutional Review Board of our 2 institutions. Setting: This study consists of patients from 2 large academic centers. Methods: Patients with VCFs who underwent their first PVP and met the inclusion criteria between January 2007 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included. In the training cohort, the independent risk factors for new VCFs after PVP were identified by multivariate stepwise backward Cox regression analysis from the risk factors selected by univariate analysis and Harrell’s C-statistics and used to develop the score system (assessment for new VCFs after PVP [ANVCFV]) to predict the probability of new VCFs. Results: In total, 397 patients (training cohort: n = 241; validation cohort: n = 156) were included in this study. In the training cohort, the ANVCFV score was developed based on 5 independent risk factors for the new VCFs after PVP, including lower computed tomography (CT) values, pre-existing old VCFs, intradiscal cement leakage, more than one vertebra treated, and superior or inferior marginal cement distribution in the vertebra. The patients were divided into 2 groups by the ANVCFV score of -1.5 to 8.5 vs. > 8.5 points in the probability of new VCFs (median fracture-free time: 1846 vs. 732 days; P < 0.001) in the training cohort. The accuracy of this score system was 77.4% for the training cohort and 85.3% for the validation cohort. Limitations: The main limitations of this study are that it is a retrospective study and that there is a significant difference of the treated vertebrae of PVP per session between the 2 cohorts. Conclusion: Patients who underwent their first PVP with an ANVCFV score > 8.5 points may exhibit an increased chance of suffering from new VCFs. Key words: Vertebral compression fracture, percutaneous vertebroplasty, newly developed, risk factors, risk score system, Cox regression model, accuracy, validation

scholarly journals Nomograms Predicting the Occurrence of Sepsis in Patients following Major Hepatobiliary and Pancreatic Surgery

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haoyun Zhang ◽  
Fanyu Meng ◽  
Shichun Lu
Keyword(s):  
Risk Factors ◽  
Predictive Value ◽  
Pancreatic Surgery ◽  
Clinical Characteristics ◽  
Prediction Models ◽  
Validation Cohort ◽  
Postoperative Sepsis ◽  
Training Cohort ◽  
Hepatobiliary And Pancreatic Surgery ◽  
Independent Risk Factors

Purpose. Sepsis is a severe complication in patients following major hepatobiliary and pancreatic surgery. The purpose of this study was to develop and validate a nomogram based on inflammation biomarkers and clinical characteristics. Methods. Patients who underwent major hepatobiliary and pancreatic surgery between June 2015 and April 2017 were retrospectively collected. Multivariate logistic regression was used to identify the independent risk factors associated with postoperative sepsis. A training cohort of 522 patients in an earlier period was used to develop the prediction models, and a validation cohort of 136 patients thereafter was used to validate the nomograms. Results. Sepsis developed in 55 of 522 patients of the training cohort and 19 of 136 patients in the validation cohort, respectively. In the training cohort, one nomogram based on clinical characteristics was developed. The clinical independent risk factors for postoperative sepsis include perioperative blood transfusion, diabetes, operative time, direct bilirubin, and BMI. Another nomogram was based on both clinical characteristics and inflammation biomarkers. Multivariate regression analyses showed that previous clinical risk factors, PCT, and CRP were independent risk factors for postoperative sepsis. The last nomogram showed a good C-index of 0.844 (95% CI, 0.787-0.900) compared with the previous one of 0.777 (95% CI, 0.713-0.840). Patients with a total score more than 109 in the second model are at high risk. The positive predictive value and negative predictive value of the second nomogram were 27% and 97%, respectively. Conclusion. The nomogram achieved good performances for predicting postoperative sepsis in patients by combining clinical and inflammation risk factors. This model can provide the early risk estimation of sepsis for patients following major hepatobiliary and pancreatic surgery.

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 JAK2V617F variant allele frequency >50% identifies patients with polycythemia vera at high risk for venous thrombosis

2021 ◽  
Vol 11 (12) ◽  
Author(s):  
Paola Guglielmelli ◽  
Giuseppe G. Loscocco ◽  
Carmela Mannarelli ◽  
Elena Rossi ◽  
Francesco Mannelli ◽  
...  
Keyword(s):  
Risk Factors ◽  
Polycythemia Vera ◽  
Allele Frequency ◽  
Validation Cohort ◽  
Multivariable Analysis ◽  
Variant Allele ◽  
Variant Allele Frequency ◽  
Training Cohort ◽  
Independent Risk Factors ◽  
The Impact

AbstractArterial (AT) and venous (VT) thrombotic events are the most common complications in patients with polycythemia vera (PV) and are the leading causes of morbidity and mortality. In this regard, the impact of JAK2V617F variant allele frequency (VAF) is still debated. The purpose of the current study was to analyze the impact of JAK2V617F VAF in the context of other established risk factors for thrombosis in a total of 865 2016 WHO-defined PV patients utilizing two independent cohorts: University of Florence (n = 576) as a training cohort and Policlinico Gemelli, Catholic University, Rome (n = 289) as a validation cohort. In the training cohort VT free-survival was significantly shorter in the presence of a JAK2V617F VAF > 50% (HR 4; p < 0.0001), whereas no difference was found for AT (HR 0.9; p = 0.8). Multivariable analysis identified JAK2V617F VAF > 50% (HR 3.8, p = 0.001) and previous VT (HR 2.2; p = 0.04) as independent risk factors for future VT whereas diabetes (HR 2.4; p = 0.02), hyperlipidemia (HR 2.3; p = 0.01) and previous AT (HR 2; p = 0.04) were independent risk factors for future AT. Similarly, JAK2V617F VAF > 50% (HR 2.4; p = 0.01) and previous VT (HR 2.8; p = 0.005) were confirmed as independent predictors of future VT in the validation cohort. Impact of JAK2V617F VAF > 50% on VT was particularly significant in conventional low-risk patients, both in Florence (HR 10.6, p = 0.005) and Rome cohort (HR 4; p = 0.02). In conclusion, we identified JAK2V617F VAF > 50% as an independent strong predictor of VT, supporting that AT and VT are different entities which might require distinct management.

A Nomogram Combing Peripheral Parameters for Estimation of CRSwNP Recurrence

2020 ◽  
pp. 194589242097895
Author(s):  
Kun Du ◽  
Ming Zheng ◽  
Yan Zhao ◽  
Chunyuan Jiao ◽  
Wenbin Xu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Validation Cohort ◽  
Univariate Analysis ◽  
Sinus Surgery ◽  
Total Serum ◽  
Training Cohort ◽  
Serum Ige ◽  
Preoperative Prediction ◽  
Risk Factors For Recurrence ◽  
Blood Eosinophils

Background The preoperative prediction of the recurrence of chronic rhinosinusitis with nasal polyps (CRSwNP) remains difficult in clinical practice. Objective We aimed to develop a nomogram that combined peripheral risk factors to clinically predict the recurrence of CRSwNP. Methods Data from 158 CRSwNP patients who underwent endoscopic sinus surgery (ESS) from January 2012 to December 2016 were collected, and the patients were followed up for 3 years. Of these, 96 patients who underwent ESS in an earlier period formed the training cohort for nomogram development, and 62 patients who underwent ESS thereafter formed the validation cohort to confirm the model’s performance. Risk factors for recurrence identified by univariate and multivariate logistic regression were used to create a nomogram. Results The recurrence rate was 29.2% (28/96) for the training cohort and 35.5% (22/62) for the validation cohort. Univariate analysis identified blood eosinophils (Eos), serum IgE level, asthma comorbidity, and the number of previous ESSs as risk factors for recurrence. Among those four parameters, serum IgE level and a previous ESS surgery were identified as two independent risk factors. A nomogram consisting of blood Eos, total serum IgE level, asthma comorbidity, and the number of previous ESSs was constructed, demonstrating a C index of 0.81 (95% CI, 0.79-0.83) and 0.80 (95% CI, 0.77-0.83) for predicting recurrence in the training and validation cohorts, respectively. The nomogram had well-fitted calibration curves. Conclusion The nomogram might be able to preoperatively predict the recurrence of CRSwNP by using currently available and objective parameters. Further studies are required to validate its reliability and effectiveness.

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 A Nomogram Based on Preoperative Inflammatory Indices and ICG-R15 for Prediction of Liver Failure After Hepatectomy in HCC Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Tongdi Fang ◽  
Guo Long ◽  
Dong Wang ◽  
Xudong Liu ◽  
Liang Xiao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Failure ◽  
Validation Cohort ◽  
Meld Score ◽  
Decision Curve Analysis ◽  
Independent Risk Factors ◽  
Icg R15 ◽  
Score Model ◽  
End Stage ◽  
Central South

ObjectiveTo establish a nomogram based on inflammatory indices and ICG-R15 for predicting post-hepatectomy liver failure (PHLF) among patients with resectable hepatocellular carcinoma (HCC).MethodsA retrospective cohort of 407 patients with HCC hospitalized at Xiangya Hospital of Central South University between January 2015 and December 2020, and 81 patients with HCC hospitalized at the Second Xiangya Hospital of Central South University between January 2019 and January 2020 were included in the study. Totally 488 HCC patients were divided into the training cohort (n=378) and the validation cohort (n=110) by random sampling. Univariate and multivariate analysis was performed to identify the independent risk factors. Through combining these independent risk factors, a nomogram was established for the prediction of PHLF. The accuracy of the nomogram was evaluated and compared with traditional models, like CP score (Child-Pugh), MELD score (Model of End-Stage Liver Disease), and ALBI score (albumin-bilirubin) by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsCirrhosis (OR=2.203, 95%CI:1.070-3.824, P=0.030), prothrombin time (PT) (OR=1.886, 95%CI: 1.107-3.211, P=0.020), tumor size (OR=1.107, 95%CI: 1.022-1.200, P=0.013), ICG-R15% (OR=1.141, 95%CI: 1.070-1.216, P&lt;0.001), blood loss (OR=2.415, 95%CI: 1.306-4.468, P=0.005) and AST-to-platelet ratio index (APRI) (OR=4.652, 95%CI: 1.432-15.112, P=0.011) were independent risk factors of PHLF. Nomogram was built with well-fitted calibration curves on the of these 6 factors. Comparing with CP score (C-index=0.582, 95%CI, 0.523-0.640), ALBI score (C-index=0.670, 95%CI, 0.615-0.725) and MELD score (C-ibasedndex=0.661, 95%CI, 0.606-0.716), the nomogram showed a better predictive value, with a C-index of 0.845 (95%CI, 0.806-0.884). The results were consistent in the validation cohort. DCA confirmed the conclusion as well.ConclusionA novel nomogram was established to predict PHLF in HCC patients. The nomogram showed a strong predictive efficiency and would be a convenient tool for us to facilitate clinical decisions.

Independent risk factors for recurrence of apparent life-threatening events in infants

2017 ◽  
Vol 176 (4) ◽  
pp. 443-448 ◽  
Author(s):  
Riyo Ueda ◽  
Osamu Nomura ◽  
Takanobu Maekawa ◽  
Hirokazu Sakai ◽  
Satoshi Nakagawa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Life Threatening ◽  
Risk Factors For Recurrence ◽  
Independent Risk Factors

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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yixing Yu ◽  
Ximing Wang ◽  
Min Li ◽  
Lan Gu ◽  
Zongyu Xie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Severity Score ◽  
Early Stage ◽  
Area Under The Curve ◽  
Clinical Impact ◽  
Imaging Data ◽  
Net Benefit ◽  
Training Cohort ◽  
Independent Risk Factors

Abstract Background To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. Results In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5–9) was significantly higher than that of the mild group (4, IQR,2–5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889–0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867–1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. Conclusion The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

Sign in / Sign up

Export Citation Format

Share Document