scholarly journals Prediction of Graft Survival Post-liver Transplantation by L-GrAFT Risk Score Model, EASE Score, MEAF Scoring, and EAD

2021 ◽  
Vol 8 ◽  
Author(s):  
Shirui Chen ◽  
Tielong Wang ◽  
Tao Luo ◽  
Shujiao He ◽  
Changjun Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Transplantation ◽  
High Risk ◽  
Graft Survival ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Liver Graft ◽  
C Statistic ◽  
Score Model

Background: Early allograft dysfunction (EAD) is correlated with poor patient or graft survival in liver transplantation. However, the power of distinct definitions of EAD in prediction of graft survival is unclear.Methods: This retrospective, single-center study reviewed data of 677 recipients undergoing orthotopic liver transplant between July 2015 and June 2020. The following EAD definitions were compared: liver graft assessment following transplantation (L-GrAFT) risk score model, early allograft failure simplified estimation score (EASE), model for early allograft function (MEAF) scoring, and Olthoff criteria. Risk factors for L-GrAFT7 high risk group were evaluated with univariate and multivariable logistic regression analysis.Results: L-GrAFT7 had a satisfied C-statistic of 0.87 in predicting a 3-month graft survival which significantly outperformed MEAF (C-statistic = 0.78, P = 0.01) and EAD (C-statistic = 0.75, P < 0.001), respectively. L-GrAFT10, EASE was similar to L-GrAFT7, and they had no statistical significance in predicting survival. Laboratory model for end-stage liver disease score and cold ischemia time are risk factors of L-GrAFT7 high-risk group.Conclusion: L-GrAFT7 risk score is capable for better predicting the 3-month graft survival than the MEAF and EAD in a Chinese cohort, which might standardize assessment of early graft function and serve as a surrogate endpoint in clinical trial.

scholarly journals A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

scholarly journals Diabetes Risk Score in a Young Student Population in Jordan: A Cross-Sectional Study

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Abdel-Ellah Al-Shudifat ◽  
Amjad Al-Shdaifat ◽  
Ahmad Ali Al-Abdouh ◽  
Mohammad Ibrahim Aburoman ◽  
Sara Mohammad Otoum ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Score ◽  
Plasma Glucose ◽  
Risk Group ◽  
High Risk Group ◽  
Student Population ◽  
Diabetes Risk ◽  
Young Student ◽  
Diabetes Risk Score

Background. The Middle East is the home to the most obese population in the world, and type 2 diabetes mellitus is endemic in the region. However, little is known about risk factors for diabetes in the younger age groups. Methods. The Finnish Diabetes Risk Score (FINDRISC) is a simple, validated tool to identify persons at risk of diabetes. We investigated students at Hashemite University in Jordan with FINDRISC and measured fasting plasma glucose in those who were categorized in the high-risk group. Results. Overall, 1821 students (881 [48.4%] female) were included in the study. Risk factors for diabetes were common: 422 (23.2%) were overweight or obese and 497 (27.3%) had central obesity. Using the FINDRISC score, 94 (5.2%) students were at moderate risk and 32 (1.8%) at high risk of diabetes. The mean FINDRISC score was significantly higher in men than women (5.9 versus 5.4; p=0.002). Twenty-eight students in the high-risk group had a subsequent plasma glucose measurement, and 8 (29%) of them fulfilled the diagnostic criteria for diabetes. Conclusions. Risk factors for diabetes were common in a young student population in Jordan, suggesting that preventive measures should be initiated early in adulthood to turn the diabetes epidemic in the region.

scholarly journals Comprehensive transcriptomic characterization reveals core genes and module associated with immunological changes via 1619 samples of brain glioma

2021 ◽  
Vol 12 (12) ◽  
Author(s):  
Ying Zhang ◽  
Wenping Ma ◽  
Wenhua Fan ◽  
Changyuan Ren ◽  
Jianbao Xu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Treatment Options ◽  
Gene Signature ◽  
Immune Checkpoint Blockade ◽  
High Risk Group ◽  
Hub Genes ◽  
Score Model ◽  
Prediction Of Prognosis

AbstractGlioma is the most common primary malignant brain tumor with limited treatment options and poor prognosis. To investigate the potential relationships between transcriptional characteristics and clinical phenotypes, we applied weighted gene co-expression network analysis (WGCNA) to construct a free-scale gene co-expression network yielding four modules in gliomas. Turquoise and yellow modules were positively correlated with the most malignant glioma subtype (IDH-wildtype glioblastomas). Of them, genes in turquoise module were mainly involved in immune-related terms and were regulated by NFKB1, RELA, SP1, STAT1 and STAT3. Meanwhile, genes in yellow module mainly participated in cell-cycle and division processes and were regulated by E2F1, TP53, E2F4, YBX1 and E2F3. Furthermore, 14 genes in turquoise module were screened as hub genes. Among them, five prognostic hub genes (TNFRSF1B, LAIR1, TYROBP, VAMP8, and FCGR2A) were selected to construct a prognostic risk score model via LASSO method. The risk score of this immune-related gene signature is associated with clinical features, malignant phenotype, and somatic alterations. Moreover, this signature showed an accurate prediction of prognosis across different clinical and pathological subgroups in three independent datasets including 1619 samples. Our results showed that the high-risk group was characterized by active immune-related activities while the low-risk group enriched in neurophysiological-related pathway. Importantly, the high-risk score of our immune signature predicts an enrichment of glioma-associated microglia/macrophages and less response to immune checkpoint blockade (ICB) therapy in gliomas. This study not only provides new insights into the molecular pathogenesis of glioma, but may also help optimize the immunotherapies for glioma patients.

scholarly journals Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Shenbin Xu ◽  
Zefeng Wang ◽  
Juan Ye ◽  
Shuhao Mei ◽  
Jianmin Zhang
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Iron Metabolism ◽  
Risk Group ◽  
High Risk Group ◽  
Lower Grade ◽  
Who Grade ◽  
Lower Grade Glioma ◽  
Score Model

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6‐methylguanine‐DNA methyl‐transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

scholarly journals Construction and Analysis of lncRNA-mediated ceRNA Network in Nasopharyngeal Carcinoma Based on Weighted Correlation Network Analysis

2020 ◽  
Author(s):  
Zhenning Zou ◽  
Shuguang Liu ◽  
Yanping Ha ◽  
Bowan Huang
Keyword(s):  
Survival Analysis ◽  
High Risk ◽  
Network Analysis ◽  
Nasopharyngeal Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Cerna Network ◽  
Score Model ◽  
Weighted Correlation

Abstract Increasing evidence indicated that aberrant expression of long noncoding RNAs (lncRNAs) are involved in tumorigenesis of nasopharyngeal carcinoma (NPC). The purpose of this study was to construct a lncRNA-mediated ceRNA network based on weighted correlation network analysis (WGCNA). First, modules with highly correlated genes were identified from GSE102349 via WGCNA, and the preservation of the modules was evaluated by GSE68799. Then, the differentially expressed lncRNAs and mRNAs identified from GSE12452 which belonged to the same WGCNA modules and the differentially expressed miRNAs identified from GSE32960 were used to construct a ceRNA network. The prognostic value of the network was evaluated by survival analysis. Furthermore, a risk score model for predicting progression-free survival (PFS) of NPC patients was established via lasso penalized Cox regression, and the differences in the expression of the lncRNAs between high- and low-risk groups were investigated. Finally, 14 stable modules were identified, and a ceRNA network composed of 11 lncRNAs, 15 miRNAs and 40 mRNAs was established. The lncRNAs and mRNAs in the network belonged to the turquoise and salmon modules. Survival analysis indicated that ZNF667-AS1, LDHA, LMNB2, TPI1, UNG and hsa-miR-142-3p were significantly correlated with the prognosis of NPC. Gene set enrichment analysis indicated that the up-regulation of ZNF667-AS1 was associated with some immune-related pathways. Besides, a risk score model consisting of 12 genes was constructed and showed a good performance in predicting PFS for NPC patients. Among the 11 lncRNAs in the ceRNA network, SNHG16, SNHG17, and THAP9-AS1 were up-regulated in the high-risk group of NPC, while ZNF667-AS1 was down-regulated in the high-risk group of NPC. These results will promote our understanding of the crosstalk among lncRNAs, miRNAs, and mRNAs in the tumorigenesis and progression of NPC.

scholarly journals Molecular Analysis of Prognosis and Immune Pathways of Pancreatic Cancer Based on TNF Family Members

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zemin Zhu ◽  
Caixi Tang ◽  
Tao Xu ◽  
Zhijian Zhao
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Family Members ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Score Model ◽  
Vesicle Cycle

Background. Tumor necrosis factor (TNF) family members play a vital role in anticancer therapy. This study aimed to screen the critical markers for the prognostic analysis of pancreatic adenocarcinoma (PAAD) by analyzing the clustering patterns of TNF family members in PAAD. Methods. In this study, the NMF clustering method was adopted to cluster samples from The Cancer Genome Atlas (TCGA) to acquire the clustering pattern of the TNF family in PAAD. Differential gene analysis was performed according to TNF family gene clusters. The support vector machine (SVM) method was conducted for further gene screening, and the risk score model of the screened genes was constructed by Lasso. The single sample gene set enrichment analysis (ssGSEA) method was adopted for immunoenrichment analysis and tumor immune cycle analysis. Genes associated with risk scores were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results. We clustered PAAD into two groups based on TNF family genes. Nineteen TNF family genes were significantly associated with the clinical characteristics of PAAD patients. The risk score formula was composed of RHOD, UBE2C, KLHDC7b, MSLN, ADAM8, NME3, GNG2, and MCOLN3. GSE57495 and GSE62452 datasets verified that patients in the high-risk group had a worse prognosis than those in the low-risk group. The risk score-related genes analyzed by GO and KEGG were mainly involved in the modulation of chemical synaptic transmission and synaptic vesicle cycle pathway. There were significant differences in the expression of 15 immune cells between the high-risk group and the low-risk group. The risk score was positively correlated with HCK, interferon, MHC-I, and STAT1. The expression of genes relevant to chemokine, immunostimulator, MHC, and receptor was strongly associated with the risk score. Conclusion. The risk score model based on the TNF family can predict the prognosis and immune status of PAAD patients. Further research is needed to verify the clinical prognostic value of risk scores.

scholarly journals Construction and Analysis of lncRNA-Mediated ceRNA Network in Nasopharyngeal Carcinoma Based on Weighted Correlation Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhenning Zou ◽  
Shuguang Liu ◽  
Yanping Ha ◽  
Bowan Huang
Keyword(s):  
Survival Analysis ◽  
High Risk ◽  
Network Analysis ◽  
Nasopharyngeal Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Cerna Network ◽  
Score Model ◽  
Weighted Correlation

Increasing evidence indicated that aberrant expression of long noncoding RNAs (lncRNAs) are involved in tumorigenesis of nasopharyngeal carcinoma (NPC). The purpose of this study was to construct a lncRNA-mediated ceRNA network based on weighted correlation network analysis (WGCNA). First, modules with highly correlated genes were identified from GSE102349 via WGCNA, and the preservation of the modules was evaluated by GSE68799. Then, the differentially expressed lncRNAs and mRNAs identified from GSE12452 which belonged to the same WGCNA modules and the differentially expressed miRNAs identified from GSE32960 were used to construct a ceRNA network. The prognostic value of the network was evaluated by survival analysis. Furthermore, a risk score model for predicting progression-free survival (PFS) of NPC patients was established via LASSO-penalized Cox regression, and the differences in the expression of the lncRNAs between high- and low-risk groups were investigated. Finally, 14 stable modules were identified, and a ceRNA network composed of 11 lncRNAs, 15 miRNAs, and 40 mRNAs was established. The lncRNAs and mRNAs in the network belonged to the turquoise and salmon modules. Survival analysis indicated that ZNF667-AS1, LDHA, LMNB2, TPI1, UNG, and hsa-miR-142-3p were significantly correlated with the prognosis of NPC. Gene set enrichment analysis indicated that the upregulation of ZNF667-AS1 was associated with some immune-related pathways. Besides, a risk score model consisting of 12 genes was constructed and showed a good performance in predicting PFS for NPC patients. Among the 11 lncRNAs in the ceRNA network, SNHG16, SNHG17, and THAP9-AS1 were upregulated in the high-risk group of NPC, while ZNF667-AS1 was downregulated in the high-risk group of NPC. These results will promote our understanding of the crosstalk among lncRNAs, miRNAs, and mRNAs in the tumorigenesis and progression of NPC.

scholarly journals Prognostic model based on autophagy-related lncRNAs in gastric cancer

2020 ◽  
Author(s):  
Mengqiu Cheng ◽  
Wei Cao ◽  
Bo Chen
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Lasso Regression ◽  
Training Set ◽  
Kegg Analysis ◽  
Kaplan Meier ◽  
Score Model

Abstract Background: Gastric cancer (GC) is one of the most pravelent cancer in the world. Although increasing studies have indicated that autophagy-related long non-coding RNA (lncRNA) plays an essential role in the occurrence of GC, the prognosis of GC based on autophagy is still deficient.Method: Autophagy-related lncRNAs were obtained by using the correlation test with the autophagy-related gene. Data was downloaded from The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset. The prognostic autophagy-related lncRNAs significantly correlated with survival of TCGA-STAD dataset were obtained by using Kaplan-Meier and univariate Cox regression analysis. TCGA-STAD dataset was separated into a training set and a testing set randomly. The model was constructed based on the training set through the least absolute shrinkage and selection operator (LASSO) regression. The testing set and TCGA-STAD were used to validate the accuracy of the model. Every patient got a risk score (RS) and patients were separate into high-risk group and low-risk group due to the median RS. The prognostic network was built and the mRNAs in the system were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The signaling pathways that the differentially expressed genes (DEGs) between two types of risk group mainly participated in were distinguished through Gene Set Enrichment Analysis (GSEA). The individual’s survival rate was predicted through the nomogram.Results: 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. Among them, 11 lncRNAs were selected to build the risk score model through LASSO regression. The multivariate Cox analysis showed that the RS could be an independent prognosis predictor. The Kaplan-Meier survival analysis and the Receiver Operating Characteristic (ROC) curve indicated the model had an excellent prediction effect. GO, and KEGG analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy and ubiquitin-like protein ligase binding. GSEA analysis uncovered that the DEGs in high-risk group partially participated in the ECM receptor interaction and other signaling pathways.Conclusions: Our results indicated that the risk score model based on the autophagy-related lncRNAs performed well in the prediction of prognosis for patients with GC.

scholarly journals Comparative risk assessment for the development of cardiovascular diseases in the Hungarian general and Roma population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Piko ◽  
Zsigmond Kosa ◽  
Janos Sandor ◽  
Roza Adany
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Cardiovascular Risk ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Average Risk ◽  
Cvd Risk ◽  
Roma Population

AbstractCardiovascular diseases (CVDs) are the number one cause of death globally, and the early identification of high risk is crucial to prevent the disease and to reduce healthcare costs. Short life expectancy and increased mortality among the Roma are generally accepted (although not indeed proven by mortality analyses) which can be partially explained by the high prevalence of cardiovascular risk factors (CVRF) among them. This study aims to elaborate on the prevalence of the most important CVD risk factors, assess the estimation of a 10-year risk of development of fatal and nonfatal CVDs based on the most used risk assessment scoring models, and to compare the Hungarian general (HG) and Roma (HR) populations. In 2018 a complex health survey was accomplished on the HG (n = 380) and HR (n = 347) populations. The prevalence of CVRS was defined and 10-year cardiovascular risk was estimated for both study populations using the following systems: Framingham Risk Score for hard coronary heart disease (FRSCHD) and for cardiovascular disease (FRSCVD), Systematic COronary Risk Evaluation (SCORE), ACC/AHA Pooled Cohort Equations (PCE) and Revised Pooled Cohort Equations (RPCE). After the risk scores had been calculated, the populations were divided into risk categories and all subjects were classified. For all CVD risk estimation scores, the average of the estimated risk was higher among Roma compared to the HG independently of the gender. The proportion of high-risk group in the Hungarian Roma males population was on average 1.5–3 times higher than in the general one. Among Roma females, the average risk value was higher than in the HG one. The proportion of high-risk group in the Hungarian Roma females population was on average 2–3 times higher compared to the distribution of females in the general population. Our results show that both genders in the Hungarian Roma population have a significantly higher risk for a 10-year development of cardiovascular diseases and dying from them compared to the HG one. Therefore, cardiovascular interventions should be focusing not only on reducing smoking among Roma but on improving health literacy and service provision regarding prevention, early recognition, and treatment of lipid disorders and diabetes among them.

scholarly journals Multiple Myeloma Patients Undergoing Carfilzomib: Development and Validation of a Risk Score for Cardiovascular Adverse Events Prediction

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1631
Author(s):  
Anna Astarita ◽  
Giulia Mingrone ◽  
Lorenzo Airale ◽  
Fabrizio Vallelonga ◽  
Michele Covella ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Risk Assessment ◽  
Multiple Myeloma ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Adverse Events ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Cardiovascular Risk Assessment

Cardiovascular adverse events (CVAEs) are linked to Carfilzomib (CFZ) therapy in multiple myeloma (MM); however, no validated protocols on cardiovascular risk assessment are available. In this prospective study, the effectiveness of the European Myeloma Network protocol (EMN) in cardiovascular risk assessment was investigated, identifying major predictors of CVAEs. From January 2015 to March 2020, 116 MM patients who had indication for CFZ therapy underwent a baseline evaluation (including blood pressure measurements, echocardiography and arterial stiffness estimation) and were prospectively followed. The median age was 64.53 ± 8.42 years old, 56% male. Five baseline independent predictors of CVAEs were identified: office systolic blood pressure, 24-h blood pressure variability, left ventricular hypertrophy, pulse wave velocity value and global longitudinal strain. The resulting ‘CVAEs risk score’ distinguished a low- and a high-risk group, obtaining a negative predicting value for the high-risk group of 90%. 52 patients (44.9%) experienced one or more CVAEs: 17 (14.7%) had major and 45 (38.7%) had hypertension-related events. In conclusion, CVAEs are frequent and a specific management protocol is crucial. The EMN protocol and the risk score proved to be useful to estimate the baseline risk for CVAEs during CFZ therapy, allowing the identification of higher-risk patients.

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