scholarly journals Proteomics-based prognostic signature and nomogram construction of hypoxia microenvironment on deteriorating glioblastoma (GBM) pathogenesis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ya-Dan Wen ◽  
Xiao-San Zhu ◽  
Dong-Jie Li ◽  
Qing Zhao ◽  
Quan Cheng ◽  
...  
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Survival Curve ◽  
Statistical Significance ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Curve Analysis ◽  
Glioblastoma Cells ◽  
Molecular Features ◽  
Score Model

AbstractThe present study aimed to construct and evaluate a novel experiment-based hypoxia signature to help evaluations of GBM patient status. First, the 426 proteins, which were previously found to be differentially expressed between normal and hypoxia groups in glioblastoma cells with statistical significance, were converted into the corresponding genes, among which 212 genes were found annotated in TCGA. Second, after evaluated by single-variable Cox analysis, 19 different expressed genes (DEGs) with prognostic value were identified. Based on λ value by LASSO, a gene-based survival risk score model, named RiskScore, was built by 7 genes with LASSO coefficient, which were FKBP2, GLO1, IGFBP5, NSUN5, RBMX, TAGLN2 and UBE2V2. Kaplan–Meier (K–M) survival curve analysis and the area under the curve (AUC) were plotted to further estimate the efficacy of this risk score model. Furthermore, the survival curve analysis was also plotted based on the subtypes of age, IDH, radiotherapy and chemotherapy. Meanwhile, immune infiltration, GSVA, GSEA and chemo drug sensitivity of this risk score model were evaluated. Third, the 7 genes expression were evaluated by AUC, overall survival (OS) and IDH subtype in datasets, importantly, also experimentally verified in GBM cell lines exposed to hypoxic or normal oxygen condition, which showed significant higher expression in hypoxia than in normal group. Last, combing the hypoxia RiskScore with clinical and molecular features, a prognostic composite nomogram was generated, showing the good sensitivity and specificity by AUC and OS. Meanwhile, univariate analysis and multivariate analysis were used for performed to identify variables in nomogram that were significant in independently predicting duration of survival. It is a first time that we successfully established and validated an independent prognostic risk model based on hypoxia microenvironment from glioblastoma cells and public database. The 7 key genes may provide potential directions for future biochemical and pharmaco-therapeutic research.

Abstract 13530: A Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Ed-vad Risk Score

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Jonathan B Edelson ◽  
Jonathan J Edwards ◽  
Hannah Katcoff ◽  
Antara Mondal ◽  
Feiyan Chen ◽  
...  
Keyword(s):  
Emergency Department ◽  
Risk Score ◽  
Ventricular Assist Device ◽  
Risk Model ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Assist Device ◽  
Ventricular Assist ◽  
Predictors Of Mortality ◽  
Risk Of Death

Introduction: The past decade has seen tremendous growth in ambulatory ventricular assist device (VAD) patients. We sought to identify patients that present to the emergency department (ED) who are at the highest risk of death. Methods: We performed a retrospective analysis of ED encounters of VAD patients using data from the Nationwide Emergency Department Sample (NEDS) from 2010-2017. Demographic and clinical variables significantly associated with mortality (p < 0.2) in a univariate analysis were evaluated in a multivariate model. Using a random sampling of patient encounters, 80% were assigned to development and 20% to validation cohorts. A risk model was derived from independent predictors of mortality, which were weighted using integer-normalized beta coefficients. Each patient encounter was assigned to one of three groups based on risk score. Results: A total of 44,042 ED encounters of VAD patients were included in the study. The majority of patients were male (73.6%), <65 years old (60.1%), and 29% presented with bleeding, ischemic/hemorrhagic stroke, or device complication. Independent predictors of mortality during the ED visit or subsequent admission included age ≥65 years (OR 1.8, 95% CI 1.3, 4.6), primary diagnoses [stroke (OR 19.4, 95% CI 13.1, 28.8), device complication (OR 10.1, 95% CI 6.5, 16.7), cardiac (OR 4.0 95% CI 2.7, 6.1), infection (OR 5.8, 95% CI 3.5, 8.9)], and blood transfusion (OR 2.6, 95% CI 1.8, 4.0), while history of hypertension was protective (OR 0.69, 95% CI 0.5, 0.9)]. The risk score predicted mortality with an area under the curve of 0.78 and 0.71 for development and validation, respectively. Encounters in the highest risk score strata tertile had a 16-fold higher mortality compared to lowest risk tertile (15.8% vs 1.0%). Conclusions: We present a novel risk score and its validation for predicting mortality of VAD patients who present to the ED, which can serve as useful tool for clinicians caring for this high-risk, and growing, population.

scholarly journals Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12554
Author(s):  
Liming Zheng ◽  
Xi Gu ◽  
Guojun Zheng ◽  
Xin Li ◽  
Meifang He ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Early Recurrence ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Cox Regression Analysis ◽  
Score Model ◽  
Hcc Recurrence

Background Early recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients’ risk of early recurrence and their response to adjuvant therapy. Methods In this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan–Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively. Results The risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895–7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155–4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001–6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78–5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category. Conclusions Our risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.

scholarly journals Short-Term Risk of Progression of Patients with Asymptomatic Monoclonal Gammopathies to Active Multiple Myeloma: The Critical Impact of the Tumoral Mass

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1795-1795
Author(s):  
Anna Benedetta Dalla Palma ◽  
Laura Notarfranchi ◽  
Jessica Crosara ◽  
Mario Pedrazzoni ◽  
Fabrizio Accardi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multiple Myeloma ◽  
Risk Score ◽  
Statistical Significance ◽  
Prognostic Score ◽  
Univariate Analysis ◽  
M Protein ◽  
Short Term ◽  
Risk Of Progression ◽  
Risk Patients

The identification of risk factors for progression is critical in the clinical management and appropriate follow up of patients with pre-malignant Asymptomatic Monoclonal Gammopathies (AMG) including Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM). The development of prognostic score and consequently the early identification of patients with possible short-term progression to Multiple Myeloma (MM) could lead to anticipate the treatment. In this study, we retrospectively evaluated possible risk factors of short-term progression to active MM in a large cohort of MGUS and SMM patients admitted to a single haematological center (Hematology and BMT Unit, University Hospital of Parma) between 2010 and 2018. We analysed a total cohort of 235 patients diagnosed with AMG (81 MGUS and 154 SMM) according to the IMWG recently updated diagnostic criteria. All patients analysed underwent to Bone Marrow (BM) examination; moreover, imaging evaluation was performed in 22 MGUS and 123 SMM patients, in order to exclude the presence of bone disease. In a subgroup of AMG patients (n=50), bone mineral density (BMD) evaluation by Dual-energy X-ray Absorptiometry (DXA) was also available. Median age of the AMG patients analysed was 68 years (range 35-93 years). Median percentage of BM plasma cells (BMPCs) was 12% (range 2-55%) in the entire population, 7% (range 2-9) in MGUS and 15% (range 10-55) in SMM patients. Median serum M-protein was 1.7 g/dL (range: 0.17-4.5), 1.5 g/dL (range 0.17-4.5) in MGUS and 1.8 g/dL (range 0.4-2.7) in SMM patients. An abnormal free light chain (FLC) ratio was found in 70% of AMG patients, among the ones that performed the analysis; regarding SMM patients, FLC ratio value was available in 97 patients: in 72 (76%) the ratio was unbalanced, 37 (39%) had a FLC ratio ≤ 0.125 or ≥ 8 and in 14 (15%) it was > 20; among MGUS patients, value was collected in 41 patients and in 21 (51%) it was <0.26 or >1.65. The presence of immunoparesis in one or two uninvolved immunoglobulins occurred in 59% of the entire population. The median follow up time was 18 months (range 0 - 111 months) for whole population. Overall 44 patients of the entire cohort progressed to MM (41 SMM and 3 MGUS) with a median TTP of 14.5 months. By univariate analysis we found that percentage of BMPCs, entity of M-protein and presence of immunoparesis were significantly correlated with progression to active MM (p<0.001 for each variable). On the other hand, abnormal FLC ratio did not reach a statistical significance, as well as value of the involved FLC (p=0.059). Nevertheless, the presence of a FLC ratio < 0.125 or > 8 (as used in Mayo scoring system for SMM) showed a relationship at the limit of statistical significance in this subgroup of patients (p=0.052). Any significant correlation was not observed with age, sex, Ig isotype, light chain's type and the BMD values (p=NS). Afterwards, we applied Kaplan Meier method on risk factors resulted significant in univariate analysis demonstrating that they also significantly influenced the time to progression to MM. Finally, through a binomial logistic regression, we developed a new prognostic score for whole population. By combining the values of M-protein (< 2, score=0 or ≥ 2 g/dL, score=1) and the percentage of BMPC (<10%, score=0, 10-20%, score=1 and >20%, score=2), we obtained six groups at different probability of progression to active MM (Table 1). Given that result, we stratified patients in 3 groups: low-risk (score=0), intermediate-risk (score=1) and high-risk (score≥2); log-rank test confirmed that high-risk patients had a significantly shorter time to progression to symptomatic MM as compared to intermediate and low-risk patients (p<0.001). In conclusion, our results show that in patients with AMG the clinical factors, which mostly impact on the short-term risk of progression to active MM, are the entity of the PCs infiltrate and the MC related to the tumoral mass. The development of a clinical score based on BMPCs and M-protein will permit to overcome the traditional distinction between MGUS and SMM in the evaluation of the progression of AMG patients to active MM. Disclosures Giuliani: Janssen: Research Funding.

scholarly journals Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Guangzhao Huang ◽  
Zhi-yun Li ◽  
Yu Rao ◽  
Xiao-zhi Lv
Keyword(s):  
Survival Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

scholarly journals Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kang-Wen Xiao ◽  
Zhi-Bo Liu ◽  
Zi-Hang Zeng ◽  
Fei-Fei Yan ◽  
Ling-Fei Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Risk Group ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Set ◽  
Cox Regression Analysis

Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p  < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

scholarly journals In-Hospital Mortality of COVID-19 Patients Treated with High-Flow Nasal Oxygen: Evaluation of Biomarkers and Development of the Novel Risk Score Model CROW-65

Life ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 735
Author(s):  
Toni Kljakovic Gaspic ◽  
Mirela Pavicic Ivelja ◽  
Marko Kumric ◽  
Andrija Matetic ◽  
Nikola Delic ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Hospital Mortality ◽  
Risk Score ◽  
Univariate Analysis ◽  
High Flow ◽  
The Novel ◽  
Single Center ◽  
Therapeutic Possibility ◽  
Male Patients ◽  
Score Model

To replace mechanical ventilation (MV), which represents the cornerstone therapy in severe COVID-19 cases, high-flow nasal oxygen (HFNO) therapy has recently emerged as a less-invasive therapeutic possibility for those patients. Respecting the risk of MV delay as a result of HFNO use, we aimed to evaluate which parameters could determine the risk of in-hospital mortality in HFNO-treated COVID-19 patients. This single-center cohort study included 102 COVID-19-positive patients treated with HFNO. Standard therapeutic methods and up-to-date protocols were used. Patients who underwent a fatal event (41.2%) were significantly older, mostly male patients, and had higher comorbidity burdens measured by CCI. In a univariate analysis, older age, shorter HFNO duration, ventilator initiation, higher CCI and lower ROX index all emerged as significant predictors of adverse events (p < 0.05). Variables were dichotomized and included in the multivariate analysis to define their relative weights in the computed risk score model. Based on this, a risk score model for the prediction of in-hospital mortality in COVID-19 patients treated with HFNO consisting of four variables was defined: CCI > 4, ROX index ≤ 4.11, LDH-to-WBC ratio, age > 65 years (CROW-65). The main purpose of CROW-65 is to address whether HFNO should be initiated in the subgroup of patients with a high risk of in-hospital mortality.

scholarly journals Development and validation of a nomogram with an autophagy-related gene risk model for predicting survival in patients with melanoma

2020 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
High Morbidity ◽  
Clinical Variables ◽  
Kaplan Meier ◽  
Score Model

Abstract Background: Melanoma is the third most common skin malignant tumor in the clinic, with high morbidity and mortality. Autophagy plays an important role in the development and progression of melanoma. We aimed to establish an autophagy-related genes(ARGs) expression based risk model for individualized prognosis prediction in patients with melanoma.Methods: Differentially expressed autophagy-related genes (DEARGs) in melanoma and normal skin samples were screened using TCGA and GTEx database. These DEARGs were used to perform KEGG functional enrichment analysis and GO analysis. Univariate and multivariate Cox regression analyses were performed on DEARGs to identify the optimal prognosis-related genes. These prognosis-related DEARGs were used to construct a risk score model, and the predictive effect of this risk model on the prognosis of melanoma patients was tested by the Kaplan-Meier curve, log-rank test, and ROC curve. Method of univariate and multivariate analysis were used to confirmed that the risk model of independent predictive value relative to other clinical variables, and build a nomogram based on the independent prognostic factors in the univariate analysis to predict overall survival(OS) in patients with melanoma, we used internal validation and calculation of concordance index (C-index) to test prediction effect of the nomogram. We also used the t-test to analyze the relationship between risk factors (risk genes and risk score) and clinical variables in the risk model.Results: We screened and finally obtained 6 optimal DEARGs (risk gene) through univariate and multivariate Cox analysis to construct the risk model: EIF2AK2(HR=0.403, P=0.007), IFNG(HR=0.659, P=0.003), DAPK2(HR=0.441, P=0.022), PTK6(HR=1.609, P=6.04E-05), BIRC5(HR=2.479, P=0.001), and EGFR(HR=1.474, P=0.004) were selected to establish the prognostic risk score model and validated in the entire melanoma cohort. The results of GO enrichment analysis showed that the gene function of the DEARGs was concentrated in the functions of gland morphogenesis, protein insertion into membrane, and autophagy. The results of KEGG enrichment analysis showed that the function of the DEARGs was concentrated in the autophagy–animal, p53 signaling pathway, and platinum drug resistance. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P=6.402E−11). The model was identified as an independent prognostic factor. Finally, a prognostic nomogram including the risk model, T-stage, N-stage, and radiotherapy was constructed, and the calibration plots indicated its excellent predictive performance.Conclusion: The autophagy-related six-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for melanoma. The prognostic nomogram could help individualized survival prediction and improve treatment strategies.

Predictive Factors for Response and Outcome in Patients (pts) Treated with Second Generation Tyrosine Kinase Inhibitors (2-TKI) for Chronic Myeloid Leukemia in Chronic Phase (CML-CP) Post Imatinib Failure.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 509-509
Author(s):  
Elias Jabbour ◽  
Hagop Kantarjian ◽  
Susan O'Brien ◽  
Jenny Shan ◽  
Guillermo Garcia-Manero ◽  
...  
Keyword(s):  
Risk Score ◽  
Predictive Factors ◽  
Research Funding ◽  
Disease Burden ◽  
Univariate Analysis ◽  
Cytogenetic Response ◽  
Imatinib Therapy ◽  
Factors Associated ◽  
Score Model

Abstract Abstract 509 The availability of 2-TKIs has provided new therapeutic options for pts with CML post imatinib failure. We assessed the predictive factors of outcome of pts in CML-CP treated with 2-TKI. A total of 128 pts with CML-CP after imatinib failure treated with dasatinib (n=76) or nilotinib (n=52) were analyzed. Median age was 56 years (range, 21-83). The median duration of CP (CML diagnosis to start of 2-TKI) was 66 months (range, 2-241). Their best response to imatinib was complete hematologic response only in 33%, and cytogenetic response in 55% (23% complete, 16% partial, 15% minor). 4 pts were refractory to imatinib, 3 had unknown response, and 8 were intolerant. At the start of 2-TKI, 94 (73%) pts had active disease. 23% had clonal evolution (CE), and 73% had more than 90% Philadelphia positivity. The median follow-up time was 39 months (range, 15-61) from the start of the 2-TKI. At the time of last follow-up, 108 of the 128 pts (85%) were alive, 86 (67%) in CP on 2-TKI therapy; 17 pts had died. Responses to 2-TKI are shown in Table 1. In the univariate analysis (UA) for event-free survival (EFS), factors associated with poor EFS were splenomegaly, anemia (hemoglobin ≤12g/dL), lack of any cytogenetic response to previous imatinib therapy, ≥ 90% Philadelphia-positive (Ph+) metaphases at the start of 2-TKI therapy, nilotinib therapy, and high sokal risk score disease. In the subsequent multivariate analysis (MVA), splenomegaly, anemia (hemoglobin ≤12g/dL), lack of any cytogenetic response to previous imatinib therapy, and ≥ 90% Ph+ metaphases were selected as independent factors associated with poor EFS. Factors associated with poor overall survival (OS) in the UA were CE, performance status (PS) ≥1, and high sokal risk score at the start of 2-TKI therapy. In the MVA, only CE and a PS ≥1 were selected as independent poor prognostic factors for OS. High hemoglobin level (≥12g/dL), 0% bone marrow blasts, previous cytogenetic response to imatinib therapy, ≤90% Ph+ metaphases, and low sokal risk score were associated with the achievement of a major cytogenetic response (MCyR) by 12 months of therapy with 2-TKI in the UA. In the subsequent MVA for response, the lack of any cytogenetic response to imatinib therapy, anemia (hemoglobin ≤12g/dL) and ≥90% Ph+ metaphases at the start of 2-TKI therapy were selected as poor predictive factors for 12-month MCyR. Pts with 0, 1, 2, or 3 adverse factors had a 12-month probability of achieving a MCyR with 2-TKI therapy of 85%, 79%, 35%, and 14%, respectively. Based on these findings, we developed a score model that included the factors identified as independent predictive for a MCyR by 12 months of therapy with 2-TKI. Three prognostic risk groups are proposed for the new score model: 1) low score (no adverse factors; 16% of pts), in which pts have a 12-month probability of achieving a MCyR of 85%, after therapy with 2-TKI; 2) intermediate score (1-2 adverse factors; 67% of pts), in which pts have a 12-month probability of achieving a MCyR of 56%; and 3) high score (3 adverse factors; 17% of pts), in which pts have a 12-month probability of achieving a MCyR of 14% (Table 2). This score model predicts significantly for EFS (p=0.003) with a trend for OS (p=0.18). In conclusion, the outcome of pts post imatinib failure treated with 2-TKIs is dependent on previous cytogenetic response to imatinib, absence of anemia, and disease burden at the start of therapy. Pts with no previous cytogenetic response to imatinib therapy with anemia and high disease burden have a low likelihood of responding to 2-TKI with poor EFS, and therefore should be offered alternative treatment options. Disclosures: Jabbour: Novartis: Speakers Bureau; Bristol Myers Squibb: Speakers Bureau. Kantarjian:Novartis: Research Funding; Bristol Myers Squibb : Research Funding; Wyeth: Research Funding. Wierda:Bayer: Research Funding; Sanofi Aventis: Research Funding; Abbott: Consultancy, Research Funding; GSK: Consultancy, Research Funding; Trubion: Consultancy; Ligand: Consultancy; Genetech: Consultancy, Honoraria; Medimmune: Consultancy; Celegene: Speakers Bureau. Borthakur:Novartis: Speakers Bureau; Bristol Myers Squibb : Speakers Bureau. Rios:Novartis: Consultancy, Honoraria, Speakers Bureau; Bristol Myers Squibb : Consultancy, Honoraria, Speakers Bureau. Cortes:Bristol Myers Squibb: Research Funding; Novartis: Research Funding.

Estimation of cardiovascular risk: a comparison between the Framingham and the SCORE model in people under 60 years of age

2008 ◽  
Vol 15 (5) ◽  
pp. 562-566 ◽  
Author(s):  
Tjarda Scheltens ◽  
W.M. Monique Verschuren ◽  
Hendriek C. Boshuizen ◽  
Arno W. Hoes ◽  
Nicolaas P. Zuithoff ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cardiovascular Risk ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Treatment Guidelines ◽  
Risk Function ◽  
Area Under The Curve ◽  
Risk Models ◽  
Discriminative Ability ◽  
Score Model

Background The Framingham Heart Study risk model has been used in the majority of cardiovascular risk management guidelines. Recently, a new model based on the SCORE system has been proposed. We compared both risk models with regard to their ability to predict cardiovascular mortality in the Netherlands. Design Cohort study. Methods In a Dutch cohort study of 39 719 persons, three properties of the risk models were investigated: discriminating ability (ranking persons in order of risks, expressed in area under the curve); calibrating ability (prediction of events compared with actual events expressed in goodness of fit); and the number of persons assigned to treatment according to the guideline. Results The discriminative ability of both models was similar: the area under the curve of Framingham was 0.86 and of SCORE 0.85. Calibration of both functions was inadequate. The goodness of fit of the SCORE model was 35 and of the Framingham model 64, whereas a goodness of fit less than 20 is considered acceptable. Using the Dutch guideline treatment threshold of 10% mortality risk, the SCORE risk function assigned 0.4% of the population to drug treatment where the Framingham function assigned 0.7%. Conclusion The findings of this study show that both the SCORE and the Framingham model function have a good discriminative ability but are insufficient in predicting absolute risks. SCORE assigned fewer participants to treatment than Framingham. If a new risk model is implemented in treatment guidelines, comparison with the model in use and evaluation of calibrating features is needed.

scholarly journals A Novel Dosimetric Metrics-Based Risk Model to Predict Local Recurrence in Nasopharyngeal Carcinoma Patients Treated With Intensity-Modulated Radiation Therapy

2021 ◽  
Author(s):  
Wenjun M.D. ◽  
Jinlan He ◽  
Zijian Liu ◽  
Maolang Tian ◽  
Jiangping Yang ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Local Recurrence ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Intensity Modulated Radiation Therapy ◽  
Pterygopalatine Fossa ◽  
Dosimetric Analysis

Abstract Background To develop a risk model based on dosimetric metrics to predict local recurrence in nasopharyngeal carcinoma (NPC) patients treated with intensive modulated radiation therapy (IMRT). Methods 493 consecutive patients were included, among whom 44 were with local recurrence. One-to-two propensity score matching (PSM) was used to balance variables between recurrent and non-recurrent groups. Dosimetric metrics were extracted, and critical dosimetric predictors of local recurrence were identified by Cox regression model. Moreover, recurrent sites and patterns were examined by transferring the recurrent tumor to the pretreatment planning computed tomography. Results After PSM, 44 recurrent and 88 non-recurrent patients were used for dosimetric analysis. The univariate analysis showed that eight dosimetric metrics and homogeneity index were significantly associated with local recurrence. The risk model integrating D5 and D95 achieved a C-index of 0.706 for predicting 3-year local recurrence free survival (LRFS). By grouping patients using median value of risk score, patients with risk score ˃ 0.885 had significantly lower 3-year LRFS (66.2% vs. 86.4%, p = 0.023). As for recurrent features, the proportion of relapse in nasopharynx cavity, clivus, and pterygopalatine fossa was 61.4%, 52.3%, and 40.9%, respectively; and in field, marginal, and outside field recurrence constituted 68.2%, 20.5% and 11.3% of total recurrence, respectively. Conclusions The current study developed a novel risk model that could effectively predict the LRFS in NPC patients. Additionally, nasopharynx cavity, clivus, and pterygopalatine fossa were common recurrent sites and in field recurrence remained the major failure pattern of NPC in the IMRT era.

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