Validation of New Prognostic Model Including Comorbidities in Patients with Myleodysplastic Syndrome Receiving Hypomethylating Therapy

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1708-1708
Author(s):  
Young-Don Joo ◽  
Je-Hwan Lee ◽  
Sung-Doo Kim ◽  
Yunsuk Choi ◽  
Young-Hun Park ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Model ◽  
Conflicts Of Interest ◽  
Risk Group ◽  
Risk Groups ◽  
Cancer Center ◽  
Risk Category ◽  
Hypomethylating Agents ◽  
Group Response ◽  
Md Anderson

Abstract Abstract 1708 Introduction: A new prognostic model including comorbidities, which are assessed using the Adult Comorbidity Evaluation-27 (ACE-27), was proposed for the patients with MDS by researchers from MD Anderson Cancer Center, Houston, TX (J Clin Oncol 2011;29: 2240). The model was developed after analysis of 600 patients who presented to the center. We aimed to validate the new prognostic model for the Korean patients with MDS who were treated with hypomethylating agents. Methods: Between September 2006 and October 2010, 149 patients were treated with either azacitidine or decitabine for MDS defined by the WHO classification and chronic myelomonocytic leukemia (CMML) in 3 Korean institutes. The new prognostic model included age (> 65 years, 2 score points), comorbidity assessed by ACE-27 (mild/moderate, 1 point; severe, 3 points), and IPSS (intermediate-2, 2 points; high, 3 points). Patients were divided into one of 3 risk groups: low (score 0–1), intermediate (scores 2–4), and high (scored 5–8). Risk group by the new prognostic model could not be assigned in 10 patients, thus a total of 139 patients were included in this analysis. Azacitidine 75 mg/m2/day was administered as a subcutaneous injection for 7 consecutive days (n=68) and decitabine 20 mg/m2/day as a 1-hour intravenous infusion for 5 consecutive days (n=71). Both agents were repeated every 4 weeks. Clinico-patholoical data including comorbidities were collected at time of hypomethylating therapy. Treatment response was evaluated using modified International Working Group response criteria. Results: Median age was 61 years (range, 23–83); 47 patients were over 65 years old. Overall comorbidity score assessed by ACE-27 was as follows: none (n=65), mild (n=53), moderate (n=15), and severe (n=6). IPSS risk category was low/intermediate-1 in 72, intermediate-2 in 55, and high in 12. Risk group measured by the prognostic model was low in 42 (30.2%), intermediate in 79 (56.8%), and high in 18 (12.9%). Hypomethylating therapy induced hematologic responses (CR/PR/mCR) in 28 patients (20.1%) and rate for overall responses (CR/CR/mCR/HI) was 57.6% (80/139). The treatment responses were not significantly different between 3 risk groups. At a median follow-up time of 27.0 months (range, 3.3–55.5) among surviving patients, 70 patients died and overall survival (OS) probability was 49.5% at 2 years; median OS was 24.1 months (95% CI, 11.7–36.5). OS was significantly different according to the presence of comorbidities (OS at 2 years, none vs. mild/moderate vs. severe, 63.4% vs. 37.5% vs. 33.3%, P=0.006) or risk groups by the prognostic model (OS at 2 years, low vs. intermediate vs. high, 71.1% vs. 46.1% vs. 18.5%, P<0.001). The survival differences by the prognostic model were maintained after adjustment for other variables (low vs. intermediate, HR, 2.810, 95% CI, 1.444–5.468, P=0.002; low vs. high, HR, 6.037, 95% CI, 2.708–13.459, P<0.001). Conclusions: The new prognostic model including comorbidities assessed by ACE-27 was useful to predict overall survival in patients with MDS receiving azacitidine or decitabine, although the model could not predict response to the hypomethylating agents. Disclosures: No relevant conflicts of interest to declare.

A Comparison of Prognostic Models for Chronic Myelomonocytic Leukemia (CMML) in the Era of Hypomethylating Agents

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1695-1695 ◽  
Author(s):  
Eric Padron ◽  
Najla H Al Ali ◽  
Deniz Peker ◽  
Jeffrey E Lancet ◽  
Pearlie K Epling-Burnette ◽  
...  
Keyword(s):  
Overall Survival ◽  
Scoring System ◽  
Prognostic Model ◽  
Median Overall Survival ◽  
Prognostic Score ◽  
Risk Groups ◽  
Prognostic Models ◽  
Rank Test ◽  
Log Rank Test ◽  
Md Anderson

Abstract Abstract 1695 Introduction: CMML is a genetically and clinically heterogeneous malignancy characterized by peripheral monocytosis, cytopenias, and a propensity for AML transformation. Several prognostic models attempt to stratify patients into subcategories that are predictive for overall survival (OS), six models of which are specific to CMML. However, these models have either never been externally validated in the context of CMML or were externally validated prior to the use of hypomethylating agents. We externally validate and perform a detailed statistical comparison between the International Prognostic Scoring System (IPSS), MD Anderson Scoring System (MDASC), MD Anderson Prognostic Score (MDAPS), Dusseldorf Score (DS), and Spanish Scoring Systems (SS) in a large, single institution cohort. Methods: Data were collected retrospectively from the Moffitt Cancer Center (MCC) CMML database and charts were reviewed of patients that satisfied the WHO criteria for the diagnosis of CMML. The primary objective of the study was to validate the above prognostic models calculated at the time of initial presentation to MCC. All prognostic models were calculated as previously published. All analyses were conducted using SPSS version 15.0 (SPSS Inc, Chicago, IL). The Kaplan–Meier (KM) method was used to estimate median overall survival and the log rank test was used to compare KM survival estimates between two groups. Results: Between January 2000 and February 2012, 123 patients were captured by the MCC CMML database. The median age at diagnosis was 69 (30–90) years and the majority of patients were male (69%). By the WHO classification, the majority of patients had CMML-1 (84% vs. 16%) and most patients were subcategorized as MPN-CMML (59%) versus MDS-CMML (39%) by the FAB CMML criteria. The median overall survival of the entire cohort was 30 months and the rate of AML transformation was 44% (54). Twenty-two patients (18%) were treated with decitabine and 66 (54%) patients were treated with 5-azacitidine. Risk group stratification according to specific prognostic model is summarized in Table 1. The IPSS, MDASC, DS, and SS all predicted OS (p<0.05) while the MDASP could not be validated (p=0.924). When only patients who were treated with 5-azacitadine were considered, the MDASC, DS, and SS continued to predict OS (p<0.05) while the IPSS (p=0.15) and MDASP (p=0.239) did not. Previous reports have demonstrated that the MDASC provides further discrimination to refine stratification by the IPSS in Myelodysplastic Syndromes (MDS). Except for the low-risk DS patients, we grouped patients in our CMML cohort into lower and higher risk disease with each prognostic score and attempted to further stratify patients by the MDASC using KM and the log rank test. The MDASC was able to further risk stratify patients in each group for all prognostic models except those in the higher risk groups by the SS (p=0.07) and DS (P=0.45). When a similar statistical analysis was applied to each prognostic scoring system, only the MDASC was consistently able to further stratify the majority of risk groups as described in Table 2. The Dusseldorf scoring system was able to further stratify all lower risk groups regardless of model but was not able to do so in higher risk disease. Conclusions: This represents the first external validation of existing CMML prognostic models in the era of hypomethylating agent therapy. Except for the MDASP, we were able to validate the prognostic value all models tested. The MDASC represents the most robust model as it consistently refined the stratification of other models tested and remained predictive of OS in 5-azacitidine treated patients. Multi-institution collaboration is needed to construct a robust CMML specific prognostic model. Comparison to the IPSS-R is in progress. Disclosures: No relevant conflicts of interest to declare.

Validation of Alternative Chronic Myelomonocytic Leukemia (CMML)-Specific Prognostic Scoring (CPSS) System in UT MD Anderson Cancer Center Cohort

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3278-3278
Author(s):  
Priyanka Priyanka ◽  
Janhavi Raut ◽  
Patricia S Fox ◽  
Francesco Stingo ◽  
Tariq Muzzafar
Keyword(s):  
Overall Survival ◽  
Scoring System ◽  
Risk Groups ◽  
Risk Classification ◽  
Chronic Myelomonocytic Leukemia ◽  
Cancer Center ◽  
Newly Diagnosed ◽  
Md Anderson ◽  
Myelomonocytic Leukemia

Abstract INTRODUCTION: Chronic myelomonocytic leukemia (CMML) is a myeloid neoplasm that belongs to the category of myelodysplastic syndrome / myeloproliferative neoplasms (MDS / MPN). The International Prognostic Scoring System for Myelodysplastic Syndromes (IPSS) classification and its revised version (IPSS-R) addressed patients with newly diagnosed, untreated MDS and excluded CMML. While numerous investigators have attempted to devise a prognostic risk scoring system for CMML, no system has been generally accepted for this entity. A CMML-specific prognostic scoring (CPSS) system proposed by Such, et al [Blood. 2013; 11;121(15):3005-15] defines 4 different prognostic risk categories for estimating both overall survival (OS) and risk for AML transformation; the alternative version replaces RBC transfusion dependency with hemoglobin levels. AIM: The aim of the study is to validate the alternative CPSS scoring system on the CMML patient cohort at UT MD Anderson Cancer Center (UTMDACC). METHODS: The databases of the Department of Hematopathology at UTMDACC were searched for patients diagnosed with CMML presenting from 2005 to 2012. Cases were classified by WHO 2008 criteria. Inclusion criteria were: confirmed diagnosis of CMML, age > 18 years, persistent absolute monocyte count >1 × 109/L, marrow blasts < 20%, peripheral blood blasts < 20%. The alternative CPSS score was calculated as a function of WHO subtype, FAB subtype, CMML-specific cytogenetic risk classification, and hemoglobin score. Cox proportional hazards regression was used to model overall survival and time to AML progression from date of diagnosis. For time to AML progression, patients who did not experience AML progression were censored at their date of death or last follow-up. Kaplan-Meier curves were used to estimate survival and the log-rank test was used to test for significant differences by CPSS score. All statistical analyses were performed using SAS 9.3 for Windows. RESULTS: Two hundred and three patients with newly diagnosed, untreated CMML were identified in the clinical databases. These included 132 males and 71 females; median age was 70 (range 55-80) years. 149 had CMML-1 and 54 had CMML-2. A total of 107 deaths and 38 progressions were observed. The median (range) follow-up time for all patients was 1.9 (2 days-10.8) years. The variables that compose the alternative CPSS (WHO subtype, FAB subtype, CMML-specific cytogenetic risk classification, hemoglobin) as well as a description of how the score is calculated are given in Tables 1-2. In univariate Cox models, the alternative CPSS score was a significant predictor of both OS and time to AML progression (Type III p-values <.0001 and 0.0037, respectively). Median survival times for OS were 4.07, 3.32, 2.14, and 1.23 years in the low, intermediate-1, intermediate-2, and high risk groups, respectively. Since less than half the patients progressed, the median time to AML progression could not be estimated for all groups but was 6.40 and 1.60 in the intermediate-2 and high risk groups, respectively. Overall, the alternative CPSS score was highly predictive of both OS and progression free survival (PFS) and clearly delineated the patient risk groups in this sample. CONCLUSIONS: These data reinforce the validity of the alternative CPSS and serve as an additional validation cohort. Table 1. Alternative CMML-specific prognostic scoring system (CPSS) score criteria Variable Each level assigned the following value(sum to get the composite CPSS score): 0 1 2 WHO subtype CMML-1 blasts (including promonocytes) <5% in the PB and <10% in the BM CMML-2 blasts (including promonocytes) from 5% to 19% in the PB and from 10% to 19% in the BM, or when Auer rods are present irrespective of blast count — FAB subtype CMML-MD (WBC <13 × 109/L) CMML-MP (WBC ≥13 × 109/L) — CMML-specific cytogenetic risk classification* Low Intermediate High Hemoglobin ≥10 g/dL <10/dL WBC: white blood cell * CMML-specific cytogenetic risk classification; low: normal and isolated –Y; intermediate: other abnormalities; and high: trisomy 8, complex karyotype (≥3 abnormalities), chromosome 7 abnormalities Table 2. Alternative CPSS: scores used for predicting likelihood of survival and leukemic evolution in individual patient with CMML Risk group Overall CPSS score Low 0 Intermediate-1 1 Intermediate-2 2-3 High 4-5 Figure 1 Overall Survival by alternative CPSS Score Figure 1. Overall Survival by alternative CPSS Score Figure 2 Time to AML Progression by alternative CPSS Score Figure 2. Time to AML Progression by alternative CPSS Score Disclosures No relevant conflicts of interest to declare.

scholarly journals Association of Comorbidities With Overall Survival in Myelodysplastic Syndrome: Development of a Prognostic Model

2011 ◽  
Vol 29 (16) ◽  
pp. 2240-2246 ◽  
Author(s):  
Kiran Naqvi ◽  
Guillermo Garcia-Manero ◽  
Sagar Sardesai ◽  
Jeong Oh ◽  
Carlos E. Vigil ◽  
...  
Keyword(s):  
Myelodysplastic Syndrome ◽  
Median Survival ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
International Prognostic Scoring System ◽  
Cancer Center ◽  
Kaplan Meier ◽  
Severe Comorbidity ◽  
Md Anderson

Purpose Patients with cancer often experience comorbidities that may affect their prognosis and outcome. The objective of this study was to determine the effect of comorbidities on the survival of patients with myelodysplastic syndrome (MDS). Patients and Methods We conducted a retrospective cohort study of 600 consecutive patients with MDS who presented to MD Anderson Cancer Center from January 2002 to December 2004. The Adult Comorbidity Evaluation-27 (ACE-27) scale was used to assess comorbidities. Data on demographics, International Prognostic Scoring System (IPSS), treatment, and outcome (leukemic transformation and survival) were collected. Kaplan-Meier methods and Cox regression were used to assess survival. A prognostic model incorporating baseline comorbidities with age and IPSS was developed to predict survival. Results Overall median survival was 18.6 months. According to the ACE-27 categories, median survival was 31.8, 16.8, 15.2, and 9.7 months for those with none, mild, moderate, and severe comorbidities, respectively (P < .001). Adjusted hazard ratios were 1.3, 1.6, and 2.3 for mild, moderate, and severe comorbidities, respectively, compared with no comorbidities (P < .001). A final pognostic model including age, IPSS, and comorbidity score predicted median survival of 43.0, 23.0, and 9.0 months for lower-, intermediate-, and high-risk groups, respectively (P < .001). Conclusion Comorbidities have a significant impact on the survival of patients with MDS. Patients with severe comorbidity had a 50% decrease in survival, independent of age and IPSS risk group. A comprehensive assessment of the severity of comorbidities helps predict survival in patients with MDS.

scholarly journals Bioinformatic identification of genomic instability-associated lncRNAs signatures for improving the clinical outcome of cervical cancer by a prognostic model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian Zhang ◽  
Nan Ding ◽  
Yongxing He ◽  
Chengbin Tao ◽  
Zhongzhen Liang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Patients ◽  
Genomic Instability ◽  
Prognostic Model ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Training Set

AbstractThe research is executed to analyze the connection between genomic instability-associated long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We set a prognostic model up and explored different risk groups' features. The clinical datasets and gene expression profiles of 307 patients have been downloaded from The Cancer Genome Atlas database. We established a prognostic model that combined somatic mutation profiles and lncRNA expression profiles in a tumor genome and identified 35 genomic instability-associated lncRNAs in cervical cancer as a case study. We then stratified patients into low-risk and high-risk groups and were further checked in multiple independent patient cohorts. Patients were separated into two sets: the testing set and the training set. The prognostic model was built using three genomic instability-associated lncRNAs (AC107464.2, MIR100HG, and AP001527.2). Patients in the training set were divided into the high-risk group with shorter overall survival and the low-risk group with longer overall survival (p < 0.001); in the meantime, similar comparable results were found in the testing set (p = 0.046), whole set (p < 0.001). There are also significant differences in patients with histological grades, FIGO stages, and different ages (p < 0.05). The prognostic model focused on genomic instability-associated lncRNAs could predict the prognosis of cervical cancer patients, paving the way for further research into the function and resource of lncRNAs, as well as a key approach to customizing individual care decision-making.

Validation of the Newly Proposed MD Anderson Prognostic Risk Model for Patients with Myelodysplastic Syndromes

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 444-444
Author(s):  
Maria Corrales-Yepez ◽  
Jeffrey E. Lancet ◽  
Alan F. List ◽  
Mohamed A. Kharfan-Dabaja ◽  
Teresa Field ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Myelodysplastic Syndromes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Refractory Anemia ◽  
Kaplan Meier ◽  
Md Anderson

Abstract Abstract 444 Background: The international prognostic scoring system (IPSS) is the most widely used clinical tool for risk stratification and tailoring treatment in myelodysplastic syndromes (MDS). Despite its utility, the IPSS has several limitations. The IPSS was developed using outcomes of untreated primary MDS patients at time of diagnosis, and does not account for patient age, performance, and degree of cytopenia. The recently reported MD Anderson risk model (MDAS) addresses many of the limitations of IPSS (Kantarjian et al, CANCER September 15, 2008 / Volume 113 / Number 6). We validated this new risk model in a large external single institution cohort of patients. Methods: Data were collected retrospectively from Moffitt Cancer Center (MCC) MDS database and chart review of patients with MDS. The primary objective was to validate the new risk model calculated at time of initial presentation MCC. The MDAS was calculated as published based on age, performance status, blast%, degree of thrombocytopenia, cytogenetics, white blood cell count, and prior history if transfusion. Patients were divided into four risk groups: low (0-4 points), int-1 (5-6 points), int-2 (7-8 points), and high risk (≥ 9 points). All analyses were conducted using SPSS version 15.0. (SPSS Inc, Chicago, IL). The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between two groups. Cox regression was used for multivariable analysis. Results: Between January 2001 and December 2009, 844 patients were captured by MCC MDS database. The median age was 69 years, MDS subtypes were coded as Refractory anemia (RA) 98 (12%), refractory anemia with ring sideroblasts (RARS) 76 (9%), MDS with del(5q) 20 (2.4%), refractory cytopenia with multi-lineage dysplasia (RCMD) 96 (11%), refractory anemia with excess blasts (RAEB) 255 (30%), therapy related MDS 22 (2.6%), and MDS-nos 275 (33%). IPSS risk groups were low risk in 158 (18.7%), intermediate-1 (int-1) 362 (42.9%), intermediate-2 (int-2) 168 (19.9%), high risk 45 (5.3%), and missing in 111 (13.2%). Based on the new risk model 169 patients (20%) were low risk, 250 (29.6%) int-1, 182 (21.6%) int-2, 135 (16%) high risk, and 94 (11.1%) were unknown. The median OS for all patients was 36 months (95% CI 31.5–40.5 mo). Age, IPSS risk group, serum ferritin, and RBC transfusion dependence were all significant prognostic factors in univariable analysis. The median OS was 92 mo (95%CI 68.1–115.9 mo), 49 mo (95%CI 40.4–57.6 mo), 26 mo (95%CI 21.2–30.8 mo), and 15 month (95%CI 11.8–42.1 mo) respectively for patients with low, int-1, int-2 and high risk patients according to MDAS. (Figure-1) (P < 0.005). In patients with low/int-1 IPSS risk group the median OS according to MDAS was 92 mo (95%CI 68.3–115.7 mo), 49 mo (95%CI 49.3–58.7 mo), 28 mo (95%CI 20.7–35.3), and 19 mo(95% CI 9.9–28.1 mo) respectively for patients with low, int-1, int-2, high risk MDAS (p<0.005). In patients with int-2/high IPSS risk categories only 4 patients were reclassified as low MDAS risk and the median OS for those patients was 10 month (95% CI 0–38 mo). The median OS was 49 mo (95%CI 23.5–74.5 mo), 23 mo (95%CI 19.4–26.6 mo), 14 mo (95% CI 11.5–16.5 mo). (p<0.005). For all the patients the rate of AML transformation according to MDAS was 5.9%, 16.8%, 36.3%, and 50.4% respectively for low, int-1, int-2, and high risk MDAS groups. (p <0.005). In Cox regression analysis, higher risk MDAS predicted inferior OS (Hazard ratio (HR) 1.54 (95%CI 1.35–1.75) (p <0.005) independent of IPSS risk group (HR 1.25 95%CI 1.1–1.45) (p =0.004). Conclusion: Our data validates the prognostic value of the MDAS risk model which was predictive for overall survival and AML transformation. The MDAS complements the IPSS particularly in low/int-1 risk group by identifying patients with higher risk disease behavior and inferior outcome. The utility of this model as a treatment decision tool should be studied prospectively. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Development and validation of an individualized immune prognostic model in stage I–III lung squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qi-Fan Yang ◽  
Di Wu ◽  
Jian Wang ◽  
Li Ba ◽  
Chen Tian ◽  
...  
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Stage I ◽  
Tissue Samples ◽  
Mutation Burden

AbstractLung squamous cell carcinoma (LUSC) possesses a poor prognosis even for stages I–III resected patients. Reliable prognostic biomarkers that can stratify and predict clinical outcomes for stage I–III resected LUSC patients are urgently needed. Based on gene expression of LUSC tissue samples from five public datasets, consisting of 687 cases, we developed an immune-related prognostic model (IPM) according to immune genes from ImmPort database. Then, we comprehensively analyzed the immune microenvironment and mutation burden that are significantly associated with this model. According to the IPM, patients were stratified into high- and low-risk groups with markedly distinct survival benefits. We found that patients with high immune risk possessed a higher proportion of immunosuppressive cells such as macrophages M0, and presented higher expression of CD47, CD73, SIRPA, and TIM-3. Moreover, When further stratified based on the tumor mutation burden (TMB) and risk score, patients with high TMB and low immune risk had a remarkable prolonged overall survival compared to patients with low TMB and high immune risk. Finally, a nomogram combing the IPM with clinical factors was established to provide a more precise evaluation of prognosis. The proposed immune relevant model is a promising biomarker for predicting overall survival in stage I–III LUSC. Thus, it may shed light on identifying patient subset at high risk of adverse prognosis from an immunological perspective.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

scholarly journals 236 Predictors of ICI renal toxicity: a pathological approach

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A252-A252
Author(s):  
Ala Abudayyeh ◽  
Liye Suo ◽  
Heather Lin ◽  
Omar Mamlouk ◽  
Cassian Yee ◽  
...  
Keyword(s):  
Overall Survival ◽  
Renal Toxicity ◽  
Checkpoint Inhibitors ◽  
Interstitial Fibrosis ◽  
Transplant Rejection ◽  
Categorical Variables ◽  
Rank Test ◽  
Cancer Center ◽  
Renal Response ◽  
Md Anderson

BackgroundInflammatory response in unintended tissues and organs associated with the use of immune checkpoint inhibitors also known as immune related adverse events (irAEs) is a management challenge, and renal irAEs are associated with increased patient morbidity and mortality. The most common renal toxicity is acute interstitial nephritis (AIN), characterized by infiltration of renal tissue with immune cells, and may be analogous to kidney transplant rejection. Using both clinical variables and tissue findings we evaluated a large cohort of ICI cases to determine predictors of renal response and overall survival.MethodsWe retrospectively reviewed all patients treated with ICI (August 2007 to August 2020) at MD Anderson Cancer Center. A total of 38 patients with biopsy confirmed AIN and available tissue were identified. All slides were reviewed by two board certified renal pathologists and the severity of inflammation and chronicity was graded using transplant rejection BANFF criteria. Patients were categorized as renal responders if creatinine improved or returned to baseline after treatment and non-responders if it did not. Fisher’s exact tests for categorical variables and t-test/ANOVA or the counterparts of the non-parametric approaches (Wilcoxon rank-sum or Kruskal-Wallis) for continuous variables were used to compare patient‘s characteristics between groups. The distribution of overall survival (OS) was estimated by the Kaplan-Meier method. Log-rank test was performed to test the difference in survival between groups.ResultsBased on the detailed pathological findings, patients with increased interstitial fibrosis were less likely to have renal response with treatment compared to patients with less fibrosis, (p < 0.05). Inflammation, tubulitis, number of eosinophils and neutrophils had no impact on renal response. Patients with response within 3 months of AKI treatment had a superior OS in comparison to patients who responded late (12-month OS rate: 77% vs 27%, p < 0.05). Notably, patients who received concurrent ICI and achieved renal response within 3 months had the best OS while those who did not receive concurrent ICI nor achieved renal response had worst OS (12-month OS rate: 100% (renal response and concurrent ICI) vs 72% ( renal response with no concurrent ICI), vs 27% ( no renal response and nonconcurrent ICI) (p < 0.05).ConclusionsThis is the first analysis of ICI induced nephritis where a detailed pathological and clinical evaluation was performed to predict renal response. Our findings highlight the importance of early diagnosis and treatment of ICI-AIN while continuing concurrent ICI therapy.Ethics ApprovalThis retrospective study was approved by the institutional review board at The University of Texas MD Anderson Cancer Center, and the procedures followed were in accordance with the principles of the Declaration of Helsinki.

scholarly journals A novel nomogram to predict the overall survival in esthesinoeroblastoma

2020 ◽  
Author(s):  
Lijie Jiang ◽  
Tengjiao Lin ◽  
Yu Zhang ◽  
Wenxiang Gao ◽  
Jie Deng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Validation Cohort ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cancer Center ◽  
P Value ◽  
Training Cohort

Abstract Background Increasing evidence indicates that the pathology and the modified Kadish system have some influence on the prognosis of esthesioneuroblastoma (ENB). However, an accurate system to combine pathology with a modified Kadish system has not been established. Methods This study aimed to set up and evaluate a model to predict overall survival (OS) accurately in ENB, including clinical characteristics, treatment and pathological variables. We screened the information of patients with ENB between January 1, 1976, and December 30, 2016 from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program as a training cohort. The validation cohort consisted of patients with ENB at Sun Yat-sen University Cancer Center and The First Affiliated Hospital of Sun Yat-sen University in the same period, and 87 patients were identified. The Pearson’s chi-squared test was used to assess significance of clinicopathological and demographic characteristics. We used the Cox proportional hazards model to examine univariate and multivariate analyses. The model coefficients were used to calculate the Hazard ratios (HR) with 95% confidence intervals (CI). Prognostic factors with a p- value < 0.05 in multivariate analysis were included in the nomogram. The concordance index (c-index) and calibration curve were used to evaluate the predictive power of the nomogram. Results The c-index of training cohort and validation cohort are 0.737 (95% CI, 0.709 to 0.765) and 0.791 (95% CI, 0.767 to 0.815) respectively. The calibration curves revealed a good agreement between the nomogram prediction and actual observation regarding the probability of 3-year and 5-year survival. We used a nomogram to calculate the 3-year and 5-year growth probability and stratified patients into three risk groups. Conclusions The nomogram provided the risk group information and identified mortality risk and can serve as a reference for designing a reasonable follow-up plan.

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