Development of a Risk Score for Prediction of Overall Survival Following Umbilical Cord Blood Transplantation in Acute Leukemia Patients: A Study from the Acute Leukemia Working Party (WP) and Paediatric Disease WP of the European Society for Blood and Marrow Transplantation (EBMT), and Eurocord

Abstract Background: Prognostic scoring systems for allogeneic stem cell transplantation (HSCT) are of clinical value when determining a leukemic patient's suitability for this curative, but risky, procedure. Several such scores have been developed over the years for HSCT from sibling or unrelated donors, but no predictive score has been developed specifically for umbilical cord blood transplantation (UCBT). Although individual parameters have been identified to be associated with UCBT outcomes in acute leukemia (AL) patients, integrative tools for risk evaluation in this setting are lacking. We sought to develop a prediction model for overall survival (OS) (primary objective) and leukemia free survival (LFS) (secondary objective) at 2 years following UCBT in acute leukemia patients. Methods: A retrospective, international registry-based study, of 3140 acute leukemia patients who underwent UCBT from 2004 through 2014. Inclusion criteria were patients with AL receiving single or double cord blood units transplantation. Median follow up was 30 months. The dataset was geographically split into a derivation (65%) and validation set (35%). A Random Survival Forest was utilized to identify predictive factors. Top predictors were introduced into a Cox regression model, and a risk score was constructed according to each variable's hazard. Results: The median age at UCBT was 21.9 years. The 2 years OS rate was 47.7% (95% CI: 45.8-49.6). After identifying the top predictive variables, the UCBT risk score (Table 1) was constructed using 9 variables (disease status, diagnosis, cryopreserved cell dose, age, center experience, recipient cytomegalovirus sero-status, degree of HLA mismatch, previous autograft and anti thymocyte globulin administration). Over the derivation and validation datasets, a higher score was associated with decreasing probabilities for 2 years OS and LFS, ranging over the validation set from 0.72 (0.64-0.8, 95% CI) and 0.68 (0.6-0.76, 95% CI) to 0.13 (0.06-0.27, 95% CI) and 0.14 (0.07-0.28, 95% CI), respectively (Figure 1). An increasing score was also associated with increasing hazard of the predictive outcomes (Table 2). The score's discrimination (AUC) over the validation set for 2 years OS and LFS was 68.26 (64.25-72.27, 95% CI) and 66.95 (62.88-71.02, 95% CI), respectively. Calibration was excellent. Conclusion: We have developed the first integrative score for prediction of overall survival and leukemia free survival in acute leukemia patients undergoing a UCBT. The score is simple and stratifies patients into distinct risk groups. Table 1 The UCBT Risk Score Table 1. The UCBT Risk Score Table 2 Association between the UCBT risk score and 2 years OS and LFS over the validation dataset Table 2. Association between the UCBT risk score and 2 years OS and LFS over the validation dataset Figure 1 Overall survival stratified by the UCBT risk score over the validation data set Figure 1. Overall survival stratified by the UCBT risk score over the validation data set Disclosures Bader: Servier: Consultancy, Honoraria; Neovii Biotech: Research Funding; Riemser: Research Funding; Medac: Consultancy, Research Funding; Novartis: Consultancy, Honoraria.

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A Prognostic System Predictive of Outcomes in Persons Undergoing Allogeneic Hematopoietic Cell Transplantation for Myelodysplastic Syndrome

Blood ◽

10.1182/blood.v126.23.64.64 ◽

2015 ◽

Vol 126 (23) ◽

pp. 64-64

Author(s):

Brian C Shaffer ◽

Kwang Woo Ahn ◽

Zhen-Huan Hu ◽

Uday R. Popat ◽

Matt Kalaycio ◽

...

Keyword(s):

Risk Score ◽

Scoring System ◽

Hematopoietic Cell Transplantation ◽

Proportional Hazards ◽

Training Data ◽

Validation Data ◽

Data Set ◽

Training Cohort ◽

Validation Set ◽

Significant Covariates

Abstract Allogeneic hematopoietic cell transplantation (allo HCT) is a curative therapy for myelodysplastic syndrome (MDS) that may result in toxicity and mortality, limiting its efficacy in patients with this disease. There is no standardized criterion to guide selection of patients with MDS for allo HCT. In order to address this problem we examined outcomes in 2,133 patients undergoing HLA matched or mismatched allo HCT for MDS reported to the Center for International Blood and Marrow Transplant Research from 2000-2012. The primary aim of this study is to develop a prognostic scoring system predictive of overall survival (OS) in this population. We additionally addressed whether the model is predictive of transplant related mortality (TRM), relapse, and disease free survival (DFS). Patients undergoing haploidentical, syngeneic, umbilical cord blood, or with missing donor data (N = 663) and pediatric patients (N = 262) were excluded from this analysis. An additional 84 patients were removed due to missing date of diagnosis, unknown graft versus host disease (GVHD) prophylaxis, or who were missing any 100-day follow up data. 1,728 patients met these criteria and underwent HLA-matched allo HCT. An additional 405 patients underwent HLA mismatched allo HCT and formed the HLA-mismatched set. The HLA-matched allo HCT set were randomly divided into a training data set comprising 67% (N = 1,151) of the cohort and a validation data set using the remaining 33% (N = 577). The training data set was used to develop a prognostic scoring system and the validation data set was used to assess the predictive ability of the scoring system. A Cox proportional hazards model with the stepwise selection procedure was used to select significant covariates for OS. Interactions between significant covariates were examined and proportional hazards assumption was examined. Based on the magnitude of the hazard ratios (HR) associated with variables a weighted score was assigned to factors that were positively associated with OS in the training cohort. Scores were grouped based on associated HRs into good, intermediate, high, and very high risk groups. Patients with missing data were included in the multivariate Cox model analysis but were excluded from the analysis of the final risk score in the training and validation set. This analysis identified five factors predictive of mortality in the HLA matched allo HCT training set: Peripheral blood blasts ≥ 3% or platelet count < 50 × 109/μL at transplantation, IPSS-R cytogenetic risk score, poor Karnofsky performance status, and older age at transplantation (Table 1). Using these variables we developed a MDS prognostic score (Table 2). We then used the scoring system defined in Table 2 to calculate a risk score for individuals in the training cohort that had complete data on all five variables (N = 839). Increasing score was associated with greater HR for death (p-overall < 0.0001). Based on these data we applied the score to the HLA-matched validation cohort, where increasing score was predictive of overall survival (p < 0.001). Because the training set was developed based on OS and not other outcomes, we combined the 839 cases from the training cohort with the 427 cases from the validation cohort for the analyses of the secondary endpoints. In the combined HLA-matched cohort the scoring system was associated with relapse (p < 0.0001), TRM (p < 0.0001), and DFS (p < 0.0001). We then tested this model in an additional set of individuals undergoing HLA-mismatched allo HCT (N = 405). Here, the score was predictive of relapse (p < 0.0001) but not OS, DFS, or TRM. In order to determine if the proposed scoring system is superior to the IPSS or IPSS-R prognostic tools we compared the three scoring systems in the HLA-matched validation set using concordance probabilities and Brier scores in 384 patients that had complete data for all three prognostic systems. The proposed scoring system was more predictive of OS when compared to the IPSS and IPSS-R using Brier (0.241, 0.252, and 0.249, respectively) and concordance probability tools (0.575, 0.538, and 0.554, respectively). In summary, we propose a system for prediction of outcomes in transplant recipients for MDS. Such a tool may be used to inform clinical decisions and to standardize mortality risk index in clinical trials examining transplantations in this patient population. Table 1. Table 1. Table 2. Table 2. Disclosures Maziarz: Athersys: Consultancy, Patents & Royalties, Research Funding; Novartis: Consultancy.

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Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Dose-Response ◽

10.1177/1559325819894179 ◽

2019 ◽

Vol 17 (4) ◽

pp. 155932581989417 ◽

Cited By ~ 6

Author(s):

Zhi Huang ◽

Jie Liu ◽

Liang Luo ◽

Pan Sheng ◽

Biao Wang ◽

...

Keyword(s):

Colorectal Cancer ◽

Signaling Pathway ◽

Risk Score ◽

Low Risk ◽

Training Data ◽

The Cancer Genome Atlas ◽

Training Set ◽

Data Set ◽

Validation Set ◽

Cox Analysis

Background: Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC). Methods: Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis. Results: After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction. Conclusions: Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

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Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

Journal of Translational Medicine ◽

10.1186/s12967-020-02545-z ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Pancheng Wu ◽

Yi Zheng ◽

Yanyu Wang ◽

Yadong Wang ◽

Naixin Liang

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Early Stage ◽

Prognostic Signature ◽

Training Set ◽

Validation Data ◽

Data Set ◽

Screening Programs ◽

Immune Signature ◽

Immune Related Genes

Abstract Background The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD. Methods The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts. Results A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design. Conclusions Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

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P549CIED infection risk score validation using US health claims data

EP Europace ◽

10.1093/europace/euaa162.069 ◽

2020 ◽

Vol 22 (Supplement_1) ◽

Author(s):

F Z Ahmed ◽

C Blomstrom Lundqvist ◽

H Bloom ◽

C Cooper ◽

C Ellis ◽

...

Keyword(s):

Risk Score ◽

Predictive Value ◽

Claims Data ◽

Frailty Model ◽

Infection Risk ◽

Health Claims ◽

Validation Dataset ◽

Data Set ◽

Device Infection ◽

Health Claims Data

Abstract Funding Acknowledgements This work was supported by Medtronic Background/Introduction: The increasing number of cardiac implantable electronic device (CIED) infections has led to increased interest in the identification of patients who may benefit from additional infection prevention measures. Purpose The purpose of this evaluation was to validate the predictive value of the Prevention of Arrhythmia Device Infection Trial (PADIT) risk score to identify patients at increased risk of CIED infection using a U.S. health claims data set. Methods A retrospective analysis using the Optum® Clinformatics® claims database was conducted to create a dataset of index procedures which either did or did not result in an infection. The study population included both commercial and Medicare Advantage patients aged ≥18 years with at least one record of a CIED procedure between January 2011 and September 2014. Major CIED infections, defined as an infection associated with system removal, invasive procedure without system removal, or death attributable to infection, were identified through diagnosis and procedure codes. The dataset was randomized (stratified by PADIT score, which included prior procedures, age, depressed renal function, immunocompromised, and procedure type) into a Development Dataset (60%) and a Validation dataset (40%). A frailty model allowing multiple procedures per patient was fit using the Development Dataset, with PADIT score as the only predictor, excluding patients with prior infection. Prior CIED infection, which was not available in the original PADIT data, was examined for additional predictive value. Results The data extraction resulted in a dataset of 53,554 index procedures among 51,583 patients, with 30,950 patients randomized to the Development Dataset. The distribution of procedures was pacemakers (52%), ICD (20%), CRT (12%), and Revision/Upgrade (16%), while prior procedures were none (62%), 1 (37%), and 2 (1%). Among patients with no history of prior CIED infection, the frailty model showed that a 1 unit increase in the PADIT score predicts higher infection risk (20%) in the U.S. claims data set (Table 1). Prior CIED infection was associated with strong additional predictive value (HR 4.77, p < 0.0001) after adjusting for PADIT score. Conclusion In the largest external validation of a CIED risk score, the PADIT risk score predicts increased CIED infection risk, identifying higher risk patients that can benefit from targeted interventions to reduce the risk of CIED infection. Prior CIED infection brings additional predictive value to the PADIT score.

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Reduced-Intensity Versus Myeloablative Conditioning for Unrelated Cord Blood Transplantation in Adults with Acute Leukemia: A Report from Eurocord, the Acute Leukemia Working Party and the Cord Blood Committee of the Cellular Therapy & Immunobiology Working Party of the European Group for Blood and Marrow Transplantation

Blood ◽

10.1182/blood.v126.23.155.155 ◽

2015 ◽

Vol 126 (23) ◽

pp. 155-155

Author(s):

Frederic Baron ◽

Ruggeri Annalisa ◽

Eric Beohou ◽

Myriam Labopin ◽

Guillermo Sanz ◽

...

Keyword(s):

Acute Leukemia ◽

Cord Blood ◽

Research Funding ◽

Cord Blood Transplantation ◽

Working Party ◽

Myeloablative Conditioning ◽

Unrelated Cord Blood Transplantation ◽

Blood Transplantation ◽

Unrelated Cord Blood ◽

Reduced Intensity

Abstract BACKRGOUND. Non-relapse mortality (NRM) is the first cause of treatment failure after unrelated cord blood transplantation (UCBT) following myeloablative conditioning (MAC). In the last decade, reduced-intensity conditionings (RIC) for UCBT have been developed with the aim of reducing NRM and allowing older patients and those with medical comorbidities to benefit from UCBT. The aim of our retrospective registry study was to compare outcomes of acute leukemia (AL) adult patients given UCBT after either RIC or MAC regimens. Regimens were classified as MAC or RIC based on EBMT criteria. PATIENTS AND METHODS. Data from 1352 adult (> 18 yrs) patients with AL (acute myeloid leukemia [AML; n=894] or acute lymphoblastic leukemia [ALL; n=458]) given a first single or double UCBT from 2004 to 2013 at EBMT-affiliated centers were included in this retrospective study. RESULTS. 518 patients were given UCB after RIC, while 834 patients were administered MAC. The most frequently used conditioning regimens combined either TBI, cyclophosphamide and Flu (TCF regimen, given in 22% of MAC vs 75% of RIC recipients, P<0.001), or thiotepa, Bu and Flu (TBF, given in 32% of MAC vs 6% of RIC recipients, P<0.001). In comparison to MAC recipients, RIC recipients were almost 2 decades older (median age 52.5 vs 33.7 yrs, P<0.001), were more often transplanted for AML (80% vs 57%, P=0.001), received more frequently 2 cord blood units (61 vs 32%, P<0.001), received more frequently units with > or = 2 HLA-mismatches (69% vs 58%, P<0.001), received more TNC (median 3.5x10E7 vs 2.9x10E7, P<0.001), and received less frequently ATG in the conditioning (23% versus 57%). Disease status at UCBT was comparable in both groups (51% of patients in CR1 and 17% >CR). Median follow-up for survivors was 25 months. In univariate analyses, in comparison to patients given MAC, RIC recipients had a similar rate of neutrophil engraftment (89.5 vs 89%, P=0.7), and a similar incidence of grade II-IV acute (34% vs 29%, P=0.1) and chronic (22% vs 26%, P= 0.22) GVHD. In contrast, at 2-yr, RIC recipients had a higher incidence of disease relapse (41 vs 24%, P<0.001) but a lower NRM (19 vs 37%, P<0.001), translating to similar leukemia-free survival (LFS, 40% vs 38%, P=0.6) but better overall survival (OS, 47 vs 42%, P=0.01) than MAC recipients (Figure 1). Further, among ALL patients, the use of TCF regimen (n=159) was associated lower NRM (21 vs 40% at 2-yr, P<0.001), lower relapse incidence (24 vs 34%, P=0.07), and better OS (63 vs 34%, P<0.001) and LFS (55 vs 27%, P<0.001). We performed separate multivariate analyses (MVA) for patients with AML and ALL. In MVA for AML patients, the use of RIC regimen was associated with a higher incidence of relapse (HR=1.6, P=0.005) but a suggestion for lower NRM (HR=0.7, P=0.1) translating to similar OS (HR=1.0, P=0.9) and LFS (HR=1.1, P=0.3). Similarly, in MVA for ALL patients, the use of RIC regimen was associated with a higher incidence of relapse (HR=2.0, P=0.002) but a lower NRM (HR=0.6, P=0.04) translating to similar OS (HR=0.8, P=0.2) and LFS (HR=1.1, P=0.5). Further, interestingly, conditioning with TCF-based regimen was associated with a lower incidence of relapse (HR=0.5, P=0.004) translating into better OS (HR=0.6, P=0.013) and LFS (HR 0.6, P=0.002) in ALL patients in MVA adjusted for conditioning intensity (RIC vs MAC). CONCLUSIONS. These data suggest that LFS and OS might be as good with RIC than with MAC in adults AL patients offered UCBT. These observations could serve as basis for future prospective randomized studies. Figure 1. Unadjusted UCBT outcomes in patients transplanted following RIC versus MAC. Figure 1. Unadjusted UCBT outcomes in patients transplanted following RIC versus MAC. Disclosures Milpied: Celgene: Honoraria, Research Funding. Sierra:Amgen: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Mohty:Janssen: Honoraria; Celgene: Honoraria. Schmid:Neovii: Consultancy; Janssen Cilag: Other: Travel grand.

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Preoperative risk stratification of deep sternal wound infection after coronary surgery

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2019.375 ◽

2020 ◽

Vol 41 (4) ◽

pp. 444-451

Author(s):

Fausto Biancari ◽

Giuseppe Gatti ◽

Stefano Rosato ◽

Giovanni Mariscalco ◽

Aniello Pappalardo ◽

...

Keyword(s):

Coronary Artery ◽

Coronary Artery Bypass Grafting ◽

Risk Score ◽

Artery Bypass ◽

Deep Sternal Wound Infection ◽

Bypass Grafting ◽

Sternal Wound Infection ◽

Validation Data ◽

Data Set ◽

Additive Risk

AbstractObjective:To develop a risk score for deep sternal wound infection (DSWI) after isolated coronary artery bypass grafting (CABG).Design:Multicenter, prospective study.Setting:Tertiary-care referral hospitals.Participants:The study included 7,352 patients from the European multicenter coronary artery bypass grafting (E-CABG) registry.Intervention:Isolated CABG.Methods:An additive risk score (the E-CABG DSWI score) was estimated from the derivation data set (66.7% of patients), and its performance was assessed in the validation data set (33.3% of patients).Results:DSWI occurred in 181 (2.5%) patients and increased 1-year mortality (adjusted hazard ratio, 4.275; 95% confidence interval [CI], 2.804–6.517). Female gender (odds ratio [OR], 1.804; 95% CI, 1.161–2.802), body mass index ≥30 kg/m2 (OR, 1.729; 95% CI, 1.166–2.562), glomerular filtration rate <45 mL/min/1.73 m2 (OR, 2.410; 95% CI, 1.413–4.111), diabetes (OR, 1.741; 95% CI, 1.178–2.573), pulmonary disease (OR, 1.935; 95% CI, 1.178–3.180), atrial fibrillation (OR, 1.854; 95% CI, 1.096–3.138), critical preoperative state (OR, 2.196; 95% CI, 1.209–3.891), and bilateral internal mammary artery grafting (OR, 2.088; 95% CI, 1.422–3.066) were predictors of DSWI (derivation data set). An additive risk score was calculated by assigning 1 point to each of these independent risk factors for DSWI. In the validation data set, the rate of DSWI increased along with the E-CABG DSWI scores (score of 0, 1.0%; score of 1, 1.8%; score of 2, 2.2%; score of 3, 6.9%; score ≥4: 12.1%; P < .0001). Net reclassification improvement, integrated discrimination improvement, and decision curve analysis showed that the E-CABG DSWI score performed better than other risk scores.Conclusions:DSWI is associated with poor outcome after CABG, and its risk can be stratified using the E-CABG DSWI score.Trial registration:clinicaltrials.gov identifier: NCT02319083

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Proposal of A Prognostic Score for Previously Untreated Patients with Chronic Lymphocytic Leukemia Based on An Overall Survival Analysis of Three German CLL Study Group Phase III Trials

Blood ◽

10.1182/blood.v118.21.2831.2831 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2831-2831

Author(s):

Jasmin Bahlo ◽

Natali Pflug ◽

Thomas Elter ◽

Kathrin Bauer ◽

Barbara Eichhorst ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Score ◽

Research Funding ◽

Prognostic Score ◽

Phase Iii ◽

Mutational Status ◽

Phase Iii Trials ◽

Binet Stage ◽

Very High

Abstract Abstract 2831 Introduction Prognosis and need of treatment in CLL is currently determined by clinical staging systems of Binet and Rai. Recent research has focused on prognostic factors that may predict a poor prognosis independent of the clinical stage. Markers which have shown independent prognostic information are serum parameters and genetic factors (genomic aberrations, IgHV and p53 mutational status). To investigate the relevance of these different factors, we performed a pooled analysis using the data of three multicenter German CLL Study Group phase III trials (CLL1, CLL4 and CLL8). Based on this analysis we propose a prognostic score for previously untreated patients with early and advanced CLL. Material and Methods Patients were recruited between 1997 and 2006 into three phase III trials: 715 in CLL1 (“watch and wait” versus fludarabine (F)), 362 in CLL4 (F versus F and cyclophosphamide (FC)) and 817 patients in the CLL8 trial (FC versus FC and rituximab (FCR)). Serum parameters and genetic factors were centrally analyzed prior to treatment. The main end point of all statistical analyses was overall survival. First, univariate analyses were performed including variables of different groups such as baseline characteristics, stage of disease, laboratory results, molecular cytogenetics, mutational status and serum parameters. Next, multivariate Cox regressions were applied including all parameters that showed a significant association with overall survival in univariate analyses. To create a prognostic score we developed a weighted grading algorithm for independent factors based on ranges of hazard ratios. Finally, a prognostic score was defined as the sum of single ratings of adverse factors. According to this score, four different risk groups for overall survival could be identified. Results In total 1948 patients were eligible for the pooled analysis with a median age of 60 years (range, 30 to 81 years). After a median observation time of 63.4 months 485 deaths were reported. At study entry, 799 patients (42.4%) were at Binet stage A, 717 (38.0%) at Binet stage B and 370 (19.6%) at Binet stage C. Almost all considered variables were significantly associated with outcome and therefore included in the multivariate analysis. Based on the data of 1223 patients for whom all parameters were available, multivariate Cox regressions were performed and identified gender, age, ECOG score, del(17p), del(11q), IgHV mutational status, serum β2-microglobulin and serum thymidine kinase as independent factors for overall survival. Deletion 17p was the strongest adverse factor. Neither the clinical staging (Rai, Binet) nor the treatment modality were independent prognostic factors for overall survival. Similarly, the time interval between first diagnosis and study entry was not an independent prognostic factor. Due to the great differences between hazard ratios of independent factors, we developed a weighted grading system based on a simple algorithm to assign an individual grade to each adverse factor. By using this weighted grading, four different prognostic groups could be separated: low risk (score 0 – 2), intermediate risk (score 3 – 5), high risk (score 6 – 10) and very high risk (score 11 – 14) (figure 1). Overall survival rates were significantly different for these four groups with 95.2%, 86.9%, 67.7% and 18.7% survival after 5 years for the low, intermediate, high and very high risk group, respectively (p<0.0001). Moreover, within the group of patients showing a deletion 17p the score could distinguish patients of a high risk and a very high risk group (p<0.0001). Finally, the score could predict the individual risk for short overall survival independent of and within the different Binet or Rai stages (p<0.0001) (figure 2). Conclusion While Binet and Rai staging systems may remain important for the initial clinical assessment due to their simplicity, our prognostic score using a weighted combination of genetic and serum markers is superior to predict the overall survival of CLL patients. Disclosures: Pflug: Hoffmann-la Roche: Travel grant; Mundipharma: Travel grant. Eichhorst:Hoffmann La Roche: Honoraria, Research Funding, Travel Grants; Mundipharma: Research Funding, Travel Grants; Gilead: Consultancy. Bergmann:Celgene: Honoraria. Döhner:Hoffmann-la Roche: Research Funding. Stilgenbauer:Hoffmann La Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Travel Grants. Fischer:Hoffmann La Roche: Travel Grants. Hallek:Hoffmann-la Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees.

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An empirical investigation of alternative semi-supervised segmentation methodologies

South African Journal of Science ◽

10.17159/sajs.2019/5359 ◽

2019 ◽

Vol 115 (3/4) ◽

Author(s):

Douw G. Breed ◽

Tanja Verster

Keyword(s):

Logistic Regression ◽

Model Performance ◽

Predictive Modelling ◽

Data Sets ◽

Validation Data ◽

Data Set ◽

Supervised Segmentation ◽

Improved Performance ◽

Validation Set ◽

Combination Approach

Segmentation of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main types of segmentation and examples of improved performance of predictive models exist for both approaches. However, both focus on a single aspect – either target separation or independent variable distribution – and combining them may deliver better results. This combination approach is called semi-supervised segmentation. Our objective was to explore four new semi-supervised segmentation techniques that may offer alternative strengths. We applied these techniques to six data sets from different domains, and compared the model performance achieved. The original semi-supervised segmentation technique was the best for two of the data sets (as measured by the improvement in validation set Gini), but others outperformed for the other four data sets. Significance: We propose four newly developed semi-supervised segmentation techniques that can be used as additional tools for segmenting data before fitting a logistic regression. In all comparisons, using semi-supervised segmentation before fitting a logistic regression improved the modelling performance (as measured by the Gini coefficient on the validation data set) compared to using unsegmented logistic regression.

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Identification of a methylomics-associated nomogram for predicting overall survival of stage I–II lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-89429-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Heng Wang ◽

Chuangye Wei ◽

Peng Pan ◽

Fengfeng Yuan ◽

Jiancheng Cheng

Keyword(s):

Dna Methylation ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Stage I ◽

The Cancer Genome Atlas ◽

Validation Dataset ◽

Major Advance ◽

Operating Characteristics ◽

Tcga Dataset

AbstractThe aim of this paper was to identify DNA methylation based biomarkers for predicting overall survival (OS) of stage I–II lung adenocarcinoma (LUAD) patients. Methylation profile data of patients with stage I–II LUAD from The Cancer Genome Atlas (TCGA) database was used to determine methylation sites-based hallmark for stage I–II LUAD patients’ OS. The patients were separated into training and validation datasets by using median risk score as cutoff. Univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to develop a DNA methylation signature for OS of patients with stage I–II LUAD. As a result, an 11-DNA methylation signature was determined to be critically associated with the OS of patients with stage I–II LUAD. Analysis of receiver operating characteristics (ROC) suggested a high prognostic effectiveness of the 11-DNA methylation signature in patients with stage I–II LUAD (AUC at 1, 3, 5 years in training set were (0.849, 0.879, 0.831, respectively), validation set (0.742, 0.807, 0.904, respectively), entire TCGA dataset (0.747, 0.818, 0.870, respectively). Kaplan–Meier survival analyses exhibited that survival was significantly longer in the low-risk cohort compared to the high-risk cohort in the training dataset (P = 7e − 07), in the validation dataset (P = 1e − 08), and in the all-cohort dataset (P = 6e − 14). In addition, a nomogram was developed based on molecular factor (methylation risk score) as well as clinical factors (age and cancer status) (AUC at 1, 3, 5 years entire TCGA dataset were 0.770, 0.849, 0.979, respectively). The result verified that our methylomics-associated nomogram had a strong robustness for predicting stage I–II LUAD patients’ OS. Furthermore, the nomogram combined clinical and molecular factors to determine an individualized probability of recurrence for patients with stage I–II LUAD, which stood for a major advance in the field of personalized medicine for pulmonary oncology. Collectively, we successfully identified a DNA methylation biomarker and a DNA methylation-based nomogram to predict the OS of patients with stage I–II LUAD.

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Impact of Hemoglobin Level As a Continuous Variable in Calculating Prognosis in MDS: Incorporation into LSC4 and IPSS-R

Blood ◽

10.1182/blood-2020-140029 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 36-37

Author(s):

Faezeh Darbaniyan ◽

Guillermo Montalban Bravo ◽

Yue Wei ◽

Rashmi Kanagal-Shamanna ◽

Koji Sasaki ◽

...

Keyword(s):

Overall Survival ◽

Scoring System ◽

Research Funding ◽

Continuous Variable ◽

Classification Performance ◽

International Prognostic Scoring System ◽

Data Set ◽

Hematopoietic Stem ◽

Survival Status ◽

Cd34 Cells

INTRODUCTION: Myelodysplastic syndromes (MDS) are a group of hematopoietic stem-cell disorders, with heterogenous prognosis. The Revised International Prognostic Scoring System (IPSS-R) (Greenberg et al., Blood, 2012) is the standard prognostic scoring system that uses several clinical and cytogenetic criteria as categorical parameters to predict prognosis of patients with newly diagnosed MDS. A recent study compared the performance of IPSS-R with a panel comprised of 4 stemness-related genes: LAPTM4B, NGFRAP1, EMP1, and CPXM1 (LSC4) and showed a significant improvement of survival classification performance when focusing on gene expression panel (Wang et al., Blood Advances, 2020). Despite the recent upgrade, the performance of none of these markers is sufficient enough and there is still need for improvement. METHODS: In order to evaluate the predictive power of the LSC4 and develop novel models integrating RNA-sequencing data with clinical variables, we evaluated bone marrow samples from 56 independent MDS patients prior to any therapy. Patient samples were collected using institutional guidelines. BM mononuclear cells (MNCs) were enriched by Ficoll (GE Healthcare, Chicago, IL) protocol, following manufacture's guidance. BM CD34+ cells were enriched using magnetic cell separation (MACS) and CD34+ magnetic beads (Miltenyi Biotech, Germany). RNA from sorted BM CD34+ cells was isolated using the TRIzol RNA isolation kit (Fisher Scientific, Waltham, MA) followed by RNA-Seq library construction. FASTQ files were processed in TopHat2 using the default options. We implemented cox proportional hazard model to create the survival panel and time dependent AUCs was used in order to evaluate the performance of the model. We divided the patients into high versus low groups using optimum cutoff method and used Kaplan-Meier estimator to show the survival behavior in each group. RESULTS: In this work we first validated the LSC4 panel and compared it with the IPSS-R on our data set. Furthermore, in order to improve the classifier performance, we calculated the correlation of clinical features with survival status. We observed that Hemoglobin (Hgb), as a continuous variable, has a significant effect on predicting the overall survival and it can significantly improve the survival classification performance when combined with the IPSS-R or LSC4 scores (HR = 0.63, 95% CI 0.44 - 0.89, P <0.001 when Hgb combines with IPSS-R and HR = 0.67, 95% CI 0.48 - 0.93, P<0.001 when Hgb combines with LSC4). Our proposed models show an enhanced time AUC performance when focusing on survival status beyond 20 months of follow up compare to IPSS-R and LSC4 scores (Figure 1a). We have also observed that combining Hgb with LSC4 or IPSS-R significantly improves the confidence interval for long term survivals (Figure 1b). In fact, in these panels, unlike the IPSS-R panel, we treat Hgb as a continuous variable and we believe factorial consideration of Hgb can underestimates the true effect of this feature on survival. We also showed a distinct separation between two survival curves when we used new proposed panels amongst all patients (Figures 1c and 1d). CONCLUSIONS: In this work, while providing further validation for LSC4 model, we showed that Hemoglobin, as a continuous variable, has a significant prognostic effect when integrated with either IPSS-R or LSC4. Hereby, we observed that increasing Hgb even by a single unit can improve patient's overall survival. Figure 1 Disclosures Sasaki: Daiichi Sankyo: Consultancy; Novartis: Consultancy, Research Funding; Pfizer Japan: Consultancy; Otsuka: Honoraria. Garcia-Manero:H3 Biomedicine: Research Funding; Acceleron Pharmaceuticals: Consultancy, Honoraria; AbbVie: Honoraria, Research Funding; Novartis: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Amphivena Therapeutics: Research Funding; Onconova: Research Funding; Helsinn Therapeutics: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy; Astex Pharmaceuticals: Consultancy, Honoraria, Research Funding; Merck: Research Funding.

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