scholarly journals Clinical Relevance of Somatic Mutations in Chronic Myelomonocytic Leukemia Undergoing Allogeneic Stem Cell Transplantation

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1713-1713 ◽  
Author(s):  
Nico Gagelmann ◽  
Anita Badbaran ◽  
Rashit Bogdanov ◽  
Olivier Nibourel ◽  
Friedrich Stoelzel ◽  
...  
Keyword(s):  
High Risk ◽  
Genetic Risk ◽  
Research Funding ◽  
Risk Group ◽  
Performance Status ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
Research Support ◽  
Total Cohort

Current risk stratification for newly diagnosed patients with chronic myelomonocytic leukemia (CMML) includes clinical and genetic features accounting for its variable disease course. Allogeneic stem cell transplantation still remains the only curative treatment option, and prognostication of posttransplant outcome may be improved using molecular information. Here, we aim to evaluate the molecular profile and its role on posttransplant outcome in a multicenter CMML cohort. Mutation analysis was performed on DNA from bone marrow mononuclear cells or peripheral granulocytes collected prior to transplant and included previously published CMML-associated genes (i.a. SETBP1, ASXL1, RUNX1, NRAS, KRAS, TET2, CBL, IDH1/2, SF3B1, DNMT3A, EZH2, ZRSR2, U2AF1). Current prognostic models were calculated at time of transplant. Patients with transformation to acute leukemia were excluded. Top predictors of posttransplant outcome were identified using the Random Forest algorithm. Main end points were overall survival (OS) and non-relapse mortality (NRM). The total cohort consisted of 185 patients of whom seven had CMML-0, 100 CMML-1, and 78 CMML-2 at time of transplant. The median follow-up was 74 months and 6-year OS was 37% for the total cohort and differed for CMML-0 (57%), CMML-1 (43%), and CMML-2 (29%). Relapse and NRM were 27% and 44% for the total cohort being 17% and 31% for CMML-0, 23% and 40% for CMML-1, and 34% and 40% for CMML-2. Most frequently mutated genes were: TET2 (55%), ASXL1 (41%), SF3B1 (38%), DNMT3A (27%), ZRSR2 (22%), NRAS (21%), EZH2 (21%), RUNX1 (17%), and SETBP1 (17%). Ninety-two percent of patients showed at least one somatic mutation. More than three mutations were present in 49% of all patients and in 29% of CMML-0, 50% of CMML-1, and 49% of CMML-2 patients. Frequencies according to CMML-specific prognostic scoring system (CPSS) and its molecular refinement (CPSS-mol) were 8% and 6% (low risk), 31% and 18% (intermediate-1 risk), 43% and 40% (intermediate-2 risk), and 18% and 36% (high risk). Transplants were received from matched unrelated (51%), mismatched unrelated (25%), matched related (21%), or mismatched related donors (3%). Conditioning intensity was reduced (49%), myeloablative (43%), or non-myeloablative (8%). Median age of patients was 60 years, 29% were female, 30% had a Karnofsky performance status <90%, and 15% had a comorbidity index >3. In the first step of the OS analysis, the algorithm identified mutations in ASXL1, KRAS, SF3B1, ZRSR2 as high-risk mutations (HRM) predicting worse OS. In addition, the number of the HRMs was associated with worse OS. In the next step, the algorithm automatically stratified this information into three distinct risk groups: the absence of HRMs (reference; low risk), presence of 1-2 HRMs (HR, 1.81; intermediate-risk), and 3-4 HRMs (HR, 2.93; high-risk). Corresponding 6-year OS was 59% for the low-risk, 34% for the intermediate-risk, and 14% for the high-risk group (P<.001; Figure 1A). Furthermore, the absence of HRMs was associated with lower NRM (15%) compared with present HRMs (46%; P=.01). In contrast, the CPSS-mol genetic risk classification including ASXL1, RUNX1, NRAS, and SETBP1 mutations showed no distinct 6-year OS or NRM (P=.15, respectively). Next, we adjusted the impact on OS of the proposed genetic risk for other factors included in the CPSS-mol. Higher genetic risk was independently associated with increased hazard for death (with the low-risk group as reference) showing HRs of 1.70 for the intermediate-risk and 2.83 for the high-risk group (P<.001). This model showed a concordance index of 0.633 versus CPSS-mol (0.597) or the CPSS (0.572) suggesting utility of transplant-specific prognostication. Therefore, we evaluated the multivariable effect on posttransplant outcome including the following independent clinical and molecular predictors: genetic risk, % of peripheral and bone marrow blasts, leukocyte count, and performance status. This model was predictive of OS and NRM (P<.001, respectively), and showed increased prognostic precision for OS, reflected in a concordance index of 0.684. In conclusion, mutations in ASXL1, KRAS, SF3B1, ZRSR2, and the number of these mutations predict OS and NRM in CMML undergoing transplantation. Accounting for these genetic lesions may improve the prognostic precision and patient counseling in the transplant setting. Figure 1 Disclosures Bogdanov: Jazz Pharmaceuticals, MSD.: Other: Travel subsidies. Stoelzel:Neovii: Other: Travel funding; JAZZ Pharmaceuticals: Consultancy; Shire: Consultancy, Other: Travel funding. Rautenberg:Jazz Pharmaceuticals: Other: Travel Support; Celgene: Honoraria, Other: Travel Support. Dreger:Neovii, Riemser: Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees, Other: Sponsoring of Symposia; AbbVie, AstraZeneca, Gilead, Janssen, Novartis, Riemser, Roche: Consultancy; AbbVie, Gilead, Novartis, Riemser, Roche: Speakers Bureau. Finke:Riemser: Honoraria, Other: research support, Speakers Bureau; Neovii: Honoraria, Other: research support, Speakers Bureau; Medac: Honoraria, Other: research support, Speakers Bureau. Kobbe:Pfizer: Honoraria, Other: Travel support; Takeda: Honoraria, Other: Travel support; Celgene: Honoraria, Other: Travel support, Research Funding; Jazz: Honoraria, Other: Travel support; Amgen: Honoraria, Other: Travel support, Research Funding; Biotest: Honoraria, Other: Travel support; MSD: Honoraria, Other: Travel support; Neovii: Honoraria, Other: Travel support; Abbvie: Honoraria, Other: Travel support; Novartis: Honoraria, Other: Travel support; Roche: Honoraria, Other: Travel support; Medac: Honoraria, Other: Travel support. Platzbecker:Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding. Robin:Novartis Neovii: Research Funding. Beelen:Medac GmbH Wedel Germany: Consultancy, Honoraria. Kroeger:JAZZ: Honoraria; Neovii: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Sanofi-Aventis: Honoraria; Novartis: Honoraria, Research Funding; Medac: Honoraria; DKMS: Research Funding; Riemser: Research Funding.

Application of a Validated Predictive Model for Venous Thromboembolism in Cancer to Patients with Multiple Myeloma

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 534-534
Author(s):  
Natasha Catherine Edwin ◽  
Jesse Keller ◽  
Suhong Luo ◽  
Kenneth R Carson ◽  
Brian F. Gage ◽  
...  
Keyword(s):  
High Risk ◽  
Research Funding ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
Discriminative Ability ◽  
Blood Institute ◽  
C Statistic

Abstract Background Patients with multiple myeloma (MM) have a 9-fold increased risk of developing venous thromboembolism (VTE). Current guidelines recommend pharmacologic thromboprophylaxis in patients with MM receiving an immunomodulatory agent in the presence of additional VTE risk factors (NCCN 2015, ASCO 2014, ACCP 2012). However, putative risk factors vary across guidelines and no validated VTE risk tool exists for MM. Khorana et al. developed a VTE risk score in patients with solid organ malignancies and lymphoma (Blood, 2008). We sought to apply the Khorana et al. score in a population with MM. Methods We identified patients diagnosed with MM within the Veterans Health Administration (VHA) between September 1, 1999 and December 31, 2009 using the International Classification of Diseases (ICD)-03 code 9732/3. We followed the cohort through October 2014. To eliminate patients with monoclonal gammopathy of undetermined significance and smoldering myeloma, we excluded patients who did not receive MM-directed therapy within 6 months of diagnosis. We also excluded patients who did not have data for hemoglobin (HGB), platelet (PLT) count, white blood count (WBC), height and weight, as these are all variables included in the Khorana et al. risk model. Height and weight were assessed within one month of diagnosis and used to calculate body mass index (BMI). We measured HGB, PLT count, and WBC count prior to treatment initiation: within two months of MM diagnosis. A previously validated algorithm, using a combination of ICD-9 code for VTE plus pharmacologic treatment for VTE or IVC filter placement, identified patients with incident VTE after MM diagnosis (Thromb Res, 2015). The study was approved by the Saint Louis VHA Medical Center and Washington University School of Medicine institutional review boards. We calculated VTE risk using the Khorana et al. score: We assigned 1 point each for: PLT ≥ 350,000/μl, HGB < 10 g/dl, WBC > 11,000/μl, and BMI ≥ 35 kg/m2. Patients with 0 points were at low-risk, 1-2 points were considered intermediate-risk and ≥3 points were termed high-risk for VTE. We assessed the relationship between risk-group and development of VTE using logistic regression at 3- and 6-months. We tested model discrimination using the area under the receiver operating characteristic curve (concordance statistic, c) with a c-statistic range of 0.5 (no discriminative ability) to 1.0 (perfect discriminative ability). Results We identified 1,520 patients with MM: 16 were high-risk, 802 intermediate-risk, and 702 low-risk for VTE using the scoring system in the Khorana et al. score. At 3-months of follow-up, a total of 76 patients developed VTE: 27 in the low-risk group, 48 in the intermediate-risk group, and 1 in the high-risk group. At 6-months of follow-up there were 103 incident VTEs: 41 in the low-risk group, 61 in the intermediate-risk group, and 1 in the high-risk group. There was no significant difference between risk of VTE in the high- or intermediate-risk groups versus the low-risk group (Table 1). The c-statistic was 0.56 at 3-months and 0.53 at 6-months (Figure 1). Conclusion Previously, the Khorana score was developed and validated to predict VTE in patients with solid tumors. It was not a strong predictor of VTE risk in MM. There is a need for development of a risk prediction model in patients with MM. Figure 1. Figure 1. Disclosures Carson: American Cancer Society: Research Funding. Gage:National Heart, Lung and Blood Institute: Research Funding. Kuderer:Janssen Scientific Affairs, LLC: Consultancy, Honoraria. Sanfilippo:National Heart, Lung and Blood Institute: Research Funding.

scholarly journals SARS-CoV-2 Seroprevalence in Healthcare Workers in Germany: A Follow-Up Study

2021 ◽  
Vol 18 (9) ◽  
pp. 4540
Author(s):  
Johannes Korth ◽  
Benjamin Wilde ◽  
Sebastian Dolff ◽  
Jasmin Frisch ◽  
Michael Jahn ◽  
...  
Keyword(s):  
High Risk ◽  
Healthcare Workers ◽  
Risk Group ◽  
Close Contact ◽  
Virus Transmission ◽  
High Risk Group ◽  
Low Risk ◽  
University Hospital ◽  
Intermediate Risk ◽  
Igg Antibody

SARS-CoV-2 is a worldwide challenge for the medical sector. Healthcare workers (HCW) are a cohort vulnerable to SARS-CoV-2 infection due to frequent and close contact with COVID-19 patients. However, they are also well trained and equipped with protective gear. The SARS-CoV-2 IgG antibody status was assessed at three different time points in 450 HCW of the University Hospital Essen in Germany. HCW were stratified according to contact frequencies with COVID-19 patients in (I) a high-risk group with daily contacts with known COVID-19 patients (n = 338), (II) an intermediate-risk group with daily contacts with non-COVID-19 patients (n = 78), and (III) a low-risk group without patient contacts (n = 34). The overall seroprevalence increased from 2.2% in March–May to 4.0% in June–July to 5.1% in October–December. The SARS-CoV-2 IgG detection rate was not significantly different between the high-risk group (1.8%; 3.8%; 5.5%), the intermediate-risk group (5.1%; 6.3%; 6.1%), and the low-risk group (0%, 0%, 0%). The overall SARS-CoV-2 seroprevalence remained low in HCW in western Germany one year after the outbreak of COVID-19 in Germany, and hygiene standards seemed to be effective in preventing patient-to-staff virus transmission.

P3609Temporal trends of the management and outcomes of patients after myocardial infarction according to the risk for recurrent cardiovascular events

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
T Grinberg ◽  
T Bental ◽  
Y Hammer ◽  
A R Assali ◽  
H Vaknin-Assa ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
High Risk ◽  
Cardiovascular Events ◽  
Risk Group ◽  
Temporal Trends ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
High Risk Patients ◽  
Risk Patients

Abstract Background Following Myocardial Infarction (MI), patients are at increased risk for recurrent cardiovascular events, particularly during the immediate period. Yet some patients are at higher risk than others, owing to their clinical characteristics and comorbidities, these high-risk patients are less often treated with guideline-recommended therapies. Aim To examine temporal trends in treatment and outcomes of patients with MI according to the TIMI risk score for secondary prevention (TRS2°P), a recently validated risk stratification tool. Methods A retrospective cohort study of patients with an acute MI, who underwent percutaneous coronary intervention and were discharged alive between 2004–2016. Temporal trends were examined in the early (2004–2010) and late (2011–2016) time-periods. Patients were stratified by the TRS2°P to a low (≤1), intermediate (2) or high-risk group (≥3). Clinical outcomes included 30-day MACE (death, MI, target vessel revascularization, coronary artery bypass grafting, unstable angina or stroke) and 1-year mortality. Results Among 4921 patients, 31% were low-risk, 27% intermediate-risk and 42% high-risk. Compared to low and intermediate-risk patients, high-risk patients were older, more commonly female, and had more comorbidities such as hypertension, diabetes, peripheral vascular disease, and chronic kidney disease. They presented more often with non ST elevation MI and 3-vessel disease. High-risk patients were less likely to receive drug eluting stents and potent anti-platelet drugs, among other guideline-recommended therapies. Evidently, they experienced higher 30-day MACE (8.1% vs. 3.9% and 2.1% in intermediate and low-risk, respectively, P<0.001) and 1-year mortality (10.4% vs. 3.9% and 1.1% in intermediate and low-risk, respectively, P<0.001). During time, comparing the early to the late-period, the use of potent antiplatelets and statins increased among the entire cohort (P<0.001). However, only the high-risk group demonstrated a significantly lower 30-day MACE (P=0.001). During time, there were no differences in 1-year mortality rate among all risk categories. Temporal trends in 30-day MACE by TRS2°P Conclusion Despite a better application of guideline-recommended therapies, high-risk patients after MI are still relatively undertreated. Nevertheless, they demonstrated the most notable improvement in outcomes over time.

A Critical Analysis of Prognostic Factors in Patients with HTLV-1 Adult T-Cell Leukemia/Lymphoma: A Multicenter Clinicopathologic Experience and New Prognostic Score.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1784-1784
Author(s):  
Adrienne A. Phillips ◽  
Iuliana Shapira ◽  
Robert D. Willum ◽  
Jasotha Sanmugarajah ◽  
William B. Solomon ◽  
...  
Keyword(s):  
T Cell ◽  
High Risk ◽  
Median Survival ◽  
Risk Group ◽  
Performance Status ◽  
Recursive Partitioning ◽  
Prognostic Index ◽  
Low Risk ◽  
Intermediate Risk ◽  
A Performance

Abstract Purpose: Adult T-Cell Leukemia/Lymphoma (ATLL) is a rare aggressive Human T-cell Lymphotropic Virus Type-I (HTLV-I) associated peripheral T-cell neoplasm with 4 recognized clinicopathologic subtypes: acute, lymphomatous, chronic, and smoldering. Since the initial description of these variants, several studies have sought to identify additional prognostic factors. We assessed prognostic models already in use for aggressive non-Hodgkin lymphomas to develop a novel risk stratification scheme. Methods: Data regarding patients with ATLL were collected from 3 medical centers between 8/92 and 5/07. Descriptive statistics were used to assess categorical and continuous variables. Overall survival (OS) was defined as time from diagnosis to death. Survival curves for OS were estimated using the Kaplan-Meier method. Univariate associations between individual clinical factors and OS were evaluated using the log-rank test for categorical variables and the Cox model for continuous variables. Maximum logrank analysis was used to select the optimal cut-off for calcium. In order to develop a simple risk model and allow for interactions of factors independently associated with OS, we used recursive partitioning analysis. Results: 89 patients with ATLL were identified; 37 males (41.6%) and 52 females (58.4%) and median age 50 years (range 22 to 82). The acute subtype of ATLL predominated (68.5%), followed by lymphomatous (20.2%), chronic (6.8%) and smoldering (4.5%). Median OS for all sub-types was 24 weeks (range 0.9 to 315). According to the International Prognostic Index (IPI), 8 patients (9.1%) were classified as low risk, 11 patients (12.5 %) as low intermediate risk, 13 patients (14.8 %) as high intermediate risk, and 56 patients (63.6 %) as high risk, 1 patient could not be evaluated due to missing data. Median OS by IPI risk group was 271, 65, 31 and 16 weeks, respectively (p&lt;0.01). The Prognostic Index for PTCL-U (PIT) could be determined in 68 patients; 10 patients (14.7 %) had a score of 0–1 (group 1), 19 patients (27.9 %) had a score of 2 (group 2), 31 patients (45.6 %) had a score of 3 (group 3), and 8 patients (11.8 %) had a score of 4 (group 4). Median OS by PIT risk group was 61.1, 28, 24, and 11.3 weeks respectively (p&lt;0.01). A new risk model was developed using the variables of the IPI and PIT. In addition, calcium level at diagnosis was also included as it had independent prognostic value. Recursive partitioning of OS based on these variables gave a tree with 5 nodes, which fell into three risk categories: low risk patients with Stage I–II disease and a performance status &lt;2; the medium risk group composed of two sets of patients: those with Stage III–IV disease with an ECOG performance status &lt; 2 or those with an ECOG performance status ≥ 2 with calcium ≤ 11 mg/dL and age ≤ 60; and the high risk group (also comprising 2 sets of patients): those with a performance status ≥ 2 with calcium ≤ 11 mg/dL and age &gt; 60 or those with a performance status ≥ 2 and calcium &gt; 11 mg/dL. There were 10 patients (11.2%) in the low risk (median survival= 156.6 weeks), 31 (34.8%) in the intermediate risk (median survival = 45.4 weeks), and 48 (53.9%) in the high risk (median survival= 13 weeks) categories (p&lt;0.01). Conclusion: This retrospective series confirms a poor outcome for North American patients with HTLV-1 related ATLL. Although the IPI and PIT identified subsets of patients, these models had liabilities. We propose a new prognostic model based on recursive partitioning analysis that successfully identifies three prognostic categories based on performance status, stage, age and calcium level at diagnosis in a more robust and distinct fashion. Table 1. Comparison of Prognostic Scores and Kaplan Meier Survival Estimates (%) of patients with ATLL International Prognostic Index (IPI) (n = 88) Prognostic Index for PTCL-U (PIT) (n = 68) ATLL Prognostic Score (APS) (n= 89) Time (wks) Low n= 8 Low-Intermed n= 11 High-Intermed n= 13 High n= 56 Group 1 n= 10 Group 2 n= 19 Group 3 N= 31 Group 4 n= 8 Low n= 10 Intermed n= 31 High n= 48 13 8 (100%) 10 (100%) 9 (75.5%) 31 (53.1%) 10 (100%) 13 (68.4%) 19 (66.3%) 3 (25.0%) 9 (100%) 27 (87.1%) 23 (46.4%) 26 8 (100%) 9 (90.0%) 6 (56.6%) 17 (31.1%) 10 (100%) 9 (51.3%) 13 (45.4%) 0 (0%) 9 (100%) 23 (77.0%) 9 (19.9%) 52 6 (75.0%) 6 (60.0%) 3 (28.3%) 9 (17.6%) 5 (50%) 5 (28.5%) 8 (30.7%) 0 (0%) 8 (88.9%) 13 (46.0%) 4 (8.8%) 78 5 (75.0%) 4 (40.0%) 2 (18.9%) 2 (4.0%) 4 (40%) 3 (17.1%) 2 (7.7%) 0 (0%) 7 (88.9%) 7 (24.8%) 0 (0%) 104 3 (56.2%) 3 (30.0%) 2 (18.9%) 2 (2.0%) 2 (30%) 3 (17.1%) 2 (3.8%) 0 (0%) 4 (61.0%) 6 (17.7%) 0 (0%) Median OS (wks) 271 65 31 16 61.1 28 24 11.3 156.6 45.4 13

Utilization of oncotype DX in node-negative, ER-positive breast cancer patients

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 11067-11067 ◽  
Author(s):  
H. Patel ◽  
K. Hook ◽  
C. Kaplan ◽  
R. Davidson ◽  
A. DeMichele ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Oncotype Dx ◽  
Intermediate Risk ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Node Negative ◽  
Er Positive

11067 Background: The 21 gene RT-PCR assay Oncotype DX (Genomic Health, CA) stratifies patients into low, intermediate and high risk for systemic recurrence. The objective of this study was to examine the patterns of use of Oncotype DX in a single institution. Methods: All patients who had ODX testing requested by the University of Pennsylvania were identified and recurrence scores (RS) obtained. Patient and tumor characteristics, as well as treatment administered, were obtained by chart review for analysis. Results: 100 ODX tests were ordered between 1/1/05–11/30/06. RS results classified 51% of breast cancers as low risk, 38% intermediate risk, and 11% high risk. Characteristics of the tumors of the overall population and by RS group are shown in Table . 99% of patients received hormonal therapy. Of the low risk patients, only one patient was treated with chemotherapy (2%) while 34% of the intermediate risk group and 80% of the high risk group received chemotherapy. Notably, only 4/100 patients with ODX were under age 35 and 17/100 had tumors over 2cm. Conclusions: In this series, ODX use is accelerating. The results of the ODX tests appear to be used clinically as demonstrated by the very low use of chemotherapy in the low risk group. Comparison to the overall population of ER positive, node negative patients seen at this institution is underway. [Table: see text] No significant financial relationships to disclose.

Varying Importance of Prognostic Factors in Subgroups of Myelodysplastic Syndromes Defined by Blast Count, Cytogenetic Risk Group, or WHO Classification.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2776-2776
Author(s):  
Andrea Kuendgen ◽  
Corinna Strupp ◽  
Kathrin Nachtkamp ◽  
Barbara Hildebrandt ◽  
Rainer Haas ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Prognostic Factors ◽  
High Risk ◽  
Research Funding ◽  
Risk Group ◽  
Univariate Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Intermediate Risk ◽  
Blast Count

Abstract Abstract 2776 Poster Board II-752 Introduction: We wondered whether prognostic factors have similar relevance in different subpopulations of MDS patients. Methods: Our analysis was based on patients with primary, untreated MDS, including 181 RA, 169 RARS, 649 RCMD, 322 RSCMD, 79 5q-syndromes, 290 RAEB I, 324 RAEB II, 266 CMML I, 64 CMML II, and 209 RAEB-T. The impact of prognostic variables in univariate analysis was compared in subpopulations of patients defined by medullary blast count, namely <5%, ≥5% (table), ≥10%, and ≥20% (not shown), as well as 3 subpopulations defined by the cytogenetic risk groups according to IPSS (table). Multivariate analysis of prognostic factors was performed for cytogenetically defined subgroups and WHO-subtypes. Results: Strong prognostic factors in all blast-defined subgroups were hemoglobin, transfusion dependency, increased WBC, age, and LDH. However, all variables became less important in patients with ≥20% blasts (RAEB-T) and increased WBC was rare. Platelet count and cytogenetic risk groups were relevant in patients with <5%, ≥5%, and ≥10% marrow blasts, but not in RAEB-T. Marrow fibrosis was important in patients with <5% or ≥5% blasts, but not ≥10%. Gender and ANC <1000/μl were significant only in patients with a normal blast count. Furthermore, we looked for the effect of the karyotypes, relevant for IPSS scoring (-Y, del5q, del20q, others, del7q/-7, complex), and found a comparable influence on survival, irrespective whether patients had < or ≥5% marrow blasts. In subpopulations defined by cytogenetic risk groups, several prognostic factors were highly significant in univariate analysis, if patients had a good risk karyotype. These included hemoglobin, sex, age, LDH, increased WBC, transfusion need, and blast count (cut-offs 5%, 10%, and 20%). In the intermediate risk group only LDH, platelets, WBC, and blasts were significant prognostic factors, while in the high risk group only platelets and blast count remained significant. Multivariate analysis was performed for the cytogenetic risk groups and for subgroups defined by WHO subtypes. The analysis considered blast count (</≥5%), hemoglobin, platelets, ANC, cytogenetic risk group, transfusion need, sex, and age. In the subgroup including RA, RARS, and 5q-syndrome, LDH, transfusion, and age in descending order were independent prognostic parameters. In the RCMD+RSCMD group, karyotype, age, transfusion, and platelets were relevant factors. In the RAEB I+II subgroup, the order was hemoglobin, karyotype, age, and platelets, while in CMML I+II only hemoglobin had independent influence. In RAEB-T none of the factors examined was of independent significance. Looking at cytogenetic risk groups, in the favorable group, several variables independently influenced survival, namely transfusion, blasts, age, sex, and LDH (in this order). Interestingly, in the intermediate and high risk group, only blast count and platelets retained a significant impact. Conclusion: Univariate analysis showed prognostic factors (except ANC) included in IPSS and WPSS are relevant in most subgroups defined by marrow blast percentage. However, they all lose their impact if the blast count exceeds 20%. Regarding cytogenetic risk groups, several prognostic factors lose their influence already in the intermediate risk group. This underscores the prognostic importance of MDS cytogenetics. Multivariate analysis showed MDS subpopulations defined by WHO types also differ with regard to prognostic factors. In particular, CMML and RAEB-T stand out against the other MDS types. Disclosures: Kuendgen: Celgene: Honoraria. Hildebrandt:Celgene: Research Funding. Gattermann:Novartis: Honoraria, Participation in Advisory Boards on deferasirox clinical trials. Germing:Novartis, Celgene: Honoraria, Research Funding.

The Combination of SKY92 and ISS Provides a Powerful Tool to Identify Both High Risk and Low Risk Multiple Myeloma Cases, Validation in Two Independent Cohorts

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2970-2970 ◽  
Author(s):  
Martin van Vliet ◽  
Joske Ubels ◽  
Leonie de Best ◽  
Erik van Beers ◽  
Pieter Sonneveld
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Research Funding ◽  
Prognostic Markers ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Standard Risk

Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients. Materials and Methods The SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR>1). Results Figure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used. Conclusions The SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Disclosures van Vliet: SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

Clinical assessment and angiographic finding in patients with non ST-elevation myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Elbeyali
Keyword(s):  
Myocardial Infarction ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
St Elevation Myocardial Infarction ◽  
Intermediate Risk ◽  
Coronary Syndrome ◽  
Clinical Risk ◽  
St Elevation

Abstract Background Non ST-elevation myocardial infarction is considered the intermediate form of acute coronary syndrome between unstable angina and ST-elevation myocardial infarction. Blockage either occurs in a minor coronary artery or causes partial obstruction of a major coronary artery. The rate of NSTEMI has increased to be 50% of all acute coronary syndrome. Purpose To compare some demographic, clinical risk assessments and angiographic data among high, intermediate and low risk NSTEMI patients. Methods We classified one hundred twenty (120) NSTEMI patients into 3groups by GRACE risk score (high risk group &gt;140, intermediate risk group from 109 to 140 and low risk group ≤108). The patients were evaluated by personal history taking, risk factors, clinical examination, ECG, laboratory investigations, echocardiography and percutaneous coronary intervention. Results We found that low risk group percentage was 47.5%, intermediate risk group percentage was 32.5% and high-risk group percentage was 20%. As regarding culprit lesion, LAD represent most affected artery (48.3% of patients).Recurrent ischemia and MI represent the highest percentage of major adverse cardiac event (MACE) among studied groups. All patients with LM disease have a MACE while 41.2% of MACE patients have significant LAD lesion. As time of intervention delayed the incidence of MACE increases among different groups. High risk group has significantly high percentage of type C lesion and TIMI 0/1 while type A lesion and TIMI III lesion highest among low risk patients. As regarding contour of the lesion, the irregularity increases as the clinical risk increases. Also as regarding occlusion of culprit artery, the incidence of total occlusion increases as the clinical risk increases. Conclusions We recommend selection of high-risk NSTEMI patient to direct them for early invasive therapy. Very high-risk directed for immediate revascularization like STEMI patient. NSTEMI considered precursors to STEMI and an early warning signal that aggressive medical intervention needed. Association between time to intervention Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): University budget

scholarly journals Clinical Performance Status and Technical Factors Affecting Outcomes from Percutaneous Transhepatic Biliary Interventions; A Multicentre, Prospective, Observational Cohort Study

2021 ◽  
Author(s):  
Gregory C. Makris ◽  
Andrew C. Macdonald ◽  
Kader Allouni ◽  
Hannah Corrigall ◽  
Charles R. Tapping ◽  
...  
Keyword(s):  
Cohort Study ◽  
High Risk ◽  
Risk Group ◽  
Performance Status ◽  
Group Versus ◽  
High Risk Group ◽  
Low Risk ◽  
Karnofsky Score ◽  
The Uk ◽  
Biliary Interventions

Abstract Purpose The purpose of this study was to evaluate the predictive value of a ‘Modified Karnofsky Scoring System’ on outcomes and provide real-world data regarding the UK practice of biliary interventions. Materials and Methods A prospective multi-centred cohort study was performed. The pre-procedure modified Karnofsky score, the incidence of sepsis, complications, biochemical improvement and mortality were recorded out to 30 days post procedure. Results A total of 292 patients (248 with malignant lesions) were suitable for inclusion in the study. The overall 7 and 30 day mortality was 3.1% and 16.1%, respectively. The 30 day sepsis rate was 10.3%. In the modified Karnofsky ‘high risk’ group the 7 day mortality was 9.7% versus 0% for the ‘low risk’ group (p = 0.002), whereas the 30 day mortality was 28.8% versus 13.3% (p = 0.003). The incidence of sepsis at 30 days was 19% in the high risk group versus 3.3% at the low risk group (p = 0.001) Conclusion Percutaneous biliary interventions in the UK are safe and effective. Scoring systems such as the Karnofsky or the modified Karnofsky score hold promise in allowing us to identify high risk groups that will need more careful consideration and enhanced patient informed consent but further research with larger studies is warranted in order to identify their true impact on patient selection and outcomes post biliary interventions.

scholarly journals EP.TH.49The Use of MRCP to Investigate Bile Duct Stones

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Jennifer Ma ◽  
Paras Jethwa
Keyword(s):  
High Risk ◽  
Bile Duct ◽  
Risk Group ◽  
Gallstone Disease ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Liver Function Tests ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
Cbd Stone

Abstract Aim Association of Upper Gastrointestinal Surgeons (AUGIS) latest guideline advocates stratification of patients with gallstone disease to determine their risks of common bile duct (CBD) stone and to perform Magnetic resonance cholangiopancreatography (MRCP) for those at intermediate risk. The study assessed the appropriateness of our local hospital's MRCP requests in accordance to the AUGIS standard. Method Inpatient MRCP requests for suspected ductal gallstones between June and December 2019 were identified retrospectively. Admission history, ultrasound, MRCP findings and liver function tests were collected from hospital electronic records. Patients with previous cholecystectomy were excluded. Patients were categorized into ‘low risk’, ‘intermediate risk’ and ‘high risk’. Results 67 patients were included in the study and 24 patients were discovered to have CBD stones on MRCP. The majority of patients (54%) were considered ‘intermediate risk’, whilst the ‘low risk’ group consisted of 13% of the MRCP requests and ‘high risk’ group comprised of 33%. Amongst those in the ‘low risk’ group, only 1 of 9 patients (11%) had cbd stone identified on MRCP. 19% patients in the intermediate group were found to have CBD stone, whilst 73% patients in the high risk group were identified to have CBD stone. On average, patients underwent MRCP within a day of request. Conclusion A high proportion of patients at high risk for CBD stone were referred for MRCP, contrary to AUGIS guideline. Inpatient MRCP referrals should be considered carefully in this category as it potentially increases length of stay without change in clinical management.

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