scholarly journals Predicting Risk of Venous Thromboembolism in Multiple Myeloma: The Impede VTE Score

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 141-141
Author(s):  
Kristen M. Sanfilippo ◽  
Suhong Luo ◽  
Tzu-Fei Wang ◽  
Tanya Wildes ◽  
Joseph Mikhael ◽  
...  

Abstract Introduction: Venous thromboembolism (VTE) is a common cause of morbidity and mortality among patients with multiple myeloma (MM). Thromboprophylaxis is a safe and effective way to decrease VTE in other high-risk populations. Current guidelines recommend use of thromboprophylaxis in MM patients at high-risk of VTE, but no validated model predicts VTE in MM. A risk prediction model for VTE in MM would allow for use of thromboprophylaxis in MM patients at high-risk of VTE while sparing those at low risk. Therefore, we sought to develop a risk prediction model for VTE in MM. Patients and Methods: Using a nationwide cohort of Veterans, we identified 4,448 patients diagnosed with MM between 1999 and 2014. We retrospectively followed patients for 180 days after start of MM chemotherapy. We identified candidate risk factors through literature review for inclusion into the time-to-event models. We used the methods of Fine and Gray to model time to VTE while accounting for the competing risk of (non-VTE) death. To minimize immortal time bias, all treatment variables were entered as time-varying variables. Using a backward, step-wise approach, we retained variables in the model with a p ≤ 0.05, or with a p < 0.50 with findings consistent with prior literature. Using beta coefficients, we developed a risk score by multiplying by a common factor and rounding to the nearest integer. The risk score for each patient was the sum of all scores for each predictor variable. We assessed model performance with Harrell's c-statistic and with the inverse probability of censoring weighting approach. Through bootstrap analysis, we validated the model internally. We carried out all statistical analyses using SAS version 9.4 (SAS Institute, Cary, NC). Results: The median time from MM diagnosis to start of treatment was 37 days. A total of 53 patients (5.7%) developed VTE within 6 months after start of MM-specific therapy. The mean time from chemotherapy start to VTE was 69 days, with 69% of VTE events occurring in the first 3 months of chemotherapy. The factors associated with VTE were combined to develop the IMPEDE VTE score (IMID 3 points, BMI 1 point, Pathologic fracture pelvis/femur 2 points, ESA 1 point, Dexamethasone (High-dose 4 points, Low-Dose 2 points)/Doxorubicin 2 points, Ethnicity/Race= Asian -3 points, history of VTE 3 points, Tunneled line/CVC 2 points) (Table 1). In addition, use of therapeutic anticoagulation (-5 points) with warfarin or low molecular weight heparin (LWMH) and use of prophylactic LMWH or aspirin (-2 points) were associated with a decreased risk of VTE. The model showed satisfactory discrimination in both the derivation cohort (Harrell's c-statistic = 0.66) and in the bootstrap validation, c-statistic = 0.65 (95% CI: 0.62 - 0.69). Using three risk groups, the incident rate of VTE with the first 6-months of starting chemotherapy was 3.1% for scores ≤ 3 (low-risk), 7.5% for a score of 4-6 (intermediate-risk), and 13.3% for patients with a score of ≥ 7 (high-risk). The risk of developing VTE within 6 months after starting chemotherapy was significantly higher for patients with intermediate- and high-risk scores compared to low-risk (Table 2). Conclusions and Relevance: We developed a risk prediction rule, IMPEDE VTE, which can identify patients with MM at high-risk of developing VTE after starting chemotherapy. IMPEDE VTE could guide clinicians in selecting patients for thromboprophylaxis in MM. Disclosures Sanfilippo: BMS/Pfizer: Speakers Bureau. Wang:Daiichi Sankyo: Consultancy, Other: Travel. Wildes:Janssen: Research Funding. Mikhael:Onyx, Celgene, Sanofi, AbbVie: Research Funding. Carson:Flatiron Health: Employment; Washington University in St. Louis: Employment; Roche: Consultancy.

2021 ◽  
Author(s):  
Yaping Zhou ◽  
Liu Yang ◽  
Xiangxin Zhang ◽  
Xiaotong Zhao ◽  
Jianfeng Fu ◽  
...  

Abstract Background: LncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs' clinical significance, and find new targeted treatment strategies. Methods: In our study, RNA-seq and clinical data of normal and HCC patients were obtained from the TCGA database, and autophagy genes were obtained from the human autophagy database. Results: The risk prediction model containing seven autophagy-related lncRNAs was constructed. Overall survival (OS) curves show that the high-risk group patients significantly shorter than the low-risk group (P=2.292e-10), and the five years survival rate of the high-risk group (HR 0.286, 95%CI 0.199-0.411) is less than half of the low-risk group (HR 0.694, 95%CI 0.547-0.77). Univariate Cox regression indicated that risk score of the risk prediction model (P<0.001, 95%CI 1.210-1.389 ), T (P<0.001, 95%CI 1.443-2.287), and stage (P<0.001 ,95%CI 1.466-2.408 ) were independent prognostic indicators. However, only the risk score remains the independent prognostic indicator(P<0.001, 95%CI 1.197-1.400 ) based on the multivariate analysis. This risk model's prediction efficiency is significantly higher than other clinicopathological factors for 1-, 3- and 5-year survival rate prediction (AUC are 0.853, 0.794, and 0.764, respectively). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing. Conclusion: In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.


2021 ◽  
Author(s):  
Ke Han ◽  
Jukun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Yi Zhang

Purpose: ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In this study, a non–small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores. Methods: An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail. Results: A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model’s effectiveness. Univariate and multivariate Cox regression analyses showed that the model’s risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions. Conclusion: The risk prediction model established with seven ADME genes in this study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e20553-e20553
Author(s):  
Jianchun Duan ◽  
Hua Bai ◽  
Yiting Sun ◽  
Fei Gai ◽  
Shenya Tian ◽  
...  

e20553 Background: Clinical characters cannot precisely evaluate long-term survival of patients with resectable lung adenocarcinoma. Genomics studies of lung adenocarcinoma (LUAD) have advanced our understanding of LUAD's biology. Thus, genomics-based robust models predicting survival outcome for patients with operatable LUAD needs to be investigated. Here, we aimed to identify new gene signatures to construct a risk prediction model via integrating Omics data from The Cancer Genome Atlas (TCGA) to better evaluate the long-term clinical outcome of LUAD patients. Methods: A cohort of one hundred and eighty-nine stage II-IIIA lung adenocarcinoma cases receiving tumor resection were screened out and downloaded from TCGA database. Tumor samples without survival information and genes with low or no expression were removed. Genes associated with cancer and immune were further narrowed down using a Master Panel Gene Set (Amoydx). Lasso-Cox regression analysis was used to screen gene-survival outcome, and then a risk prediction model was established. LUAD cases were divided into high-risk or low-risk groups as per the scores, to assess differential expressed genes and pathways. Results: A total of 8 most survival outcome related genes (CLEC7A, PAX5, XCR1, KRT7, PLCG1, DKK1, CLEC10A, IKZF3) were identified after Lasso-Cox regression analysis and used for model construction. The overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) from the subgroups within the high- and low-risk groups were assessed and showed significant prolonged in low-risk group, the hazard ratio (HR) of OS was 2.72 (95%CI: 2.04-3.61, P = 5.91e-12) in high-risk group. Hierarchical clustering analysis, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) revealed that genes involved in immune responses were significantly suppressed in high-risk group, while as genes involved in antioxidative metabolism were activated, which gave us a hint that immune-metabolism interaction might play a vital role in determining the distal survival outcome of LUAD. Conclusions: Our risk prediction model enables precise evaluation of long-term survival for patients with LUAD. Further, it provides a novel and comprehensive understanding of biological impacts on LUAD prognosis, which offers new insights for future development of precise diagnostic and therapeutic approaches.[Table: see text]


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
S Vohra ◽  
R Sethi ◽  
P Sharma ◽  
A Pradhan ◽  
P Vishwakarma ◽  
...  

Abstract Background Ever since the concept of preventive cardiology has come into vogue, several risk identification models have come up which combine several risk factors to create a risk prediction score for occurrence of cardiovascular (CV) event. While carrying a proven validation in Western population, none of the risk prediction model has been satisfactorily evaluated in Indians especially young &lt;40 years old. Objectives To compare Artificial Intelligence based novel risk score with traditional risk scores in young (less than 40 years age) patients presenting with acute coronary syndrome (ACS) and to estimate the relative efficacy of different coronary artery disease (CAD) risk scores in young Indian Patients. Design Single center, Observational, Non-interventional study. Participants Cohort of Patients more than 20 but less than 40 years old with ACS in the department of Cardiology from 1st January 2019 to 31st October 2019. Methods 314 young patients [mean age 36.14±4.17 years] presenting with acute coronary syndrome (ACS) were enrolled. The three clinically most pertinent risk assessment models [Framingham Risk score (FRS), World Health Organization risk prediction charts (WHO/ISH), and QRISK3 scores] and Artificial Intelligence based novel risk score (AICVD) were applied on day 1 of presentation, and tried to see whether one risk score versus other risk score could have predicted the event earlier had we applied it before the occurrence of ACS. Risk factors considered included those already in traditional scoring systems and new risk factors (diet, alcohol, tobacco, dyslipidemia, physical activity, family history of heart disease, history of heart disease, heart rate, respiratory rate, chronic heart symptoms and psychological stress). Results WHO/ISH provided the lowest high risk estimate with only 1 (0.9%) patient estimated to be having &gt;20% 10-year risk. The FRS estimated high risk (&gt;20% 10-year risk) in 3 (1%) patients. The QRISK3 estimated high risk (&gt;10% 10-year risk) in 20 (6.5%) patient. In comparison, AICVD risk prediction model stood tall by identifying 73 (23.2%) patients as high risk and 62.74% patients as more than moderate risk for having CV events at 7 years (p&lt;0.001). Conclusion Perhaps, this is the first study which has compared artificial intelligence based novel risk prediction model with the three most commonly applied models, in the young Indian patients. We found that a cohort of young Indian patients presenting with ACS, when studied retrospectively, was identified as “high risk” most likely by AICVD risk prediction model rather than the traditional counterparts. The WHO/ISH risk prediction charts and FRS were the poorest predictors. Performance of QRISK3 score also remained less than satisfactory. These findings suggested that AICVD risk prediction model is a promising tool to assess for CV risk in Indian population. FUNDunding Acknowledgement Type of funding sources: None. Predictability of risk prediction models


2011 ◽  
Vol 32 (4) ◽  
pp. 360-366 ◽  
Author(s):  
Erik R. Dubberke ◽  
Yan Yan ◽  
Kimberly A. Reske ◽  
Anne M. Butler ◽  
Joshua Doherty ◽  
...  

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.


2018 ◽  
Vol 106 (1) ◽  
pp. 129-136 ◽  
Author(s):  
Damien J. LaPar ◽  
Donald S. Likosky ◽  
Min Zhang ◽  
Patty Theurer ◽  
C. Edwin Fonner ◽  
...  

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Qi Wang ◽  
Yi Tang ◽  
Jiaojiao Zhou ◽  
Wei Qin

Abstract Background Acute kidney injury (AKI) has high morbidity and mortality in intensive care units (ICU). It can also lead to chronic kidney disease (CKD), more costs and longer hospital stay. Early identification of AKI is important. Methods We conducted this monocenter prospective observational study at West China Hospital, Sichuan University, China. We recorded information of each patient in the ICU within 24 h after admission and updated every two days. Patients who reached the primary outcome were accepted into the AKI group. Of all patients, we randomly drew 70% as the development cohort and the remaining 30% as the validation cohort. Using binary logistic regression we got a risk prediction model of the development cohort. In the validation cohort, we validated its discrimination by the area under the receiver operator curve (AUROC) and calibration by a calibration curve. Results There were 656 patients in the development cohorts and 280 in the validation cohort. Independent predictors of AKI in the risk prediction model including hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male. In the validation cohort, the AUROC is 0.783 (95% CI 0.730–0.836) and the calibration curve shows good calibration of this prediction model. The optimal cut-off value to distinguish high-risk and low-risk patients is 4.5 points (sensitivity is 78.4%, specificity is 73.2% and Youden’s index is 0.516). Conclusions This risk prediction model can help to identify high-risk patients of AKI in ICU to prevent the development of AKI and treat it at the early stages. Trial registration TCTR, TCTR20170531001. Registered 30 May 2017, http://www.clinicaltrials.in.th/index.php?tp=regtrials&menu=trialsearch&smenu=fulltext&task=search&task2=view1&id=2573


2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Mansoor Husain ◽  
Stephen C. Bain ◽  
Anders Gaarsdal Holst ◽  
Thomas Mark ◽  
Søren Rasmussen ◽  
...  

Abstract Background Semaglutide is a glucagon-like peptide-1 (GLP-1) analog treatment for type 2 diabetes (T2D) available in subcutaneous (s.c.) and oral formulations. Two cardiovascular (CV) outcomes trials showed that in subjects with T2D at high risk of CV events there were fewer major adverse CV events (MACE; defined as CV death, non-fatal stroke, non-fatal myocardial infarction) with semaglutide than with placebo (hazard ratio [95% CI]: 0.74 [0.58;0.95] for once-weekly s.c. semaglutide and 0.79 [0.57;1.11] for once-daily oral semaglutide). However, there is little evidence for an effect of semaglutide on MACE in subjects not at high risk of CV events. This post hoc analysis examined CV effects of semaglutide in subjects across a continuum of baseline CV risk. Methods Data from the s.c. (SUSTAIN) and oral (PIONEER) semaglutide phase 3a clinical trial programs were combined according to randomized treatment (semaglutide or comparators) and analyzed to assess time to first MACE and its individual components. A CV risk model was developed with independent data from the LEADER trial (liraglutide vs placebo), considering baseline variables common to all datasets. Semaglutide data were analyzed to assess effects of treatment as a function of CV risk predicted using the CV risk prediction model. Results The CV risk prediction model performed satisfactorily when applied to the semaglutide data set (area under the curve: 0.77). There was a reduced relative and absolute risk of MACE for semaglutide vs comparators across the entire continuum of CV risk. While the relative risk reduction tended to be largest with low CV risk score, the largest absolute risk reduction was for intermediate to high CV risk score. Similar results were seen for relative risk reduction of the individual MACE components and also when only placebo comparator data were included. Conclusion Semaglutide reduced the risk of MACE vs comparators across the continuum of baseline CV risk in a broad T2D population. Trial registrations ClinicalTrials.gov identifiers: NCT02054897, NCT01930188, NCT01885208, NCT02128932, NCT02305381, NCT01720446, NCT02207374, NCT02254291, NCT02906930, NCT02863328, NCT02607865, NCT02863419, NCT02827708, NCT02692716, NCT02849080, NCT03021187, NCT03018028, NCT03015220.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 534-534
Author(s):  
Natasha Catherine Edwin ◽  
Jesse Keller ◽  
Suhong Luo ◽  
Kenneth R Carson ◽  
Brian F. Gage ◽  
...  

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.


2014 ◽  
Vol 111 (03) ◽  
pp. 531-538 ◽  
Author(s):  
Drahomir Aujesky ◽  
Daniel Hayoz ◽  
Jürg Beer ◽  
Marc Husmann ◽  
Beat Frauchiger ◽  
...  

SummaryThere is a need to validate risk assessment tools for hospitalised medical patients at risk of venous thromboembolism (VTE). We investigated whether a predefined cut-off of the Geneva Risk Score, as compared to the Padua Prediction Score, accurately distinguishes low-risk from high-risk patients regardless of the use of thromboprophylaxis. In the multicentre, prospective Explicit ASsessment of Thromboembolic RIsk and Prophylaxis for Medical PATients in SwitzErland (ESTIMATE) cohort study, 1,478 hospitalised medical patients were enrolled of whom 637 (43%) did not receive thromboprophylaxis. The primary endpoint was symptomatic VTE or VTE-related death at 90 days. The study is registered at ClinicalTrials.gov, number NCT01277536. According to the Geneva Risk Score, the cumulative rate of the primary endpoint was 3.2% (95% confidence interval [CI] 2.2–4.6%) in 962 high-risk vs 0.6% (95% CI 0.2–1.9%) in 516 low-risk patients (p=0.002); among patients without prophylaxis, this rate was 3.5% vs 0.8% (p=0.029), respectively. In comparison, the Padua Prediction Score yielded a cumulative rate of the primary endpoint of 3.5% (95% CI 2.3–5.3%) in 714 high-risk vs 1.1% (95% CI 0.6–2.3%) in 764 lowrisk patients (p=0.002); among patients without prophylaxis, this rate was 3.2% vs 1.5% (p=0.130), respectively. Negative likelihood ratio was 0.28 (95% CI 0.10–0.83) for the Geneva Risk Score and 0.51 (95% CI 0.28–0.93) for the Padua Prediction Score. In conclusion, among hospitalised medical patients, the Geneva Risk Score predicted VTE and VTE-related mortality and compared favourably with the Padua Prediction Score, particularly for its accuracy to identify low-risk patients who do not require thromboprophylaxis.


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