Relationship between CD33 Expression, P-Glycoprotein-Mediated Drug Efflux, and Clinical Outcome in Patients Treated in Phase II Trials with Gemtuzumab Ozogamicin Monotherapy.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2324-2324 ◽  
Author(s):  
Roland B. Walter ◽  
Ted A. Gooley ◽  
Vincent H.J. van der Velden ◽  
Michael R. Loken ◽  
Jacques J.M. van Dongen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clinical Outcome ◽  
Regression Model ◽  
Phase Ii ◽  
Inverse Relationship ◽  
Logistic Regression Model ◽  
Gemtuzumab Ozogamicin ◽  
Drug Efflux ◽  
Expression Levels ◽  
P Glycoprotein

Abstract Background: The anti-acute myeloid leukemia (AML) immunoconjugate, gemtuzumab ozogamicin (GO; Mylotarg™), contains a humanized anti-CD33 antibody (hP67.6) to facilitate uptake of the toxic calicheamicin-γ1 derivative in CD33-positive AML cells. This putative mechanism implies a critical role for the intracellular accumulation of the toxic moiety for GO-induced cytotoxicity. Indeed, drug efflux by P-glycoprotein (Pgp) mediates resistance to GO and correlates with clinical outcome after GO monotherapy. Furthermore, recent in vitro data obtained in human myeloid cell lines have unequivocally demonstrated a quantitative relationship between CD33 expression and GO-induced cytotoxicity. In light of these findings, we have now re-examined the significance of CD33 expression levels on AML blasts and relationship with Pgp activity for clinical outcome of patients treated with GO monotherapy. Methods: Pre-treatment bone marrow samples from patients enrolled in multicenter phase II protocols evaluating the safety and efficacy of GO monotherapy (generally 2 doses of 9 mg/m2 14 days apart) were used for analysis. Relative CD33 expression was quantified by flowcytometry immunophenotyping using the hP67.6 antibody, and linear fluorescence values used for calculations. Pgp function was cytofluorometrically determined by efflux of the fluorescent dye, DiOC2. Results are presented as mean values and 95% confidence intervals. Unpaired t-tests, Pearson correlations, and logistic regression models were used for statistical analysis. Results: Patients achieving a complete remission (CR) or CR with incomplete recovery of platelet counts (CRp) had statistically significantly higher mean CD33 expression levels (71.20 [57.20–85.19], n=69) compared to non-responders (54.44 [48.38–60.51], n=203; p=0.01). There was an inverse relationship between CD33 expression and Pgp efflux (r=−0.23) and this contributed to responders having a statistically significantly lower mean Pgp efflux (1.40 [1.28–1.52], n=57) compared to non-responders (1.83 [1.72–1.95], n=173; p<0.0001). The addition of Pgp statistically significantly improved a logistic regression model containing only CD33 (p<.0001), whereas the addition of CD33 did not lead to a statistically significantly improved logistic regression model containing only Pgp (p=0.14). Conclusion: These data indicate that CD33 expression levels as well as Pgp efflux are associated with clinical outcome of patients treated with GO monotherapy, and that knowledge of Pgp provides important information regarding the probability of achieving a response, even after consideration of CD33 expression level. The inverse relationship between CD33 abundance and Pgp efflux is consistent with the notion of maturation-stage dependent expression of these proteins, and offers the rationale for the use of cell-differentiation-promoting agents, for example cytokines, in combination with GO to enhance GO-induced cytotoxicity and possibly improve clinical outcome of patients undergoing GO-containing AML therapy.

scholarly journals The significance of C-reactive protein for the prediction of net-adverse clinical outcome in patients with acute pulmonary embolism

2020 ◽  
Vol 77 (1) ◽  
pp. 35-40
Author(s):  
Rade Milic ◽  
Boris Dzudovic ◽  
Bojana Subotic ◽  
Slobodan Obradovic ◽  
Ivan Soldatovic ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Logistic Regression ◽  
Clinical Outcome ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Acute Pulmonary Embolism ◽  
Clinical Manifestations ◽  
Multivariate Logistic Regression Model ◽  
Significant Independent Predictor ◽  
End Point

Background/Aim. Acute pulmonary embolism (APE) may have different clinical manifestations. Also, its outcome can range from complete recovery to early death. Major bleeding (MB) as a due of the therapy also contributes to the overall adverse outcome. So far, it is unknown what the best predictors are for short-term mortality and MB among the several commonly used biomarkers. The aim of this study was to evaluate the significance of Creactive protein (CRP) and other biomarkers for the prediction of adverse clinical outcomes. Methods. This clinical, observational, retrospective-prospective study included 219 consecutive adult patients treated for APE. Results. Among 219 patients, 22 (10%) died within the first month after diagnosis. Twenty seven patients (12.3%) had at least one episode of MB. Composite end-point [netadverse clinical outcome (NACO)] was estimated in 47 (21.5%) of patients. The average values of all biomarkers were higher in the group of patient who died, and differences were statistically significant. Similar results were obtained for composite end-point. In terms of MB, none of biomarkers did not have significance, but CRP had a slight tendency toward significance. Results from univariate logistic regression model showed that troponin was statistically significant predictor of 30-day mortality. However, after adjusting for other variables, in multivariate logistic regression model troponin failed to be significant independent predictor of 30-day mortality. Unlike troponin, CRP and brain natriuretic peptide (BNP) were significant in all models ? uni and multivariate (they were independent predictors of 30-day mortality). Conclusion. CRP has a good predictive value for 30-day mortality and NACO, and potential for MB in patients treated for APE.

scholarly journals Logistic regression model of fotemustine toxicity combining independent phase II studies

Cancer ◽  
1996 ◽  
Vol 78 (9) ◽  
pp. 1980-1987 ◽  
Author(s):  
Eric Raymond ◽  
Corinne Haon ◽  
Catherine Boaziz ◽  
Maylis Coste
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Phase Ii ◽  
Logistic Regression Model ◽  
Phase Ii Studies

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

Sign in / Sign up

Export Citation Format

Share Document