J. Blake Bartlett
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View article: Data from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Data from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Purpose:We designed mathematical models to describe and quantify the mechanisms and dynamics of minimal residual disease (MRD) in order to better understand these MRD dynamics; inform future treatment design, including when to stop or chan…
View article: Figure 6 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Figure 6 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Model validation from the MAIA trial: PFS predictions from original data compared with actual results from updated data. CI, confidence interval. Kaplan–Meier curves are shown with censoring ticks removed.
View article: Figure 4 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Figure 4 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Time to achievement of CR or better by arm in patients achieving CR or better.
View article: Figure 2 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Figure 2 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Y-intercept versus log doubling time (days). CR, complete response; MRD-ve, minimal residual disease-negative.
View article: Supplementary Data 1 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Supplementary Data 1 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
supplementary online material, tables, figures, mathematics.
View article: Figure 5 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Figure 5 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Overall PFS model fits to DRd and Rd arms, combining response subgroups. Kaplan–Meier curves are shown with censoring ticks removed.
View article: Figure 1 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Figure 1 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Example curves showing back-extrapolation of Y-intercepts and estimated growth rates from sequential MRD values. DT, doubling time; r, correlation coefficient. Note: Example results are shown for four individual patients. Green dashed line…
View article: Figure 3 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Figure 3 from Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential Open
Minimum time spent below the LOD (months) by whether values increased above the LOD again or not. aLOD group 3, values go below the LOD and stay below the LOD (≥2 values below the LOD). bLOD group 4, values at or belo…
View article: Daratumumab for patients with myeloma with early or late relapse after initial therapy: subgroup analysis of CASTOR and POLLUX
Daratumumab for patients with myeloma with early or late relapse after initial therapy: subgroup analysis of CASTOR and POLLUX Open
High-risk multiple myeloma (MM) is often defined based on cytogenetic abnormalities, but patients who relapse early after initial therapy are considered a functional high-risk group. In the phase 3 CASTOR and POLLUX studies, daratumumab pl…
View article: POSTER: MM-304 Efficacy Outcomes and Characteristics of Patients With Multiple Myeloma (MM) Who Achieved Sustained Minimal Residual Disease Negativity After Treatment With Ciltacabtagene Autoleucel (cilta-cel) in CARTITUDE-1
POSTER: MM-304 Efficacy Outcomes and Characteristics of Patients With Multiple Myeloma (MM) Who Achieved Sustained Minimal Residual Disease Negativity After Treatment With Ciltacabtagene Autoleucel (cilta-cel) in CARTITUDE-1 Open
View article: Oral Abstract: MM-304 Efficacy Outcomes and Characteristics of Patients With Multiple Myeloma (MM) Who Achieved Sustained Minimal Residual Disease Negativity After Treatment With Ciltacabtagene Autoleucel (cilta-cel) in CARTITUDE-1
Oral Abstract: MM-304 Efficacy Outcomes and Characteristics of Patients With Multiple Myeloma (MM) Who Achieved Sustained Minimal Residual Disease Negativity After Treatment With Ciltacabtagene Autoleucel (cilta-cel) in CARTITUDE-1 Open
View article: Daratumumab plus lenalidomide, bortezomib and dexamethasone in newly diagnosed multiple myeloma: Analysis of vascular thrombotic events in the <scp>GRIFFIN</scp> study
Daratumumab plus lenalidomide, bortezomib and dexamethasone in newly diagnosed multiple myeloma: Analysis of vascular thrombotic events in the <span>GRIFFIN</span> study Open
Summary Patients with multiple myeloma are at increased risk of vascular thromboembolic events (VTEs). This post hoc analysis evaluated VTEs in the randomised phase 2 GRIFFIN study ( ClinicalTrials.gov Identifier: NCT02874742) that investi…
View article: Daratumumab plus lenalidomide/bortezomib/dexamethasone in Black patients with transplant-eligible newly diagnosed multiple myeloma in GRIFFIN
Daratumumab plus lenalidomide/bortezomib/dexamethasone in Black patients with transplant-eligible newly diagnosed multiple myeloma in GRIFFIN Open
View article: Meetings and Conferences
Meetings and Conferences Open