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Статті в журналах з теми "Multiple Stratification"

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Автор: Grafiati

Опубліковано: 8 вересня 2023

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1

JOURDAIN, BENJAMIN, BERNARD LAPEYRE, and PIERGIACOMO SABINO. "CONVENIENT MULTIPLE DIRECTIONS OF STRATIFICATION." International Journal of Theoretical and Applied Finance 14, no. 06 (September 2011): 867–97. http://dx.doi.org/10.1142/s0219024911006772.

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This paper investigates the use of multiple directions of stratification as a variance reduction technique for Monte Carlo simulations of path-dependent options driven by Gaussian vectors. The precision of the method depends on the choice of the directions of stratification and the allocation rule within each strata. Several choices have been proposed but, even if they provide variance reduction, their implementation is computationally intensive and not applicable to realistic payoffs, in particular not to Asian options with barrier. Moreover, all these previously published methods employ orthogonal directions for multiple stratification. In this work we investigate the use of algorithms producing convenient directions, generally non-orthogonal, combining a lower computational cost with a comparable variance reduction. In addition, we study the accuracy of optimal allocation in terms of variance reduction compared to the Latin Hypercube Sampling. We consider the directions obtained by the Linear Transformation and the Principal Component Analysis. We introduce a new procedure based on the Linear Approximation of the explained variance of the payoff using the law of total variance. In addition, we exhibit a novel algorithm that permits to correctly generate normal vectors stratified along non-orthogonal directions. Finally, we illustrate the efficiency of these algorithms in the computation of the price of different path-dependent options with and without barriers in the Black-Scholes and in the Cox-Ingersoll-Ross markets.

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2

Ooi, Melissa Gaik-Ming, Sanjay de Mel, and Wee Joo Chng. "Risk Stratification in Multiple Myeloma." Current Hematologic Malignancy Reports 11, no. 2 (February 16, 2016): 137–47. http://dx.doi.org/10.1007/s11899-016-0307-4.

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3

Skinner, C. J., D. J. Holmes, and D. Holt. "Multiple Frame Sampling for Multivariate Stratification." International Statistical Review / Revue Internationale de Statistique 62, no. 3 (December 1994): 333. http://dx.doi.org/10.2307/1403765.

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4

Chang, Kuang-Chao, Jeng-Fu Liu, and Chien-Pai Han. "Multiple inverse sampling in post-stratification." Journal of Statistical Planning and Inference 69, no. 2 (June 1998): 209–27. http://dx.doi.org/10.1016/s0378-3758(97)00157-2.

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5

Gharbi, Alya, Saloua Mrabet, Amina Nasri, Firas Larnaout, Amina Gargouri, Amina Gargouri, Imen Kacem, and Riadh Gouider. "Prognostic Factors Stratification in Multiple Sclerosis." Multiple Sclerosis and Related Disorders 37 (January 2020): 101534. http://dx.doi.org/10.1016/j.msard.2019.11.009.

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6

Ghaemsaidi, S. J., H. V. Dosser, L. Rainville, and T. Peacock. "The impact of multiple layering on internal wave transmission." Journal of Fluid Mechanics 789 (January 25, 2016): 617–29. http://dx.doi.org/10.1017/jfm.2015.682.

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Given the ubiquity of layering in environmental stratifications, an interesting example being double-diffusive staircase structures in the Arctic Ocean, we present the results of a joint theoretical and laboratory experimental study investigating the impact of multiple layering on internal wave propagation. We first present results for a simplified model that demonstrates the non-trivial impact of multiple layering. Thereafter, utilizing a weakly viscous linear model that can handle arbitrary vertical stratifications, we perform a comparison of theory with experiments. We conclude by applying this model to a case study of a staircase stratification profile obtained from the Arctic Ocean, finding a rich landscape of transmission behaviour.

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7

Chang, Kuang-Chao, Chien-Pai Han, and Doyle L. Hawkins. "Truncated multiple inverse sampling in post-stratification." Journal of Statistical Planning and Inference 76, no. 1-2 (February 1999): 215–34. http://dx.doi.org/10.1016/s0378-3758(98)00135-9.

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8

Ontaneda, Daniel, Samuel Cohn, and Robert J. Fox. "Risk stratification and mitigation in multiple sclerosis." Multiple Sclerosis and Related Disorders 3, no. 5 (September 2014): 639–49. http://dx.doi.org/10.1016/j.msard.2014.05.003.

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9

Hussain, Mohsin, Asad Mahmood, Rafia Mahmood, Hamid Iqbal, Ayesha Khurshid, and Nabeela Khan. "Multiple Myeloma-Clinicopathological Features and Risk Stratification." Pakistan Armed Forces Medical Journal 72, SUPPL-2 (May 31, 2022): S95–98. http://dx.doi.org/10.51253/pafmj.v72isuppl-2.3100.

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Objective: To evaluate the clinic pathological features and to risk stratify patients of multiple myeloma in our population. Study Design: Cross sectional study. Place and Duration of Study: Department of Hematology, Armed Forces Institute of Pathology (AFIP) Rawalpindi Pakistan, from Jan to Jun 2019. Methodology: Patients that were newly diagnosed multiple myeloma on the basis of International Myeloma Working Group (IMWG) 2014 criteria were included in the study. Blood counts, peripheral film examination, bone marrow aspirate and trephine were examined. Biochemical profile, serum protein electrophoresis and skeletal survey was assessed. Results: A total of 65 newly diagnosed Multiple Myeloma patients were included. Of these, 43 (66.2%) were males and 22 (33.8%) females. Mean age of the patients was 58.5 years with a range of 36-76 years. The most common presenting symptom was bone pain in 33 (50.8%) patients, followed by backache in 32 (49.2%) patients. Mean percentage of plasma cell on bone marrow examination was 40.89% ± 23.2. On risk stratification based on International staging system, 20 (30.7%) patients were in stage I, 19 (29.1%) patients were on stage II while 26 (40.2%) patients were in stage III. Conclusion: Bone pain and backache along with anemia were found the predominant complaints of patients presenting with multiple myeloma in our setup with male predominance. Risk stratification of multiple myeloma according to ISS revealed that stage III was the most predominant in our population.

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10

van de Donk, Niels W. C. J., and Pieter Sonneveld. "Diagnosis and Risk Stratification in Multiple Myeloma." Hematology/Oncology Clinics of North America 28, no. 5 (October 2014): 791–813. http://dx.doi.org/10.1016/j.hoc.2014.06.007.

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11

Hansen, Rickard. "Smoke Stratification in a Mine Drift with Multiple Objects Downstream." Mining Revue 29, no. 1 (March 1, 2023): 1–18. http://dx.doi.org/10.2478/minrv-2023-0001.

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Abstract The smoke behaviour and smoke stratification of a fire in a mine drift will be one of the decisive factors affecting the risk to mining personnel during a fire. This paper studies the smoke stratification in a mine drift with multiple objects downstream of the fire, at varying distances and number of objects. Data for the study was provided from earlier model-scale fire experiments and CFD modelling was performed for in-depth analysis of specific phenomena. It was found that at considerable downstream distances from the fire, the smoke stratification differences were significant, reflecting the high impact of multiple objects. With an increasing distance between the objects downstream, an increased degree of mixing and decreased stratification occurred. With an increasing distance between the burning object and the second object, the smoke layer will descend further before encountering the object and the smoke stratification on the upstream side of the second object will decrease. The increased mixing of the hot gases flowing from the burning object will have a more significant effect on the overall stratification due to the higher temperatures. An increasing number of objects downstream will not by itself lead to increased stratification, with shorter distances between the objects and an increasing number of objects, the smoke stratification may instead be retained for a longer distance. An increasing flow velocity will result in decreasing stratification found foremost downstream of the burning object, as the tilt of the plume will increase and interact increasingly with the second object.

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12

Lussier, Tyler, Natalie Schoebe, and Sabine Mai. "Risk Stratification and Treatment in Smoldering Multiple Myeloma." Cells 11, no. 1 (December 31, 2021): 130. http://dx.doi.org/10.3390/cells11010130.

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Smoldering multiple myeloma is a heterogeneous asymptomatic precursor to multiple myeloma. Since its identification in 1980, risk stratification models have been developed using two main stratification methods: clinical measurement-based and genetics-based. Clinical measurement models can be subdivided in three types: baseline measurements (performed at diagnosis), evolving measurements (performed over time during follow-up appointments), and imaging (for example, magnetic resonance imaging). Genetic approaches include gene expression profiling, DNA/RNA sequencing, and cytogenetics. It is important to accurately distinguish patients with indolent disease from those with aggressive disease, as clinical trials have shown that patients designated as “high-risk of progression” have improved outcomes when treated early. The risk stratification models, and clinical trials are discussed in this review.

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13

Wong, A. B. D., and R. W. Griffiths. "Stratification and convection produced by multiple turbulent plumes." Dynamics of Atmospheres and Oceans 30, no. 2-4 (December 1999): 101–23. http://dx.doi.org/10.1016/s0377-0265(99)00022-6.

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14

Fernández-Paredes, Lidia, Armanda Casrouge, Jérémie Decalf, Clara de Andrés, Luisa Maria Villar, Rebeca Pérez de Diego, Bárbara Alonso, et al. "Multimarker risk stratification approach at multiple sclerosis onset." Clinical Immunology 181 (August 2017): 43–50. http://dx.doi.org/10.1016/j.clim.2017.05.019.

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15

Scott, James G. "Nonparametric Bayesian multiple testing for longitudinal performance stratification." Annals of Applied Statistics 3, no. 4 (December 2009): 1655–74. http://dx.doi.org/10.1214/09-aoas252.

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16

Chng, W. J., A. Dispenzieri, C.-S. Chim, R. Fonseca, H. Goldschmidt, S. Lentzsch, N. Munshi, et al. "IMWG consensus on risk stratification in multiple myeloma." Leukemia 28, no. 2 (August 26, 2013): 269–77. http://dx.doi.org/10.1038/leu.2013.247.

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17

Kapoor, Rajan, Rajiv Kumar, and A. P. Dubey. "Risk Stratification in Multiple Myeloma in Indian Settings." Indian Journal of Hematology and Blood Transfusion 36, no. 3 (December 14, 2019): 464–72. http://dx.doi.org/10.1007/s12288-019-01240-4.

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18

Segges, Priscilla, and Esteban Braggio. "Genetic Markers Used for Risk Stratification in Multiple Myeloma." Genetics Research International 2011 (September 13, 2011): 1–4. http://dx.doi.org/10.4061/2011/798089.

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While no specific genetic markers are required in the diagnosis of multiple myeloma (MM), multiple genetic abnormalities and gene signatures are used in disease prognostication and risk stratification. This is particularly important for the adequate identification of the high-risk MM group, which does not benefit from any of the current therapies, and novel approaches need to be proposed. Fluorescence in situ hybridization (FISH) has been employed for establishing risk-based stratification and still remains the most used genetic technique in the clinical routine. The incorporation of gene expression profiling (GEP) in the study of MM has shown to be a very powerful test in the patient stratification, but its incorporation in clinical routine depends on some technical and logistic resolutions. Thus, FISH still remains the gold standard test for detecting genomic abnormalities and outcome discrimination in MM.

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19

O'Dowd, E., C. Bellinger, T. Dotson, C. Filippini, L. Pickup, Q. Chometon, C. Arteta, et al. "P42.01 AI Assistance for Pulmonary Nodule Stratification: An Multiple-Reader Multiple-Case Study." Journal of Thoracic Oncology 16, no. 3 (March 2021): S477. http://dx.doi.org/10.1016/j.jtho.2021.01.825.

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20

Smilowitz, Nathaniel R., Vuthy Nguy, Yindalon Aphinyanaphongs, Jonathan D. Newman, Yuhe Xia, Harmony R. Reynolds, Judith S. Hochman, Glenn I. Fishman, and Jeffrey S. Berger. "Multiple Biomarker Approach to Risk Stratification in COVID-19." Circulation 143, no. 13 (March 30, 2021): 1338–40. http://dx.doi.org/10.1161/circulationaha.120.053311.

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21

Yoon, Sung-Soo. "Multiple myeloma in Korea: risk stratification and initial treatment." Blood Research 53, no. 3 (2018): 183. http://dx.doi.org/10.5045/br.2018.53.3.183.

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22

Jiang, Allan, Donna Reece, and Hong Chang. "Genomic stratification of multiple myeloma treated with novel agents." Leukemia & Lymphoma 53, no. 2 (September 19, 2011): 202–7. http://dx.doi.org/10.3109/10428194.2011.608449.

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23

Shores, Kindal A., David Scott, and Myron F. Floyd. "Constraints to Outdoor Recreation: A Multiple Hierarchy Stratification Perspective." Leisure Sciences 29, no. 3 (May 10, 2007): 227–46. http://dx.doi.org/10.1080/01490400701257948.

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24

Lee, KangJae Jerry, and David Scott. "Participation in Wildlife Watching: A Multiple Hierarchy Stratification Perspective." Human Dimensions of Wildlife 16, no. 5 (September 2011): 330–44. http://dx.doi.org/10.1080/10871209.2011.597825.

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25

Bayes-Genis, Antoni, and Jordi Ordonez-Llanos. "Multiple biomarker strategies for risk stratification in heart failure." Clinica Chimica Acta 443 (March 2015): 120–25. http://dx.doi.org/10.1016/j.cca.2014.10.023.

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26

Tur, Carmen, and Xavier Montalban. "Natalizumab: Risk Stratification of Individual Patients with Multiple Sclerosis." CNS Drugs 28, no. 7 (June 19, 2014): 641–48. http://dx.doi.org/10.1007/s40263-014-0168-0.

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27

Graf, Jonas, Verena I. Leussink, Thomas Dehmel, Marius Ringelstein, Norbert Goebels, Ortwin Adams, Colin R. MacKenzie, et al. "Infectious risk stratification in multiple sclerosis patients receiving immunotherapy." Annals of Clinical and Translational Neurology 4, no. 12 (November 24, 2017): 909–14. http://dx.doi.org/10.1002/acn3.491.

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28

Koohestani, Kamran, Yury Stepanyants, and Mohammad Nabi Allahdadi. "Analysis of Internal Solitary Waves in the Gulf of Oman and Sources Responsible for Their Generation." Water 15, no. 4 (February 13, 2023): 746. http://dx.doi.org/10.3390/w15040746.

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A combination of multiple data sources has been used to study the characteristics of internal solitary waves (ISWs) in the Gulf of Oman (GoO). Water column stratification in the Gulf has been examined using field observations and World Ocean Atlas 2018 datasets. The spatiotemporal distribution of ISWs has been obtained from satellite images obtained by means of Synthetic Aperture Radar (SAR) and optical sensors taken from 2018 to 2020. The mechanisms of ISW generation in the GoO have been studied using the data revealed from different available sources. The results show that there are annually two major typical stratifications in the GoO throughout the year, strong stratification in May through September and weak stratification during other months. Dispersion relations corresponding to these types of stratification have been obtained with acceptable accuracy for both deep and shallow regions. The spatiotemporal distribution of ISWs demonstrates that the western and southern regions of the GoO are the hotspots for generation of ISWs in this basin. Several mechanisms of ISW generation in the GoO are discussed including tide, eddies, lee waves, and atmospheric perturbation; the latter one is, apparently, responsible for the appearance of large-amplitude ISWs.

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29

Ishino, Mitsunori, Yasuchika Takeishi, Takeshi Niizeki, Tetsu Watanabe, Joji Nitobe, Takuya Miyamoto, Takehiko Miyashita, et al. "Risk Stratification of Chronic Heart Failure Patients by Multiple Biomarkers." Circulation Journal 72, no. 11 (2008): 1800–1805. http://dx.doi.org/10.1253/circj.cj-08-0157.

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30

Fox, R. J., and R. A. Rudick. "Risk stratification and patient counseling for natalizumab in multiple sclerosis." Neurology 78, no. 6 (January 25, 2012): 436–37. http://dx.doi.org/10.1212/wnl.0b013e318245d2d0.

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31

Mellors, Patrick, Moritz Binder, Rhett P. Ketterling, Patricia Griepp, Linda B. Baughn, Francis K. Buadi, Martha Q. Lacy, et al. "Metaphase Cytogenetics for Risk Stratification in Newly Diagnosed Multiple Myeloma." Blood 134, Supplement_1 (November 13, 2019): 4396. http://dx.doi.org/10.1182/blood-2019-122291.

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Introduction: Abnormal metaphase cytogenetics are associated with inferior survival in newly diagnosed multiple myeloma (MM). These abnormalities are only detected in one third of cases due to the low proliferative rate of plasma cells. It is unknown if metaphase cytogenetics improve risk stratification when using contemporary prognostic models such as the revised international staging system (R-ISS), which incorporates interphase fluorescence in situ hybridization (FISH). Aims: The aims of this study were to 1) characterize the association between abnormalities on metaphase cytogenetics and overall survival (OS) in newly diagnosed MM treated with novel agents and 2) evaluate whether the addition of metaphase cytogenetics to R-ISS, age, and plasma cell labeling index (PCLI) improves model discrimination with respect to OS. Methods: We analyzed a retrospective cohort of 483 newly diagnosed MM patients treated with proteasome inhibitors (PI) and/or immunomodulators (IMID) who had metaphase cytogenetics performed prior to initiation of therapy. Abnormal metaphase cytogenetics were defined as MM specific abnormalities, while normal metaphase cytogenetics included constitutional cytogenetic variants, age-related Y chromosome loss, and normal metaphase karyotypes. Multivariable adjusted proportional hazards regression models were fit for the association between known prognostic factors and OS. Covariates associated with inferior OS on multivariable analysis included R-ISS stage, age ≥ 70, PCLI ≥ 2, and abnormal metaphase cytogenetics. We devised a risk scoring system weighted by their respective hazard ratios (R-ISS II +1, R-ISS III + 2, age ≥ 70 +2, PCLI ≥ 2 +1, metaphase cytogenetic abnormalities + 1). Low (LR), intermediate (IR), and high risk (HR) groups were established based on risk scores of 0-1, 2-3, and 4-5 in modeling without metaphase cytogenetics, and scores of 0-1, 2-3, and 4-6 in modeling incorporating metaphase cytogenetics, respectively. Survival estimates were calculated using the Kaplan-Meier method. Survival analysis was stratified by LR, IR, and HR groups in models 1) excluding metaphase cytogenetics 2) including metaphase cytogenetics and 3) including metaphase cytogenetics, with IR stratified by presence and absence of metaphase cytogenetic abnormalities. Survival estimates were compared between groups using the log-rank test. Harrell's C was used to compare the predictive power of risk modeling with and without metaphase cytogenetics. Results: Median age at diagnosis was 66 (31-95), 281 patients (58%) were men, median follow up was 5.5 years (0.04-14.4), and median OS was 6.4 years (95% CI 5.7-6.8). Ninety-seven patients (20%) were R-ISS stage I, 318 (66%) stage II, and 68 (14%) stage III. One-hundred and fourteen patients (24%) had high-risk abnormalities by FISH, and 115 (24%) had abnormal metaphase cytogenetics. Three-hundred and thirteen patients (65%) received an IMID, 119 (25%) a PI, 51 (10%) received IMID and PI, and 137 (28%) underwent upfront autologous hematopoietic stem cell transplantation (ASCT). On multivariable analysis, R-ISS (HR 1.59, 95% CI 1.29-1.97, p < 0.001), age ≥ 70 (HR 2.32, 95% CI 1.83-2.93, p < 0.001), PCLI ≥ 2, (HR 1.52, 95% CI 1.16-2.00, p=0.002) and abnormalities on metaphase cytogenetics (HR 1.35, 95% CI 1.05-1.75, p=0.019) were associated with inferior OS. IR and HR groups experienced significantly worse survival compared to LR groups in models excluding (Figure 1A) and including (Figure 1B) the effect of metaphase cytogenetics (p < 0.001 for all comparisons). However, the inclusion of metaphase cytogenetics did not improve discrimination. Likewise, subgroup analysis of IR patients by the presence or absence of metaphase cytogenetic abnormalities did not improve risk stratification (Figure 1C) (p < 0.001). The addition of metaphase cytogenetics to risk modeling with R-ISS stage, age ≥ 70, and PCLI ≥ 2 did not improve prognostic performance when evaluated by Harrell's C (c=0.636 without cytogenetics, c=0.642 with cytogenetics, absolute difference 0.005, 95% CI 0.002-0.012, p=0.142). Conclusions: Abnormalities on metaphase cytogenetics at diagnosis are associated with inferior OS in MM when accounting for the effects of R-ISS, age, and PCLI. However, the addition of metaphase cytogenetics to prognostic modeling incorporating these covariates did not significantly improve risk stratification. Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Alnylam: Research Funding; Celgene: Research Funding; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding. Kapoor:Celgene: Honoraria; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Glaxo Smith Kline: Research Funding. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

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32

Ariel, Barak, and Justice Tankebe. "Racial stratification and multiple outcomes in police stops and searches." Policing and Society 28, no. 5 (May 19, 2016): 507–25. http://dx.doi.org/10.1080/10439463.2016.1184270.

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33

Chng, W. J., T.-H. Chung, S. Kumar, S. Usmani, N. Munshi, H. Avet-Loiseau, H. Goldschmidt, B. Durie, and P. Sonneveld. "Gene signature combinations improve prognostic stratification of multiple myeloma patients." Leukemia 30, no. 5 (December 16, 2015): 1071–78. http://dx.doi.org/10.1038/leu.2015.341.

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34

Maura, Francesco, Arjun Raj Rajanna, Andriy Derkach, Bachisio Ziccheddu, Niels Weinhold, Kylee H. Maclachlan, Benjamin Diamond, et al. "Individualized Treatment-Adjusted Risk Stratification in Newly Diagnosed Multiple Myeloma." Blood 140, Supplement 1 (November 15, 2022): 1561–63. http://dx.doi.org/10.1182/blood-2022-160215.

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35

Rajkumar, S. Vincent. "Multiple myeloma: 2013 update on diagnosis, risk-stratification, and management." American Journal of Hematology 88, no. 3 (February 25, 2013): 225–35. http://dx.doi.org/10.1002/ajh.23390.

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36

Rajkumar, S. Vincent. "Multiple myeloma: 2014 Update on diagnosis, risk-stratification, and management." American Journal of Hematology 89, no. 10 (September 16, 2014): 998–1009. http://dx.doi.org/10.1002/ajh.23810.

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37

Rajkumar, S. Vincent. "Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management." American Journal of Hematology 91, no. 7 (June 12, 2016): 719–34. http://dx.doi.org/10.1002/ajh.24402.

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38

Rajkumar, S. Vincent. "Multiple myeloma: 2018 update on diagnosis, risk-stratification, and management." American Journal of Hematology 93, no. 8 (August 2018): 1091–110. http://dx.doi.org/10.1002/ajh.25117.

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39

Rajkumar, S. Vincent. "Multiple myeloma: 2020 update on diagnosis, risk‐stratification and management." American Journal of Hematology 95, no. 5 (April 13, 2020): 548–67. http://dx.doi.org/10.1002/ajh.25791.

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40

Rajkumar, S. Vincent. "Multiple myeloma: 2011 update on diagnosis, risk-stratification, and management." American Journal of Hematology 86, no. 1 (December 22, 2010): 57–65. http://dx.doi.org/10.1002/ajh.21913.

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41

Rajkumar, S. Vincent. "Multiple myeloma: 2012 update on diagnosis, risk-stratification, and management." American Journal of Hematology 87, no. 1 (December 17, 2011): 78–88. http://dx.doi.org/10.1002/ajh.22237.

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42

Rajanna, Arjun Raj, Francesco Maura, Andriy Derkach, Bachisio Ziccheddu, Niels Weinhold, Kylee Maclachlan, Benjamin Diamond, et al. "Abstract 5453: Individualized risk stratification in newly diagnosed multiple myeloma." Cancer Research 83, no. 7_Supplement (April 4, 2023): 5453. http://dx.doi.org/10.1158/1538-7445.am2023-5453.

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Abstract Background: Clinical outcomes for newly diagnosed multiple myeloma (NDMM) patients are heterogenous with survival ranging from months to > 10 years. Though several clinical and genomic features predict outcomes, the “one-size-fits-all” treatment paradigm remains dominant for NDMM. Hypothesis: By integrating clinical, genomic and therapeutic data, using artificial intelligence, an individualized risk-prediction model for NDMM (IRM) can facilitate individually-tailored therapeutic decisions. Methods: We included 1933 patients with clinical and genomic data from 5 cohorts: MMRF CoMMpass (n=1062), MGP (n=492), Moffit AVATAR (n=177), UAMS (n=93), and MSKCC (n=109). The median follow-up was 43 months. Overall, we considered 160 clinical (e.g., age, ECOG, race), therapeutics, and genomic variables. To correct for time-dependent variables such as autologous stem cell transplant (ASCT) and continuous treatment, a multi-state model was designed across two phases: induction (phase 1), and post-induction (phase 2). Neural Cox Non-proportional-hazards (NCNPH) was used to integrate the data and build the model. Results: Overall, the 5-year overall survival (OS) c-index for IRM was 0.73, significantly higher than all existing prognostic models: R2-ISS (0.62), ISS (0.61) and R-ISS (0.56). The overall model accuracy was significantly improved by the inclusion of 12 genomic features, including 1q21 gain/amp, TP53 loss, t(4;14)(NSD2;IGH), complex copy number signatures, APOBEC mutational signature contribution, and del1p. Prescribed therapy emerged as a key determinant of risk, suggesting that effective combinations may have a different impact in the context of individual patient features, with the potential to significantly change clinical outcomes despite poor historical prognostication (i.e., treatment variance). Leveraging these concepts, we interrogated the clinical impact of ASCT and continuous treatment in the context of NDMM treated with bortezomib, lenalidomide and dexamethasone (VRd). Integrating predicted outcomes and treatment variance for all 4 possible treatment combinations (i.e., VRd +/- ASCT +/- continuous treatment) we identified 3 patient groups. In the first group (n=632), patients were characterized by complex genomic features, older age, high ISS, poor outcomes and limited treatment variance, reflecting aggressive and refractory myeloma. The second group (n=571) was characterized by high treatment variance, with favorable outcomes if ASCT and continuous treatment are provided. The last group (n=730) included patients with favorable clinical and genomic profiles, achieving good outcomes, with minimal advantage from ASCT. Conclusion: Integrating historical and emerging genomic features with clinical and therapeutic data, we developed the first individualized risk-prediction model for personally-tailored therapeutic decisions in NDMM. Citation Format: Arjun Raj Rajanna, Francesco Maura, Andriy Derkach, Bachisio Ziccheddu, Niels Weinhold, Kylee Maclachlan, Benjamin Diamond, Faith Davies, Eileen Boyle, Brian Walker, Alexandra Pos, Malin Hulcrantz, Ariosto Silva, Oliver Hampton, Jamie K. Teer, Niccolò Bolli, Graham Jackson, Martin Kaiser, Charlotte Pawlyn, Gordon Cook, Dennis Verducci, Dickran Kazandjian, Fritz Van Rhee, Saad Usmani, Kenneth H. Shain, Marc S. Raab, Gareth Morgan, Ola Landgren. Individualized risk stratification in newly diagnosed multiple myeloma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5453.

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Kurian, Allison W., Antonis C. Antoniou, and Susan M. Domchek. "Refining Breast Cancer Risk Stratification: Additional Genes, Additional Information." American Society of Clinical Oncology Educational Book, no. 36 (May 2016): 44–56. http://dx.doi.org/10.1200/edbk_158817.

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Recent advances in genomic technology have enabled far more rapid, less expensive sequencing of multiple genes than was possible only a few years ago. Advances in bioinformatics also facilitate the interpretation of large amounts of genomic data. New strategies for cancer genetic risk assessment include multiplex sequencing panels of 5 to more than 100 genes (in which rare mutations are often associated with at least two times the average risk of developing breast cancer) and panels of common single-nucleotide polymorphisms (SNPs), combinations of which are generally associated with more modest cancer risks (more than twofold). Although these new multiple-gene panel tests are used in oncology practice, questions remain about the clinical validity and the clinical utility of their results. To translate this increasingly complex genetic information for clinical use, cancer risk prediction tools are under development that consider the joint effects of all susceptibility genes, together with other established breast cancer risk factors. Risk-adapted screening and prevention protocols are underway, with ongoing refinement as genetic knowledge grows. Priority areas for future research include the clinical validity and clinical utility of emerging genetic tests; the accuracy of developing cancer risk prediction models; and the long-term outcomes of risk-adapted screening and prevention protocols, in terms of patients’ experiences and survival.

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44

Manier, Salomon, Chia-Jen Liu, Hervé Avet-Loiseau, Jihye Park, Jiantao Shi, Federico Campigotto, Karma Z. Salem, et al. "Prognostic role of circulating exosomal miRNAs in multiple myeloma." Blood 129, no. 17 (April 27, 2017): 2429–36. http://dx.doi.org/10.1182/blood-2016-09-742296.

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Key Points Two circulating exosomal microRNAs, let-7b and miR-18a, improved survival prediction in patients with MM. Circulating exosomal miRNAs enhanced the stratification of patients with high-risk factors.

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45

Chen, Nan, and Chiung-Tzu Lucetta Tsai. "Rural-Urban Divide and the Social Stratification in Leisure Participation in China: Application of Multiple Hierarchy Stratification Perspective." Applied Research in Quality of Life 15, no. 5 (July 8, 2019): 1535–48. http://dx.doi.org/10.1007/s11482-019-09750-z.

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46

Hájek, Roman, Sebastian Gonzalez-McQuire, Zsolt Szabo, Michel Delforge, Lucy DeCosta, Marc S. Raab, Walter Bouwmeester, Marco Campioni, and Andrew Briggs. "Novel risk stratification algorithm for estimating the risk of death in patients with relapsed multiple myeloma: external validation in a retrospective chart review." BMJ Open 10, no. 7 (July 2020): e034209. http://dx.doi.org/10.1136/bmjopen-2019-034209.

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Objectives and designA novel risk stratification algorithm estimating risk of death in patients with relapsed multiple myeloma starting second-line treatment was recently developed using multivariable Cox regression of data from a Czech registry. It uses 16 parameters routinely collected in medical practice to stratify patients into four distinct risk groups in terms of survival expectation. To provide insight into generalisability of the risk stratification algorithm, the study aimed to validate the risk stratification algorithm using real-world data from specifically designed retrospective chart audits from three European countries.Participants and settingPhysicians collected data from 998 patients (France, 386; Germany, 344; UK, 268) and applied the risk stratification algorithm.MethodsThe performance of the Cox regression model for predicting risk of death was assessed by Nagelkerke’s R2, goodness of fit and the C-index. The risk stratification algorithm’s ability to discriminate overall survival across four risk groups was evaluated using Kaplan-Meier curves and HRs.ResultsConsistent with the Czech registry, the stratification performance of the risk stratification algorithm demonstrated clear differentiation in risk of death between the four groups. As risk groups increased, risk of death doubled. The C-index was 0.715 (95% CI 0.690 to 0.734).ConclusionsValidation of the novel risk stratification algorithm in an independent ‘real-world’ dataset demonstrated that it stratifies patients in four subgroups according to survival expectation.

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Meißner, Tobias, Anja Seckinger, Thierry Rème, Thomas Hielscher, Thomas Möhler, Kai Neben, Hartmut Goldschmidt, Bernard Klein, and Dirk Hose. "Metascoring and Gene Expression Profiling in Clinical Routine in Multiple Myeloma,." Blood 118, no. 21 (November 18, 2011): 3940. http://dx.doi.org/10.1182/blood.v118.21.3940.3940.

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Abstract Abstract 3940 BACKGROUND. Multiple myeloma is characterized by molecular heterogeneity transmitting to survival ranging from several months to over 15 years. Gene expression profiling allows assessment of biological entities, risk, and targets. Its translation into clinical routine alongside conventional prognostic factors has been prevented by lack of appropriated reporting tools and the integration with other prognostic factors into a single risk stratification (metascoring). METHODS. We present here a non-commercial open source software-framework developed in the open source language R (GEP-report) containing a graphic user interphase based on Gtk2. Affymetrix microarray raw-data and a documentation-by-value strategy to directly apply thresholds and grouping-algorithms from a reference cohort of 262 myeloma patients are used. Gene expression-based and conventional prognostic factors are integrated within one risk-stratification (HM-metascore) and the final report is created as a PDF-file. RESULTS. The GEP-report comprises i) quality control, ii) test of sample identity, iii) biological classifications of multiple myeloma, iv) risk stratification, v) assessment of target-genes, and vi) integration of expression-based and clinical risk factors within one metascore. This HM-metascore sums over the weighted factors gene-expression based risk-assessment (UAMS-, IFM-score), proliferation, ISS-stage, t(4;14), and expression of prognostic target-genes (AURKA, IGF1R) for which clinical grade inhibitors exist. It delineates three significantly different groups of 13.1, 72.1 and 14.7% of patients with a 6-year survival of 89.3, 60.6 and 18.6%, respectively. CONCLUSION. GEP-reporting allows prospective assessment of target gene expression and integration of current prognostic factors within one risk stratification (metascoring), being customizable regarding novel parameters or other cancer entities. Disclosures: No relevant conflicts of interest to declare.

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48

Gertz, Morie A. "Deciding on the therapy of multiple myeloma using genetic risk stratification." Leukemia & Lymphoma 52, no. 2 (January 24, 2011): 157–58. http://dx.doi.org/10.3109/10428194.2010.542601.

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49

Radocha, Jakub, Vladimír Maisnar, Ludek Pour, Zdenek Adam, Ivan Spicka, Jan Straub, Vlastimil Scudla, et al. "Multiple Myeloma R-ISS Prognostic Stratification System in Real Life Population." Blood 128, no. 22 (December 2, 2016): 3333. http://dx.doi.org/10.1182/blood.v128.22.3333.3333.

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Abstract Introduction: Revised prognostic scoring system R-ISS (standard ISS plus cytogenetic changes) has been introduced as a possible tool for evaluation of patients with multiple myeloma. This system is based on pooled data from various clinical trials but has not been validated in patients´ population outside the clinical trial setting. Aim: To evaluate clinical relevance of R-ISS in real life population of multiple myeloma patients. Methods: Registry of monoclonal gammopathies (RMG) was established in 2007 and has become one of the flagship projects of the Czech Myeloma Group. The registry collects prospective data from patients with myeloma and other gammopathies (https://trials.cba.muni.cz/trialdb2/interface_forms/login_rmg.asp). Registry is regularly monitored and data are validated by an external monitor. Data from registry were retrieved to identify patients in whom all above mentioned parameters were available. These patients were then stratified according to R-ISS and TTP and OS were calculated as primary endpoints. Results: 555 patients (260 females, 295 males, median age 66 years) with multiple myeloma who had full set of necessary data available were identified. Median follow-up of this cohort was 22.2 months. 97 17.5% (97/555) patients were R-ISS stage I, 55.7% 309/555 were R-ISS stage II and 26.8% (149/555) patients were R-ISS III. Median overall survival was not reached for stage I, 3.9 years for stage II and 2.5 years for stage III. The differences were statistically significant (p<0.001, log-rank test). Median time to progression was 3.3 years for stage I, 1.9 years for stage II and 1.3 years for stage III. The differences were statistically significant (p<0.001, log-rank test). Stage I versus II showed HR (95% CI): 2.84 (1.66-4.87), p<0.001 and stage I versus III HR (95% CI): 5.20 (2.99-9.03), p<0.001 for overall survival and HR (95% CI): 2.02 (1.37-2.96), p<0.001 and (95% CI): 2.49 (1.64-3.77), p< 0.001 for time to progression. Similar survival pattern can be seen in a subgroup of patients treated with autologous stem cell transplantation, without autologous stem cell transplantation and the system provides valuable information even in a subgroup of patients who were never treated with novel agents. Figure 1 shows overall survival and figure 2 time to progression of the cohort. Conclusion: Revised ISS provides valuable information about the long term prognosis in a mixed cohort of real life multiple myeloma patients. This system enables to estimate the prognostic category of each specific patient in a horizon of several years ahead. Supported by PRVOUK P37. Table 1 Table 1. Figure 1 Overall survival for R-ISS stages Figure 1. Overall survival for R-ISS stages Figure 2 Time to progression for R-ISS stages Figure 2. Time to progression for R-ISS stages Disclosures Spicka: BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria; Millenium: Honoraria. Hájek:Janssen: Honoraria; Celgene: Consultancy, Research Funding; BMS: Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy.

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Lonial, Sagar. "Presentation and risk stratification – improving prognosis for patients with multiple myeloma." Cancer Treatment Reviews 36 (May 2010): S12—S17. http://dx.doi.org/10.1016/s0305-7372(10)70007-4.

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