PRO78 Real-World Treatment Patterns in Patients with Light Chain (AL) Amyloidosis: Analysis of the Optum US Electronic Health Records (EHR) and Commercial Claims Database

2021 ◽  
Vol 24 ◽  
pp. S212
Author(s):  
A. Dispenzieri ◽  
J. Zonder ◽  
J. Hoffman ◽  
S.W. Wong ◽  
M. Liedtke ◽  
...  
2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin

Author(s):  
E.D. Farrand ◽  
O. Gologorskaya ◽  
H. Mills ◽  
L. Radhakrishnan ◽  
H.R. Collard ◽  
...  

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  

SummaryObjective: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.Methods: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generaliz-ability of clinical trials and for defining population-representative clinical trial eligibility criteria.Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5608-5608
Author(s):  
Noa Biran ◽  
Camille Johnson ◽  
Andrew D Norden ◽  
Roger Kumar ◽  
David H. Vesole ◽  
...  

Abstract Background: Racial disparities in incidence and prevalence in multiple myeloma (MM) are well known with black (B) individuals having 2-3 fold increased risks compared to white non-Hispanic (WNH) populations. An ECOG review (Baker, Blood 2013) and a Mayo Clinic study (Greenberg, Blood Cancer Journal 2015) found a lower frequency of IgH translocations in B patients (pts). However a Veterans Health Administration study (Blue, BJH 2017) did not identify racial differences by classical karyotype procedures. Paulus (ASH 2016) using the COMMPASS database also failed to identify cytogenetic differences by race or ethnicity among approximately 1000 patients but did identify gene expression profiling , gene copy number variations, and gene single nucleotide variations. Our group presented a single institution review (Velez ASCO 2017) of 482 pts and noted that WNH pts had more IgH rearrangements (similar to Mayo) and t(11;14) than minorities. We sought to extend these observations by reviewing the Cota database containing diagnostic information drawn from 12 cancer centers (85 hematologists-oncologists) within the northeast USA. Methods: The Cota database contains diagnostic, clinical and outcome information abstracted and enriched from the electronic health records. The database is purely observational and has been de-identified for secondary research purposes. Cytogenetics and FISH studies were performed at various laboratories per physician discretion and not all pts were tested for all genomic abnormalities. Fisher-exact test comparisons were calculated. Results: The electronic health records of 935 pts with MM diagnosed between January 1, 2012 and May 1, 2017 were reviewed. FISH genomic data on 819 pts obtained at the time of initial diagnosis was available including 581 (71%) WNH, 66 (8%) White Hispanic (H), 138 (17%) B, and 34 (4%) Asian/Indian (AI) pts. 454 (55%) were male with median age at initial presentation of 65 years. Subtypes included IgG kappa NHW: 36%, H: 38% B: 38% AI: 32%; IgG lambda NHW: 23%, H: 18% B: 27% AI: 35%; IgA kappa NHW: 14%, H: 15% B: 9% AI: 6%; kappa light chain NHW: 10%, H: 11% B:12% AI: 21%; IgA lambda NHW: 7%, H: 11% B: 5% AI 6%; lambda light chain NHW: 7%, H: 4% B: 4% AI: 6%; other subtypes all 1% or less. Durie-Salmon stage I, II, III was NHW: 8%, 13%, 78%; H: 6%, 8%, 86%; B: 5%, 9%, 86%; AI: 12%, 0%, 88%. As detailed in the Table, FISH identified abnormalities were similar across race and ethnicity, with the exception of higher rates of t(4:14) in minorities and higher rates of 1q amplifications in Asian/Indians (p<0.02). Conclusions: Unlike the Mayo/ECOG experiences, but similar to the COMMPASS and Veteran's reports, we found no significant differences in the frequency of IgH translocations. We also noted no differences in R-ISS defining high risk abnormality frequencies by racial or ethnicity groupings. Thus differences in incidence or severity of MM appear unlikely to be explained by genomics at the FISH level. Table. Table. Disclosures Biran: Merck: Research Funding; Amgen: Consultancy, Speakers Bureau; Celgene: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; BMS: Research Funding. Norden:Cota Inc: Employment. Siegel:Merck: Consultancy, Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau. Goldberg:COTA Inc.: Employment, Equity Ownership.


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