Sheila R. Reddy's research while affiliated with Beverly Hills Cancer Center and other places

Introduction Antiviral therapy may be underutilized in patients at high risk for increased clinical and economic burden (e.g. older adults). We aimed to examine the benefits associated with antiviral treatment of seasonal influenza among treated and untreated Medicare beneficiaries. Methods This retrospective study of Medicare Claims Research Identifiable Files identified patients ≥66 years old with an influenza diagnosis in outpatient setting between October 2016–March 2019 (flu seasons 2016–2018). Index date defined as date of first claim with influenza diagnosis; baseline as the 12 months pre-index. Treated patients received antivirals ≤2 days from index. Untreated patients had no antivirals ≤6 months post-index. Treated/untreated patients were 1:1 propensity score matched. Outcomes (death, all-cause and respiratory-related healthcare resource utilization [HCRU] and costs) were assessed until death or up to 6 months post-index. Descriptive statistics were reported; Kaplan-Meier estimation was used for survival over time. Results Among 116,901 matched patient pairs, all-cause mortality within 6 months from index diagnosis was 1.6% among treated versus 4.3% among untreated patients. Rates (treated versus untreated) of all-cause inpatient hospitalizations during follow-up were 13.9% versus 22.7% and respiratory-related hospitalizations were 4.2% versus 9.0%. Mean (SD) total all-cause and respiratory-related costs were $9,830 ($18,616.0) and $900 ($4016.4) among the treated, respectively, versus $13,207 ($24,405.1) and $2,024 ($7,623.7) among untreated, respectively. All differences were statistically significant (p < 0.001). Conclusions Lack of antiviral treatment is associated with increased mortality, HCRU, and economic burden in older Medicare beneficiaries with seasonal influenza. Future research should investigate whether the choice of antivirals affects influenza burden.

Aims: To provide more recent estimates of healthcare utilization and costs in Huntington's disease (HD) in the Medicaid population. Materials and methods: This retrospective analysis used administrative claims data for HD beneficiaries (≥1 HD claim; ICD-9-CM 333.4) from Medicaid Analytic eXtract data files from 01/01/2010-12/31/2014. The date of the first HD claim during the identification period (01/01/2011-12/31/13) was assigned as the index date. If a beneficiary had multiple HD claims during the identification period, one was randomly chosen as the index date. Beneficiaries were required to be continuously enrolled in fee-for-service plans during the one-year pre-index and post-index periods. Medicaid beneficiaries without HD were drawn from a 100% random sample and matched (3:1) to those with HD. Beneficiaries were classified by disease stage (early/middle/late). All-cause and HD-related (any utilization related to HD diagnosis or symptoms associated with HD) healthcare utilization and costs were reported. Results: A total of 1,785 beneficiaries without HD were matched to 595 beneficiaries with HD (139 early-, 78 middle-, and 378 late-stage). The mean (SD) annual total costs were higher for beneficiaries with HD than beneficiaries without HD ($73,087 [$75,140] vs. $26,834 [$47,659], p < 0.001) and driven by inpatient costs ($45,190 [$48,185] vs. $13,808 [$39,596], p < 0.001). Total healthcare costs were highest among beneficiaries with late-stage HD (mean [SD] cost: $22,797 [$31,683] for early-stage HD vs. $55,294 [$129,290] for middle-stage HD vs. $95,251 [$60,197] for late-stage HD; p < 0.001). Limitations: Administrative claims are intended for billing purposes and subject to coding errors. This study did not address functional status, which may provide further insight to late-stage and end-of-life burden of HD, and indirect costs. Conclusions: Medicaid beneficiaries with HD have higher acute healthcare utilization and costs compared to beneficiaries without HD, which tend to increase with disease progression, indicating that HD beneficiaries at later disease stages have greater burden.

Introduction: Treatment with dasatinib for chronic myeloid leukemia (CML) has been associated with development of pleural effusion, however data regarding its optimal management are limited. We examined treatment patterns and healthcare resource utilization (HCRU) and costs among patients with CML treated with dasatinib who experienced a subsequent pleural effusion. Methods: Adults with CML and ≥1 pharmacy claim for dasatinib in 2015-2018 who experienced pleural effusion after dasatinib were identified using data from claims databases. Results: Overall, 123 patients were eligible. After 1 year, of the 38.2% of patients with a dose modification, 72.3% did not switch treatment; amongst these patients, 70.6% continued treatment. Among patients with a stable dose after pleural effusion (61.8%), 57.9% later switched to another TKI. The mean (SD) duration of dasatinib treatment after pleural effusion was 262.0 (124.0) days for patients with a dose modification versus 149.1 (155.2) days for those with a stable dose (p < 0.001). HCRU and costs were similar between groups. Discussion/conclusion: Dasatinib dose modification after pleural effusion was not always required; however, patients with dose modifications continued therapy for a longer duration with a lower rate of switching to another TKI versus patients who remained on a stable dose.

Introduction: Initial clinical manifestations of transthyretin amyloidosis (ATTR) are not well understood, making timely diagnosis challenging. Methods: Patients aged ≥68 years newly diagnosed with ATTR were identified using Medicare Research Identifiable Files. Symptom manifestation and healthcare utilization were measured during 3 years pre-diagnosis; demographics and comorbidity index during 1-year pre-diagnosis. Controls (ATTR-free) were matched 1:1 to patients with ATTR based on age, sex and region; same index date and enrollment as match. Results: We identified 552 matched ATTR-control pairs: mean age 78.3 (standard deviation 6.3) and 64.5% male. Among patients with ATTR (vs controls), cardiovascular conditions (92.9 vs 75.9%) and hospitalization (54.0 vs 35.5%) were frequent during 3 years pre-diagnosis. Conclusion: Patients with ATTR have multiple symptoms and hospitalizations pre-diagnosis, recognition of which may facilitate earlier diagnosis and treatment.

Introduction: Huntington's disease (HD) is a rare, genetic, and ultimately fatal neurodegenerative disease, with a devastating impact on individuals and families across generations. Few estimates of HD epidemiology in the US exist. Methods: This study employed a retrospective cross-sectional design to examine the epidemiology of HD in the US Medicare and Medicaid beneficiary populations using 2016-2017 claims data from the Medicare 100% Research Identifiable Files (RIFs) and 2014 claims data from the Medicaid Analytic eXtract (MAX) files for 17 states. Medicare beneficiaries ≥65 years with a diagnosis of HD (≥1 claim with ICD-10-CM code G10) in 2017 and Medicaid beneficiaries <65 years with a diagnosis of HD (≥1 claim with ICD-9-CM code 333.4) in 2014 were identified. The study outcomes included the 2017 prevalence proportion and incidence rate of HD in the Medicare population and the 2014 prevalence proportion of HD in the Medicaid population. Results: In the Medicare population, 1,941 prevalent and 819 incident cases of HD were identified in 2017, corresponding to a prevalence proportion of 13.1 per 100,000 persons and incidence rate of 6.1 per 100,000 person-years. In the Medicaid population, 353 prevalent cases of HD were identified in 2014, corresponding to a prevalence proportion of 15.2 per 100,000 persons. Discussion/conclusion: This study suggests that prevalence and incidence of HD in the US may be higher than previously estimated. This has important implications in raising awareness of HD among providers and payers, and ensuring availability of and access to services for HD patients and care partners in the Medicare and Medicaid populations.

Objective: Estimate the annual cost of care in the 5 years following a cancer diagnosis for 17 invasive cancer types, by stage at diagnosis. Methods: We used 2012-2016 data from the Surveillance, Epidemiology, and End Results (SEER) registry-Medicare claims database to examine cost of care among Medicare beneficiaries with a confirmed cancer diagnosis based on International Classification of Diseases for Oncology, Third Edition histology codes reported in SEER. Beneficiaries contributed to the annual cost calculations (Years 1-5) using their observed time after diagnosis. Beneficiaries were continuously enrolled in fee-for-service Medicare Parts A/B and Part D during follow-up. Total, inpatient, outpatient, and pharmacy cancer-related service costs were calculated. Results: From 2012-2016, we identified 597,778 Medicare beneficiaries with incident cancer diagnosis within 5 years (Stage I, II, III, and IV: 32.6%, 33.4%, 15.9%, and 18.0%, respectively). In Year 1, mean (standard deviation) total costs for Stage I diagnoses varied from $7,640 ($17,378) (prostate) to $94,636 ($117,636) (pancreas). Total costs increased by stage and reached $58,783 ($92,344) (prostate) to $156,982 ($175,009) (stomach) for Stage IV diagnoses in Year 1. Costs in Year 1 were significantly higher for Stage IV diagnoses than for earlier stages across all cancer types. In Years 2-5, total costs were lower than in Year 1 but continued to increase by stage. Conclusions: Beneficiaries diagnosed at later stages of cancer have higher costs of care (up to 7 times as much) than those diagnosed at earlier stages. Earlier cancer diagnosis may lead to more efficient treatment and decreased management cost.

Background Despite emerging treatments for hereditary transthyretin (ATTRv) amyloidosis, the disease is often misdiagnosed, with reported diagnostic delays of up to several years. Knowledge of the patient journey leading up to diagnosis may help to promote earlier intervention. The study’s objective was to examine patient clinical characteristics and healthcare utilization prior to ATTRv amyloidosis diagnosis. Methods Patients ≥ 18 years and newly diagnosed with ATTRv amyloidosis identified in IBM® MarketScan® Commercial and Medicare Supplemental data using a claims-based algorithm as follows: diagnosis required ≥ 1 medical claim with relevant amyloidosis diagnosis code (ICD-10-CM: E85.0-.4, E85.89, E85.9; excludes light chain and wild type) during identification (ID) period (1/1/2016–12/31/2017), and ≥ 1 occurrence of qualifying criteria during 2011–2017: ≥ 15 days diflunisal use without > 30-day gap, liver transplant, or claim with specific codes E85.1 or E85.2. The index date was defined as the date of first claim with amyloidosis diagnosis code in ID period. Patients had continuous enrollment ≥ 5 years pre-index date (look-back period). Occurrence of selected comorbid conditions and symptoms and healthcare utilization (testing, emergency department visits and hospitalization) measured during the look-back period; demographics, physician specialty, and Charlson comorbidity index (CCI) measured 1 year pre-index. Patients with an ICD-9/10 amyloidosis code during the look-back period were excluded. An ATTRv-free reference cohort was created from a random sample of enrollees who lacked any diagnosis of amyloidosis and matched 3:1 to ATTRv patients on age, gender, and region to provide reference values; same index and enrollment requirement as match. Results For the 141 qualifying patients with ATTRv and 423 matched controls, mean (standard deviation) age was 62.5 (14.2) years and 53.9% were female. Mean CCI for ATTRv cohort was 2.7 (3.0) versus 1.1 (1.9) among controls. Selected comorbidities, testing, visits, and hospitalization were common among patients with ATTRv during the look-back period with higher rates versus controls. Conclusions Patients with ATTRv amyloidosis experience multiple neurological, cardiovascular, and other clinical manifestations, testing, and hospitalization prior to diagnosis. Occurrence of potential markers of illness is most common in the year before diagnosis.

BACKGROUND: Additional real-world studies are needed to more fully elucidate the effectiveness of antifibrotic treatment in slowing the progression of idiopathic pulmonary fibrosis (IPF). OBJECTIVE: To compare mortality and hospitalization between Medicare beneficiaries with IPF who initiate antifibrotic therapy and those who did not receive treatment. METHODS: A retrospective observational study of Medicare beneficiaries using the 100% Medicare Research Identifiable File was conducted. We included patients aged 67 years and over diagnosed with IPF (≥ 1 inpatient or ≥ 2 outpatient claims with IPF diagnosis, J84.112]) during the study period (January 1, 2010-December 31, 2017). Patients who initiated antifibrotic treatment (pirfenidone or nintedanib) between October 15, 2014 (FDA approval date) and December 31, 2017 (ie, treated patients) were compared with those who did not receive treatment during a historical period (January 1, 2012-October 14, 2014) before the availability of antifibrotics (ie, untreated historical controls). Patients were matched by propensity score, and the outcomes, mortality, and hospitalization (all cause and respiratory related) were compared using a Cox proportional hazards model. RESULTS: We identified 4,641 treated patients and 4,641 propensity score-matched controls who met all study criteria; 352 treated patients who lacked matches were excluded from the study. Cox regression analysis of treated patients vs matched controls showed a significantly lower risk of mortality (HR = 0.62, 95% CI = 0.57-0.68); lower risk of hospitalization (HR = 0.71, 95% CI = 0.67-0.76; HR = 0.70, 95% CI = 0.64-0.76); and lower rate in number of hospitalizations per month (incident rate ratio [IRR] = 0.65, 95% CI = 0.60-0.71; IRR = 0.65, 95% CI = 0.58-0.73). CONCLUSIONS: This study suggests that treatment with antifibrotics may confer a survival benefit and protection against all-cause and respiratory-related hospitalization for IPF patients. DISCLOSURES: This work was sponsored by F. Hoffmann-La Roche/Genentech, Inc. Corral is employed by Genentech, Inc. Reddy, Chang, Broder, and Gokhale are employed by Partnership for Health Analytic Research LLC, a health services research company, which was hired by Genentech to conduct this research. Mooney has received advisory board/consulting fees and research support from Genentech, unrelated to this work. Mooney also reports advisory board/consulting fees and research support from Boehringer Ingelheim; personal fees from Imvaria; and grants from Celgene and Pliant, unrelated to this work. Through their employment with Partnership for Health Analytic Research, Reddy, Chang, Broder, and Gokhale have been compensated to conduct research for AbbVie, Akcea, ASPC, Amgen, AstraZeneca, BMS, Boston Scientific Corporation, Celgene, Eisai, Ethicon, GRAIL, Helsinn, Illumina, Innovation and Value Initiative, Ionis, Jazz, Kite, Novartis, Otsuka, Pathnostics, PhRMA, Prothena, Sage, Verde Technologies, Genentech, Inc., Greenwich Biosciences, Inc., Mirum Pharmaceuticals, Inc., Sanofi US Services, Inc., Sunovion Pharmaceuticals, Inc., and Dompe US, Inc., unrelated to this work. This research was presented as an abstract at CHEST 2020 Annual Meeting (virtual), October 18-21, 2020, and American Thoracic Society 2020 Virtual Meeting, June 2020.

Introduction Decision-making in the treatment (Tx) of chronic myeloid leukemia (CML) is complex and dependent on many factors, including disease phase and drug tolerability. The tyrosine kinase inhibitor (TKI) dasatinib is an effective long-term Tx option for most patients (pts) with newly diagnosed CML, based on the deep and durable responses reported in the DASISION trial (Cortes JE, et al. J Clin Oncol 2016); however, dasatinib was associated with the development of pleural effusion, an adverse event caused by the build-up of excess fluid in the pleural space outside of the lungs (Jany and Welte Dtsch Arztebl Int 2019). Data regarding the optimal strategy for managing pleural effusion among pts treated with dasatinib are limited. Dose reductions, interruptions or switching to another TKI are commonly used strategies but in the SIMPLICITY trial, pts who remained on first-line Tx had better clinical outcomes than pts who switched Tx (Gambacorti-Passerini C, et al. Eur J Haematol 2020). Here we present results of a study examining Tx patterns, including duration of dasatinib use after pleural effusion, and healthcare resource utilization (HCRU) and costs among pts with CML treated with dasatinib who experienced a subsequent pleural effusion. Methods Data from the IBM MarketScan ® Commercial and Medicare Supplemental Databases were used to identify pts diagnosed with CML (ICD-9-CM code 205.1x, 205.8x; ICD-10-CM code C92.1x, C92.Zx) during the study period (Jan 1, 2014 - Sep 30, 2019). Eligible pts were ≥ 18 y, had ≥ 1 pharmacy claim for dasatinib, and experienced a pleural effusion after dasatinib Tx (ICD-9-CM: 511.1, 511.89; ICD-10-CM: J90) during the identification (ID) period (Jan 1, 2015 - Sep 30, 2018). All pts had ≥ 1 filled prescription for dasatinib before the index date (defined as date of first pleural effusion during ID period), with dasatinib available on the index date, and no code for pleural effusion recorded during the baseline period (1-y pre-index); pts were also required to be continuously enrolled (baseline to follow-up at 1-y post-index). Demographic and clinical characteristics were described, and endpoints evaluated included Tx patterns (dose modification, switching to another TKI, duration of dasatinib Tx), HCRU, and cost. Results In total, 123 pts met the study criteria. The mean (standard deviation [SD]) age was 62.2 (10.9) y, 23.6% were female, and the mean (SD) Charlson Comorbidity Index was 3.8 (2.1). Overall, 38.2% of pts had a dose modification and 61.8% a stable dose after pleural effusion. At the 1-y follow-up, most pts (72.3%) with a dose modification did not switch Tx, and, of those, 70.6% continued Tx whereas the majority (57.9%) of pts with a stable dose switched to another TKI (Figure A). The mean (SD) number of days from first pleural effusion to end of dasatinib Tx (duration of dasatinib Tx) was significantly greater in pts with a dose modification compared with those with a stable dose (262.0 [124.0] vs 149.1 [155.2]; P < 0.001). In pts with a dose modification, the mean (SD) number of days from pleural effusion to dose modification was 73.7 (77.1) days, and from dose modification to end of Tx was 188.3 (128.7) days. Pts with a dose modification took a significantly longer time to switch from dasatinib to another TKI compared with pts with a stable dose (mean [SD]: 164.7 [105.8] vs 74.8 [76.0] days; P < 0.001; Figure B). Overall, 48.0% of pts had an inpatient hospitalization, with a mean (SD) total stay of 11.3 (14.3) days, and 37.4% had a visit to an emergency department. The mean (SD) number of physician office visits was 24.3 (16.7). Total mean (SD) costs were $196,797 (143,848). There were no statistically significant differences in HCRU and costs between pts with dose modification and stable dose. Conclusions These findings demonstrate that dasatinib discontinuation may not be necessary after development of pleural effusion. Pts who had a dose modification of dasatinib after development of pleural effusion were able to continue on dasatinib for a longer duration and had a lower rate of switching to another TKI but with similar HCRU and costs compared with pts who maintained a stable dose. Although not all pts required a dose modification to continue dasatinib Tx after pleural effusion, these findings suggest that in some pts, dose modification of dasatinib may allow for continued Tx with the potential to sustain outcomes. Study support Funded by Bristol Myers Squibb Figure 1 Figure 1. Disclosures Brokars: Bristol Myers Squibb: Current Employment. Kee: Bristol Myers Squibb: Current Employment. McBride: Bristol Myers Squibb: Current Employment. Reddy: Amgen: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Partnership for Health Analytic Research (PHAR), LLC: Current Employment; Greenwich Biosciences: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; GRAIL: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Prothena: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Jazz: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Kite: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Genentech: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Verde Technologies: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Exact Sciences Corporation: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Eisai: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Dompe: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Celgene: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Sage: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Otsuka: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Novartis: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Mirum Pharmaceuticals: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Boston Scientific Corporation: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Bristol Myers Squibb: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Sanofi US Services: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Takeda Pharmaceuticals: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Akcea: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; BioMarin Pharmaceutical: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months. Chang: AstraZeneca: Other: I am an employee of PHAR, LLC, which was paid by AstraZeneca to conduct research; Bristol Myers Squibb: Other: I am an employee of PHAR, LLC, which was paid by BMS to conduct research; Boston Scientific Corporation: Other: I am an employee of PHAR, LLC, which was paid by Boston Scientific Corporation to conduct research; Celgene: Other: I am an employee of PHAR, LLC, which was paid by Celgene to conduct research; Eisai: Other: I am an employee of PHAR, LLC, which was paid by Eisai to conduct research; Ethicon: Other: I am an employee of PHAR, LLC, which was paid by Ethicon to conduct research; GRAIL: Other: I am an employee of PHAR, LLC, which was paid by GRAIL to conduct research; Helsinn: Other: I am an employee of PHAR, LLC, which was paid by Helsinn to conduct research; Illumina: Other: I am an employee of PHAR, LLC, which was paid by Illumina to conduct research; Ionis: Other: I am an employee of PHAR, LLC, which was paid by Ionis to conduct research; Jazz: Other: I am an employee of PHAR, LLC, which was paid by Jazz to conduct research; Kite: Other: I am an employee of PHAR, LLC, which was paid by Kite to conduct research; Novartis: Other: I am an employee of PHAR, LLC, which was paid by Novartis to conduct research; Otsuka: Other: I am an employee of PHAR, LLC, which was paid by Otsuka to conduct research; Pathnostics: Other: I am an employee of PHAR, LLC, which was paid by Pathnostics to conduct research; Prothena: Other: I am an employee of PHAR, LLC, which was paid by Prothena to conduct research; Sage: Other: I am an employee of PHAR, LLC, which was paid by Sage to conduct research; Verde Technologies: Other: I am an employee of PHAR, LLC, which was paid by Verde Technologies to conduct research; Genentech, Inc.: Other: I am an employee of PHAR, LLC, which was paid by Genentech to conduct research; Greenwich Biosciences, Inc.: Other: I am an employee of PHAR, LLC, which was paid by Greenwich Biosciences to conduct research; Mirum Pharmaceuticals, Inc.: Other: I am an employee of PHAR, LLC, which was paid by Mirum Pharmaceuticals, Inc. to conduct research; Dompe US, Inc.: Other: I am an employee of PHAR, LLC, which was paid by Dompe US, Inc. to conduct research; Sanofi US Services, Inc.: Other: I am an employee of PHAR, LLC, which was paid by Sanofi US Services Inc. to conduct research; Sunovion Pharmaceuticals, Inc.: Other: I am an employee of PHAR, LLC which was paid by Sunovion Pharmaceuticals, Inc. to conduct research. ; BioMarin Pharmaceuticals Inc.: Other: I am an employee of PHAR, LLC which was paid by BioMarin Pharmaceuticals, Inc. to conduct research. ; Takeda Pharmaceuticals U.S.A., Inc.: Other: I am an employee of PHAR, LLC which was paid by Takeda Pharmaceuticals U.S.A., Inc., to conduct research. ; Exact Sciences Corporation: Other: I am an employee of PHAR, LLC which was paid by Exact Sciences Corporation to conduct research. ; Amgen: Other: I am an employee of PHAR, LLC, which was paid by Amgen to conduct research; Akcea: Other: I am an employee of PHAR, LLC, which was paid by Akcea to conduct research; AbbVie: Other: I am an employee of PHAR, LLC, which was paid by AbbVie to conduct research; Partnership for Health Analytic Research (PHAR), LLC: Current Employment, Other. Tarbox: Partnership for Health Analytic Research (PHAR), LLC: Current Employment, Other: I am an employee of PHAR, LLC, which was paid by Celgene/BMS to conduct this research.; AbbVie: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Akcea: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Amgen: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; BioMarin Pharmaceuticals: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Bristol Myers Squibb: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Boston Scientific Corporation: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Celgene: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Dompe: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Eisai: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Exact Sciences Corporation: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Genentech: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; GRAIL: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Greenwich Biosciences: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Jazz: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Kite: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Mirum Pharmaceuticals: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Novartis: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Otsuka: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Prothena: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Sage: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Sanofi US Services: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Takeda Pharmaceuticals USA: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months; Verde Technologies: Other: I am an employee of PHAR, LLC, which was paid by this company to conduct research in the last 24 months. LeBlanc: Jazz Pharmaceuticals: Research Funding; Daiichi-Sankyo: Consultancy, Honoraria, Other: Advisory board; Astellas: Consultancy, Honoraria, Other: Advisory board; AstraZeneca: Consultancy, Honoraria, Other: Advisory board, Research Funding; Amgen: Consultancy, Other: travel; American Cancer Society: Research Funding; CareVive: Consultancy, Other, Research Funding; Flatiron: Consultancy, Other: Advisory board; Pfizer: Consultancy, Other: Advisory Board; Helsinn: Consultancy, Research Funding; BMS/Celgene: Consultancy, Honoraria, Other: Travel fees, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Other: Advisory board, Research Funding; Otsuka: Consultancy, Honoraria, Other; Duke University: Research Funding; UpToDate: Patents & Royalties; NINR/NIH: Research Funding; Agios: Consultancy, Honoraria, Other: Advisory board; Travel fees, Speakers Bureau; AbbVie: Consultancy, Honoraria, Other: Advisory board; Travel fees, Speakers Bureau; Heron: Consultancy, Honoraria, Other: advisory board.