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Sastry RA, Setty A, Liu DD, Zheng B, Ali R, Weil RJ, Roye GD, Doberstein CE, Oyelese AA, Niu T, Gokaslan ZL, Telfeian AE. Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions. PLoS One 2024; 19:e0303519. [PMID: 38723044 PMCID: PMC11081267 DOI: 10.1371/journal.pone.0303519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/04/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVE To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging. MATERIALS AND METHODS A training and testing dataset of reports of 979 CT or MRI scans of the brain for patients admitted to the neurosurgery service of a single hospital in June 2021 or to the Emergency Department between July 1-8, 2021, was identified. A variety of machine learning and deep learning algorithms utilizing natural language processing were trained on the training set (84% of the total cohort) and tested on the remaining images. A subset comparison cohort (n = 76) was then assessed to compare output of the best algorithm against real-life inpatient documentation. RESULTS For "brain compression", a random forest classifier outperformed other candidate algorithms with an accuracy of 0.81 and area under the curve of 0.90 in the testing dataset. For "brain edema", a random forest classifier again outperformed other candidate algorithms with an accuracy of 0.92 and AUC of 0.94 in the testing dataset. In the provider comparison dataset, for "brain compression," the random forest algorithm demonstrated better accuracy (0.76 vs 0.70) and sensitivity (0.73 vs 0.43) than provider documentation. For "brain edema," the algorithm again demonstrated better accuracy (0.92 vs 0.84) and AUC (0.45 vs 0.09) than provider documentation. DISCUSSION A natural language processing-based machine learning algorithm can reliably and reproducibly identify selected common neurosurgical comorbidities from radiology reports. CONCLUSION This result may justify the use of machine learning-based decision support to augment provider documentation.
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Affiliation(s)
- Rahul A. Sastry
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Aayush Setty
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
- Department of Computer Science, Brown University, Providence, RI, United States of America
| | - David D. Liu
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Bryan Zheng
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Rohaid Ali
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Robert J. Weil
- Department of Neurosurgery, Brain & Spine, Southcoast Health, Dartmouth, MA, United States of America
| | - G. Dean Roye
- Department of Surgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Curtis E. Doberstein
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Adetokunbo A. Oyelese
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Tianyi Niu
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Ziya L. Gokaslan
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
| | - Albert E. Telfeian
- Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America
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Ganga A, Leary OP, Setty A, Xi K, Telfeian AE, Oyelese AA, Niu T, Camara-Quintana JQ, Gokaslan ZL, Zadnik Sullivan P, Fridley JS. Optimizing surgical management of facet cysts of the lumbar spine: systematic review, meta-analysis, and local case series of 1251 patients. J Neurosurg Spine 2023; 39:793-806. [PMID: 37728373 DOI: 10.3171/2023.6.spine23404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/22/2023] [Indexed: 09/21/2023]
Abstract
OBJECTIVE Lumbar facet cysts (LFCs) can cause neurological dysfunction and intractable pain. Surgery is the current standard of care for patients in whom conservative therapy fails, those with neurological deficits, and those with evidence of spinal instability. No study to date has comprehensively examined surgical outcomes comparing the multiple surgical treatment options for LFCs. Therefore, the authors aimed to perform a combined analysis of cases both in the literature and of patients at a single institution to compare the outcomes of various surgical treatment options for LFC. METHODS The authors performed a literature review in accordance with PRISMA guidelines and meta-analysis of the PubMed, Embase, and Cochrane Library databases and reviewed all studies from database inception published until February 3, 2023. Studies that did not contain 3 or more cases, clearly specify follow-up durations longer than 6 months, or present new cases were excluded. Bias was evaluated using Cochrane Collaboration's Risk of Bias in Nonrandomised Studies-of Interventions (ROBINS-I). The authors also reviewed their own local institutional case series from 2015 to 2020. Primary outcomes were same-level cyst recurrence, same-level revision surgery, and perioperative complications. ANOVA, common and random-effects modeling, and Wald testing were used to compare treatment groups. RESULTS A total of 1251 patients were identified from both the published literature (29 articles, n = 1143) and the authors' institution (n = 108). Patients were sorted into 5 treatment groups: open cyst resection (OCR; n = 720), tubular cyst resection (TCR; n = 166), cyst resection with arthrodesis (CRA; n = 165), endoscopic cyst resection (ECR; n = 113), and percutaneous cyst rupture (PCR; n = 87), with OCR being the analysis reference group. The PCR group had significantly lower complication rates (p = 0.004), higher recurrence rates (p < 0.001), and higher revision surgery rates (p = 0.001) compared with the OCR group. Patients receiving TCR (3.01%, p = 0.021) and CRA (0.0%, p < 0.001) had significantly lower recurrence rates compared with those undergoing OCR (6.36%). The CRA group (6.67%) also had significantly lower rates of revision surgery compared with the OCR group (11.3%, p = 0.037). CONCLUSIONS While PCR is less invasive, it may have high rates of same-level recurrence and revision surgery. Recurrence and revision rates for modalities such as ECR were not significantly different from those of OCR. While concomitant arthrodesis is more invasive, it might lead to lower recurrence rates and lower rates of subsequent revision surgery. Given the limitations of our case series and literature review, prospective, randomized studies are needed.
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Affiliation(s)
- Arjun Ganga
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Owen P Leary
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Aayush Setty
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Kevin Xi
- 2Brown University School of Public Health, Providence, Rhode Island
| | - Albert E Telfeian
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Adetokunbo A Oyelese
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Tianyi Niu
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | | | - Ziya L Gokaslan
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Patricia Zadnik Sullivan
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
| | - Jared S Fridley
- 1Department of Neurosurgery, Warren Alpert School of Medicine, Brown University, Providence; and
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Tillett W, Coates L, Kishimoto M, Setty A, Gao T, Lippe R, Helliwell P. AB0904 Evaluating Numeric Rating Scale Versions of the 3 and 4 Visual Analog Scale (3/4-VAS) Composite Measures in Patients with Active Psoriatic Arthritis from the SELECT-PsA Program. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.2498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundThe multifaceted nature of psoriatic arthritis (PsA) can make it challenging to evaluate treatment targets and disease activity. Moreover, most existing assessment tools are time-consuming and not always feasible in routine clinical care, indicating a need for new disease measures that are easy to perform and calculate. Composite measures using 3-visual analog scale (VAS; physician’s global assessment, patient’s global assessment, and skin) or 4-VAS (physician’s global assessment, joints, skin, and pain) have been proposed as simpler alternatives.1 Given potential advantages of numeric rating scales (NRS) over VAS, we here adapted 3/4-VAS for use with NRS components and tested its validity via post hoc analysis of the upadacitinib (UPA) SELECT-PsA program.ObjectivesEvaluate the ability of 3/4-NRS scores to assess treatment response in SELECT-PsA 1 and 2, as well as the correlation of 3/4-NRS with other common disease activity measures.MethodsData are from the SELECT-PsA 1 and 2 phase 3 trials in patients with prior inadequate response or intolerance to ≥1 non-biologic DMARD or ≥1 biologic DMARD, respectively. In both trials, patients received once daily UPA 15 mg, UPA 30 mg, or placebo (PBO); SELECT-PsA 1 also included the active comparator adalimumab (ADA) 40 mg every other week (wk). 3-NRS scores were determined using the mean of SAPS questions 1–10, physician’s global assessment of disease activity, and patient’s global assessment of disease activity; 4-NRS scores were determined using the mean of SAPS questions 1–10, physician’s global assessment of disease activity, patient’s assessment of pain, and BASDAI question 3 related to joint pain and swelling. The 3/4-NRS scale ranges from 0 (no disease activity) to 10 (severe activity). 3/4-NRS and cDAPSA (DAPSA without the CRP component) were assessed at all available visits through wk 56. Correlations between 3/4-NRS with PsA disease activity score (PASDAS), routine assessment of patient index data 3 (RAPID3), DAPSA, cDAPSA, and other disease activity measures were determined by nonparametric Spearman rank correlation coefficient for UPA 15 mg patients from both trials and ADA for SELECT-PsA 1. All data are shown as observed; nominal p-values are provided throughout.ResultsA total of 1281 and 423 patients were included from SELECT-PsA 1 and 2, respectively. For both cDAPSA and 3/4-NRS scores, patients receiving UPA 15 mg showed clear numerical improvements compared with PBO at wk 24 in both trials (Table 1). 3/4-NRS scores were highly correlated with RAPID3 and PASDAS measures (r >0.6, P <0.0001) for UPA 15 mg patients at baseline (Figure 1). Moderate correlations were observed between 3/4-NRS and DAPSA/cDAPSA (r = ~0.4, P <0.0001), as well as physical function (HAQ-DI) and quality of life measures (SF-36). Nominally significant but weaker correlations were detected for joints, skin, and other disease activity assessments. Similar overall results were observed for patients receiving ADA.Table 1.3/4-NRS and cDAPSA Disease Activity Scores at Week 24 and 56 (As Observed)SELECT-PsA 1Wk 24Wk 56Mean score [n]PBOUPA 15 mgADAUPA 15 mgADA3-NRS3.7 [370]2.2 [398]2.4 [398]1.8 [372]2.0 [359]4-NRS3.8 [367]2.3 [392]2.6 [395]1.9 [367]2.2 [357]cDAPSA24.0 [372]14.9 [399]16.6 [400]10.2 [372]11.3 [358]SELECT-PsA 2Wk 24Wk 56Mean score [n]PBOUPA 15 mgUPA 15 mg3-NRS4.7 [172]2.9 [190]2.4 [164]4-NRS4.9 [170]3.1 [188]2.7 [162]cDAPSA37.1 [172]21.6 [190]15.3 [166]3/4-NRS ranges from 0–10; cDAPSA ranges from 0–154. Lower scores indicate decreased disease activity.Conclusion3/4-NRS was able to successfully discriminate between PBO and therapeutic groups in SELECT-PsA 1 and 2. 3/4-NRS scores correlated well with other clinical and patient reported outcome measures, including those focused on joints (DAPSA) or multiple manifestations (PASDAS), supporting 3/4-NRS as a viable and easy to use tool in daily clinical practice.References[1]Tillett W, et al. J Rheumatol 2021; 201675.AcknowledgementsAbbVie and the authors thank the patients, study sites, and investigators who participated in these clinical trials. AbbVie funded these studies and participated in the study design, research, analysis, data collection, interpretation of data, reviewing, and approval of the publication. All authors had access to relevant data and participated in the drafting, review, and approval of this publication. No honoraria or payments were made for authorship. Medical writing support was provided by Matthew Eckwahl, PhD, of AbbVie.Disclosure of InterestsWilliam Tillett Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, MSD, Novartis, Pfizer, and UCB., Laura Coates Speakers bureau: AbbVie, Amgen, Biogen, BMS, Boehringer Ingelheim, Celgene, Galapagos, Gilead, GSK, Janssen, Lilly, Medac, Novartis, Pfizer, Serac, and UCB., Consultant of: AbbVie, Amgen, Biogen, BMS, Boehringer Ingelheim, Celgene, Galapagos, Gilead, GSK, Janssen, Lilly, Medac, Novartis, Pfizer, Serac, and UCB., Mitsumasa Kishimoto Consultant of: AbbVie, Amgen-Astellas BioPharma, Asahi-Kasei Pharma, Astellas, Ayumi Pharma, BMS, Celgene, Chugai, Daiichi-Sankyo, Eisai, Eli Lilly, Gilead, Janssen, Kyowa Kirin, Novartis, Ono Pharma, Pfizer, Tanabe-Mitsubishi, Teijin Pharma, and UCB Pharma., Arathi Setty Shareholder of: AbbVie, Employee of: AbbVie, Tianming Gao Shareholder of: AbbVie, Employee of: AbbVie, Ralph Lippe Shareholder of: AbbVie, Employee of: AbbVie, Philip Helliwell Paid instructor for: Educational services: Abbvie, Amgen, Novartis, Janssen, Consultant of: Eli Lilly
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Mease PJ, Setty A, Papp K, Van den Bosch F, Tsuji S, Keiserman M, Bu X, Chen L, Mccaskill R, Mcdearmon-Blondell E, Wung P, Tillett W. POS1041 LONG-TERM EFFICACY AND SAFETY OF UPADACITINIB IN PATIENTS WITH PSORIATIC ARTHRITIS REFRACTORY TO BIOLOGIC THERAPIES: 2-YEAR RESULTS FROM THE PHASE 3 SELECT-PsA 2 STUDY. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundUpadacitinib (UPA), an oral Janus kinase (JAK) inhibitor, demonstrated efficacy and safety in patients (pts) with psoriatic arthritis (PsA) and prior inadequate response or intolerance to ≥1 biologic disease-modifying antirheumatic drug (bDMARD) at week (wk) 56 in the phase 3 SELECT-PsA 2 study.1ObjectivesTo evaluate the efficacy and safety of UPA at wk 104 from the ongoing long-term extension of SELECT-PsA 2.MethodsPts were randomized to UPA 15 mg (UPA15), UPA 30 mg (UPA30), or placebo (PBO) for 24 wks; PBO pts were then switched to UPA15 or UPA30. For continuous UPA treatment groups, efficacy endpoints at wk 104 were analyzed using non-responder imputation (NRI) and as observed (AO) (binary endpoints) or mixed-effect model repeated measures (MMRM) and AO (continuous endpoints). Treatment-emergent adverse events (TEAEs) were summarized for pts who received ≥1 dose of study drug using visit-based cut-off at wk 104.ResultsA total of 641 pts received ≥1 dose of study drug. At wk 104, 38.4% of all patients had discontinued study drug, with the highest discontinuation observed in patients randomized to PBO at baseline (all PBO: 46.7%). The most common reasons for discontinuation were lack of efficacy (UPA15: 12.3%, UPA30: 8.7%, all PBO: 21.7%) and adverse event (UPA15: 10.9%, UPA30: 13.3%, all PBO: 12.7%). The proportion of UPA pts that achieved ACR20/50/70, MDA, PASI75/90/100, and resolution of dactylitis and enthesitis were generally similar, or further improved, with 104 wks of treatment vs 56 wks1 (Table 1). Similarly, mean change from baseline in HAQ-DI, patient’s assessment of pain, BASDAI, and ASDAS was improved with UPA treatment. At 104 wks of therapy, clinical responses were largely similar with UPA15 and UPA30. Generally, safety data at wk 104 (Figure 1) were consistent with that reported at wk 56.1 Rates of serious infection, herpes zoster, hepatic disorder, anemia, neutropenia, lymphopenia, and CPK elevation remained numerically higher with UPA30 vs UPA15, while rates of malignancies, MACE, and VTE were similar for both UPA groups. One death was reported with UPA15 (unexplained due to lack of information; however, the patient had recently been diagnosed with ovarian cancer) and 2 with UPA30 (pancytopenia and COVID-19 pneumonia).Table 1.Efficacy Endpoints at Week 104EndpointUPA15 (n=211)UPA30 (n=218)Proportion of Pts (%)aNRIAONRIAOACR2055.580.354.681.8ACR5044.562.939.959.4ACR7023.232.221.631.5Minimal Disease Activity (MDA)29.441.333.949.3PASI75b47.769.852.781.1PASI90b37.755.244.367.8PASI100b23.135.435.955.6Resolution of enthesitis by LEIc39.867.837.568.4Resolution of dactylitis by LDId54.597.452.096.9Change from BLeMMRMAOMMRMAOHealth Assessment Questionnaire - Disability Index (HAQ-DI)-0.36-0.39-0.50-0.53Patient’s assessment of pain (numeric rating scale)-2.7-3.0-2.9-3.1Bath Ankylosing Spondylitis Disease Activity Index (BASDAI)f-2.6-3.0-2.6-2.9Ankylosing Spondylitis Disease Activity Score (ASDAS)f-1.4-1.7-1.3-1.5ACR20/50/70, ≥20%/50%/70% improvement in American College of Rheumatology criteria; AO, as observed; BL, baseline; LDI, Leeds Dactylitis Index; LEI, Leeds Enthesitis Index; MMRM, mixed effect model repeated measurement; NRI, non-responder imputation; PASI75/90/100, ≥75%/90%/100% improvement in Psoriasis Area and Severity Index; pts, patients; UPA, upadacitinib.aData shown as NRI and AO for binary endpoints.bFor pts with psoriasis affecting ≥3% of body surface area at BL.cFor pts with LEI >0 at BL; resolution LEI=0.dFor pts with LDI >0 at BL; resolution LDI=0.eData shown as MMRM (LS mean) and AO (mean) for continuous endpoints.fFor pts with psoriatic spondylitis at BL.ConclusionIn PsA pts with prior inadequate response or intolerance to ≥1 bDMARD, clinical responses were maintained with UPA15 and UPA30 up to 2 years of treatment. No new safety signals were identified in this long-term extension.References[1]Mease PJ, et al. Rheumatol Ther. 2021;8:903-19.AcknowledgementsAbbVie and the authors thank the patients, study sites, and investigators who participated in this clinical trial (NCT03104374). AbbVie funded this study and participated in the study design, research, analysis, data collection, interpretation of data, reviewing, and approval of the publication. All authors had access to relevant data and participated in the drafting, review, and approval of this publication. No honoraria or payments were made for authorship. Medical writing support was provided by Monica R.P. Elmore, PhD of AbbVie.Disclosure of InterestsPhilip J Mease Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squib, Celgene, Eli Lilly, Galapagos, Genentech, Gilead, GSK, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squib, Celgene, Eli Lilly, Galapagos, Genentech, Gilead, GSK, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squib, Celgene, Eli Lilly, Galapagos, Genentech, Gilead, GSK, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Arathi Setty Shareholder of: Employee of AbbVie and may hold stock options, Employee of: Employee of AbbVie, Kim Papp Speakers bureau: AbbVie, Akros, Allergan, Almirall, Amgen, Bausch Health, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Dermavant, Dermira, Eli Lilly, Galderma, Genentech/Roche, Janssen, Kyowa Kirin, LEO, Meiji, MSD, Novartis, Pfizer, Regeneron, Sanofi Genzyme, Sienna Pharmaceuticals, Sun Pharma, Takeda, UCB, and Valeant, Consultant of: AbbVie, Akros, Allergan, Almirall, Amgen, Arcutis, Avillion, Bausch Health, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Dermavant, Dermira, Eli Lilly, Galderma, Genentech/Roche, GSK, Janssen, Kyowa Kirin, LEO, Meiji, MSD, Novartis, Pfizer, Regeneron, Sanofi Genzyme, Sienna Pharmaceuticals, Sun Pharma, Takeda, UCB, and Valeant, Grant/research support from: AbbVie, Akros, Allergan, Almirall, Amgen, Arcutis, Avillion, Bausch Health, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Dermavant, Dermira, Eli Lilly, Galderma, Genentech/Roche, GSK, Janssen, Kyowa Kirin, LEO, Meiji, MSD, Novartis, Pfizer, Regeneron, Sanofi Genzyme, Sienna Pharmaceuticals, Sun Pharma, Takeda, UCB, and Valeant, Filip van den Bosch Speakers bureau: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Merck, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Merck, Novartis, Pfizer, and UCB, Shigeyoshi Tsuji Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, and UCB, Consultant of: AbbVie, Eli Lilly, Janssen, Novartis, and UCB, Grant/research support from: AbbVie, Eli Lilly, Janssen, Novartis, and UCB, MAURO KEISERMAN Speakers bureau: AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Xianwei Bu Shareholder of: Employee of AbbVie and may hold stock options, Employee of: Employee of AbbVie, Liang Chen Shareholder of: Employee of AbbVie and may hold stock options, Employee of: Employee of AbbVie, Reva McCaskill Shareholder of: Employee of AbbVie and may hold stock options, Employee of: Employee of AbbVie, Erin McDearmon-Blondell Shareholder of: Employee of AbbVie and may hold stock options, Employee of: Employee of AbbVie, Peter Wung Shareholder of: Employee of AbbVie and may hold stock options, Employee of: Employee of AbbVie, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, MSD, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, and Janssen
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