Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data.

INTRODUCTION: Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high-quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non-small cell lung cancer (NSCLC) protocols in China, on the risk-benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. METHODS: To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross-dimensional real-world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit-risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. DISCUSSION: This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies.

Revisiting the lung cancer screening eligibility criteria to promote equity for Black individuals.

BACKGROUND: Early detection using low-dose computed tomography reduces lung-cancer-specific mortality by 20% among high-risk individuals. Blacks are less likely than Whites to meet lung cancer screening (LCS) criteria under both the former and the updated United States Preventive Services Task Force (USPSTF) guidelines. The purpose of this study was to assess racial disparities in LCS eligibility and to propose tailored eligibility criteria for Blacks to enable equitable screening rate between Whites and Blacks. METHODS: Data for this study were obtained from the Behavioral Risk Factor Surveillance System (2017-2021). 101,552 subjects were included in the final analysis. By employing a systematic approach, we sought cut-off points at which Blacks were equally likely as Whites to be eligible for LCS. We evaluated the minimum age and smoking pack-years for Blacks while we retained the 2021 USPSTF criteria for Whites. The final decision was based on the minimum Wald's Chi-square statistics. RESULTS: The model we employed identified cut-off points at which Blacks were equally likely as Whites to be eligible for LCS. Retaining the 2021 USPSTF criteria for Whites, the model discovered a new pair of points for Blacks by reducing the minimum age to 43 years and decreasing the cumulative number of cigarettes smoked to 15 pack-years. Based on these cut-off points, we created tailored criteria for Blacks. Under the tailored criteria, Blacks (OR: 1.00; 95 %CI: 0.88-1.14) had the same odds of eligibility for LCS as Whites. The odds of eligibility for LCS by sex under the tailored criteria did not differ significantly for Black men (OR: 1.02; 95 %CI: 0.85-1.24) and Black women (OR: 0.95; 95 %CI: 0.81-1.12) compared to their respective White counterparts. CONCLUSIONS: These tailored criteria for Blacks eliminate the disparities between Blacks and Whites in LCS eligibility. Future studies should test the sensitivity and specificity of these tailored criteria.

[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening]. / Développement d'une solution d'intelligence artificielle pour améliorer le screening en recherche clinique.

INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of an artificial intelligence driven tool (text mining, machine learning, classification…) for the screening and detection of patients, potentially eligible for recruitment in one of the clinical trials open at the "Institut de Cancérologie de Lorraine". METHODS: Computerized clinical data during the first medical consultation among patients managed in an anticancer center over the 2019-2023 period were used to study the performances of an artificial intelligence tool (SAS® Viya). Recall, precision and F1-score were used to determine the artificial intelligence algorithm effectiveness. Time saved on screening was determined by the difference between the time taken using the artificial intelligence-assisted method and that taken using the standard method in clinical trial participant screening. RESULTS: Out of 9876 patients included in the study, the artificial intelligence algorithm obtained the following scores: precision of 96 %, recall of 94 % and a 0.95 F1-score to detect patients with breast cancer (n=2039) and potentially eligible for inclusion in a clinical trial. The screening of 258 potentially eligible patient's files took 20s per file vs. 5min and 6s with standard method. DISCUSSION: This study suggests that artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient screening process, as well as in approaches to feasibility, site selection, and trial selection.

Fabry-specific treatment in Australia: time to align eligibility criteria with international best practices.

BACKGROUND: Disease-specific therapy aims to improve symptoms, stabilise current disease and delay progression in patients with Fabry disease. In Australia, treatment access is subject to eligibility criteria initially established in 2004. Patients and their clinicians question why these criteria have remained unchanged despite significant progress in disease understanding. AIMS: Appraise the clinical quality of the Australian treatment access criteria. METHODS: The Fabry Australia Medical Advisory Committee (N = 6) used the Appraisal of Guidelines for REsearch and Evaluation Global Rating Scale (AGREE II GRS) to assess the clinical quality of the current treatment eligibility criteria. They reviewed the literature, developed 17 clinical statements to help guide reforms of the eligibility criteria and achieved consensus (achievement of ≥75% agreement in the range 5-7 on a 7-point Likert scale) through anonymous voting. The findings were applied to develop proposals for revised classification and treatment initiation criteria. RESULTS: The current treatment eligibility criteria underperformed on the AGREE II GRS. They are pragmatic but out-of-step with contemporary data. Consensus was achieved on all 17 proposed clinical statements. There was strong agreement to differentiate classical male Fabry patients to facilitate timelier access to Fabry-specific treatment. There was also agreement on the value of adopting relevant organ involvement criteria in classical female patients and patients with non-classical disease. CONCLUSIONS: Australian access criteria are misaligned with current clinical evidence. The clinical statements and proposed classification and initiation criteria should prompt discussions to support more equitable access to treatment and better align Australian practice with contemporary evidence and international guidelines.

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.

OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the model's capability to extract criteria entities along with contextual attributes including values, temporality, and modifiers and present the strengths and limitations of this system. MATERIALS AND METHODS: The clinical trial data were acquired from https://ClinicalTrials.gov/. We developed a system, AutoCriteria, which comprises the following modules: preprocessing, knowledge ingestion, prompt modeling based on GPT, postprocessing, and interim evaluation. The final system evaluation was performed, both quantitatively and qualitatively, on 180 manually annotated trials encompassing 9 diseases. RESULTS: AutoCriteria achieves an overall F1 score of 89.42 across all 9 diseases in extracting the criteria entities, with the highest being 95.44 for nonalcoholic steatohepatitis and the lowest of 84.10 for breast cancer. Its overall accuracy is 78.95% in identifying all contextual information across all diseases. Our thematic analysis indicated accurate logic interpretation of criteria as one of the strengths and overlooking/neglecting the main criteria as one of the weaknesses of AutoCriteria. DISCUSSION: AutoCriteria demonstrates strong potential to extract granular eligibility criteria information from trial documents without requiring manual annotations. The prompts developed for AutoCriteria generalize well across different disease areas. Our evaluation suggests that the system handles complex scenarios including multiple arm conditions and logics. CONCLUSION: AutoCriteria currently encompasses a diverse range of diseases and holds potential to extend to more in the future. This signifies a generalizable and scalable solution, poised to address the complexities of clinical trial application in real-world settings.

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

PURPOSE: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility. MATERIALS AND METHODS: Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient. RESULTS: In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation. CONCLUSION: To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

Call to action: overcoming enrollment disparities in cancer clinical trials with modernized eligibility criteria.

Traditional clinical trial eligibility criteria restrict study populations, perpetuating enrollment disparities. We aimed to assess implementation of modernized eligibility criteria guidelines among pancreatic cancer (PC) clinical trials. Interventional PC trials in the United States since January 1, 2014, were identified via clinicaltrials.gov with December 31, 2017, as the transition for pre- and postguidance eras. Trials were assessed for guideline compliance and compared using Fisher exact test. In total, 198 trials were identified: 86 (43.4%) were pre- and 112 (56.6%) postguidance era. Improvements were seen in allowing patients with history of HIV (8.6% vs 43.8%; P < .0001), prior cancer (57.0% vs 72.3%; P = .034), or concurrent and/or stable cancer (2.1% vs 31.1%; P < .0001) to participate. Most (>95%) trials were compliant with laboratory reference ranges, QT interval corrected for heart rate (QTc) cutoffs, and rationalizing excluding prior therapies both pre- and postguidance eras. However, overall compliance with modernized criteria remains poor. We advocate for stakeholders to update protocols and scrutinize traditionally restrictive eligibility criteria.

Text Classification of Cancer Clinical Trial Eligibility Criteria.

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility are stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yield the highest average performance across all criteria.

Implementing Modernized Eligibility Criteria in US National Cancer Institute Clinical Trials.

In 2018, the Cancer Therapy Evaluation Program (CTEP) at the US National Cancer Institute published new protocol template language that focused on organ function and prior and concurrent cancers in an effort to modernize eligibility criteria for cancer treatment trials. We conducted an analysis of CTEP-supported trials to evaluate the uptake and incorporation of the new language. The analysis included evaluation of 122 protocols approved in the years 2018-2020 for inclusion of the modernized eligibility criteria and consistency with new protocol template language related to 7 major eligibility criteria. These were cardiac function, liver function, kidney function, HIV status, prior and/or concurrent malignancies, treated and/or stable brain metastasis, and new and/or progressive brain metastases. Overall, CTEP trials evaluated in this period demonstrated that eligibility criteria were implemented to a relatively high degree ranging from a low of 54.1% for prior and/or concurrent malignancies to a high of 93.4% for eligibility criteria related to HIV infection. The findings demonstrate that modernized eligibility criteria can be successfully implemented but that consistent implementation requires sustained focused effort. As a result of these findings, CTEP began a new initiative in January 2022 that incorporates a specific review of eligibility criteria for new protocols to promote and improve consistency with the modernization effort.

A New Approach to Simplifying and Harmonizing Cancer Clinical Trials-Standardizing Eligibility Criteria.

Importance: Clinical trial sponsors rely on eligibility criteria to control the characteristics of patients in their studies, promote the safety of participants, and optimize the interpretation of results. However, in recent years, complex and often overly restrictive inclusion and exclusion criteria have created substantial barriers to patient access to novel therapies, hindered trial recruitment and completion, and limited generalizability of trial results. A LUNGevity Foundation working group developed a framework for lung cancer clinical trial eligibility criteria. The goals of this framework are to (1) simplify eligibility criteria, (2) facilitate stakeholders' (patients, clinicians, and sponsors) search for appropriate trials, and (3) harmonize trial populations to support intertrial comparisons of treatment effects. Observations: Clinicians and representatives from the pharmaceutical industry, the National Cancer Institute, the US Food and Drug Administration (FDA), the European Medicines Agency, and the LUNGevity Foundation undertook a process to identify and prioritize key items for inclusion in trial eligibility criteria. The group generated a prioritized library of terms to guide investigators and sponsors in the design of first-line, advanced non-small cell lung cancer clinical trials intended to support marketing application. These recommendations address disease stage and histologic features, enrollment biomarkers, performance status, organ function, brain metastases, and comorbidities. This effort forms the basis for a forthcoming FDA draft guidance for industry. Conclusions and Relevance: As an initial step, the recommended cross-trial standardization of eligibility criteria may harmonize trial populations. Going forward, by connecting diverse stakeholders and providing formal opportunity for public input, the emerging FDA draft guidance may also provide an opportunity to revise and simplify long-standing approaches to trial eligibility. This work serves as a prototype for similar efforts now underway for other cancers.

Pancreatic Cancer Clinical Treatment Trials Accrual: A Closer Look at Participation Rates.

OBJECTIVES: Low rates of participation in cancer clinical trials are commonly reported, raising concerns about missed opportunities to engage patients in treatment trials. We reviewed eligibility for and enrollment in pancreatic cancer clinical trials for patients seen at a National Cancer Institute (NCI)-designated cancer center during 1 year, to calculate participation rates with detailed information to determine the best-case participation rate. MATERIALS AND METHODS: This retrospective cohort study used the Abramson Cancer Center Cancer Registry, clinical trial protocols, and electronic medical records (EMRs) to determine eligibility for all available pancreatic cancer clinical trials. Patient characteristics and reasons for ineligibility were abstracted from EMRs. We then computed participation rates based on enrollment in trials using EMR and clinical trials monitoring data. RESULTS: Of 233 new pancreatic cancer patients in 2014, 47 or 20% enrolled in a clinical trial (enrollment fraction). According to the EMR, of the 66 patients who were eligible for a trial, 54 (82% of eligible) accepted and 47 (71% of eligible) ultimately enrolled in a trial, 8 (12% of eligible) declined, and 4 (6% of eligible) had no record of patient decision. Enrollment in a trial by both the EMR and clinical trials database was confirmed for 71% of eligible patients. CONCLUSIONS: This study reveals that 71% of newly diagnosed pancreatic cancer patients who were eligible for a trial were enrolled in a treatment trial. We contend that in-depth analysis, rather than enrollment fraction, should be used to inform the gap between actual participation and optimal clinical trial participation for cancer patients.

Improvement Initiative to Develop and Implement a Tool for Detecting Drug-Drug Interactions During Oncology Clinical Trial Enrollment Eligibility Screening.

OBJECTIVES: Screening subjects for drug-drug interactions (DDIs) before enrollment in oncology clinical trials is integral to ensuring safety, but standard procedures or tools are not readily available to screen DDI in this setting. Our objectives were to develop a DDI screening tool for use during oncology clinical trial enrollment and to test usability in single-center and multicenter pilot studies. METHODS: A multistage approach was used for this quality improvement intervention. Semistructured interviews with individuals responsible for DDI screening were conducted to develop a prototype tool. The tool was used for screening DDI in subjects enrolling in National Clinical Trials Network trials of commercially available agents during a single-center 3-month pilot. Improvements were made, and a 3-month multicenter pilot was conducted at volunteer SWOG Cancer Research Network sites. Participants were surveyed to determine tool usability and efficiency. RESULTS: A tool was developed from semistructured interviews. A critical feature was reporting which medications had specific pharmacokinetic and pharmacodynamic characteristics including transporter and cytochrome P450 substrates, inhibitors, or inducers and QT prolongation. In the 12-site study, average (SD) DDI screening time for each patient decreased by 15.7 (10.2) minutes (range, 3-35 minutes; P < 0.001). Users reported the tool highly usable, with >90% agreeing with all positive usability characterizations and disagreeing with all negative complexity characterizations. CONCLUSIONS: A DDI screening tool for oncology clinical trial enrollment was created and its usability confirmed. Further testing with more diverse investigator sites and study drugs during eligibility screening is warranted to improve safety and data accuracy within clinical trials.

Comorbidity Assessment Is Uneven Across Veterans Health Administration and Medicare for the Same Patient: Implications for Risk Adjustment.

OBJECTIVE: Compare comorbidity identification in Medicare and Veterans Health Administration (VA) data for the purposes of risk adjustment. DATA SOURCES: Analysis of Medicare and VA datasets for dually-enrolled Veterans receiving care in both settings, fiscal years 2010-2014. STUDY DESIGN: A retrospective analysis of administrative data for a national sample of cancer decedents. DATA EXTRACTION METHODS: Comorbidities were evaluated using Elixhauser and Charlson coding algorithms. PRINCIPAL FINDINGS: Clinical comorbidities were more likely to be recorded in Medicare than in VA datasets. Of 42 comorbidities, 36 (86%) were recorded at a different frequency. For example, congestive heart failure was recorded for 22.0% of patients in Medicare data and for 11.3% of patients in VA data (P<0.001). CONCLUSION: There are large differences in comorbidity assessment across VA and Medicare administrative data for the same patient, posing challenges for risk adjustment.

The implementation and utility of patient screening logs in a multicentre randomised controlled oncology trial.

BACKGROUND: The utility of patient screening logs and their impact on improving trial recruitment rates are unclear. We conducted a retrospective exploratory analysis of screening data collected within a multicentre randomised controlled trial investigating chemotherapy for upper tract urothelial carcinoma. METHODS: Participating centres maintained a record of patients meeting basic screening criteria stipulated in the trial protocol, submitting logs regularly to the clinical trial coordinating centre (CTC). Sites recorded the number of patients ineligible, not approached, declined and randomised. The CTC monitored proportions of eligible patients, approach rate (proportion of eligible patients approached) and acceptance rate (proportion recruited of those approached). Data were retrospectively analysed to identify patterns of screening activity and correlation with recruitment. RESULTS: Data were collected between May 2012 and August 2016, during which time 71 sites were activated-a recruitment period of 2768 centre months. A total of 1138 patients were reported on screening logs, with 2300 requests for logs sent by the CTC and 47% of expected logs received. A total of 758 patients were reported as ineligible, 36 eligible patients were not approached and 207 declined trial participation. The approach rate was 91% (344/380), and the acceptance rate was 40% (137/344); these rates remained consistent throughout the data collection. The main reason patients provided for declining (99/207, 48%) was not wanting to receive chemotherapy. There was a moderately strong correlation (r = 0.47) between the number reported on screening logs and the number recruited per site. Considerable variation in data between centres was observed, and 54/191 trial participants (28%) enrolled during this period were not reported on logs. CONCLUSIONS: Central collection of screening logs can identify reasons for patients declining trial participation and help monitor trial activity at sites; however, obtaining complete data can be challenging. There was a correlation between the number of patients reported on logs and recruitment; however, this was likely confounded by sites' available patient population. The use of screening logs may not be appropriate for all trials, and their use should be carefully considered in relation to the associated workload. No evidence was found that central collection of screening logs improved recruitment rates in this study, and their continued use warrants further investigation. TRIAL REGISTRATION: ISRCTN98387754 . Registered on 31 January 2012.

Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

PURPOSE: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials. PATIENTS AND METHODS: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS: Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION: In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.

Lymphoma Study Titles on ClinicalTrials.gov Lack Details Necessary for Study Identification.

BACKGROUND: ClinicalTrials.gov is used by clinicians and patients to identify clinical trials. We assessed the ease with which users could identify relevant trials related to lymphoma using the short and official titles. We hypothesized that lymphoma titles frequently lack important information. MATERIALS AND METHODS: We performed 2 searches on ClinicalTrials.gov. The first search was performed before June 2017, when ClinicalTrials.gov underwent updates to improve usability. The second was performed after 2017. We assessed whether the short and official titles of each trial provided information on the study phase, eligible disease status, lymphoma histologic subtype, study intervention, primary objective, and the presence of randomization and placebo control. RESULTS: Of the pre-overhaul lymphoma trials, the official versus short titles included information regarding study intervention (99% vs. 96%), study phase (82% vs. 14%), lymphoma histologic subtype (78% vs. 72%), disease status (46% vs. 35%), randomization (13% vs. 2%), presence of placebo (6% vs. 2%), and primary objective (38% vs. 26%). Of the post-overhaul trials, the official versus short titles included information regarding study intervention (97% vs. 96%), lymphoma histologic subtype (83% vs. 78%), study phase (78% vs. 8%), disease status (64% vs. 50%), primary objective (38% vs. 23%), presence of placebo (11% vs. 0%), and randomization (18% vs. 0%). CONCLUSION: The official titles were more informative than were the short titles on ClinicalTrials.gov. However, the short and official titles both often lacked the basic information needed to understand a clinical trial. This has persisted despite updates to the platform. These results highlight the need for standardization of the format and content included in study titles.

Singapore chapter of rheumatologists' updated consensus statement on the eligibility for government subsidization of biologic and targeted therapy for the treatment of psoriatic arthritis.

AIM: There have been major advances in biologic treatment options for psoriatic arthritis (PsA) since the publication of the 2015 consensus recommendations by the Chapter of Rheumatologists, College of Physicians, Academy of Medicine, Singapore, for government-assisted funding, thus warranting a revision of this guideline. METHODS: Recent trials and nine published guidelines on the use of biologic therapy for PsA were reviewed. Based on the synthesized evidence, a task force panel (TFP), consisting of 10 practicing rheumatologists in Singapore, rated the statements pertaining to the use of biologic therapy, using a modified Delphi approach. Consensus was obtained if >70% agreed on a statement. RESULTS: The TFP agreed on 10 recommendations pertaining to the initiation, choice and continuation of biologic therapy. A biologic is indicated in patients with PsA: (a) with at least three swollen and tender joints, digits or entheses; and (b) who have failed at least two conventional synthetic disease-modifying anti-rheumatic drug (csDMARD) strategies for a minimum of 3 months each. Any approved drug class including tumor necrosis factor inhibitors, interleukin-17 inhibitors (IL-17i), IL-12/23i or targeted synthetic DMARDs may be considered as first-line treatment, and continued only if a response is achieved by 6 months. CONCLUSION: These recommendations developed through a formal consensus method may be useful to guide funding considerations for appropriate and equitable use of biologic therapy for eligible patients with PsA.