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  string(2053) "Background: As the emergence of novel targeted therapeutic options, monitoring disease activity and predicting therapeutic efficacy has become essential processes in CD management. However, longitudinal dynamics of disease activity have not been fully understood.

Objective: This study aims to explore distinct trajectories of disease activity within one year of targeted therapy in CD patients, so as to provide evidence for disease monitoring and personalized medicine. The scientific hypothesis of this study is that the trajectories of major predictors could help to monitor disease courses and predict different therapeutic outcomes in CD.

Study design: This is a post-hoc analysis study including data from one or more of the seven clinical trials (VERSIFY, UNITI-1 , UNITI-2 , IM-UNITI , EXTEND, U-EXCEL,and U-EXCEED). The major predictors include dynamics of disease activity (including clinical activity, patient-report outcomes, biomarker and endoscopic activity) within follow-up time.

Participants: Moderate-to-severe CD patients with at least three time of disease activity assessment would be included. Participants who meet any of the following criteria are not eligible for study inclusion: lacking data of corresponding predictors during induction therapy; having concomitant intestinal infection disease when assessing predictors after induction therapy.

Main Outcome Measure(s): Outcomes include therapeutic efficacy, such as clinical response, clinical remission, mucosal healing, endoscopic improvement, patient-reported outcomes etc.) at the end of maintenance treatment.

Statistical analysis: The latent class growth mixed model is performed to fit the trajectory of disease activity dynamic trajectory. Cross-lagged structural equation models are used to explore temporal relationships of the predictors. Multivariate logistic regression is conducted to adjust potential confounders and to analyze the association of candidate predictors and outcomes. " ["project_brief_bg"]=> string(909) "Crohn's disease(CD) is a chronic, recurrent inflammatory bowel disease. Recent years have witnessed a significant surge in the development of targeted therapies, which has expanded the therapeutic options for CD, such as adalimumab, ustekinumab, infliximab, and vedolizumab . Timely monitoring disease activity and predicting therapeutic response are vital processes during biologics and small molecules, especially in the initial year of treatment. However, for CD patients with targeted therapy, relatively limited studies describing trajectories of disease course have been reported. Therefore, in order to investigate the role of disease activity dynamic trajectories in therapeutic efficacy monitoring and prognosis predicting, we will perform post-hoc analysis based on one or more of the seven randomized clinical trials, including VERSIFY, UNITI-1 , UNITI-2 , IM-UNITI, EXTEND, U-EXCEL, and U-EXCEED ." ["project_specific_aims"]=> string(369) "This study aims to explore distinct trajectories of disease activity within one year of targeted therapy in CD patients, so as to provide evidence for disease monitoring and personalized medicine. The scientific hypothesis of this study is that the trajectories of major predictors could help to monitor disease courses and predict different therapeutic outcomes in CD." ["project_study_design"]=> array(2) { ["value"]=> string(14) "indiv_trial_an" ["label"]=> string(25) "Individual trial analysis" } ["project_purposes"]=> array(1) { [0]=> array(2) { ["value"]=> string(50) "research_on_clinical_prediction_or_risk_prediction" ["label"]=> string(50) "Research on clinical prediction or risk prediction" } } ["project_software_used"]=> array(2) { ["value"]=> string(7) "rstudio" ["label"]=> string(7) "RStudio" } ["project_research_methods"]=> string(643) "Participants from one or more clinical trials (UNITI-1 , UNITI-2, and IM-UNITI from the YODA Project; VERSIFY, EXTEND, U-EXCEL, and U-EXCEED from the Vivli Project) will be included in the study as either a development cohort for trajectory fitting or a external validation cohort. Moderate-to-severe CD patients with at least three time of disease activity assessment would be included. Participants who meet any of the following criteria are not eligible for study inclusion: lacking data of corresponding predictors during induction therapy; having concomitant intestinal infection disease when assessing predictors after induction therapy." ["project_main_outcome_measure"]=> string(641) "Outcomes include therapeutic efficacy (such as clinical response, clinical remission, mucosal healing, endoscopic response, patient-reported outcomes etc.) at the end of maintenance treatment, as well as colorectal resection during long-term follow-up.
Clinical response is defined as a decrease from baseline in the CDAI ≥100 points or a CDAI score <150. And clinical remission is defined as CDAI<150. As for endoscopic outcomes, endoscopic response is defined as Simple Endoscopic Score for CD(SES-CD) decrease ≥50% from baseline, while mucosal healing is defined as SES-CD 20 points and IBDQ ≥170 points, respectively." ["project_main_predictor_indep"]=> string(420) "The major predictors include clinical activity (e.g., CDAI), patient-reported outcomes(e.g., patient-reported outcome 2), serum and fecal biomarkers (e.g., C-reactive protein, hemoglobin, albumin, and fecal calprotectin), endoscopic scores, patient report outcomes (e.g., IBDQ scores), and their long-term trajectories. They will be collected during one-year targeted treatment and before the end of maintenance therapy." ["project_other_variables_interest"]=> string(280) "Other variables of interest include baseline characteristics (e.g., gender, age, disease duration, body mass index, medication history, treatment allocation and concomitant therapy) and baseline disease activity evaluation (e.g., CDAI, fecal calprotectin and C-reactive protein). " ["project_stat_analysis_plan"]=> string(1347) "Continuous and categorical variables are described as median (interquartile range) or frequency (percentage), respectively. The latent class growth mixed model (LCGMM) is performed to fit the trajectory of disease activity dynamic trajectory. LCGMM is a validated approach used to analyze longitudinal data and identify subgroups with distinct trajectories,which has been applied in various diseases. Based on lcmm package in R software, LCGMM usually uses linear, quadratic, or cubic polynomial functions with different class numbers ranging from 2 to 5 for identifying subgroups with distinct trajectories. The optimal trajectory was selected based on (1) the lowest Bayesian information criterion, (2) a minimum of 5% of patients in each class, and (3) the posterior probability of assignments being >0.7 in each class. Cross-lagged structural equation models are used to explore temporal relationships of the predictors. Multivariate logistic regression is conducted to adjust potential confounders (such as age, sex, medications, and baseline CDAI) and to assess whether variables of interest could independently predict outcomes. Statistical significance was set at p-value<0.05. All statistical analysis is performed via R software within the secure platform to which the YODA project or the Vivli project remote desktop is connected." ["project_timeline"]=> string(221) "Anticipated project start: 2024/7/15
Analysis completion: 2025/2/15
Manuscript drafted: 2025/2/15
First submitted for publication: 2025/3/15
Results reported back to the YODA Project: 2025/6/15" ["project_dissemination_plan"]=> string(566) "The products of this project will be submitted to scientific conference, such as Digestive Disease Week, European Crohn’s and Colitis Organization and Asian Crohn’s and Colitis Organization. A manuscript will also be submitted for publication in peer-reviewed journals, such as Clinical Gastroenterology and Hepatology (CGH), Journal of Crohn's and Colitis (JCC), American Journal of Gastroenterology (AJG) and Inflammatory Bowel Diseases (IBD) and others. The acknowledgement for Vivli Project and YODA Project will be presented in all products of this study." ["project_bibliography"]=> string(1181) "
  1. Dolinger, M., J. Torres and S. Vermeire, Crohn’s disease. Lancet, 2024. 403(10432): p. 1177-1191.
  2. Liu, Y.H., et al., One-Year Trajectory of Cognitive Changes in Older Survivors of COVID-19 in Wuhan, China: A Longitudinal Cohort Study. JAMA Neurol, 2022. 79(5): p. 509-517.
  3. Bui, D.S., et al., Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life. Lancet Respir Med, 2018. 6(7): p. 535-544.
  4. Jia, J., et al., Biomarker Changes during 20 Years Preceding Alzheimer’s Disease. N Engl J Med, 2024. 390(8): p. 712-722.
  5. Constantine-Cooke, N., et al., Longitudinal Fecal Calprotectin Profiles Characterize Disease Course Heterogeneity in Crohn’s Disease. Clin Gastroenterol Hepatol, 2023. 21(11): p. 2918-2927.e6.
  6. Van Den Houte, M., et al., Predictors of symptom trajectory in newly diagnosed ulcerative colitis: a 3-year follow-up cohort study. J Crohns Colitis, 2024.
  7. Chen, R., et al., Trajectory of fecal lactoferrin for predicting prognosis in ulcerative colitis. Precis Clin Med, 2023. 6(3): p. pbad022.
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2024-0460

Research Proposal

Project Title: Dynamics of Disease Activity in Monitoring Therapeutic Outcomes and Predicting Prognosis for Crohn’s disease

Scientific Abstract: Background: As the emergence of novel targeted therapeutic options, monitoring disease activity and predicting therapeutic efficacy has become essential processes in CD management. However, longitudinal dynamics of disease activity have not been fully understood.

Objective: This study aims to explore distinct trajectories of disease activity within one year of targeted therapy in CD patients, so as to provide evidence for disease monitoring and personalized medicine. The scientific hypothesis of this study is that the trajectories of major predictors could help to monitor disease courses and predict different therapeutic outcomes in CD.

Study design: This is a post-hoc analysis study including data from one or more of the seven clinical trials (VERSIFY, UNITI-1 , UNITI-2 , IM-UNITI , EXTEND, U-EXCEL,and U-EXCEED). The major predictors include dynamics of disease activity (including clinical activity, patient-report outcomes, biomarker and endoscopic activity) within follow-up time.

Participants: Moderate-to-severe CD patients with at least three time of disease activity assessment would be included. Participants who meet any of the following criteria are not eligible for study inclusion: lacking data of corresponding predictors during induction therapy; having concomitant intestinal infection disease when assessing predictors after induction therapy.

Main Outcome Measure(s): Outcomes include therapeutic efficacy, such as clinical response, clinical remission, mucosal healing, endoscopic improvement, patient-reported outcomes etc.) at the end of maintenance treatment.

Statistical analysis: The latent class growth mixed model is performed to fit the trajectory of disease activity dynamic trajectory. Cross-lagged structural equation models are used to explore temporal relationships of the predictors. Multivariate logistic regression is conducted to adjust potential confounders and to analyze the association of candidate predictors and outcomes.

Brief Project Background and Statement of Project Significance: Crohn's disease(CD) is a chronic, recurrent inflammatory bowel disease. Recent years have witnessed a significant surge in the development of targeted therapies, which has expanded the therapeutic options for CD, such as adalimumab, ustekinumab, infliximab, and vedolizumab . Timely monitoring disease activity and predicting therapeutic response are vital processes during biologics and small molecules, especially in the initial year of treatment. However, for CD patients with targeted therapy, relatively limited studies describing trajectories of disease course have been reported. Therefore, in order to investigate the role of disease activity dynamic trajectories in therapeutic efficacy monitoring and prognosis predicting, we will perform post-hoc analysis based on one or more of the seven randomized clinical trials, including VERSIFY, UNITI-1 , UNITI-2 , IM-UNITI, EXTEND, U-EXCEL, and U-EXCEED .

Specific Aims of the Project: This study aims to explore distinct trajectories of disease activity within one year of targeted therapy in CD patients, so as to provide evidence for disease monitoring and personalized medicine. The scientific hypothesis of this study is that the trajectories of major predictors could help to monitor disease courses and predict different therapeutic outcomes in CD.

Study Design: Individual trial analysis

What is the purpose of the analysis being proposed? Please select all that apply.: Research on clinical prediction or risk prediction

Software Used: RStudio

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: Participants from one or more clinical trials (UNITI-1 , UNITI-2, and IM-UNITI from the YODA Project; VERSIFY, EXTEND, U-EXCEL, and U-EXCEED from the Vivli Project) will be included in the study as either a development cohort for trajectory fitting or a external validation cohort. Moderate-to-severe CD patients with at least three time of disease activity assessment would be included. Participants who meet any of the following criteria are not eligible for study inclusion: lacking data of corresponding predictors during induction therapy; having concomitant intestinal infection disease when assessing predictors after induction therapy.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: Outcomes include therapeutic efficacy (such as clinical response, clinical remission, mucosal healing, endoscopic response, patient-reported outcomes etc.) at the end of maintenance treatment, as well as colorectal resection during long-term follow-up.
Clinical response is defined as a decrease from baseline in the CDAI ≥100 points or a CDAI score <150. And clinical remission is defined as CDAI<150. As for endoscopic outcomes, endoscopic response is defined as Simple Endoscopic Score for CD(SES-CD) decrease ≥50% from baseline, while mucosal healing is defined as SES-CD 20 points and IBDQ ≥170 points, respectively.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: The major predictors include clinical activity (e.g., CDAI), patient-reported outcomes(e.g., patient-reported outcome 2), serum and fecal biomarkers (e.g., C-reactive protein, hemoglobin, albumin, and fecal calprotectin), endoscopic scores, patient report outcomes (e.g., IBDQ scores), and their long-term trajectories. They will be collected during one-year targeted treatment and before the end of maintenance therapy.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Other variables of interest include baseline characteristics (e.g., gender, age, disease duration, body mass index, medication history, treatment allocation and concomitant therapy) and baseline disease activity evaluation (e.g., CDAI, fecal calprotectin and C-reactive protein).

Statistical Analysis Plan: Continuous and categorical variables are described as median (interquartile range) or frequency (percentage), respectively. The latent class growth mixed model (LCGMM) is performed to fit the trajectory of disease activity dynamic trajectory. LCGMM is a validated approach used to analyze longitudinal data and identify subgroups with distinct trajectories,which has been applied in various diseases. Based on lcmm package in R software, LCGMM usually uses linear, quadratic, or cubic polynomial functions with different class numbers ranging from 2 to 5 for identifying subgroups with distinct trajectories. The optimal trajectory was selected based on (1) the lowest Bayesian information criterion, (2) a minimum of 5% of patients in each class, and (3) the posterior probability of assignments being >0.7 in each class. Cross-lagged structural equation models are used to explore temporal relationships of the predictors. Multivariate logistic regression is conducted to adjust potential confounders (such as age, sex, medications, and baseline CDAI) and to assess whether variables of interest could independently predict outcomes. Statistical significance was set at p-value<0.05. All statistical analysis is performed via R software within the secure platform to which the YODA project or the Vivli project remote desktop is connected.

Narrative Summary: Crohn's disease(CD) is a chronic, recurrent inflammatory bowel disease. Recent years have witnessed a significant surge in the development of targeted therapies(e.g., biologics and small molecules), which has expanded the therapeutic options for CD. However, not all CD patients could benefit from the treatment of targeted therapies. Therefore, timely monitoring disease activity and predicting therapeutic response are vital processes during therapies, especially in the initial year of treatment. Describing trajectories of disease course is a promising topic in current clinical research, while relatively limited studies towards this have been reported in CD patients with biological therapies or small molecules. Therefore, this project aims to investigate the role of disease activity dynamic trajectories in therapeutic efficacy monitoring and prognosis predicting in CD.

Project Timeline: Anticipated project start: 2024/7/15
Analysis completion: 2025/2/15
Manuscript drafted: 2025/2/15
First submitted for publication: 2025/3/15
Results reported back to the YODA Project: 2025/6/15

Dissemination Plan: The products of this project will be submitted to scientific conference, such as Digestive Disease Week, European Crohn’s and Colitis Organization and Asian Crohn’s and Colitis Organization. A manuscript will also be submitted for publication in peer-reviewed journals, such as Clinical Gastroenterology and Hepatology (CGH), Journal of Crohn's and Colitis (JCC), American Journal of Gastroenterology (AJG) and Inflammatory Bowel Diseases (IBD) and others. The acknowledgement for Vivli Project and YODA Project will be presented in all products of this study.

Bibliography:

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