Disease-free survival as a surrogate for overall survival in HR+/HER2- early breast cancer: A correlation analysis.

BACKGROUND: Overall survival (OS) is a universally accepted measure of clinical benefit; however, prolonged follow-up is needed to observe sufficient events. Disease-free survival (DFS) has been widely adopted as a primary endpoint for early breast cancer (EBC) trials, as follow-up is comparatively shorter. Here, we present an analysis evaluating DFS as a surrogate for OS for adjuvant treatment of hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) EBC. METHODS: A systematic literature review which included randomized controlled trials (RCTs) with ≥80% of adult patients with HR+/HER2- EBC was conducted. The RCTs evaluated various systemic therapeutic categories; key inclusion criteria included reporting of DFS and OS hazard ratios (HRs) and mature OS data. Spearman rank correlation and weighted linear regression analyses evaluated DFS and OS HR correlation. A scenario analysis tested base-case analysis robustness, and a parallel analysis using patient-level data was conducted. RESULTS: The base case (N = 14 RCTs) showed an unweighted Spearman coefficient of 0.81 between OS and DFS (weighted: 0.81), with 84% of the variability in OS explained by DFS differences (R2 from weighted regression). The surrogate threshold effect (Burzykowski T, Buyse M. Pharm Stat. 2006;5:173-186) was 0.82 for DFS/OS HR. Scenario analysis (n = 9 RCTs), which excluded chemotherapy trials, and patient-level analysis using FACE trial data were consistent with the base-case analysis. CONCLUSIONS: These analyses support DFS as a reliable surrogate endpoint for OS in adjuvant HR+/HER2- EBC trials. Using DFS as a surrogate measure will permit timelier access to novel treatments for patients with HR+/HER2- EBC.

Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data.

INTRODUCTION: The time required to reach clinical remission varies in patients with chronic urticaria (CU). The objective of this study is to develop a predictive model using a machine learning methodology to predict time to clinical remission for patients with CU. METHODS: Adults with ≥ 2 ICD-9/10 relevant CU diagnosis codes/CU-related treatment > 6 weeks apart were identified in the Optum deidentified electronic health record dataset (January 2007 to June 2019). Clinical remission was defined as ≥ 12 months without CU diagnosis/CU-related treatment. A random survival forest was used to predict time from diagnosis to clinical remission for each patient based on clinical and demographic features available at diagnosis. Model performance was assessed using concordance, which indicates the degree of agreement between observed and predicted time to remission. To characterize clinically relevant groups, features were summarized among cohorts that were defined based on quartiles of predicted time to remission. RESULTS: Among 112,443 patients, 73.5% reached clinical remission, with a median of 336 days from diagnosis. From 1876 initial features, 176 were retained in the final model, which predicted a median of 318 days to remission. The model showed good performance with a concordance of 0.62. Patients with predicted longer time to remission tended to be older with delayed CU diagnosis, and have more comorbidities, more laboratory tests, higher body mass index, and polypharmacy during the 12-month period before the first CU diagnosis. CONCLUSIONS: Applying machine learning to real-world data enabled accurate prediction of time to clinical remission and identified multiple relevant demographic and clinical variables with predictive value. Ongoing work aims to further validate and integrate these findings into clinical applications for CU management.

Perspectives on the provision of GDM screening in general practice versus the hospital setting: a qualitative study of providers and patients.

OBJECTIVE: A novel gestational diabetes mellitus (GDM) screening programme which involved offering screening at the patient's general practitioner (GP) compared with the traditional hospital setting was trialled. This study investigates perspectives of involved stakeholders on the provision of GDM screening at both settings. DESIGN: Thematic analysis of the perspectives of stakeholders involved in the receiving and provision of GDM screening in both the GP and hospital settings drawn from focus groups and interviews. PARTICIPANTS: 3 groups of participants are included in this research--patient participants, GP screening providers and hospital screening providers. All were recruited from a larger sample who participated in a randomised controlled screening trial. Purposeful sampling was utilised to select participants with a wide variety of perspectives on the provision of GDM screening. SETTING: Participants were recruited from a geographical area covered by 3 hospitals in Ireland. RESULTS: 4 themes emerged from thematic analysis--namely (1) travel distance, (2) best care provision, (3) sense of ease created and (4) optimal screening. CONCLUSIONS: The influence of travel distance from the screening site is the most important factor influencing willingness to attend for GDM screening among women who live a considerable distance from the hospital setting. For patients who live equidistance from both settings, other factors are important; namely the waiting facilities including parking, perceived expertise of screening provider personnel, access to emergency treatment if necessary, accuracy of tests and access to timely results and treatment. Optimal screening for GDM should be specialist led, incorporate expert advice of GDM screening, treatment and management, should be provided locally, offer adequate parking and comfort levels, provide accurate tests, and timely access to results and treatment. Such a service should result in improved rates of GDM screening uptake. TRIAL REGISTRATION NUMBER: ISRCTN41202110.

Can the Onset of Type 2 Diabetes Be Delayed by a Group-Based Lifestyle Intervention in Women with Prediabetes following Gestational Diabetes Mellitus (GDM)? Findings from a Randomized Control Mixed Methods Trial.

OBJECTIVE: To evaluate a 12-week group-based lifestyle intervention programme for women with prediabetes following gestational diabetes (GDM). DESIGN: A two-group, mixed methods randomized controlled trial in which 50 women with a history of GDM and abnormal glucose tolerance postpartum were randomly assigned to intervention (n = 24) or wait control (n = 26) and postintervention qualitative interviews with participants. MAIN OUTCOME MEASURES: Modifiable biochemical, anthropometric, behavioural, and psychosocial risk factors associated with the development of type 2 diabetes. The primary outcome variable was the change in fasting plasma glucose (FPG) from study entry to one-year follow-up. RESULTS: At one-year follow-up, the intervention group showed significant improvements over the wait control group on stress, diet self-efficacy, and quality of life. There was no evidence of an effect of the intervention on measures of biochemistry or anthropometry; the effect on one health behaviour, diet adherence, was close to significance. CONCLUSIONS: Prevention programmes must tackle the barriers to participation faced by this population; home-based interventions should be investigated. Strategies for promoting long-term health self-management need to be developed and tested.