Repeated intrathecal phenol injections for cancer pain.

Intrathecal phenol neurolysis is a treatment of last resort for specific refractory cancer pains. However, there is a paucity of evidence in the academic literature and no recent documented evidence of repeat injections on the same patient. We aim to present our experience and learning from repeat interventions on the same patient in order to further the evidence base for medical professionals managing pain in a palliative care setting.

Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting.

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk.

Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials.

BACKGROUND: Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials. MATERIAL AND METHODS: Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10 P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal. RESULTS: The SDM quality metrics showed improvement in 83% (95% CI, 80-85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41-70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct. CONCLUSION: The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

Comprehensive Assessment of Risk-Based Quality Management Adoption in Clinical Trials.

BACKGROUND: Risk-based monitoring (RBM) and risk-based quality management (RBQM) offer a compelling approach to increase efficiency, speed and quality in clinical trials by prioritizing and mitigating risks related to essential safety and efficacy data. Since 2013, the FDA and EMA have encouraged the use of RBM/RBQM, however adoption has been slow with limited understanding of the barriers to adoption. METHODS: The Tufts Center for the Study of Drug Development conducted an online survey among pharmaceutical, biotechnology, and contract research organizations and gathered 206 responses on 32 distinct RBQM practices. RESULTS: On average, companies implemented RBQM in 57% of their clinical trials. Lower levels of adoption were observed among companies conducting fewer than 25 trials annually (48%) compared to those conducting more than 100 trials annually (63%). Primary barriers to adoption include lack of organizational knowledge and awareness, mixed perceptions of the value proposition of RBQM, and poor change management planning and execution. Insights into improving the level of adoption are discussed.

Self- vs provider-referral differences for coronary artery calcium testing.

Study objectives: The objectives of this study were to identify independent predictors for moderate/accentuated coronary artery calcium (CAC) score and compare patients who self-referred for CAC Computed Tomography (CT) testing to those who were provider-referred. Design: Patients underwent CAC between January to July 2019. The analysis was divided into self-referred patients influenced by a CAC community campaign who identified themselves as having cardiovascular risk factors compared to provider-referred intermediate-risk patients who were asymptomatic. SAS version 9.4 (SAS Institute, Inc., Cary, NC) was used for all analyses. Setting: Seven southwest Ohio hospitals from a single network. Participants: 2124 adult patients who received CAC CT (163 self and 1961 provider-referred). Interventions: CAC CT. Main outcome measures: Demographics, risk factors, lab values, prescriptions, and referral status were used to compare CAC score differences between self- and provider-referred patients. Results: For 2124 patients, three predictors for moderate/accentuated CAC score remained significant after multiple logistic regression: CKD (OR 0.24, CI 0.008-0.68, p < 0.05), COPD (OR 0.39, CI 0.19-0.80, p < 0.05), and CAD (OR 0.46, CI 0.22-0.98, p < 0.05). There were four differences between referred groups: history of PVD (OR 0.21, CI 0.05-0.86, p < 0.05), higher triglyceride (OR 1.004, CI 1.00-1.01, p < 0.05), higher LDL levels (OR 0.991, CI 0.98-1.00, p < 0.05), and beta blocker prescription (OR 4.38, CI 1.49-12.85, p < 0.05) in self-referred patients. Conclusions: CAC CT testing is associated with independent risk predictors and can be used to clarify cardiovascular risk in self- and provider-referred patients with statistical similarity. Patients reliably self-refer for CAC CT when risk is present during a community initiative. Such initiatives may have a preventive benefit and lead to earlier pursuit and optimization of anti-lipid therapies.