Demonstrating Benefit-Risk Profiles of Novel Therapeutic Strategies in Kidney Transplantation: Opportunities and Challenges of Real-World Evidence.

While great progress has been made in transplantation medicine, long-term graft failure and serious side effects still pose a challenge in kidney transplantation. Effective and safe long-term treatments are needed. Therefore, evidence of the lasting benefit-risk of novel therapies is required. Demonstrating superiority of novel therapies is unlikely via conventional randomized controlled trials, as long-term follow-up in large sample sizes pose statistical and operational challenges. Furthermore, endpoints generally accepted in short-term clinical trials need to be translated to real-world (RW) care settings, enabling robust assessments of novel treatments. Hence, there is an evidence gap that calls for innovative clinical trial designs, with RW evidence (RWE) providing an opportunity to facilitate longitudinal transplant research with timely translation to clinical practice. Nonetheless, the current RWE landscape shows considerable heterogeneity, with few registries capturing detailed data to support the establishment of new endpoints. The main recommendations by leading scientists in the field are increased collaboration between registries for data harmonization and leveraging the development of technology innovations for data sharing under high privacy standards. This will aid the development of clinically meaningful endpoints and data models, enabling future long-term research and ultimately establish optimal long-term outcomes for transplant patients.

ENTIMOS: A Discrete Event Simulation Model for Maximising Efficiency of Infusion Suites in Centres Treating Multiple Sclerosis Patients.

BACKGROUND: Improved multiple sclerosis (MS) diagnosis and increased availability of intravenous disease-modifying treatments can lead to overburdening of infusion centres. This study was focused on developing a decision-support tool to help infusion centres plan their operations. METHODS: A discrete event simulation model ('ENTIMOS') was developed using Simul8 software in collaboration with clinical experts. Model inputs included treatment-specific clinical parameters, resources such as infusion chairs and nursing staff, and costs, while model outputs included patient throughput, waiting time, queue size, resource utilisation, and costs. The model was parameterised using characteristics of the Charing Cross Hospital Infusion Centre in London, UK, where 12 infusion chairs were deployed for 170 non-MS and 860 MS patients as of March 2021. The number of MS patients was projected to increase by seven new patients per week. RESULTS: The model-estimated waiting time for an infusion is, on average, 8 days beyond clinical recommendation in the first year of simulation. Without corrective action, the delay in receiving due treatment is anticipated to reach 30 days on average at 30 months from the start of simulation. Such system compromise can be prevented either by adding one infusion chair annually or switching 7% of existing patients or 24% of new patients to alternative MS treatments not requiring infusion. CONCLUSION: ENTIMOS is a flexible model of patient flow and care delivery in infusion centres serving MS patients. It allows users to simulate specific local settings and therefore identify measures that are necessary to avoid clinically significant treatment delay resulting in suboptimal care.

Predictive Modeling Identifies Total Bleeds at 12-Weeks Postswitch to N8-GP Prophylaxis as a Predictor of Treatment Response.

BACKGROUND: Predicting annualized bleeding rate (ABR) during factor VIII (FVIII) prophylaxis for severe hemophilia A (SHA) is important for long-term outcomes. This study used supervised machine learning-based predictive modeling to identify predictors of long-term ABR during prophylaxis with an extended half-life FVIII. METHODS: Data were from 166 SHA patients who received N8-GP prophylaxis (50 IU/kg every 4 days) in the pathfinder 2 study. Predictive models were developed to identify variables associated with an ABR of ≤1 versus >1 during the trial's main phase (median follow-up of 469 days). Model performance was assessed using area under the receiver operator characteristic curve (AUROC). Pre-N8-GP prophylaxis models learned from data collected at baseline; post-N8-GP prophylaxis models learned from data collected up to 12-weeks postswitch to N8-GP, and predicted ABR at the end of the outcome period (final year of treatment in the main phase). RESULTS: The predictive model using baseline variables had moderate performance (AUROC = 0.64) for predicting observed ABR. The most performant model used data collected at 12-weeks postswitch (AUROC = 0.79) with cumulative bleed count up to 12 weeks as the most informative variable, followed by baseline von Willebrand factor and mean FVIII at 30 minutes postdose. Univariate cumulative bleed count at 12 weeks performed equally well to the 12-weeks postswitch model (AUROC = 0.75). Pharmacokinetic measures were indicative, but not essential, to predict ABR. CONCLUSION: Cumulative bleed count up to 12-weeks postswitch was as informative as the 12-week post-switch predictive model for predicting long-term ABR, supporting alterations in prophylaxis based on treatment response.

Preliminary Results of a Novel Algorithmic Method Aiming to Support Initial Causality Assessment of Routine Pharmacovigilance Case Reports for Medication-Induced Liver Injury: The PV-RUCAM.

INTRODUCTION: Data incompleteness in pharmacovigilance (PV) health records limits the use of current causality assessment methods for drug-induced liver injury (DILI). In addition to the inherent complexity of this adverse event, identifying cases of high causal probability is difficult. OBJECTIVE: The aim was to evaluate the performance of an improved, algorithmic and standardised method called the Pharmacovigilance-Roussel Uclaf Causality Assessment Method (PV-RUCAM), to support assessment of suspected DILI. Performance was compared in different settings with regard to applicability and differentiation capacity. METHODS: A PV-RUCAM score was developed based on the seven sections contained in the original RUCAM. The score provides cut-off values for or against DILI causality, and was applied on two datasets of bona fide individual case safety reports (ICSRs) extracted randomly from clinical trial reports and a third dataset of electronic health records from a global PV database. The performance of PV-RUCAM adjudication was compared against two standards: a validated causality assessment method (original RUCAM) and global introspection. RESULTS: The findings showed moderate agreement against standards. The overall error margin of no false negatives was satisfactory, with 100% sensitivity, 91% specificity, a 25% positive predictive value and a 100% negative predictive value. The Spearman's rank correlation coefficient illustrated a statistically significant monotonic association between expert adjudication and PV-RUCAM outputs (R = 0.93). Finally, there was high inter-rater agreement (K w = 0.79) between two PV-RUCAM assessors. CONCLUSION: Within the PV setting of a pharmaceutical company, the PV-RUCAM has the potential to facilitate and improve the assessment done by non-expert PV professionals compared with other methods when incomplete reports must be evaluated for suspected DILI. Prospective validation of the algorithmic tool is necessary prior to implementation for routine use.