Clinical Research Directory

Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

The goal of this prospective diagnostic test (correlation) study is to develop and investigate the performance of artificial intelligence in predicting peritoneum transporter status and dialysis efficiency in adult patients undergoing peritoneal dialysis (PD). The main questions it aims to answer are: Can artificial intelligence predict peritoneal transporter status based on simple clinical and biochemical measurements? Can artificial intelligence predict dialysis adequacy (Kt/V) using these features? Researchers will compare the performance of the AI model with the gold standard Peritoneal Equilibration Test (PET) and Kt/V to evaluate its accuracy and reliability. Participants will: Provide peritoneal dialysate and spot urine samples for biochemical analysis. Undergo routine dialysis adequacy and peritoneal equilibration testing (PET). Have clinical and laboratory data collected for AI model training and validation. The study will recruit approximately 350 peritoneal dialysis patients, with 280 participants in the training/validation arm and 70 participants in the test arm. The study duration is 12 months following enrollment.

Targeted Deprescribing in Patients on Hemodialysis

Hemodialysis (HD) patients take more pills per day on average than any other chronically ill patient population. On average, an HD patient takes 19 medications per day, of which 70% may not be appropriate. The reason the medications may not be appropriate is that HD patients are rarely included in clinical trials for new medications and therefore the efficacy and safety data that exists for the general population may not actually apply to them. Tools to guide the re-assessment and discontinuation (deprescribing) of these specific medications that lack evidence for efficacy and safety in HD patients are needed. These tools will help reduce the amount of medications being taken and the potential negative consequences of taking so many medications (e.g. adverse drug reactions, drug interactions, non-adherence, increased risk of cognitive impairment, impaired balance and falls, and increased risk of morbidity, hospitalization, and mortality). Nine medications that are often inappropriately prescribed to HD patients have been identified by the investigators. These medications are: Alpha-1 Blockers, Anticonvulsants, Benzodiazepines \& Z-Drugs, Loop Diuretics, Prokinetic Agents, Proton Pump Inhibitors, Quinine, Urate Lowering Agents, and Statins. The investigators developed and validated tools to help medical teams in outpatient HD units with identifying and stopping these medications in their patients. The next step will be to perform a study where test these tools are tested in practice at multiple HD centers across Canada. This initiative should decrease the average number of medications per patient and inappropriate medication use in the HD units where these tools are used. The overall objective of this study is to improve current clinical practice by optimizing medication use and prescribing patterns in the HD units across Canada.