Abstract
BACKGROUND AND OBJECTIVES: Potentially inappropriate medication refers to the prescription of drugs whose risks outweigh the benefits. There are different pharmacotherapeutic optimization strategies to detect and avoid potentially inappropriate medications (PIMs), namely deprescription. The List of Evidence-Based Deprescribing for Chronic Patients (LESS-CHRON) criteria were designed as a tool to systematize the deprescribing process. LESS-CHRON has established itself as one of the most suitable to be applied in older (≥65 years) multimorbid patients. However, it has not been applied to these patients, to measure the impact on their treatment. For this reason, a pilot study was conducted to analyze the feasibility of implementing this tool in a care pathway. RESEARCH DESIGN AND METHODS: A pre-post quasi-experimental study was conducted. Older outpatients with multimorbidity from the Internal Medicine Unit of a benchmark Hospital were included. The main variable was feasibility in clinical practice, understood as the likelihood that the deprescribing intervention recommended by the pharmacist would be applied to the patient. Success rate, therapeutic, and anticholinergic burden, and other variables related to health care utilization were analyzed. RESULTS: A total of 95 deprescribing reports were prepared. Forty-three were evaluated by the physician who assessed the recommendations made by pharmacists. This translates into an implementation feasibility of 45.3%. The application of LESS-CHRON identified 92 PIMs. The acceptance rate was 76.7% and after 3 months 82.7% of the stopped drugs remained deprescribed. A reduction in anticholinergic burden and enhanced adherence was achieved. However, no improvement was found in clinical or health care utilization variables. DISCUSSION AND IMPLICATIONS: The implementation of the tool in a care pathway is feasible. The intervention has achieved great acceptance and deprescribing has been successful in a not insignificant percentage. Future studies with a larger sample size are necessary to obtain more robust results in clinical and health care utilization variables.