The Effect of Accompaniment by Older Adults on Anesthetic Recovery

Summary:

In 2022, Mexico estimated a population of 17,958,707 older adults. With increased life expectancy, it is essential to seek strategies that improve the health of this population, as they are more vulnerable compared to other age groups due to functional and cognitive decline, along with an increase in chronic diseases and medication intake. During this stage of life, there is a possibility of requiring surgical treatment, which is the focus of this protocol proposing a maneuver that impacts patients' health without requiring economic costs. The proposal suggests the accompaniment of older adults by a family member during the immediate post-anesthetic period. Hypothesis: Accompaniment of older adults during the immediate postoperative period improves the quality of anesthetic recovery by 60%. This value is based on a study by Shem, where accompanying older adults prior to anesthetic induction resulted in a 61% reduction in anxiety among older adults. Anesthesiologists have expanded their role in perioperative medicine alongside geriatric medicine services for older surgical patients.

An experimental study will be conducted with two randomly divided groups: one group with accompaniment and one group without accompaniment in the recovery area. Both groups will be assessed using different questionnaires: 1. Pfeiffer Test for cognitive impairment diagnosis, 2. QoR-15 to assess the quality of anesthetic recovery, 3. Beck Anxiety Questionnaire, all of which will be administered 24 hours after surgery. Delirium will also be assessed using NuDESC at 24 hours, day 5, and 30 days after surgery. General data prior to surgery will be recorded, and vital signs such as heart rate, blood pressure, and pain on a verbal scale from 0 to 10 will be monitored during the postoperative period.

Statistical analysis will involve representing baseline characteristics of the population using mean and standard deviation or median and interquartile range, depending on the distribution type. X2 will be used to compare both groups in terms of outcomes. Finally, a multivariate analysis will be conducted using logistic regression to adjust for confounding variables.