Multicenter Study Protocol: Research on Evaluation and Detection of Surgical Wound Complications with AI-based Recognition. (REDSCAR-trial)

Overview

The increasing use of telemedicine in surgical care has demonstrated significant poten-tial for improving patient outcomes and optimizing healthcare resources. This study investigates the efficacy of the RedScar© app in telematic detection and monitoring of surgical site infections (SSIs), a major cause of healthcare-associated infections (HAIs) with significant economic and health impacts. RedScar© leverages a patient's smartphone to provide automated infection risk assessments without requiring clini-cian input, offering a potential solution for remote postoperative care. In a pilot study, RedScar© demonstrated 100% sensitivity and 83.13% specificity in detecting SSIs, with high patient satisfaction regarding its comfort, cost-effectiveness, and ability to reduce absenteeism. This multicenter prospective study aims to validate these findings, com-paring app-based detection with in-person evaluations. Primary objectives include as-sessing the sensitivity and specificity of RedScar© using receiver operating character-istic (ROC) analysis, while secondary objectives include evaluating patient satisfaction and standardizing telematic follow-up across centers. The study will include 168 pa-tients undergoing abdominal surgery, with follow-up assessments conducted remotely via the app and in-person at specified intervals. Data will be analysed using descrip-tive and statistical methods to assess diagnostic accuracy and patient satisfaction. This research seeks to further develop RedScar© as a reliable, scalable tool for enhancing postoperative care, reducing healthcare costs, and improving patient experiences in surgical recovery.

Conditions