⇒ The model for detecting the risk of renal injury is based on data collected in the anaesthetic consultation prior to cardiac surgery and has been published in the scientific journal Minerva Anestesiol.
It is a study designed by the Spanish Research Group on Renal Dysfunction in Cardiac Surgery (GEDRCC), of which researchers are members of INCLIVA and the Anaesthesia Service of the Hospital Clínico de Valencia, in which 23 hospitals that perform cardiac surgery in Spain were invited to participate.
The objective of each hospital was to gather a total of 50 consecutive patients, or if it was not possible to reach that number, to collect all available patient data for a maximum period of 1 year. A total of 942 patients undergoing cardiac surgery, not including emergency surgery, were studied.
Due to the poor results in our population of existing predictive models, the researchers decided to use their data to generate a new scale, called the GEDRCC option, with the aim of improving predictive ability in our population and detecting patients who are at higher risk during scheduled surgery.
As a result, a new predictive model of acute renal injury after cardiac surgery in scheduled interventions has been developed based on available preoperative information. “Our new model is easy to calculate and can be an effective tool for communicating risk to patients and guiding decision-making in the perioperative period,” say the study authors.
The purpose of predictive models in surgery is to define an individual’s risk before undergoing surgery; therefore, models based on preoperative risk factors should be applied during preanesthesia consultations, which provides an advantage over those models that take into account variables during the operation and after surgery.
It is important for any anesthesiologist to inform patients prior to surgery about their individual risk and to consider the selection of appropriate perioperative management strategies.
Therefore, predictive models are invaluable tools for informing and individualizing decisions. Their use is important to anticipate and treat complications, and to introduce more accurate preventive measures.