Elderly and frail patients are increasing among the surgical population and are associated with an increased risk of postoperative mortality and major adverse events. Consequently, the preoperative decision-making process for this population is often complex, in particular to avoid situations of futility and overtreatment. Meanwhile, this high-risk surgical population also expresses different life and health priorities when facing difficult treatment decisions such as surgery. Most of the time, they are known to prioritize patient-reported outcomes (PROs) such as independence and quality of life, over survival and longevity.

PROs are assessed using patient-reported measures (PROMs), many of which have been widely used and validated for a long time in the perioperative period. PROs integration into clinical practice is known to enhances health outcomes by improving communication between patients and caregivers, facilitating the detection of potential serious but missed symptoms, and increasing patient engagement and satisfaction. However, PROs are still poorly used during preoperative decision-making and discussions.

Incorporating PROs into preoperative decision-making seems to be essential for several reasons:

• They help frame risk-benefit discussions according to life and health priorities that may be more meaningful for this high-risk population of elderly and frail patients.
• They could value non-operative alternatives according to potential benefits on PROs despite being less effective in terms of survival.
• They could support advanced care planning discussions by clarifying the potential impact of postoperative major adverse events on PROs.

To date, a major limitation of using PROs in surgical decision-making is the lack of reliable and tailored data for specific populations and procedures. This gap impedes the development of accurate predictive models that clinicians can trustfully use. However, today, some variables have been identified as potential predictors of deteriorated postoperative PROs, and predictive models are gradually emerging. Additionally, machine learning appears to enhance the accuracy of these models, though further validation is needed. This data on PROs must be developed to enable physicians to consider more relevant outcomes for this high-risk population and to empower patients and their relatives to make decisions that respect their values and priorities.