Preoperative assessment of the older surgical patient: honing in on geriatric syndromes

Abstract

Nearly 50% of Americans will have an operation after the age of 65 years. Traditional preoperative anesthesia consultations capture only some of the information needed to identify older patients (defined as ≥65 years of age) undergoing elective surgery who are at increased risk for postoperative complications, prolonged hospital stays, and delayed or hampered functional recovery. As a catalyst to this review, we compared traditional risk scores (eg, cardiac-focused) to geriatric-specific risk measures from two older female patients seen in our preoperative clinic who were scheduled for elective, robotic-assisted hysterectomies. Despite having a lower cardiac risk index and Charlson comorbidity score, the younger of the two patients presented with more subtle negative geriatric-specific risk predictors – including intermediate or pre-frail status, borderline malnutrition, and reduced functional/mobility – which may have contributed to her 1-day-longer length of stay and need for readmission. Adequate screening of physiologic and cognitive reserves in older patients scheduled for surgery could identify at-risk, vulnerable elders and enable proactive perioperative management strategies (eg, strength, balance, and mobility prehabilitation) to reduce adverse postoperative outcomes and readmissions. Here, we describe our initial two cases and review the stress response to surgery and the impact of advanced age on this response as well as preoperative geriatric assessments, including frailty, nutrition, physical function, cognition, and mood state tests that may better predict postoperative outcomes in older adults. A brief overview of the literature on anesthetic techniques that may influence geriatric-related syndromes is also presented.

Derivation and Validation of a Geriatric‐Sensitive Perioperative Cardiac Risk Index

“…Therefore, this model is the first of a series of models that will need to be updated by integrating geriatric‐relevant data in a timely manner. Hence, our future endeavors will focus on integrating and testing the usability of biologic variables such as inflammatory factors and other significant factors including nutritional status, functional status, depression, cognition, and frailty indices . In addition, the external validation was conducted using a different data‐set year from the same organization: NSQIP.…”

“…Hence, our future endeavors will focus on integrating and testing the usability of biologic variables such as inflammatory factors and other significant factors including nutritional status, functional status, depression, cognition, and frailty indices. 2,11,[26][27][28] In addition, the external validation was conducted using a different data-set year from the same organization: NSQIP. Additional studies further validating this model using data from different external data sets are warranted.…”

“…There are 40 million people aged 65 and over living in the United States today . Although they account for just 15% of the US population, they receive one third of all inpatient surgeries . By 2030, 72 million Americans will be 65 and over, accounting for 20% of the US population and an increasing number of surgeries .…”

“…1 Although they account for just 15% of the US population, they receive one third of all inpatient surgeries. 1,2 By 2030, 72 million Americans will be 65 and over, accounting for 20% of the US population and an increasing number of surgeries. 1,3,4 All inpatient surgery carries a risk of cardiac complications for all adult patients, regardless of age.…”

“…The aims of this study were (1) to investigate the performance of the RCRI and Gupta MICA perioperative cardiac risk models in a geriatric population, (2) to evaluate the incidence of MICA after noncardiac nonemergency surgery across the age spectrum, (3) to develop a geriatricsensitive perioperative cardiac risk index (GSCRI) optimized for use with geriatric patients and sensitive to the clinical and physiologic uniqueness of this population (given that prior hypothesis-driven objectives were met), and (4) to conduct comparative performance analysis of the GSCRI, RCRI, and Gupta MICA models.…”