Our objective was to compare the association between the Clinical Frailty Scale (CFS), a validated frailty assessment tool, and psoas muscle index (PMI), representing PMA normalized for height surrogate of sarcopenia, with adverse postoperative outcomes in order to assess whether one marker is a better predictor of postoperative outcomes. We also aimed to assess the correlation between low PMI and a frailty state with adverse postoperative outcomes.
Design and Settings
We conducted a retrospective analysis of a subgroup of patients from a prospective cohort (19) undergoing elective noncardiac surgery in a tertiary academic hospital (Hôpital Maisonneuve-Rosemont, Montréal, Canada) from January 2017 to January 2018.
Study participants
All patients aged 65 years or older scheduled to undergo elective non-cardiac surgery were eligible for inclusion (vascular and general surgery in our center). We excluded patients undergoing emergent surgery or those unable to provide consent. We contacted patients by phone or met them in person to obtain consent. Our institution’s ethics review board approved the prospective study, and an amendment has been made to include this sub-study.
A total of 270 patients underwent major surgery in our center and 134 patients underwent vascular or general surgery (the other patients underwent orthopedic surgery). Data for the complete cohort was published in Canadian Journal of Anesthesia (ref). A total of 78 patients with abdominal CT performed within 6-months of surgery were included in the final analysis. Figure 1 shows the number of patients meeting the inclusion and exclusion criteria. Patients with and without abdominal CT-scans did not differ significantly in baseline characteristics and frailty state (Additional file 1). Our final cohort's surgical procedures were mostly intraperitoneal vascular bypass surgery, gastrointestinal tract surgery, hyperthermic intraperitoneal chemotherapy, and biliary tract surgery.
Of 134 potential patients, 78 met the inclusion criteria and had a CT scan performed within 6-months
before surgery.
Frailty and Sarcopenia Assessment
Frailty was assessed prospectively using the CFS, an instrument developed and validated by Rockwood and colleagues. (20) Based on self-report of comorbidities and help with instrumental activities of daily living (IADLs) and activities of daily living (ADLs), (21) the CFS is scored on an ordinal scale from 1 to 9, where a score of 1 corresponds to being robust and a score of 9 being severely frail. Patients were then classified into three categories according to their score; 1-3 being considered robust, 4 being vulnerable, and 5-8 frail.
A trained research assistant with previous experience using CFS performed a semi-structured interview either by phone or in-person to determine the frailty level. We have used this method in the past in a cohort of orthopedic surgery patients, and CFS was predictive of hospital LOS. (22) CFS has been previously used in both the vascular and general surgery population and was associated with higher postoperative mortality, greater risk of 6-months readmission, and postoperative functional decline. (23-25)
PMI was used as a surrogate of skeletal muscle loss. Preoperative CT scans were downloaded in digital DICOM format and imported into the web-based CoreSlicer software platform (www.coreslicer.com) and analysed retrospectively. After selecting the axial slice at the top of the L4 vertebral level, a research assistant unaware of frailty score and patient outcomes traced the right and left psoas muscles with a segmental brush tool as validated in previous studies.(15, 17, 26, 27) The research assistant underwent training and inter-rater reliability testing with a co-investigator (LAM) who developed the CoreSlicer software. The psoas muscles' measured areas were summed and normalized for height, as is conventional for other body composition measures, yielding PMI in cm2/m2. As muscle volume is correlated to patient height, this normalization procedure reduces patient sex and morphology variations.(28) PMI results were then stratified by gender and divided into tertiles, with patients in the first (lowest) tertile being most sarcopenic, and patients in the third-highest tertile, least sarcopenic.(15, 17, 29)
Variables
The surgical procedure and patient characteristics (age, gender, comorbidity, preoperative status, and body mass index) were collected through medical chart review. Comorbidity was defined as the co-existence of at least two separate chronic illnesses. The burden of comorbidity was quantified by the Charlson Comorbidity Index (CCI).(30) Preoperative status was defined using the American Society of Anesthesiologists classification (ASA).(31)
Outcomes
The primary clinical outcome of interest was a composite of the incidence of severe postoperative complications, defined by the American College of Surgeons National Surgical Quality Improvement Program dataset. We included only complications of Clavien-Dindo classification grade II or higher, thus requiring invasive interventions (surgical, endoscopic, or radiological), complex pharmacological treatment, intensive care management, or supportive life therapies. Outcome assessment was performed by a research assistant unaware of patient’ CFS and PMA.
Statistical Analysis
Continuous variables were summarized with the sample median and interquartile range (IQR) and compared using the Spearman rank correlation test. Dichotomous variables were summarized with frequency tables and compared across PMI tertiles using the chi-square test. A multivariable negative binomial regression model was used to determine the association between PMI or CFS and the number of severe postoperative complications after adjusting for covariates (age, sex, comorbidity, and baseline hemoglobin level). Covariates were chosen because they are known predictors of postoperative complications and are also factors associated with frailty or sarcopenia. The risk was reported as incidence risk ratios (IRR) with 95% confidence intervals (95% CI). We chose to analyze the total number of complications since patients might have more than one complication during the hospital stay. We constructed two models using the same covariates and only interchanging PMI for CFS. We compared the Akaike's Information Criterion of both models. Collinearity between PMI and CFS and other covariates was assessed using the Variance Inflation Factor (VIF). All statistical analyses were performed using SPSS 25.