Frailty as a predictor of surgical outcomes in older patients.
BACKGROUND
Preoperative risk assessment is important yet inexact in older patients because physiologic reserves are difficult to measure. Frailty is thought to estimate physiologic reserves, although its use has not been evaluated in surgical patients. We designed a study to determine if frailty predicts surgical complications and enhances current perioperative risk models.


METHODS
We prospectively measured frailty in 594 patients (age 65 years or older) presenting to a university hospital for elective surgery between July 2005 and July 2006. Frailty was classified using a validated scale (0 to 5) that included weakness, weight loss, exhaustion, low physical activity, and slowed walking speed. Patients scoring 4 to 5 were classified as frail, 2 to 3 were intermediately frail, and 0 to 1 were nonfrail. Main outcomes measures were 30-day surgical complications, length of stay, and discharge disposition. Multiple logistic regression (complications and discharge) and negative binomial regression (length of stay) were done to analyze frailty and postoperative outcomes associations.


RESULTS
Preoperative frailty was associated with an increased risk for postoperative complications (intermediately frail: odds ratio [OR] 2.06; 95% CI 1.18-3.60; frail: OR 2.54; 95% CI 1.12-5.77), length of stay (intermediately frail: incidence rate ratio 1.49; 95% CI 1.24-1.80; frail: incidence rate ratio 1.69; 95% CI 1.28-2.23), and discharge to a skilled or assisted-living facility after previously living at home (intermediately frail: OR 3.16; 95% CI 1.0-9.99; frail: OR 20.48; 95% CI 5.54-75.68). Frailty improved predictive power (p < 0.01) of each risk index (ie, American Society of Anesthesiologists, Lee, and Eagle scores).


CONCLUSIONS
Frailty independently predicts postoperative complications, length of stay, and discharge to a skilled or assisted-living facility in older surgical patients and enhances conventional risk models. Assessing frailty using a standardized definition can help patients and physicians make more informed decisions.
Makary
Martin A
MA
Department of Surgery, John Hopkins University School of Medicine, Johns Hopkins Medical Institutions, 1550 Orleans Street, Baltimore, MD 21231, USA. mmakary1@jhmi.edu
Segev
Dorry L
DL
Pronovost
Peter J
PJ
Syin
Dora
D
Bandeen-Roche
Karen
K
Patel
Purvi
P
Takenaga
Ryan
R
Devgan
Lara
L
Holzmueller
Christine G
CG
Tian
Jing
J
Fried
Linda P
LP
D000368:Aged; D019540:Area Under Curve; D015897:Comorbidity; D017558:Elective Surgical Procedures; D005260:Female; D016330:Frail Elderly; D015577:Geriatric Assessment; D006801:Humans; D015994:Incidence; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D017063:Outcome Assessment, Health Care; D011183:Postoperative Complications / Q000453:epidemiology; D011237:Predictive Value of Tests; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors