Effects of sarcopenia and frailty on postoperative recovery in elderly patients: A prospective cohort study

Kedi Guo; Xinghe Wang; Xian Lu; Yuping Yang; Wensi Lu; Shuting Wang; Xihui Tang; Yan Wu; Yuqing Xu; Qingsong Chen; Su Liu

doi:10.1002/jcsm.13337

. 2023 Sep 19;14(6):2642–2652. doi: 10.1002/jcsm.13337

Effects of sarcopenia and frailty on postoperative recovery in elderly patients: A prospective cohort study

Kedi Guo ^1,², Xinghe Wang ^1,^2,³, Xian Lu ^1,², Yuping Yang ^1,², Wensi Lu ^1,², Shuting Wang ^1,², Xihui Tang ^1,², Yan Wu ^1,², Yuqing Xu ^1,², Qingsong Chen ^1,², Su Liu ^1,^2,^✉

PMCID: PMC10751444 PMID: 37724506

Abstract

Background

Sarcopenia and frailty are both age‐related declines in functional reserve that are linked to adverse health outcomes. It is critical to know about the outcomes of a combination of these conditions. The study aimed to investigate the effects of sarcopenia and frailty on postoperative recovery in elderly patients and to explore risk factors.

Methods

This prospective cohort study was conducted among 608 patients aged ≥60 years, American Society of Anesthesiologists I‐III, who were scheduled to undergo thoracic (non‐cardiac) and abdominal surgery from 1 March 2022 to 31 October 2022 at the Affiliated Hospital of Xuzhou Medical University. Frailty was measured by the 28‐item frailty index, and sarcopenia was assessed sarcopenia was assessed by skeletal muscle index in computed tomographic scan, handgrip strength and 6‐m walk. Participants were classified as follows: Group A: both sarcopenia and frailty; Group B: sarcopenia only; Group C: frailty only; and Group D: neither frailty nor sarcopenia. The primary outcome was 90‐day morbidity. Multivariable logistic regression model was used to estimate the association between sarcopenia, frailty and 90‐day morbidity.

Results

The median (interquartile range) age of participants was 68 (64–72) years, and 62.7% were men. The prevalence rates of sarcopenia and frailty were 32.8% and 47.6%, respectively. The 90‐day morbidity in Group A was 58.5%, in Group B was 46.2%, in Group C was 42.0% and in Group D was 28.8%, and the difference was significant (P < 0.001). In the multivariable analysis, both sarcopenia and frailty [odds ratio (OR), 2.21; 95% confidence interval (CI), 1.26–3.89], sarcopenia only (OR, 1.84; 95% CI, 1.01–3.36), frailty only (OR, 1.77; 95% CI, 1.03–3.03), women (OR, 0.67; 95% CI, 0.45–0.99), body mass index (OR, 0.94; 95% CI, 0.88–0.99), pre‐operative albumin (OR, 0.96; 95% CI, 0.91–1.00) and operative stress score (OSS) [OSS 3 (OR, 2.09; 95% CI, 1.21–3.67); OSS 4–5 (OR, 3.81; 95% CI, 2.31–6.42)] were independently associated with 90‐day morbidity. In the multivariable analysis with inverse probability weighting adjusted cohort, sarcopenia and frailty were also significantly associated with 90‐day morbidity.

Conclusions

Sarcopenia and frailty were associated with higher risks of postoperative 90‐day morbidity in elderly patients alone and in combination. Sex, body mass index, pre‐operative albumin and operative stress were also independent factors for postoperative morbidity within 90 days.

Keywords: Aging, Frailty, Morbidity, Older adult, Sarcopenia

Introduction

According to the United Nations Population report, the global population aged ≥60 years is expected to increase from 13.5% in 2020 to 22% in 2050. ¹ With population aging, the proportion of elderly patients undergoing surgery will also increase significantly. In the United States, elderly patients account for up to 50% of all surgical patients, and in China, they account for 30%. ² Elderly surgical patients face significant challenges of physiological changes, increased oxidative stress, malnutrition, mental decline and social isolation. ³ Despite advances in surgical procedures and perioperative treatment, many elderly surgical patients still suffer from postoperative morbidity, prolonged hospital stay, increased medical burden and decreased quality of life. Hence, it is vital to identify modifiable risk factors and supply supportive interventions.

Sarcopenia (ICD‐10‐CM, M62.84) is an age‐related loss of skeletal muscle mass and muscle strength and/or reduced physical performance. ⁴ Sarcopenia is considered as a strong predictor of postoperative falls, hospitalizations, infection, delirium and death in older individual. ⁵ , ⁶ , ⁷ Frailty is defined as a condition of decreased physiological reserve and failure of homeostatic mechanisms. ⁸ Empirical research suggests that older individuals with frailty have an increased likelihood of hospitalization, postoperative cognitive decline and early mortality. ⁹ , ¹⁰ , ¹¹ Although sarcopenia and frailty can coexist and have been related as states of increased vulnerability due to degradation of functionality and independence, they do not necessarily represent the same disease. ¹²

In the literature, the presence of sarcopenia or frailty is associated with poorer surgical outcomes, but few studies have investigated their joint effects on multiple postoperative outcomes simultaneously. The limited studies on this topic are mostly cross‐sectional studies that have failed to control confounding variables. ¹³ , ¹⁴ Therefore, the association warrants further study. We designed a prospective cohort study to examine the predictive ability of combined sarcopenia and frailty on post‐operative recovery in elderly patients. We hypothesized that patience with sarcopenia and frailty in combination were associated with higher risks of post‐operative 90‐day morbidity.

Methods

Study participants

The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2200057073) and approved by the Ethics Committee of The Affiliated Hospital of Xuzhou Medical University (XYFY2022‐KL039‐01). We followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines. All participants provided written informed consent prior to enrolment.

Patients aged ≥60 years, American society of Anesthesiologists (ASA) I‐III, who were scheduled to undergo selective thoracic (non‐cardiac) or abdominal surgery under general anaesthesia were eligible for inclusion. All patients should have abdominal computed tomographic (CT) scan obtained within 30 days before surgery. Exclusion criteria were bone and joint diseases of both upper limbs (such as rheumatoid arthritis), cognitive dysfunction or psychiatric disease (such as Alzheimer's disease and schizophrenia), being scheduled for intensive care unit admission, or refusing to sign informed consent.

Definitions of sarcopenia

Sarcopenia is defined as an age‐related loss of skeletal muscle mass and muscle strength and/or reduced physical performance. ⁴ Muscle mass was measured by two consecutive transverse CT scans within 30 days before surgery from L3 using the Slice‐O‐Matic version 5.0 program (Tomovision, Montreal, Canada), which allows the demarcation of specific tissue using Hounsfield unit boundaries of −29 to +150 for the skeletal muscles. The skeletal muscle area (SMA, cm²) is the total muscle area of the psoas, paraspinal, internal oblique, external oblique, rectus abdominis and transversus abdominis muscles. Skeletal muscle index (SMI, SMI = SMA/(height (m) × height (m))) was normalized to height. Low muscle mass was defined as a SMI lower than 52.3 cm²/m² for men or 38.6 cm²/m² for women who did not have obesity (body mass index (BMI) < 30 kg/m²) or an SMI lower than 54.3 cm²/m² for men or 46.6 cm²/m² for women who had obesity (BMI ≥ 30 kg/m²). ¹⁵ All CT images were analysed by a single trained research staff member masked to outcomes.

Muscle strength was measured by handgrip strength. It was measured using a Jamar J00105 hydraulic hand dynamometer, taking the maximum reading of two trials with the dominant hand in a maximum‐effort isometric contraction. Low muscle strength was diagnosed as the grip strength <18 kg in women or <28 kg in men. ⁴

Physical performance was measured by the time taken to walk 6 m at a normal pace from a moving start, without deceleration, and taking the average result of at least two trials as the recorded speed. Low physical performance was diagnosed as 6‐m walk <1.0 m/s.

Definitions of frailty

Frailty was measured by using 28‐item frailty index. ¹⁶ The frailty index was calculated for each participant as the number of deficits present in a person divided by the 28 deficits considered. Then further categorized the frailty index into three levels (robust (frailty index ≤ 0.10), pre‐frail (0.10 < frailty index < 0.25) and frail (frailty index ≥ 0.25)). For ease of grouping, people were classified as not frail (frailty index < 0.25) and frail (frailty index ≥ 0.25) (Table S1).

Data collection

Demographic characteristics and clinical data were collected from each participant. The co‐morbidities graded using the age‐adjusted Charlson co‐morbidity index (aCCI), ¹⁷ the Morse Fall Scale score, ¹⁸ nutritional status (assessed using the Malnutrition Universal Screening Tool), ¹⁹ activities of daily living (evaluated using Barthel index), ²⁰ sleep quality (measured using numeric rating scale; 0–10, 0 = very dissatisfied, 10 = very satisfied) were evaluated by trained, employed nurses of our department. The operative stress score (OSS) was used to categorize procedures as low (1–2), moderate (3) and high (4–5) stress. ²¹ Post‐operative recovery was evaluated based on morbidity, length of stay, re‐admission, short‐term recovery (including extubation time, post‐operative activity time, exhaust time and defecation time) and long‐term recovery (including 90‐day sleep quality and daily activities). Morbidity was graded according to the Clavien–Dindo classification ²² (Table S2). Major morbidity was defined as Clavien–Dindo classification score ≥ 3. Post‐operative pulmonary complications (PPCs) were defined as a collapsed composite outcome of atelectasis, respiratory infection, pleural effusion, pneumonia, respiratory failure or acute respiratory distress syndrome. ²³

Outcome measures

The primary outcome was 90‐day morbidity. The secondary outcomes included: (1) extubation time; (2) length of stay in post‐anesthesia care unit; (3) out‐of‐bed mobilization; (4) post‐operative exhaust and defecation time; (5) post‐operative oral feeding time; (6) post‐operative hospital stay; (7) major morbidity; (8) incidence of PPCs; (9) 90‐day readmission rate; (10) 90‐day mortality; (11) scores of activities of daily living within 90 days; (12) sleep quality within 90 days.

Sample size

The sample size estimation was based on the post‐operative morbidity and on the principle of 10 outcome events per variable. Referring to previous literatures, the morbidity in elderly patients was about 38%. ²⁴ Using an estimated morbidity of 38% and for 20 predictors, we aimed to enrol 526 patients. Considering a dropout rate of 10–15%, the study was designed to enrol 600 patients.

Statistical analysis

The distribution of variables was assessed using the Shapiro–Wilk test. Normally distributed continuous variables were presented as the mean ± standard deviation (mean ± SD) and were compared using Student's t‐test. Non‐normally distributed continuous data were reported as median (interquartile range, IQR) and compared using the Kruskal–Wallis test. Categorical data were reported as frequency (%) and were analysed using the chi‐squared test or Fisher's exact test. Post hoc analysis was performed using Bonferroni adjustment in the case of statistically significant differences observed among multiple groups.

Data without any information about 90‐day morbidity were deleted altogether, and the remaining missing data were supplied by multiple imputation.

Multivariable logistic regression models were used to estimate the association between sarcopenia and frailty and 90‐day morbidity. The Least Absolute Shrinkage and Selection Operator regression analysis was performed including statistically significant risk factors in the univariable analysis to filter non‐zero characteristic factors. Multivariable logistic regression analysis was used to identify the independent risk factors associated with 90‐day morbidity. In addition, the inverse probability treatment weighting (IPTW) method for multiple groups was performed to control for observed potential confounding variables. The IPTW‐adjusted odds ratio (OR) was calculated by multivariable logistic regression analyses in the IPTW‐adjusted cohort. Spearman correlation analysis was performed to examine the relationship between frailty and sarcopenia. We test additive and multiplicative interactions in the same logistic regression model and measure additivity by relative excess risk due to interaction, proportion of disease attributable to interaction, and synergy index.

Statistical analysis was performed using R software (version 4.2.2). The reported statistical significance levels were all two‐sided, with statistical significance set at 0.05.

Results

Baseline characteristics

Seven hundred patients were assessed for eligibility, and 574 patients were finally analysed (Figure 1). Baseline median (interquartile range) age of participants was 68 (64–72) years, and 62.7% were men. The prevalence of sarcopenia was 32.8%, 68.1% in men and 31.9% in women (P = 0.013). The prevalence of frailty was 47.6%, 57.1% in men and 42.9% in women (P = 0.006). No differences in age, sex, BMI, ASA, aCCI, nutritional status, sleep quality, Morse Fall Scale score, score of activities of daily living, pre‐operative albumin, pre‐operative pre‐albumin and pre‐operative haemoglobin were observed among the groups as shown in Table 1 (P < 0.05).

Table 1.

Demographic characteristics of study participants

Characteristics	No. (%)
Characteristics	Group A: Both sarcopenia and frailty (n = 123)	Group B: Sarcopenia only (n = 65)	Group C: Frailty only (n = 150)	Group D: Neither frailty nor sarcopenia (n = 236)	P
Age, year	72.00 (66.00–76.00)	69.00 (64.00–72.50) ^a	68.00 (64.00–72.00) ^a	66.00 (62.00–70.00) ^a ^c	<0.001
Sex (men/women)	81/42	47/18	75/75 ^a ^b	157/79 ^c	0.002
BMI, kg/m²	24.30 (20.98–25.75)	22.95 (22.06–23.69)	26.40 (22.03–28.20) ^a , ^b	23.97 (21.80–26.21) ^a ^c	<0.001
ASA (II/III)	26/97	35/30 ^a	41/109 ^b	198/38 ^a ^c	<0.001
aCCI	7 (5–8)	5 (4–6) ^a	6 (5–7) ^a ^b	4 (4–6) ^a ^c	<0.001
Nutritional status	1 (0–1)	0 (0–1) ^a	0 (0–1) ^a	0 (0–1) ^a	<0.001
Sleep quality	6 (5–7)	7 (6–8) ^a	7 (5–8) ^a	7 (6–8) ^a ^c	<0.001
Score of MFS	15 (15, 40)	15 (15, 35) ^a	15 (15, 40)	15 (15, 15) ^a ^c	<0.001
Score of activities of daily living	100 (100, 100)	100 (100, 100)	100 (100, 100)	100 (100, 100) ^a ^b	<0.001
Smoking history, No.	44 (35.8)	19 (29.2)	46 (30.7)	80 (33.9)	0.729
Alcohol history, No.	34 (27.6)	20 (30.8)	36 (24.0)	72 (30.5)	0.540
Preoperative blood glucose, mmol/L	5.32 (4.76,5.83)	5.12 (4.52,5.49)	5.29 (4.75,5.73)	5.31 (4.85,5.56)	0.341
Preoperative albumin, g/dL	4.03 (3.71,4.27)	4.19 (3.79,4.34)	4.21 (3.89,4.45) ^a	4.19 (3.92,4.49) ^a	<0.001
Preoperative pre‐albumin, mg/L	215.00 (168.00, 241.00)	232.00 (179.00, 264.50)	224.00 (191.75, 259.00)	229.00 (197.25, 275.75) ^a	0.001
Preoperative haemoglobin, g/dL	12.70 (11.00, 13.90)	13.30 (11.55, 14.30)	13.10 (11.88, 14.00)	13.50 (12.40, 14.78) ^a ^c	<0.001
Preoperative platelet count, ×10³/μL	218.00 (177.00, 291.00)	217.00 (174.00, 263.00)	210.50 (175.75, 265.25)	204.50 (171.25, 244.25)	0.340

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Nutritional status was assessed using the Malnutrition Universal Screening Tool. Sleep quality was measured using numeric rating scale: 0–10, 0 = very dissatisfied, 10 = very satisfied. Score of activities of daily living was evaluated using Barthel index.

ASA, American society of Anesthesiologists; aCCI, age‐adjusted Charlson co‐morbidity index; BMI, body mass index; MFS, Morse Fall Scale.

^{^a}

Compared with group A, P < 0.008.

^{^b}

Compared with group B, P < 0.008.

^{^c}

Compared with group C, P < 0.008.

Clinical characteristics

No significant differences in OSS, proportion of endoscopic surgery, fluid input and blood loss were observed among the four groups (P > 0.05). Procedure type, operative time, blood transfusion and urine output did differ among the four groups (P < 0.05) (Table 2).

Table 2.

Clinical characteristics of study participants

Characteristics	No. (%)
Characteristics	Group A: Both sarcopenia and frailty (n = 123)	Group B: Sarcopenia only (n = 65)	Group C: Frailty only (n = 150)	Group D: Neither frailty nor sarcopenia (n = 236)	P
Surgical approach, No.
Laparoscopic	103 (83.7)	54 (83.1)	129 (86.0)	199 (84.3)	0.936
Procedure type, No.					<0.001
Thoracic	18 (14.6)	17 (26.2)	52 (34.7) ^a	44 (18.6) ^c
Upper abdominal	72 (58.5)	32 (49.2)	65 (43.3)	103 (43.6)
Lower abdominal	27 (22.0)	14 (21.5)	15 (10.0)	57 (24.2)
Urology	6 (4.9)	2 (3.1)	18 (12.0)	32 (13.6)
Operative stress, No.
Low (OSS 1–2)	25 (20.3)	11 (16.9)	47 (31.3)	53 (22.5)	0.084
Moderate (OSS 3)	28 (22.8)	22 (33.8)	41 (27.3)	63 (26.7)
High (OSS 4–5)	70 (56.9)	32 (49.2)	62 (41.3)	120 (50.8)
Operative time, min	190.2 (120.0–240.0)	174.9 (108.5–240.0)	150.0 (90.0–221.4)	136.5 (90.0–210.0) ^a	0.004
Fluid intake, mL	1775 (1250–2250)	1500 (1250–2250)	1500 (1000–2275)	1500 (1000–2000)	0.049
Blood transfusion, No.	14 (11.4)	4 (6.2)	6 (4.0)	8 (3.4) ^a	0.019
Urine output, mL	300 (180–400)	300 (200–400)	300 (200–400)	200 (50–300) ^b ^c	0.001
Blood loss, mL	100 (50–200)	85 (50–200)	80 (50–170)	70 (50–140)	0.397
Unplanned transfer to ICU, No.	11 (8.9)	1 (1.5)	4 (2.7)	4 (1.7)	0.007

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ICU, intensive care unit; OSS, Operative Stress Score.

^{^a}

Compared with group A, P < 0.008.

^{^b}

Compared with group B, P < 0.008.

^{^c}

Compared with group C, P < 0.008.

Associations of sarcopenia and frailty with post‐operative outcomes

The frequency of 90‐day morbidity differed significantly among the groups (Table 3). Pairwise comparisons between groups revealed significant differences only when comparing the group with neither frailty nor sarcopenia (28.8%) with any of the remaining three groups (Group A, 58.5%; Group B, 46.2%; Group C, 42.0%) (all P < 0.008). The incidence of major morbidity was difference in the four groups (P = 0.044), but subsequent pairwise comparisons with correction for multiple comparisons did not reveal statistically significant differences among the groups. The incidence of PPCs, post‐operative exhaust time, defecation time, oral feeding time and post‐operative hospital stay differed among the four groups (P < 0.05). The incidence of PPCs was higher in group A than in groups (P < 0.008). In addition, the post‐operative exhaust time, defecation time, oral feeding time and post‐operative hospital stay were longer in Group A than in the other Group D (all P < 0.008). Extubation time, length of stay in post‐anaesthesia care unit, out‐of‐bed mobilization, 90‐day readmission rate and 90‐day mortality did not differ significantly among the groups.

Table 3.

Short‐term and long‐term postoperative outcomes

Outcome	No. (%)
Outcome	Group A: Both sarcopenia and frailty (n = 123)	Group B: Sarcopenia only (n = 65)	Group C: Frailty only (n = 150)	Group D: Neither frailty nor sarcopenia (n = 236)	P
Extubation time, min	25 (20–30)	25 (15–30)	25 (20–25)	20 (20–30)	0.634
Duration stayed in PACU, min	30 (25–40)	30 (25–40)	25 (25–40)	30 (25–40)	0.534
Out‐of‐bed mobilization, day	2 (1–2)	1 (1–2)	1 (1–2)	1 (1–2)	0.153
Post‐operative exhaust time, day	2 (1–3)	2 (1–3)	2 (1–3)	2 (1–3) ^a	0.032
Post‐operative defecation time, day	4 (3–5)	3 (3–5)	4 (3–4)	3 (3–4) ^a	0.014
Post‐operative oral feeding time, year	4 (1–6)	2 (1–5)	2 (1–5) ^a	2 (1–4) ^a	0.009
Re‐operation, No.	1 (0.8)	1 (1.5)	3 (2.0)	1 (0.4)	0.399
Postoperative hospital stay, day	8.00 (5.00–11.25)	7.50 (5.25–9.75)	7.00 (4.75–10.00)	7.00 (4.00–10.00) ^a	0.014
90‐day mortality, No.	2 (1.6)	1 (1.5)	2 (1.3)	0	0.117
90‐day morbidity, No.	72 (58.5)	30 (46.2)	63 (42.0)	68 (28.8) ^a ^b ^c	<0.001
PPCs	51 (41.5)	23 (35.4)	48 (32.0)	50 (21.2) ^a	0.001
Clavien–Dindo classification, No.
1–2	61 (49.6)	24 (36.9)	52 (34.7)	60 (25.4) ^a	<0.001
3–4	12 (9.8)	6 (9.2)	12 (8.0)	8 (3.4)	0.044
Readmission rate, No.	13 (10.6)	5 (7.7)	16 (10.7)	14 (5.9)	0.298
Necessary admission rate, No.	10 (8.1)	4 (6.2)	13 (8.7)	8 (3.4)	0.106
90‐day activities of daily living scores	100 (90, 100)	100 (95, 100)	100 (100, 100) ^a	100 (100, 100)^ab	<0.001
90‐day sleep quality	6 (5–7)	7 (6.5–8) ^a	7 (5–8)	7 (6–8) ^a , ^c	<0.001

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Morbidity was graded according to the Clavien–Dindo classification. Major morbidity was defined as Clavien–Dindo classification score ≥ 3. Sleep quality was measured using numeric rating scale; 0–10, 0 = very dissatisfied, 10 = very satisfied. Score of activities of daily living was evaluated using Barthel index.

PACU, post‐anesthesia care unit; PPCs, postoperative pulmonary complications.

^{^a}

Compared with group A, P < 0.008.

^{^b}

Compared with group B, P < 0.008.

^{^c}

Compared with group C, P < 0.008.

Risk factors associated with 90‐day morbidity

According to the Least Absolute Shrinkage and Selection Operator regression analysis (Figure 2), we selected eight non‐zero characteristic variables including both sarcopenia and frailty, neither frailty nor sarcopenia, sex, operative stress, ASA grade, BMI, pre‐operative albumin and aCCI (Table 4). In the multivariable analysis, both sarcopenia and frailty [OR, 2.21; 95% confidence interval (CI), 1.26–3.89], sarcopenia only (OR, 1.84; 95% CI, 1.01–3.36), frailty only (OR, 1.77; 95% CI, 1.03–3.03), women (OR, 0.67; 95% CI, 0.45–0.99), BMI (OR, 0.94; 95% CI, 0.88–0.99), pre‐operative albumin (OR, 0.96; 95% CI, 0.91–1.00) and operative stress [OSS 3 (OR, 2.09; 95% CI, 1.21–3.67); OSS 4–5 (OR, 3.81; 95% CI, 2.31–6.42)] were independently associated with 90‐day morbidity (Figure 3). In the multivariable analysis with inverse probability weighting adjusted cohort, both sarcopenia and frailty (OR, 2.11; 95% CI, 1.25–3.58), sarcopenia only (OR, 2.51; 95% CI, 1.31–4.79), frailty only (OR, 2.24; 95% CI, 1.39–3.64) were also independently associated with 90‐day morbidity (Table 5). Sarcopenia and frailty were poor correlated (r_s = 0.259, P < 0.001). The multiplicative interaction between sarcopenia and frailty was not significant (P = 0.384). The additive interaction terms was no either statistically significant interaction (relative excess risk due to interaction, −0.39, 95% CI, −1.89–1.11; proportion of disease attributable to interaction, −0.18, 95% CI, −0.89‐0.54; synergy index, 0.76, 95% CI, 0.27–2.12).

Demographic and clinical feature selection using LASSO regression. (A) The selection of the tuning parameter (lambda) in the LASSO model used 10‐fold cross‐validation with the minimum criteria. The relationship curve between partial likelihood deviation (binomial deviation) and log (lambda) was plotted. Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 standard error (SE) of the minimum criteria (the 1‐SE criteria). (B) LASSO coefficient profiles of the eight features. A coefficient profile plot was produced against the log (lambda) sequence. Vertical line was drawn at the value selected using 10‐fold cross‐validation, where optimal lambda resulted in eight features with non‐zero coefficients.

Table 4.

Coefficients and Lambda.1se value of the LASSO regression

Characteristics	Coefficients	Lambda.1se
Both sarcopenia and frailty	0.104	0.047
Neither frailty nor sarcopenia	−0.268
Sex	−0.001
Surgery grade	0.409
ASA grade	0.261
BMI	−0.017
Preoperative albumin	−0.017
aCCI	0.007

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aCCI, age‐adjusted Charlson co‐morbidity index; ASA, American society of Anesthesiologists; BMI, body mass index.

Logistic regression analysis with 90‐day morbidity.

Table 5.

Associations between sarcopenia, frailty and 90‐day morbidity in the crude analysis, multivariable analysis, and propensity‐score analyses

Analysis	Odds ratio (95% CI)
Crude analysis
Both sarcopenia and frailty	3.49 (2.21–5.50)
Sarcopenia only	2.12 (1.21–3.72)
Frailty only	1.79 (1.16–2.75)
Neither frailty nor sarcopenia	1 [Reference]
Multivariable analysis
Both sarcopenia and frailty	2.21 (1.26–3.89)
Sarcopenia only	1.84 (1.01–3.36)
Frailty only	1.77 (1.03–3.03)
Neither frailty nor sarcopenia	1 [Reference]
Propensity‐score analysis
With inverse probability weighting
Both sarcopenia and frailty	2.11 (1.25–3.58)
Sarcopenia only	2.50 (1.31–4.79)
Frailty only	2.24 (1.39–3.64)
Neither frailty nor sarcopenia	1 [Reference]

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Discussion

In this prospective cohort study, we found that elderly patients with sarcopenia and frailty had poor post‐operative recovery, as indicated by a higher incidence of 90‐day morbidity, longer hospital stay, and reduced post‐operative self‐care capacity. Sex, operative stress, BMI, pre‐operative albumin, and co‐morbidity can significantly predict post‐operative recovery.

Our study found that sarcopenia was an independent risk factor for post‐operative morbidity, which is consistent with other studies. A study involving 234 patients after liver resection for malignant tumours reported that patients with sarcopenia had a statistically significantly higher rate of 90‐day morbidity than patients in the other groups and had longer hospital stays and higher readmission rates. ²⁵ Another study involving 404 patients undergoing radical gastrectomy for gastric cancer also found that sarcopenia significantly increased post‐operative morbidity in gastric cancer patients, resulting in longer post‐operative hospital stay and shorter overall survival and disease‐free survival. ²⁶ The reasons for the high morbidity in elderly patients with sarcopenia may be as follows: (1) The decrease of amino acids released by skeletal muscle of sarcopenia patients lead to the lack of raw materials for the synthesis of acute phase proteins by the liver. In addition, glutamine is the main energy source for the immune cells and its deficiency would lead to decreased immune cell activity. ²⁷ , ²⁸ (2) The ability of free radical scavenging in skeletal muscle of sarcopenia patients was decreased. After surgery, excessive free radicals in the body could not be removed in time, leading to tissue damage aggravated. ²⁹ (3) Sarcopenic patients with decreased muscle mass and muscle strength were prone to falls. Post‐operative exercise volume decreased, which would further aggravate sarcopenia. ³⁰

Frailty is a multifactorial disorder that affects several dimensions, including physical function, cognition and psychosocial independence. The most famous and validated methods to assess frailty are the Fried's phenotype and the FRAIL scale. These scales focus on physical frailty only and not always convenient to use in a clinical setting because of a lack of machinery to complete a handgrip test, lack of space for a walk test or lack of time to perform multiple measurements. Furthermore, because of ethnic difference, these scales based on Western constitutions are not optimal for the Chinese. In this study, we assessed patients' frailty using the frailty index. ¹⁶ It is a validated tool based on a database of half a million Chinese adults, encompassing medical conditions, symptoms, signs and physical measurements. In our study, men were significantly more likely to have frailty, although the opposite was the case elsewhere. ³¹ Potential explanations for this may be methodological, including differences in frailty assessment scales and patient selection criteria.

Our study found that frailty is also an independent risk factor for post‐operative morbidity, which is consistent with the other studies. A study involving 645 patients undergoing non‐cardiac surgery found that the need, cost and length of hospital stay were significantly increased in patients with frailty, and frailty was the only significant predictor of death or new disability. ³² Another study with long‐term follow‐up of 52 012 patients undergoing cancer surgery found that each 10% increase in frailty (patient was assessed by pre‐operative frailty index, and the frailty index was as a linear continuous variable per each 0.1 unit increase) was associated with a 1.60‐fold relative decrease in survival. Increased frailty was associated with longer hospital stays, fewer days alive and at home, more frequent discharge to a nursing facility, and increased healthcare costs. ³³ Another large study found that 57.3% of complications are the mediators of the association between frailty and mortality, and cardiopulmonary complications had the highest mortality. ³⁴

Sarcopenia and frailty share some features: high prevalence in the aged population; a strong association with adverse health outcomes; and the possibility of reversibility. Although an overlap is found between the characteristics of sarcopenia and frailty, they are not equivalent. Esmee found that sarcopenia and frailty are two separate conditions based on the current definitions. ¹² Sarcopenia was thought to be a biological substrate of frailty; however, not all sarcopenic individuals are frail. In our study, 34.6% of sarcopenic patients were not frail, while 54.9% of frail patients were not sarcopenic, which is consistent with previous findings. ³⁵ , ³⁶ A 10‐year follow‐up research of 716 community‐dwelling adults aged ≥65 years in Australia found that frailty and sarcopenia in combination are more predictive of mortality than either condition alone, and there was no significant interaction between sarcopenia and frailty, indicating that each acted independently, having an additive effect on mortality, which is similar to the results of our study. ³⁵ Sarcopenia is mainly assessed at the muscle, including muscle mass, muscle strength and physical function. However, frailty includes multiple dimensions, including physical frailty, psychological frailty, social frailty and cognitive frailty. There is a high degree of overlap between sarcopenia and frailty when assessing physical frailty. In general, sarcopenia and frailty are two different diseases, and sarcopenia cannot be simply regarded as part of frailty.

Future challenges include health consultation and management to prevent sarcopenia and frailty in primary health care or community preventive services settings, identifying high‐risk populations and targeting aggressive interventions in clinical research settings. Sarcopenia and frailty are both theoretically reversible, and the surgical morbidity associated with them is also possibly avoidable. Treatment of sarcopenia may focus on increasing muscle mass and strength through a combination of exercise and appropriate protein intake, whereas frailty may require attention to the underlying diverse pathophysiology of the different domains. ³⁷ In fact, surgical therapy, especially for malignancy, is frequently expedited, and there is no enough time for physical optimization before surgery. Hence, preventing sarcopenia and frailty in daily life is a top priority.

The prevalence of morbidity increases with the risk factors which include sex, BMI, pre‐operative albumin and operative stress. In general, men had a higher incidence of sarcopenia and women had a higher incidence of frailty. Because 62.7% of the enrolled population was male, the incidence of sarcopenia and frailty was higher in men than in women, which may further amplify the effect of gender on the morbidity. In this study, only 3% of the elderly patients had obesity (BMI ≥ 30 kg/m²). Based on this, we found that higher BMI was associated with a lower morbidity. However, for patients with sarcopenic obesity, higher BMI was positively correlated with post‐operative morbidity, because the synergistic effect of sarcopenia and obesity could lead to aggravation of metabolic disorders and increase the risk of cardiovascular disease. ³⁸ The risk of adverse outcomes such as falls, disability and death was higher than patience with normal BMI. ³⁸ Albumin, a small globular protein produced by the liver, has physiological properties that include anti‐inflammatory, antioxidant, anticoagulant and antiplatelet aggregation activity as well as colloid osmotic effect. Previous researches have confirmed that pre‐operative hypoalbuminaemia is related to the prognosis of patients. Patients with hypoalbuminaemia have a higher risk for surgical site infection, pneumonia, extended length of stay and readmission. ³⁹ Our study also found that albumin is an independent risk factor for post‐operative morbidity. In addition to hypoproteinemia, clinicians should also focus on the changes in albumin levels, which may indicate postoperative adverse events. The OSS was developed to reflect the estimated physiologic stress caused by an operation and was linked to post‐operative outcomes. Post‐operative mortality increased with the increase of OSS in robust and frail patients, which was consistent with our study. ⁴⁰

One strength of this study is that the evidence was based on elderly patients undergoing surgery. Some physiological changes occur as people age, such as altered endocrine function, increased oxidative stress, inflammation, chronic diseases and malnutrition. ³ These changes make the elderly vulnerable to stressors and frequently result in a reduced physiological reserve in multiple organs and a limited capacity to maintain homeostasis. Hence, elderly surgical patients always present with varying degrees of sarcopenia and frailty that are related to illnesses, which require surgical interventions. For them, operation is a significant physical and psychological blow that drains their limited reserves and leads to decompensation. Several surgical outcome studies focused on groups of high‐risk procedures rather than broadly assessing the risk across diverse surgeries. ⁵ , ⁶ , ¹⁵ , ²⁵ Our prospective cohort study included multiple types of surgery with different operative stress to explore the impact of sarcopenia and frailty on elderly surgical patients. In addition to tracking mortality and complication rates after surgery, we also define ‘recovery’ from the patient's point of view such as 90‐day sleep quality and daily activities. Post‐operative recovery should focus on prolonging the time of life, as equally important as maintaining functional independence and quality of life, while avoiding unnecessary admissions to hospital or longterm care facilities.

There are some limitations to our study. First, unmeasured confounders may exist because of the observational nature of this research. Second, some of the self‐reported parameters are subjective in nature and may introduce informational bias. The patients we included must have abdominal CT scan within 30 days before surgery, which may lead to selection bias. Third, in view of the deterministic influence of ethnicity on muscle characteristics, Chinese diagnostic criteria for sarcopenia based on L3‐SMI have not been established. We referred to previous literature for the cut‐off values of L3‐SMI. ¹⁵ These values may be significantly large for Chinese populations and result in a low prevalence of sarcopenia. Fourth, frailty can be divided into robust, pre‐frailty and frailty, and sarcopenia can be classified into no sarcopenia, sarcopenia and severe sarcopenia. However, we did not perform a stratified analysis. Finally, the long‐term effects of sarcopenia and frailty on post‐operative recovery in elderly patients were not observed. Hence, further multi‐centre, large‐sample trials are needed to investigate the long‐term impacts of sarcopenia and frailty on post‐operative recovery in elderly patients and explore the effective treatment for sarcopenia and frailty.

Conclusions

The morbidity risk was twice as high for patients who had both sarcopenia and frailty than for those with neither of these conditions, over a 90‐day follow‐up period. Frailty and sarcopenia acted independently and had an additive effect on morbidity. This study has established patient‐related risk factors of morbidity to be sarcopenia and frailty, sex, BMI, pre‐operative albumin and operative stress.

Funding

This study was supported by Natural Science Research Project of Jiangsu Higher Education Institutions (22KJA320007). Commercial funding was not received.

Conflict of interest

The authors declare that they have no conflict of interest.

Supporting information

Table S1. List of 28 variables included in the frailty index.

Table S2. Clavien‐Dindo Classification.

Click here for additional data file.^{(20.5KB, docx)}

Acknowledgements

The authors thank the colleagues and staff for their cooperation in data collection. All authors comply with the ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle.

Guo K., Wang X., Lu X., Yang Y., Lu W., Wang S., et al (2023) Effects of sarcopenia and frailty on postoperative recovery in elderly patients: A prospective cohort study, Journal of Cachexia, Sarcopenia and Muscle, 14, 2642–2652, doi: 10.1002/jcsm.13337

Kedi Guo, Xinghe Wang and Xian Lu contributed equally to this work.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. List of 28 variables included in the frailty index.

Table S2. Clavien‐Dindo Classification.

Click here for additional data file.^{(20.5KB, docx)}

[jcsm13337-bib-0001] 1. United Nations Department of Economic and Social Affairs . World population prospects 2022: summary of results. New York: United Nations; 2022. http://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/fles/wpp2022_summary_of_results.pdf. Accessed 1 Sept 2022. [Google Scholar]

[jcsm13337-bib-0002] 2. Catananti C, Gambassi G. Pain assessment in the elderly. Surg Oncol 2010;19:140–148. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0003] 3. Gómez‐Ríos MÁ, Abad‐Gurumeta A. Anesthesia in the elderly patient. Resilience in frailty time. Med Clin (Barc) 2022;159:486–488. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0004] 4. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc 2020;21:300–7.e2. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0005] 5. Lieffers JR, Bathe OF, Fassbender K, Winget M, Baracos VE. Sarcopenia is associated with postoperative infection and delayed recovery from colorectal cancer resection surgery. Br J Cancer 2012;107:931–936. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0006] 6. Mosk CA, van Vugt JLA, de Jonge H, Witjes CD, Buettner S, Ijzermans JN, et al. Low skeletal muscle mass as a risk factor for postoperative delirium in elderly patients undergoing colorectal cancer surgery. Clin Interv Aging 2018;13:2097–2106. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0007] 7. Cruz‐Jentoft AJ, Sayer AA. Sarcopenia. Lancet 2019;393:2636–2646. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0008] 8. Dent E, Martin FC, Bergman H, Woo J, Romero‐Ortuno R, Walston JD. Management of frailty: opportunities, challenges, and future directions. Lancet 2019;394:1376–1386. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0009] 9. Ijaz N, Buta B, Xue QL, Mohess DT, Bushan A, Tran H, et al. Interventions for frailty among older adults with cardiovascular disease: JACC state‐of‐the‐art review. J Am Coll Cardiol 2022;79:482–503. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0010] 10. Evered LA, Vitug S, Scott DA, Silbert B. Preoperative frailty predicts postoperative neurocognitive disorders after total hip joint replacement surgery. Anesth Analg 2020;131:1582–1588. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0011] 11. McIsaac DI, Taljaard M, Bryson GL, Beaulé PE, Gagne S, Hamilton G, et al. Frailty and long‐term postoperative disability trajectories: a prospective multicentre cohort study. Br J Anaesth 2020;125:704–711. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0012] 12. Reijnierse EM, Trappenburg MC, Blauw GJ, Verlaan S, de van der Schueren MA, Meskers CG, et al. Common ground? The concordance of sarcopenia and frailty definitions. J Am Med Dir Assoc 2016;17:371.e7–371.e12. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0013] 13. Davies B, García F, Ara I, Artalejo FR, Rodriguez‐Mañas L, Walter S. Relationship between sarcopenia and frailty in the Toledo study of healthy aging: a population based cross‐sectional study. J Am Med Dir Assoc 2018;19:282–286. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0014] 14. Mijnarends DM, Schols JM, Meijers JM, Tan FE, Verlaan S, Luiking YC, et al. Instruments to assess sarcopenia and physical frailty in older people living in a community (care) setting: similarities and discrepancies. J Am Med Dir Assoc 2015;16:301–308. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0015] 15. Xiao J, Caan BJ, Cespedes Feliciano EM, Meyerhardt JA, Peng PD, Baracos VE, et al. Association of low muscle mass and low muscle radiodensity with morbidity and mortality for colon cancer surgery. JAMA Surg 2020;155:942–949. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0016] 16. Fan J, Yu C, Guo Y, Bian Z, Sun Z, Yang L, et al. Frailty index and all‐cause and cause‐specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health 2020;5:e650–e660. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0017] 17. Liu J, Li J, He J, Zhang H, Liu M, Rong J. The age‐adjusted Charlson comorbidity index predicts post‐operative delirium in the elderly following thoracic and abdominal surgery: a prospective observational cohort study. Front Aging Neurosci 2022;14:979119. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0018] 18. Lohse KR, Dummer DR, Hayes HA, Carson RJ, Marcus RL. Combining the AM‐PAC ‘6‐Clicks‘ and the morse fall scale to predict individuals at risk for falls in an inpatient rehabilitation hospital. Arch Phys Med Rehabil 2021;102:2309–2315. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0019] 19. Leonard JA, Hayden JA, Tripuraneni PS, Maxwell JH, Giurintano JP. Malnutrition universal screening tool predicts postoperative complications following major head and neck cancer surgery. Head Neck 2023;45:604–611. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0020] 20. Kaambwa B, Bulamu NB, Mpundu‐Kaambwa C, Oppong R. Convergent and discriminant validity of the Barthel index and the EQ‐5D‐3L when used on older people in a rehabilitation setting. Int J Environ Res Public Health 2021;18:10314. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0021] 21. Yan Q, Kim J, Hall DE, Shinall MC Jr, Reitz KM, Stitzenberg KB, et al. Association of frailty and the expanded operative stress score with preoperative acute serious conditions, complications and mortality in males compared to females: a retrospective observational study. Ann Surg 2023;277:e294–e304. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0022] 22. Kengsakul M, Nieuwenhuyzen‐de Boer GM, Udomkarnjananun S, Kerr SJ, van Doorn HC, van Beekhuizen HJ. Clinical validation and comparison of the comprehensive complication index and Clavien‐Dindo classification in predicting post‐operative outcomes after cytoreductive surgery in advanced ovarian cancer. Int J Gynecol Cancer 2023;33:263–270. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0023] 23. Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth 2017;118:317–334. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0024] 24. Zhang F, He ST, Zhang Y, Mu DL, Wang DX. Comparison of two malnutrition assessment scales in predicting postoperative complications in elderly patients undergoing noncardiac surgery. Front Public Health 2021;9:694368. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0025] 25. Diao YK, Liang L, Yang T. Association of sarcopenia and body composition with postoperative 90‐day morbidity after liver resection for malignant tumors. JAMA Surg 2021;156:590. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0026] 26. Huang DD, Cai HY, Wang WB, Song HN, Luo X, Dong WX, et al. Measurement of muscle quantity/quality has additional predictive value for postoperative complications and long‐term survival after gastrectomy for gastric cancer in patients with probable sarcopenia as defined by the new EWGSOP2 consensus: analysis from a large‐scale prospective study. Nutrition 2021;86:111156. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0027] 27. Chen F, Chi J, Liu Y, Fan L, Hu K. Impact of preoperative sarcopenia on postoperative complications and prognosis of gastric cancer resection: a meta‐analysis of cohort studies. Arch Gerontol Geriatr 2022;98:104534. [DOI] [PubMed] [Google Scholar]

[jcsm13337-bib-0028] 28. Lutz CT, Quinn LS. Sarcopenia, obesity, and natural killer cell immune senescence in aging: altered cytokine levels as a common mechanism. Aging (Albany NY) 2012;4:535–546. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jcsm13337-bib-0029] 29. Ji LL, Kang C, Zhang Y. Exercise‐induced hormesis and skeletal muscle health. Free Radic Biol Med 2016;98:113–122. [DOI] [PubMed] [Google Scholar]

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PERMALINK

Effects of sarcopenia and frailty on postoperative recovery in elderly patients: A prospective cohort study

Kedi Guo

Xinghe Wang

Xian Lu

Yuping Yang

Wensi Lu

Shuting Wang

Xihui Tang

Yan Wu

Yuqing Xu

Qingsong Chen

Su Liu

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study participants

Definitions of sarcopenia

Definitions of frailty

Data collection

Outcome measures

Sample size

Statistical analysis

Results

Baseline characteristics

Figure 1.

Table 1.

Clinical characteristics

Table 2.

Associations of sarcopenia and frailty with post‐operative outcomes

Table 3.

Risk factors associated with 90‐day morbidity

Figure 2.

Table 4.

Figure 3.

Table 5.

Discussion

Conclusions

Funding

Conflict of interest

Supporting information

Acknowledgements

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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