Impact of pre-operative frailty on hospital length of stay in major abdominal oncological surgeries: A prospective, observational study

Abstract

Background and Aims:

Frailty increases vulnerability to stressors, negatively impacting cancer outcomes. We evaluated the association between pre-operative frailty and post-operative outcomes in Indian patients undergoing major abdominal cancer surgery and explored ultrasound-based sarcopenia markers for frailty screening.

Methods:

Frailty was assessed using the Fried Frailty Phenotype. The primary outcome was hospital length of stay (LOS); secondary outcomes included severe complications (Clavien–Dindo III/IV), intensive care unit stay, and 30- and 90-day mortality. Pre-operative ultrasound-guided quadriceps thickness and rectus femoris cross-sectional area (CSA) were measured, and their cut-off values were derived via receiver operating characteristic (ROC) curves as screening parameters for pre-operative frailty.

Results:

Among 142 patients, 68 (47.8%) were frail. Frail patients had longer hospital stays {median 9 vs. 6 days; relative risk (RR) 1.20 [95% confidence interval (CI) 1.02 to 1.41]}; P = 0.031. On multivariate analysis, frailty {RR 1.16 [95% CI 1.03 to 1.31]}; P = 0.012 and American Society of Anesthesiologists physical status (ASA-PS) > III {RR 1.35 [95% CI 1.10 to 1.65]}; P = 0.004 independently predicted prolonged LOS. ROC analysis showed poor predictive ability of ultrasonography: {area under curve 0.591 [95% CI 0.497 to 0.685]}; P = 0.061 for body mass index-normalised quadriceps thickness; {0.613 [95% CI 0.519 to 0.707]}; P = 0.020 for rectus femoris CSA, which improved to {0.721 [95% CI 0.636 to 0.806]} with pre-operative anaemia. The diagnostic accuracy was 65.5% and 64.3%, respectively; the sensitivity/specificity for quadriceps was 78%/54%, and for rectus femoris, CSA was 50%/78%. DeLong’s test showed no significant difference (P = 0.6).

Conclusion:

Frailty prolongs hospital stay but not complications or mortality. Ultrasound alone poorly predicts frailty. Combining pre-operative anaemia improves the predictive value of rectus femoris ultrasound assessment.

INTRODUCTION

Cancer alters the body’s physiological environment, which may lead to the development of frailty and cachexia. Frailty has been shown to influence outcomes of anticancer therapies. It directly correlates with chemotherapy intolerance and predicts poor post-operative outcomes in different surgical subgroups.[1,2,3] Therefore, early recognition of at-risk patients can influence the pathway to achieve an optimal functional status, enabling better outcomes of planned anti-cancer therapies.[4] International medical societies have introduced recommendations for frailty assessments in geriatric patients before cancer therapy.[5] Frailty, as a marker of physiological age rather than just chronological age, may help predict post-operative outcomes, even in younger populations.

Several assessment tools have been employed in the pre-operative setting to identify frail patients.[6] Clinical Frailty Scale (CFS), Fried Frailty Phenotype (FFP), and Frailty Index (FI) are among the most used methods globally. The Fried criteria,[7] which defines frailty by assessing the patient in five domains, is one of the widely used and validated tools to identify frailty. However, these tools have not been incorporated in routine pre-operative anaesthesia evaluation because they are either time-consuming or subjective.

To overcome the time and resource constraints in employing these tools for every pre-operative patient, the literature has suggested using ‘sarcopenia’ as a surrogate marker for frailty. Radiographic assessment for sarcopenia through computed tomography (CT) or ultrasonography (USG)-based dimensions of specific muscles like psoas and quadriceps femoris have been used as surrogate markers for frailty.[8,9] Despite the increasing incidence and prevalence of cancer in the Indian population, and a wide array of anti-cancer therapies available, the literature evaluating use of pre-operative frailty assessment through validated frailty scores or their radiographic surrogate parameters as predictors of post-operative outcomes after cancer resection is limited.

Thus, we conducted this prospective single-centre, observational study to assess the impact of pre-operative frailty on post-operative outcomes in the Indian population undergoing major abdominal surgeries for cancer resection.

Our primary objective was to evaluate the impact of pre-operative frailty on post-operative hospital length of stay (LOS) after major abdominal cancer resection.

Secondary objectives included evaluating the effect of frailty on intensive care unit (ICU) LOS, association with severe post-operative complications, 30-day mortality, and 3-month mortality and using pre-operative ultrasound as a screening tool by identifying the optimal cut-off values of ultrasound-guided quadriceps thickness and rectus femoris cross-sectional area (CSA) as screening parameters for frailty.

METHODS

This single-centre, prospective observational study was conducted between June 2023 and October 2024 at a tertiary care cancer hospital in India. It was done according to the principles of the Declaration of Helsinki, 2013 and Good Clinical practice guidelines. The study was prospectively registered on the Clinical Trials Registry - India (registration number: CTRI/2023/05/052864; dated 18/05/2023), and institute ethics committee approval (Ref. No.: IECPG-52/31.01.2023, OT-15/20.07.2023; dated 01/02/2023) was obtained. The Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist was followed. Written informed consent was obtained from all the participants, and their confidentiality was maintained throughout.

Adult patients, aged 18 years or older, undergoing major abdominal cancer surgery (defined as surgery having a skin incision, with an operative duration of 2 h or more, and an expected hospital stay of 3 days or more)[10] were included in the study. Patients who refused or were unable to provide written consent, as well as those with a history of diseases affecting muscles, such as myasthenia gravis or muscular dystrophy, or who were unable to lie down in a supine position for ultrasound examination were excluded from the study.

Patients admitted by the surgical team for the planned procedure were screened for inclusion criteria. Eligible patients were included and assessed for pre-operative frailty using the FFP questionnaire [Appendix 1] by a single anaesthesiologist. After applying the Fried phenotype, a second anaesthesiologist, blinded to the frailty status with an experience of around 40 ultrasound examinations in assessing the dimensions of the muscle groups under the guidance of a radiologist, measured the quadriceps thickness and the rectus femoris CSA using ultrasound [Figure 1a and 1b] for all patients [Appendix 2].

Figure 1.

Acquisition of ultrasound images (a) Quadriceps thickness (b) Rectus femoris cross-sectional area

On the day of the surgery, patients underwent the procedure as per standard plan of care, which was general anaesthesia with epidural catheter placement for open abdominal cancer surgeries in our institute. The intra-operative plan of management was as per the discretion of the anaesthesiologist attending the case. Post-operatively, patients were shifted to either the post-anaesthesia care unit (PACU) or ICU. Post-operative surgical and anaesthesia care was as per our standard institutional practice.

Pre-operative parameters like demographics, comorbidity status, tumour staging, pre-operative anti-cancer treatment details and recent laboratory parameters, and intra-operative variables like blood loss, fluid requirement, vasopressor requirement, core body temperature, and early versus delayed extubation (extubation in operation theatre vs. delayed extubation in ICU) were collected.

The anaesthesiologist performing the pre-operative frailty assessment followed up the patients post-operatively for hospital LOS (defined as the number of days from the day of surgery to discharge from the hospital), ICU LOS (number of days from the day of surgery to discharge from the ICU to the ward), severe post-operative complications [Clavien Dindo (CD)[11] scale, CD ≥3], 30-day mortality (all-cause mortality within 30 days of surgery), and 3-month mortality (all-cause mortality within 90 days of surgery).

Sample size was based on the duration of hospital stay in frail versus non-frail patients. The mean LOS in a study by Mueller et al.[12] was found to be 14.6 days with a standard deviation (SD) of 11 days for frail patients and 8.4 days with a SD of 5 days for non-frail patients. Considering 5 days as a clinically significant difference, with 90% power and a 5% level of significance (two-sided), 62 patients were estimated to be frail and non-frail each. Frailty was determined after recruitment. Assuming the proportion of frail patients to be lower than non-frail patients, with a 10% dropout rate, we recruited till 68 ‘frail’ patients were included.

Continuous variables were evaluated as mean (SD) or median [interquartile range (IQR)], and their differences were evaluated using either the Mann–Whitney U test or an independent samples t-test, as appropriate. Categorical variables were examined using the Chi-square test or Fisher’s exact test. To evaluate the discriminative ability of ultrasound parameters with the FFP as the reference standard, receiver operating characteristic (ROC) curve analysis was performed. Optimal cut-off values were identified using the Youden index. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated at these thresholds. Comparisons between ROC curves of quadriceps thickness and rectus femoris CSA were conducted using De Long’s test to determine if one ultrasound quantified muscle mass demonstrated superior predictive performance over the other. Analyses were done using statistical package for the social sciences (SPSS) version 20 (International Business Machines Corp. Armonk, NY), with P < 0.05 as a significant value. The LOS was evaluated using Poisson count data analysis, adjusted for plausible confounders, that is, stage of cancer (Stage 1 or 2 vs Stage 3 or 4), chemotherapy (Yes vs No), American Society of Anesthesiologists - physical status (ASA PS) classification system (I or II vs III or IV), and blood loss (<500 vs ≥500 ml) as categorical variables and age as a continuous variable.

RESULTS

A total of 160 patients were screened for eligibility, and 142 were included. Seventy-four out of 142 patients were identified as non-frail while recruiting 68 (47.8%) frail patients. The baseline demographics and duration of surgery were comparable. Ovarian and endometrial cancer surgeries were the most performed surgeries in both groups [Table 1]. Among the intra-operative variables assessed, vasopressor use and delayed extubation were found to be high in frail patients (P = 0.035 and P = 0.045, respectively) [Table 1].

Table 1.

Patient characteristics

Variable	Frail (n=68) Mean (SD)	Non-frail (n=74) Mean (SD)	Mean difference/odds ratio [95% CI]	P
Age (years)	50.26 (11.03)	50.30 (10.33)	0.03[-3.51 to 3.53]	0.986
Height (cm)	156.68 (5.024)	156.59 (6.311)	-0.08[-1.99 to 1.82]	0.112
Weight (kg)	58.29 (13.758)	60.95 (13.012)	2.66[-1.79 to 7.10]	0.239
BMI (kg/m2)	23.85 (5.73)	24.67 (4.95)	0.82[-0.96 to 2.59]	0.363
Gender, Male:Female (%)	11:57 (16.2/83.8)	15:59 (20.l/79.7)	1.0 1.32 [0.56 to 3.11]	0.529
ASA-PS n (%)				0.195
I/II     III/IV	61 (89.7) 7 (10.3)	71 (95.9) 3 (4.1)	1.0 2.29 [0.55 to 9.54]
Types of surgeries				0.713
Gynaecology oncology     Colorectal surgeries     Pancreatic surgeries     Miscellaneous (uterine/hepatic/renal/gastric)	44 (64.7) 16 (23.5) 6 (8.8) 2 (2.9)	49 (66.2) 14 (18.9) 6 (8.1) 5 (6.8)	0.90 [0.27 to 2.99] 1.14 [0.30 to 4.36] 1.0 0.37 [0.06 to 2.93]
Duration (min)	282	290		0.783
Adjuvant anticancer therapy details
Neo adjuvant chemotherapy     Radiation therapy	39 (57.4) 5 (7.4)	41 (55.4) 5 (6.8)	1.08 [0.56 to 2.10] 1.10 [0.3 to 3.96]	0.815 1.00
Staging				0.907
I/II     III/IV	26 (38.2) 42 (61.8)	29 (39.2) 45 (60.8)
Intra-operative Blood loss				0.499
<500 ml     >500 ml	50 (73.5) 18 (26.5)	58 (78.4) 16 (21.6)
Crystalloid transfusion (ml)	2308.8 (617.5)	2274.0 (581.0)	34.75[-164 to 234]	0.730
Intra-operative colloid requirement
Yes     No	31 (45.6) 37 (54.4)	41 (55.4) 33 (44.6)		0.242
Vasopressors	45 (66.2)	36 (48.6)	81	0.035
Temperature (degree centigrade)	35.56 (0.77)	35.63 (0.77)	0.07[-0.18 to 0.33]	0.583
Delayed extubation
Yes     No	18 50	10 64	2.30 [0.98–5.42]	0.045

Values are presented as mean±SD (or SEM). ASA-PS – American Society of Anesthesiologists – Physical Status; BMI – Body mass index; CI – Confidence Interval; SD – Standard Deviation; SEM: Standard error of mean

Frailty was found to be significantly associated with increased hospital LOS. Univariate analysis showed the hospital LOS in frail versus non-frail patients to be 9 vs 6 days {relative risk (RR) 1.20 [1.02 to 1.41]}; P - 0.031. Multivariate analysis after adjusting for potential confounders (age ≥ 60 years, stage III/IV, ASA-PS ≥ III, blood loss ≥ 500 ml, and neoadjuvant chemotherapy (NACT)) for hospital LOS revealed frailty {RR 1.16 [1.034 to 1.305]}; P < 0.012 and ASA–PS > III {RR 1.35[1.10 to 1.65]}; P - 0.004 to be the significant predictors of increased hospital LOS [Table 2]. Frailty was not significantly associated with severe post-operative complications, ICU LOS, and 30-day and 3-month mortality [Table 3].

Table 2.

Multivariable Poisson regression to predict independent risk factors for Hospital LOS

Variable Name	Reference category	Regression Coefficient (SE)	Risk ratio [95% CI]	P
Unadjusted analysis
Frailty	Absent	0.182 (0.08)	1.20 [1.02 to 1.41]	0.031
Adjusted analysis
Frailty	Absent	0.150 (0.06)	1.16 [1.034 to 1.305]	0.012
Stage	1+2	0.120 (0.063)	1.13 [0.997 to 1.274]	0.055
ASA-PS class	1+2	0.30 (0.10)	1.35 [1.10 to 1.65]	0.004
Blood Loss	< 500 ml	0.036 (0.07)	1.04 [0.91 to 1.19]	0.604
Age	< 60 years	-0.120 (0.08)	0.89 [0.76 to 1.04]	0.133
NACT	No	0.033 (0.0610)	1.034 [0.92 to 1.165]	0.587

ASA-PS – American Society of Anesthesiologists – Physical Status; CI – Confidence Interval; NACT – Neoadjuvant chemotherapy; SE-Standard Error; LOS: Length of stay

Table 3.

Comparison of surgical outcomes and frailty

Surgical outcomes	Frail (%) n=68	Non-frail (%) n=74	Odds ratio [95%CI]	P
Clavien–Dindo classification I/II III/IV	47 (69.1) 21 (30.9)	61 (82.4)13 (17.6)	1.0 2.10 [0.95 to 4.62]	0.063
ICU LOS	1.0 (0.0-1.00)	1.0 (0.0-1.0)		0.197
30-day mortality	3 (4.4)	1 (1.4)	3.37 [0.34 to 33.20]	0.298
3-month mortality	9 (13.2)	3 (4.1)	3.61 [0.94 to 13.94]	0.063

Values are presented as mean±standard deviation (SD or SEM) or median (1Q, 3Q) ICU LOS – Intensive care unit length of stay; SD - Standard deviation; SEM – Standard error mean; Q - Quartile

The sensitivity and specificity of body mass index (BMI)-normalised quadriceps thickness for predicting frailty were 78% and 54%, respectively, while BMI-normalised rectus femoris CSA demonstrated a sensitivity of 50% and a specificity of 78% [Table 4]. ROC analysis [Figure 2a and 2b] showed poor discriminatory ability, with an area under curve (AUC) 0.591 95% CI [0.497 to 0.685]; P = 0.061 for quadriceps thickness per unit BMI and {0.613 95% CI [0.519 to 0.707]}; P = 0.020 for rectus femoris CSA per unit BMI [Table 4]. Comparison of ROC curves using DeLong’s test revealed no significant difference in AUC between the two muscles, indicating no superiority of one muscle in determining frailty over another (P = 0.626).

Table 4.

Comparison of ROC cut-offs, sensitivity, specificity, AUC, and diagnostic accuracy in USG parameters

Characteristics	Cut-off	Sensitivity (95% CI)	Specificity (95% CI)	Negative Predictive Value (95% CI)	Positive Predictive Value (95% CI)	Area under Curve (AUC) (95% CI)	P-using De Long Test	Diagnostic accuracy (%)
Quadriceps thickness (cm)	≤2.18	0.78 [0.66 to 0.87]	0.39 [0.28 to 0.51]	0.66 [0.53 to 0.77]	0.54 [0.49 to 0.60]	0.597 [0.504 to 0.690]; P=0.045	P=0.33	57.8 [49.2 to 66.0]
Rectus femoris CSA (cm2)	≤2.65	0.66 [0.54 to 0.77]	0.60 [0.47 to 0.71]	0.66 [0.57 to 0.74]	0.60 [0.52 to 0.67]	0.641 [0.550 to 0.732]; P=0.004		62.7 [54.2 to 70.6]
Quadriceps thickness (cm) Normalised BMI	≤0.089	0.78 [0.66 to 0.87]	0.54 [0.42 to 0.66]	0.73 [0.62 to 0.81]	0.61 [0.54 to 0.67]	0.591 [0.497 to 0.685; P=0.061	P=0.62	65.5 [57.1 to 73.3]
Rectus femoris CSA (cm2) Normalised BMI	≤0.091	0.50 [0.38 to 0.62]	0.78 [0.66 to 0.87]	0.62 [0.56 to 0.68]	0.68 [0.56 to 0.78]	0.613 [0.519 to 0.707]; P=0.020		64.3 [55.8 to 72.2]

Values are presented as mean±SD (or SEM). AUC - Area under curve; BMI – Body mass index; CSA – Cross-sectional area; ROC - Receiver operating characteristic; SD – Standard deviation; SEM – Standard error mean; USG - Ultrasonography

Figure 2.

Receiver operating characteristic (ROC) curves to find cut-off values for frailty using area under the curve (AUC) of the ultrasound parameters. (a) ROC analysis for quadriceps thickness, (b) ROC analysis for rectus femoris cross-sectional area alone, and (c) ROC analysis for rectus femoris CSA with pre-operative anaemia. CSA: Cross-sectional area

The Youden index identified optimal cut-off values for BMI-normalised quadriceps thickness and rectus femoris CSA to detect frailty as <0.089 and <0.091, respectively. These thresholds yielded diagnostic accuracies of 65.5% {95% CI [57.1 to 73.3]} for quadriceps thickness and 64.3% {95% CI [55.8 to 72.2]} for rectus femoris CSA [Table 4].

DISCUSSION

This prospective observational study demonstrates a longer hospital LOS in frail patients undergoing major abdominal cancer surgeries (9 vs. 6 days; P = 0.031). Multivariate analysis demonstrated frailty and ASA-PS ≥ III as independent factors for increased hospital LOS. Our primary result aligns with other studies[13,14] showing positive correlation between frailty and length of hospital stay, which leads to increased healthcare burden, resource utilisation, and increased cost burden on the patient.[15]

Frailty did not show a significant correlation with severe post-operative complications, 30-day mortality, 3-month mortality, or ICU LOS in our study. Though intra-operative vasopressor uses and delayed extubation were more in frail patients, these parameters did not seem to affect ICU LOS clinically or statistically. This differs from the reported literature showing positive correlation of frailty with adverse outcomes.[16,17] The contradictory findings can be attributed to multiple factors. Firstly, our study was powered to detect a significant difference in the primary outcome of hospital LOS and is inadequately powered to depict significant number of severe adverse events and mortality. Secondly, optimal perioperative care in a tertiary oncological setup with standardised practices may have helped in mitigating short-term adverse events. Lastly, frailty has a pronounced effect on delayed recovery rather than discrete adverse events, thereby impacting long-term health more.

Recognising at-risk frail patients and formulating their tailored plan of perioperative care should be an area of attention for anaesthesiologists. Exhaustive, subjective tools[18,19,20] often demotivate physicians from regularly using them in the perioperative period. Hence, more data on rapid, objective, and valid screening tools are warranted for robust evidence.

Our attempt to evaluate ultrasound-guided sarcopenia as a quick bedside surrogate marker for frailty yielded inconclusive results. We made a structured attempt to evaluate the use of USG-based rectus femoris CSA and quadriceps thickness as surrogate markers for frailty, in a large, homogeneous cancer patient population in India, which, to the best of our knowledge, has not been done before. Our findings indicated low sensitivity and specificity of the USG-guided quadriceps thickness and rectus femoris CSA. Furthermore, we found that the discriminative ability of ultrasound as a standalone frailty screening tool was low {AUC for BMI normalised quadriceps thickness and rectus femoris CSA as 0.591 [0.497 to 0.685] and 0.613 [0.519 to 0.707], respectively}, which differs from the findings reported by other studies.[6,21] Canales et al.[6] performed a pilot study on 32 patients and 20 healthy volunteers undergoing heterogeneous surgeries {AUC for quadriceps thickness = 0.80 [95% CI, 0.64 to 0.97] and 0.70 [95% CI, 0.49 to 0.91] for rectus femoris CSA}. Another study[21] conducted on patients undergoing a heterogeneous group of surgeries compared pre-operative USG-guided psoas CSA and rectus femoris with perioperative mortality and found a strong positive predictive ability of the ultrasound parameters with AUC = 0.74 (psoas major) and 0.84 (rectus femoris). The discrepancy can be attributed to the differences in the study population, frailty definitions, operator expertise, surgical complexity, or sample size. The lower AUC and discriminative ability of our data may not be directly comparable with a pilot study which included heterogeneous (malignant and non-malignant) surgeries. Further, considering that frailty is a constellation of symptoms, encompassing muscle mass, endurance, and psychological components, an isolated anatomical measurement might not give a comprehensive overview.

We further analysed our data and incorporated physiological parameters like age > 60 years, female gender, BMI >30 kg/m², ASA PS ≥ III, and pre-operative anaemia (haemoglobin <10 g/dL) to enhance the discriminatory performance of ultrasound for frailty screening and found that pre-operative anaemia significantly improved the predictive ability of ultrasound-guided rectus femoris CSA, yielding an AUC of 0.721 {95% CI [0.636 to 0.806]} [Figure 2c]. This may help us in hypothesising that pre-operative anaemia identification, along with rectus femoris CSA quantification, can help in quick identification of frailty. This combined assessment can be readily implemented by an anaesthesiologist. Since our study is underpowered for ROC analysis and it being a secondary objective, it is plausible that future studies with a larger sample size may yield statistically significant and practice-changing results. Quick and easy frailty assessment methods will improve motivation among clinicians to incorporate frailty screening in pre-operative anaesthetic checks.

Our study possesses several notable strengths. The study maintains homogeneity and minimises confounding bias. Frailty assessment[22] using a widely validated tool, along with objective methods such as the Camry hand dynamometer, enhances the study’s reliability. Finally, our data support the potential utility of a conjunction of anatomical parameters like USG-guided sarcopenia with physiological parameters as a bedside screening tool for predicting frailty, providing a foundation for future studies.

However, our study has several limitations. The FFP, a widely accepted tool for assessing frailty, notably overlooks its psychosocial and emotional dimensions, thereby creating a gap in understanding its comprehensive nature. As psychological factors can significantly impact patient outcomes and recovery, this oversight is particularly notable. Certain variables such as extent of resection and surgical complexity scores were not collected, which could have a confounding effect on our results. Logistic constraints prevented us from comparing our ultrasound findings with gold-standard measurements obtained via CT.

CONCLUSION

We conclude that frailty is a significant predictor of increased hospital LOS in abdominal cancer patients undergoing surgery but lacks a correlation with short-term adverse events and mortality. Isolated ultrasound-guided quantification of quadriceps thickness and rectus femoris CSA showed poor discriminative ability for frailty screening. However, including pre-operative anaemia may improve the predictive ability of rectus femoris CSA and help develop a promising bedside screening tool for frailty.

Presentation at conferences/CMEs and abstract publication

A part of the study was displayed as a poster in KoreAnaesthesia 2025, Incheon, South Korea. No abstract was published.

Study data availability

De-identified data may be requested with reasonable justification from the authors and shall be shared after institutional approval.

Disclosure of use of AI assistive or generative tools

No generative AI tools were used for content generation in this manuscript. Grammar and reference formatting software were employed.

Declaration of use of permitted tools

The tables, tools are not copyrighted. Permission for the use of IPAQ questionnaire was duly obtained by the authors.

Supplementary material

None.

Author contributions

JS Concepts, design, definition of intellectual content, literature search, conduct of cases, data acquisition, data analyses, manuscript preparation, editing, review. SV Concepts, design, definition of intellectual content, manuscript preparation, editing, review and approval. RM Concepts, design, definition of intellectual content, data analyses, manuscript preparation, editing, review. ST Design, definition of intellectual content, manuscript preparation, editing, review and approval. SM Manuscript preparation, editing, review and approval. SB Manuscript preparation, editing, review and approval. All the authors have participated in the review, drafting and final approval of the manuscript.

Conflicts of interest

None.

APPENDIX - 1

Fried Phenotype frailty screening

We assessed the five domains of Fried criteria as follows:

• Two questions were used to assess self-reported exhaustion from the Center for Epidemiologic Studies Depression Scale. Exhaustion criteria present if the respondent answered “most of the time”/“often” to the following questions: “I felt everything I did was an effort in the last week” and “I could not get going”.[1]

• Physical strength was evaluated by grip strength. A 90 kg grip strength Camry hand dynamometer was used to calculate the grip power of the non-dominant hand, adjusted to gender and body mass index. Three measurements in kilograms (kg) were obtained, and the mean value was considered. The patient was deemed physically weak if the mean measurement was less than the following sex and BMI-based cut-offs – for males, 20.5 kg (BMI < 24), 21.5 kg (BMI 24 to 26), and 23 kg (BMI > 26), for females, 11.5 kg (BMI < 23) and 13 kg for BMI > 23).[2]

• The timed-up test was used to gauge reduced walking speed. The patient was assessed on their ability to get up from a chair, walk 3 meters, return, and sit down. A timing of more than 10 seconds was labelled as slow walking speed.[2]

• Physical activity was gauged using the English and validated Hindi versions of the International Physical Activity Questionnaire—Long Form (IPAQ-LF). Scoring was categorised as low, moderate, and high as per guidelines on IPAQ-LF scoring.[3]

• Self-reported, unintentional weight loss of more than 4.5 kg in the previous year.[1]

APPENDIX 2

Ultrasound image acquisition

The patients were positioned supine, with 30 degrees upper body elevation and legs extended. Ultrasound images of the rectus femoris cross-sectional area and the quadriceps muscle were measured. It was obtained at a mark of 60% length measured from the anterior superior iliac spine to the superior border of the patella. Three values of ultrasound parameters (Figs. 1a and b) were obtained, and the mean was calculated. Images were acquired using a Philips Innosight Diagnostic Ultrasound System (Philips Ultrasound Int., Bothell, USA) with a curved–array transducer (C6-2) operating at a frequency of 2–6 MHz. Standardisation of muscle mass compositions to body habitus was done by dividing the mean values acquired above by body mass index for normalisation.

1. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J et al. Frailty in Older Adults: Evidence for a Phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146-57.

2. Savva GM, Donoghue OA, Horgan F, O’Regan C, Cronin H, Kenny RA. Using Timed Up-and-Go to Identify Frail Members of the Older Population. J Gerontol A Biol Sci Med Sci 2013; 68:441-46.

3. Prabhu SS, Thakur AM. Reliability and validity of the Hindi version of international physical activity questionnaire-long-form (IPAQ-LF). Hong Kong Physiother J 2023; 43:33-41.

Funding Statement

Nil.