Preoperative ultrasound to identify prefrailty in patients undergoing total hip/knee replacement: A single-center, prospective, cohort study

Preoperative ultrasound to identify prefrailty in patients undergoing total hip/knee replacement: A single-center, prospective, cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preoperative ultrasound to identify prefrailty in patients undergoing total hip/knee replacement: A single-center, prospective, cohort study Heng Xue, Meng Kang, Jiuping Huang, Xiaoxiao Wang, Xuan Lai, Zhe Ma, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5304280/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To identify prefrailty in patients undergoing total hip or knee replacement using preoperative ultrasound measurements of muscle dimensions and stiffness, with the goal of detecting high-risk prefrailty patients. Methods In this prospective cohort study, patients who underwent total hip/knee replacement were enrolled. Preoperative dimensions and stiffness of the biceps brachii (BB) were assessed using grey scale ultrasound (US) and shear wave elastography (SWE). Patients were preoperatively assessed for prefrailty based on FRAIL scale. Results In this study, a total of 121 consecutive patients [median age 71 years, (IQR 68–73 years), 94 women] were included. Sixty-five patients (53.7%) had prefrailty. The proportion of females is higher in the prefrail group compared to the non-frail group (86.2% vs. 67.9%, P = 0.023). The hemoglobin value in prefrail group was lower than that in non-frail group (130.2 ± 13.9 g/L vs. 136.2 ± 12.6 g/L, P = 0.031). US measurements revealed significant differences in BB thickness and area between prefrail and non-prefrail groups on both dominant and non-dominant sides. These differences were observed in both absolute values and relative values (normalized by BMI) (all p < 0.05). BB perimeter showed a significant difference between groups on the dominant side only (all p < 0.05). The AUC of BB thickness on dominant side (after normalization by dividing by BMI) was 0.664 (0.565–0.762), which was the largest among all US variables. Conclusion Preoperative US measurements of BB dimensions demonstrated its predictive value for prefrailty in patients undergoing total hip or knee replacement surgery. However, SWE was found to be insufficient in distinguishing between prefrail and non-frail patients. Ultrasound Prefrailty Hip/knee replacement Figures Figure 1 Figure 2 Figure 3 Background Frailty is defined as increased vulnerability and impaired recovery from stressors, such as falls and infections, and is also associated with premature mortality and prolonged hospitalization [ 1 , 2 ]. Mild cognitive impairment (MCI) is defined by memory impairment and other cognitive dysfunctions, and shares similar adverse health outcomes with frailty [ 3 , 4 ]. The etiology of these two conditions is multifactorial. Identifying high-risk patients with prefrailty allows for early intervention to prevent adverse outcomes following surgery, thereby reducing the associated morbidity and mortality [ 5 , 6 ]. However, comprehensive assessment of prefrailty and MCI necessitates requires a substantial time and the expertise of experienced psychiatrists or geriatric physicians, making it impractical in the demanding clinical setting of surgical systems. Numerous studies have been conducted to identify potential risk factors associated with frailty and MCI. Recently, low skeletal muscle mass, or sarcopenia, has emerged as an commonly important factor contributing to both frailty [ 7 , 8 ] and MCI [ 9 – 12 ]. US has been used as a powerful and readily accessible tool to track the development of sarcopenia [ 13 – 15 ]. In addition, shear-wave elastography (SWE) of muscle in both static and dynamic condition provides additional information related to tissue properties by evaluating tissue elasticity, which may contribute to the diagnosis of sarcopenia [ 16 – 18 ] and the estimation of individual muscle force [ 19 ]. Therefore, in this pilot study, we hypothesized that preoperative US measurements of biceps brachii dimensions and stiffness could predict frailty and MCI in patients undergoing total hip/knee replacement, while also exploring the determination of useful cutoff values. Materials and Methods Patients and Setting This study was approved by Peking University Third Hospital Medical Science Research Ethics Committee (IRB00006761-M2022303) on May 24th, 2022, and conformed to the principles of the Declaration of Helsinki. All methods were performed in accordance with relevant guidelines and regulations. Written informed consent was obtained from all participants, and the study was registered at the Chinese Clinical Trial Registry (ChiCTR2200062483). Patients with hip/knee osteoarthritis scheduled for total hip/knee replacement were recruited from Peking University Third Hospital (Beijing, China) from April 2023 to December 2023. Patients who declined participation in the study or experienced delayed surgery were excluded. Patients with neuromuscular disorders (such as muscular dystrophy, spinal muscular atrophy, amyotrophic lateral sclerosis, or polyneuropathy) were also excluded from the study, based on their medical history and clinical presentation. Additionally, patients with significant symptoms related to upper limb were excluded, as these symptoms could affect dimensions and stiffness of the biceps brachii (BB), which were key outcome measurements in this study. The basic information collected included patient demographics such as age, sex, weight, height, body mass index (BMI), as well as preoperative laboratory findings including hemoglobin, lymphocyte count, neutrophil count, serum albumin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). To assess the nutritional status of patients prior to surgery, the geriatric nutritional risk index (GNRI) and prognostic nutritional index (PNI) were calculated using corresponding formulas [ 20 , 21 ]. US and Shear Wave Elastography (SWE) Examination US and SWE examinations were performed one day before the surgery by two experienced radiologists with more than 10 years of experience in musculoskeletal US and 5 years of experience in SWE. To minimize bias, the two radiologists conducting all ultrasound examinations in this study underwent standardized training based on the study protocol. Following the standardized training, the two radiologists collaboratively examined 10 subjects to further reinforce their learning. All grayscale and SWE images and measurements were obtained using a single US system (Aixplorer; SuperSonic Imagine, Aix en Provence, France), equipped with 4–15 MHz and 4–10 MHz liner transducers. Although the majority of US based sarcopenia studies have been performed on the quadriceps muscle and/or biceps brachii [ 22 , 23 ], considering the effect of the primary disease (hip/knee osteoarthritis) on the lower limb muscles, only biliteral biceps brachii (BB) were measured. The patient is seated in front of the examiner with forearm externally rotated to bring the muscle forward (Fig. 1 A). The muscle was measured in the transverse plane at the midpoint between the coracoid process and the elbow crease. The thickness, perimeter and area of BB muscle were measured on both dominant side and non-dominant side (Fig. 2 ). Trapezoidal extension may be used when the boundary extended beyond the field of view. In order to standardize various muscle mass composition to body habitus, these values were divided by BMI for normalization. measuring the thickness, perimeter and area of BB muscle at transverse plane. B After grey scale evaluation, SWE was performed at longitudinal plane when the BB muscle is relaxed. C Following the relaxed position assessments, the patient was instructed to hold a 2-kg dumbbell in their hand, with the arm positioned away from the knee or bed. SWE was performed for BB muscle in a tensed state. After grey scale evaluation, SWE was performed by using the 4–10 MHz linear transducer with minimum pressure. The system reports SWE readings in both Shear Wave Velocity (SWV) (meters per second) and Young modulus (kilopascals) as surrogates for tissue stiffness. SWV was used for analysis due to the anisotropy of muscle. Participants were asked to relax the upper limbs, and SWE was performed in the longitudinal plane for its better reproducibility [ 24 ](Fig. 1 B). During image acquisition, the probe was applied to the skin with minimum pressure and the state immobilization was maintained for several seconds to ensure the acquisition of stable SWE images. The frequency was switched to penetration mode. A rectangular elasticity box was fixed at a size of 2 cm×1.5cm, and a circular region of interest (Quantification BOX, Q-BOX) with a fixed diameter of 6mm was used to calculate muscle elasticity (Fig. 3 A). SWE images free from random inconsistent artifactual color patterns were selected for measurement. Q-BOX was put at the most homogeneously elastic area of the muscle, away from fascia or bone. Five reliable SWE images were obtained, and the median of measurements was used as the result, in order to provide a comparatively unbiased estimation of muscle stiffness. Following the relaxed position assessments, the patient was instructed to hold a 2-kg dumbbell in their hand, with the arm positioned away from the knee or bed (Fig. 1 C). Elasticity of contracted BB muscle was measured with the same method employed during the relaxed position (Fig. 3 B). Prefrailty and MCI Assessments Prefrailty and MCI were assessed by a geriatric physician with over 10 years of experience. Prefrailty was diagnosed using the FRAIL scale [ 25 , 26 ], which consists of 5 self-report questions. One point is given for any affirmative response, with a total score raging from 0 to 5 points. Patients with 0 point were assigned to the non-frailty group, while those 1 to 2 points were assigned to the prefrailty group. MCI was assessed based on Montreal Cognitive Assessment (MoCA) [ 27 ] and Clinical Dementia Rating Scale (CDR) [ 28 ]. Participants were considered eligible for MCI if they had a below normal score on both tests. The assessments of frailty and MCI were conducted after the US and SWE examinations, one day prior to surgery. Statistical analysis The sample size was calculated based on previous research [ 29 ] using PASS 11. A random sample of 48 subjects from the prefrail population and 48 subjects from the non-frail population produced a two-sided 95.0% confidence interval with a width of 0.220, assuming a sample AUC value of 0.650. We performed statistical analysis using SPSS software (version 25.0, IBM). Categorical variables were described using frequencies and percentages, while continuous variables were presented as mean and standard deviation (SD). Independent-samples t-test was used to compare quantitative values such as muscle dimensions and stiffness, between the prefrail and non-frail group, as well as the MCI and non-MCI groups. Binary multivariate logistic regression analyses were performed to identify multivariate predictors of prefrailty and MCI. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance of various US parameters to predict prefrailty and MCI. Areas under the ROC curves and 95% CIs were calculated in each ROC curve analysis. The optimal cutoff values were determined using the Youden index. P < 0.05 was considered indicative of a statistically significant difference. Results Prefrailty and MCI Diagnosis in Study Participants A total of 121 consecutive patients were included, with a median age of 71 years (IQR 68–73 years, 94 women). Based on the FRAIL scale, prefrailty was identified in 65 patients (53.7%), and no patients met the criteria for frailty (score ≥ 3). MCI was identified in 51 patients (42.1%). Demographics and clinical parameters of non-frail and prefrail groups are shown in Table 1 . The proportion of females is higher in the prefrail group compared to the non-frail group. Additionally, patients with prefrailty had lower hemoglobin levels. No significant differences were observed in other demographic or clinical parameters between the two groups. Table 1 Demographics and clinical parameters between Non-frail and Prefrail groups Parameter Non-frail (n = 56) Prefrail (n = 65) p value Age 71.2 ± 3.9 70.7 ± 4.3 0.503 Female (n, %) 38 (67.9%) 56 (86.2%) 0.016 BMI 27.2 ± 3.3 27.4 ± 3.3 0.655 Hemoglobin 136.2 ± 12.6 130.2 ± 13.9 0.031 Lymphocyte count 1.8 ± 0.6 1.8 ± 0.6 0.859 Neutrophil count 3.6 ± 0.9 4.0 ± 1.5 0.112 ALT 20.3 ± 9.6 20.1 ± 10.5 0.917 AST 24.1 ± 7.2 22.6 ± 7.0 0.227 Albumin 43.9 ± 2.6 43.6 ± 2.8 0.630 Creatinine 70.9 ± 16.1 69.1 ± 14.8 0.521 GNRI 118.5 ± 7.4 118.9 ± 8.2 0.774 PNI 43.9 ± 2.6 43.6 ± 2.7 0.630 MCI (n, %) 24 (42.9%) 27 (41.5%) 0.884 The data in bold represent statistically significant differences. Predictive value of ultrasound measurement for Prefrailty and MCI Ultrasound (US) measurements revealed significant differences in biceps brachii (BB) thickness and area between prefrail and non-prefrail groups on both dominant and non-dominant sides (Table 2 ). These differences were observed in both absolute values and relative values (normalized by BMI). BB perimeter showed a significant difference between groups on the dominant side only. US stiffness measurements did not reveal any significant differences between prefrail and non-prefrail groups. Table 2 Difference of ultrasound measurement for non-frail and prefrail group Parameter Non-frail (n = 56) Prefrail (n = 65) p value Thickness of BB on dominant side (cm) 1.92 ± 0.34 1.74 ± 0.27 0.003 Thickness of BB on dominant side (cm)* 0.07 ± 0.01 0.06 ± 0.01 0.002 Perimeter of BB on dominant side (cm) 11.76 ± 1.77 11.15 ± 1.68 0.021 Perimeter of BB on dominant side (cm)* 0.44 ± 0.08 0.42 ± 0.08 0.029 Area of BB on dominant side (cm 2 ) 7.63 ± 2.09 6.61 ± 1.47 0.005 Area of BB on dominant side (cm 2 )* 0.28 ± 0.08 0.25 ± 0.06 0.002 SWV of BB on dominant side (m/s) 2.89 ± 1.07 2.90 ± 1.01 0.847 SWV of contracted BB on dominant side (m/s) 6.44 ± 1.31 6.21 ± 1.27 0.569 Thickness of BB on non-dominant side (cm) 1.89 ± 0.36 1.72 ± 0.31 0.012 Thickness of BB on non-dominant side (cm)* 0.07 ± 0.01 0.06 ± 0.01 0.010 Perimeter of BB on non-dominant side (cm) 11.60 ± 1.80 11.21 ± 1.77 0.110 Perimeter of BB on non-dominant side (cm)* 0.43 ± 0.08 0.42 ± 0.09 0.112 Area of BB on non-dominant side (cm 2 ) 7.36 ± 2.09 6.60 ± 1.50 0.025 Area of BB on non-dominant side (cm 2 ) * 0.28 ± 0. 08 0.25 ± 0. 06 0.002 SWV of BB on non-dominant side (m/s) 2.84 ± 0. 96 2.92 ± 0. 83 0.318 SWV of contracted BB on non-dominant side (m/s) 6.48 ± 1.35 6.70 ± 1.37 0.600 The data in bold represent statistically significant differences. *After divided by BMI for normalization. US measurements of BB dimensions and stiffness revealed no statistically significant differences between the MCI and non-MCI groups, neither before nor after normalization for BMI (Table 3 ). Consequently, no further analysis using ROC curves to predict MCI was performed. Table 3 Difference of ultrasound measurement for MCI and non-MCI group Parameter non-MCI (n = 70) MCI (n = 51) p value Thickness of BB on dominant side (cm) 1.81 ± 0.31 1.85 ± 0.31 0.384 Thickness of BB on dominant side (cm)* 0.07 ± 0.01 0.07 ± 0.01 0.560 Perimeter of BB on dominant side (cm) 11.48 ± 1.74 11.37 ± 1.75 0.713 Perimeter of BB on dominant side (cm)* 0.43 ± 0.08 0.42 ± 0.09 0.227 Area of BB on dominant side (cm 2 ) 7.05 ± 1.80 7.13 ± 1.92 0.773 Area of BB on dominant side (cm 2 )* 0.27 ± 0.07 0.26 ± 0.08 0.502 SWV of BB on dominant side (m/s) 2.94 ± 1.14 2.84 ± 0.88 0.758 SWV of contracted BB on dominant side (m/s) 6.13 ± 1.21 6.57 ± 1.35 0.146 Thickness of BB on non-dominant side (cm) 1.77 ± 0.35 1.84 ± 0.33 0.320 Thickness of BB on non-dominant side (cm)* 0.07 ± 0.01 0.07 ± 0.01 0.850 Perimeter of BB on non-dominant side (cm) 11.45 ± 1.86 11.30 ± 1.70 0.581 Perimeter of BB on non-dominant side (cm)* 0.43 ± 0.08 0.41 ± 0.08 0.239 Area of BB on non-dominant side (cm 2 ) 6.90 ± 1.85 7.03 ± 1.82 0.731 Area of BB on dominant side (cm 2 ) * 0.27 ± 0.07 0.26 ± 0.08 0.502 SWV of BB on non-dominant side (m/s) 2.89 ± 0.90 2.87 ± 0.88 0.983 SWV of contracted BB on non-dominant side (m/s) 6.58 ± 1.35 6.61 ± 1.37 0.977 *After dividing BMI for normalization. The diagnostic performance and area under the curve (AUC) of statistically significant US features in predicting prefrailty is presented in Table 4 . All AUC values were concentrated within a narrow range (0.616–0.664) with similar accuracy (58.1%-66.1%). Table 4 Diagnostic performance of statistically significant US measurements in predicting prefrailty patients. Parameter AUC (95%CI) Optimal cut-off value Sensitivity (95% CI) Specificity (95% CI) Accuracy (95% CI) Thickness of BB on dominant side (cm) 0.654 (0.557–0.752) 2.05 33.9% (23.5%-46.0%) 89.2% (78.5%-95.0%) 59.5% (50.1%-67.8%) Thickness of BB on dominant side (cm)* 0.664 (0.565–0.762) 0.069 58.9% (46.8%-70.2%) 70.8% (57.8%-81.0%) 64.4% (55.6%-72.4%) Perimeter of BB on dominant side (cm) 0.622 (0.520–0.725) 11.15 64.3% (52.2%-74.9%) 66.2% (53.2%-77.2%) 65.1% (56.4%-73.1%) Perimeter of BB on dominant side (cm)* 0.616 (0.514–0.717) 0.406 64.3% (52.2%-74.9%) 60% (46.9%-71.8%) 62.3% (53.4%-70.4%) Area of BB on dominant side (cm 2 ) 0.649 (0.548–0.749) 7.25 58.9% (46.8%-70.1%) 69.2% (56.3%-80.0%) 63.7% (54.8%-71.7%) Area of BB on dominant side (cm 2 )* 0.663 (0.564–0.762) 0.269 57.1% (44.8%-68.2%) 70.8% (57.8%-81.0%) 63.4% (54.6%-71.5%) Thickness of BB on non-dominant side (cm) 0.632 (0.533–0.732) 2.05 33.9% (23.5%-46.0%) 86.2% (74.9%-93.0%) 58.1% (48.9%-66.3%) Thickness of BB on non-dominant side (cm)* 0.637 (0.537–0.737) 0.069 55.4% (43.3%-66.8%) 70.8% (57.8%-81.0%) 62.5% (53.7%-70.7%) Area of BB on non-dominant side (cm 2 ) 0.618 (0.517–0.719) 6.25 71.4% (59.4%-80.3%) 52.3% (40.0%-64.8%) 62.5% (53.7%-70.7%) Area of BB on non-dominant side (cm 2 ) * 0.663 (0.564–0.762) 0.254 66.1% (54.0%-76.5%) 66.2% (53.2%-77.2%) 66.1% (56.9%-74.3%) *After dividing BMI for normalization. Discussion Prefrailty and mild cognitive impairment (MCI) are well-established risk factors associated with increased morbidity and mortality after surgery. However, the use of ultrasound (US) for preoperative identification of these conditions remains under-investigated in the literature. To address this gap, our pilot study aims to explore the potential value of US and elastography measurements in predicting prefrailty. Notably, prefrailty and frailty show a stronger association with low skeletal muscle mass (sarcopenia) compared to MCI [ 7 , 8 ]. Previous research has demonstrated the promise of US measurements of quadriceps depth in discriminating frail from non-frail surgical patients. Furthermore, these measurements have also been linked to adverse outcomes, such as prolonged hospitalization, increased morbidity, and non-home discharge [ 29 , 30 ]. However, our study population specifically focused on patients with hip or knee osteoarthritis, a condition that can affect lower limb muscle mass. Consequently, we opted to measure bilateral biceps brachii muscle dimensions, as they are less likely to be directly impacted by osteoarthritis in the lower limbs. Consistent with prior research, our study found that individuals with prefrailty exhibited reduced muscle dimensions, particularly on the dominant side. However, in contrast to studies solely focused on discriminating between frail and non-frail participants, our study found limited discriminatory ability of the US method in distinguishing prefrailty from non-prefrailty within this population. A potential explanation for the discrepancy can be attributed to our study design. We exclusively recruited patients, whereas the previous study included both patients and volunteers. The inherent health differences between patients and healthy volunteers are likely to be more pronounced than variations within a patient population. This could explain why US effectively identified reduced muscle dimensions in our prefrail patients, while encountering challenges in distinguishing prefrail from non-prefrail individuals in a clinical setting. Frailty and sarcopenia are characterized by declines in both muscle quantity and quality. Shear wave elastography (SWE), by measuring muscle stiffness, offers promise for detecting these conditions due to its ability to assess muscle quality alongside traditional measures of muscle size [ 16 – 18 ]. Janczyk EM et al. conducted a systematic review to evaluate the diagnostic utility of sonoelastography for sarcopenia [ 18 ]. Nine studies employed shear wave elastography (SWE), and one utilized one-strain elastography. However, due to significant heterogeneity among the included studies, the authors were unable to draw definitive conclusions regarding the effectiveness of elastography in assessing sarcopenia. We aimed to determine whether SWE could also serve as a potential tool for identifying prefrailty patients, given the significant association between sarcopenia and frailty syndrome [ 31 ]. We hypothesized that there would be a difference in muscle stiffness between the prefrail and non-prefrail groups; however, our analysis did not reveal any statistically significant differences. These findings suggest that SWE of bilateral BB muscles may not be a reliable tool for identifying prefrailty in this patient population. In addition to investigating prefrailty, we explored the potential of US to predict MCI in this patient population undergoing total hip/knee replacement surgery. We hypothesized that individuals diagnosed with MCI would exhibit lower muscle volume and stiffness, possibly attributed to fat infiltration, in comparison to those without MCI, given the established link between sarcopenia and both conditions. However, our investigation did not reveal any significant differences between the two groups, suggesting that grayscale US and SWE may not be reliable tools for predicting or differentiating MCI from non-MCI in this context. Our study had a few limitations. Although previous studies have reported SWE is a reliable and repeatable method for measuring the SWV of muscle and tendon [ 32 , 33 ], including in both pre- and post-contraction states [ 34 ], inter-and intra-observer variability of greyscale US and SWE was not evaluated in this study. Two radiologists performed all US exams in this study, which may have potentially introduced unrecognized bias and variability. Second, our investigation did not encompass the assessment of the impact of US measurements on secondary outcomes, such as unplanned ICU admission and hospital stay duration. The omission of secondary outcomes precludes a comprehensive evaluation of the effectiveness and value of US in predicting prefrailty. Third, we selected only patients who underwent total hip/knee replacement surgeries, thereby excluding those who received other types of surgeries. This exclusion introduces a selection bias, limiting the generalizability of our findings to the broader patient population undergoing various surgical procedures. Fourth, even though US demonstrated promising potential for identifying prefrailty in our study, its clinical application is currently hindered by the absence of standardized cutoff values for different age groups, genders, and anatomical locations. This lack of standardization limits the validation of US measurements for routine clinical use. Conclusions Preoperative US measurements of BB dimensions demonstrated its predictive value for prefrailty in patients undergoing total hip or knee replacement surgery. However, SWE was found to be insufficient in distinguishing between prefrail and non-frail patients. Abbreviations US ultrasound MCI Mild cognitive impairment SWE shear-wave elastography BB biceps brachii BMI body mass index AST aspartate aminotransferase ALT alanine aminotransferase GNRI geriatric nutritional risk index PNI prognostic nutritional index SWV Shear Wave Velocity MoCA Montreal Cognitive Assessment CDR Clinical Dementia Rating Scale SD standard deviation ROC Receiver operating characteristic AUC area under the curve Declarations Ethics approval and consent to participate The study was approved by approved by Peking University Third Hospital Medical Science Research Ethics Committee (IRB00006761-M2022303) on May 24 th , 2022. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study is supported by Ministry of Science and Technology of the People's Republic of China (STI2030-Major Projects+2021ZD0204300), Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD2202), the Fundamental Research Funds for the Central Universities (Clinical Medicine Plus X - Young Scholars Project of Peking University, PKU2024LCXQ029). Authors' contributions Y.H., Y.L., X.G., and H.T. designed the study protocol. X.W. and X.L. contributed materials and analysis tools. H.X., J.H., Z.M. and Q.X. sampled the data and performed the analyses. H.X. and M.K. were major contributors in writing the manuscript. Y.H. supervised the study and revised the manuscript. 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Preoperative Point-of-Care Ultrasound to Identify Frailty and Predict Postoperative Outcomes: A Diagnostic Accuracy Study. Anesthesiology. 2022;136:268–78. Ben-Menachem E, Ashes C, Lepar G et al. Smaller rectus femoris size measured by ultrasound is associated with poorer outcomes after cardiac surgery. J Thorac Cardiovasc Surg 2022. Nascimento CM, Ingles M, Salvador-Pascual A, Cominetti MR, Gomez-Cabrera MC, Vina J. Sarcopenia, frailty and their prevention by exercise. Free Radic Biol Med. 2019;132:42–9. Tas S, Onur MR, Yilmaz S, Soylu AR, Korkusuz F. Shear Wave Elastography Is a Reliable and Repeatable Method for Measuring the Elastic Modulus of the Rectus Femoris Muscle and Patellar Tendon. J Ultrasound Med. 2017;36:565–70. Hatta T, Giambini H, Uehara K, et al. Quantitative assessment of rotator cuff muscle elasticity: Reliability and feasibility of shear wave elastography. J Biomech. 2015;48:3853–8. Dulgheriu IT, Solomon C, Muntean DD et al. Shear-Wave Elastography and Viscosity PLUS for the Assessment of Peripheric Muscles in Healthy Subjects: A Pre- and Post-Contraction Study. Diagnostics (Basel) 2022;12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5304280","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373590491,"identity":"9ee646e9-fa1b-4be1-b3a5-feea4971b83c","order_by":0,"name":"Heng Xue","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Xue","suffix":""},{"id":373590492,"identity":"c89f6a39-328d-46e0-b669-5df3382714a4","order_by":1,"name":"Meng Kang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Kang","suffix":""},{"id":373590493,"identity":"937e05ef-f812-41ed-8b86-23e2d623a039","order_by":2,"name":"Jiuping Huang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiuping","middleName":"","lastName":"Huang","suffix":""},{"id":373590494,"identity":"90bdbc9f-e23c-4225-a3f5-73280d518f87","order_by":3,"name":"Xiaoxiao Wang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiao","middleName":"","lastName":"Wang","suffix":""},{"id":373590495,"identity":"dbafe9ae-4af0-40b4-9575-b446154f5413","order_by":4,"name":"Xuan Lai","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Lai","suffix":""},{"id":373590496,"identity":"6c0a30d6-4147-4120-9789-221f07f029b1","order_by":5,"name":"Zhe Ma","email":"","orcid":"","institution":"Peking University Health Science Center","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Ma","suffix":""},{"id":373590497,"identity":"d38b49d9-e5a7-4a0c-8e86-8041a9e673b5","order_by":6,"name":"Qian Xiang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xiang","suffix":""},{"id":373590498,"identity":"86b6d2ee-f9ee-46c1-9e5a-2d99e2c93f95","order_by":7,"name":"Hua Tian","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Tian","suffix":""},{"id":373590499,"identity":"a061fd8c-3195-48d7-a061-9fe69d5c89f8","order_by":8,"name":"Xiangyang Guo","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangyang","middleName":"","lastName":"Guo","suffix":""},{"id":373590500,"identity":"c259295e-2de5-4fa4-96f9-6c887cbd87c9","order_by":9,"name":"Yang Li","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":373590501,"identity":"0416ffbe-78ce-4a46-8402-93e762be795e","order_by":10,"name":"Yongzheng Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAxTGxwY2MEeCaC2MM0nWwszbwEBYizn72WMSH3fU2puz9x5+bbuDL9rgAPPB2zwMdnm4tFj25KVJzjxznNmy51yade4ZttwNB9iSrXkYkotxOuxAjpk0b9sxNoMbOWbGuW0gLTxm0jwMBxIbcGk5/washcfg/hszY0uwFv5v+LXcANtSI2Fwg8f4MSPEFjYCWt4YW85sO2BgcCbHjLEX6JeZh9mMLecYJONxWI7hjY9tdfYGx88Yf/i541hu3/HmhzfeVNjh1AIELMBYOAxisAEZx4CxAzYKt3ogYP7AwFAHY9TgVToKRsEoGAUjEwAA7QJaXoExPU8AAAAASUVORK5CYII=","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yongzheng","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-10-21 12:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5304280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5304280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69078159,"identity":"a4f6388a-5632-411f-b1d7-0f42365735aa","added_by":"auto","created_at":"2024-11-15 11:28:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":816275,"visible":true,"origin":"","legend":"\u003cp\u003eA Photograph illustrates the patient position and transducer position when\u003c/p\u003e\n\u003cp\u003emeasuring the thickness, perimeter and area of BB muscle at transverse plane. B After grey scale evaluation, SWE was performed at longitudinal plane when the BB muscle is relaxed. C Following the relaxed position assessments, the patient was instructed to hold a 2-kg dumbbell in their hand, with the arm positioned away from the knee or bed. SWE was performed for BB muscle in a tensed state.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5304280/v1/246a11895e5b09f2bb67e91d.jpeg"},{"id":69078160,"identity":"7bf415fe-fa74-430d-815d-3bed6638d3f0","added_by":"auto","created_at":"2024-11-15 11:28:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA 73-year-old female patient, scheduled for total knee replacement. \u003c/strong\u003eThe thickness (A), perimeter and area (B) of right BB muscle were measured by grey-scale US.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5304280/v1/d6b291039acaf1fd839644b8.jpeg"},{"id":69078158,"identity":"2c7518e6-99f8-438a-b5c3-536b4990c389","added_by":"auto","created_at":"2024-11-15 11:28:11","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74590,"visible":true,"origin":"","legend":"\u003cp\u003eThe same patient with Fig 2. Stiffness of BB muscle was measured at longitudinal plane for both relaxed (A) and tensed position (B) with SWE.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5304280/v1/721a974cff39c27ee50f76b3.jpeg"},{"id":87255902,"identity":"d8fc6009-a258-4c5e-8344-c010b6f36409","added_by":"auto","created_at":"2025-07-22 05:53:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5304280/v1/0e11704e-f068-42b1-95d6-04cdd7e76eb0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative ultrasound to identify prefrailty in patients undergoing total hip/knee replacement: A single-center, prospective, cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eFrailty is defined as increased vulnerability and impaired recovery from stressors, such as falls and infections, and is also associated with premature mortality and prolonged hospitalization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mild cognitive impairment (MCI) is defined by memory impairment and other cognitive dysfunctions, and shares similar adverse health outcomes with frailty [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The etiology of these two conditions is multifactorial. Identifying high-risk patients with prefrailty allows for early intervention to prevent adverse outcomes following surgery, thereby reducing the associated morbidity and mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, comprehensive assessment of prefrailty and MCI necessitates requires a substantial time and the expertise of experienced psychiatrists or geriatric physicians, making it impractical in the demanding clinical setting of surgical systems.\u003c/p\u003e \u003cp\u003eNumerous studies have been conducted to identify potential risk factors associated with frailty and MCI. Recently, low skeletal muscle mass, or sarcopenia, has emerged as an commonly important factor contributing to both frailty [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and MCI [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. US has been used as a powerful and readily accessible tool to track the development of sarcopenia [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, shear-wave elastography (SWE) of muscle in both static and dynamic condition provides additional information related to tissue properties by evaluating tissue elasticity, which may contribute to the diagnosis of sarcopenia [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the estimation of individual muscle force [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, in this pilot study, we hypothesized that preoperative US measurements of biceps brachii dimensions and stiffness could predict frailty and MCI in patients undergoing total hip/knee replacement, while also exploring the determination of useful cutoff values.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Setting\u003c/h2\u003e \u003cp\u003e This study was approved by Peking University Third Hospital Medical Science Research Ethics Committee (IRB00006761-M2022303) on May 24th, 2022, and conformed to the principles of the Declaration of Helsinki. All methods were performed in accordance with relevant guidelines and regulations. Written informed consent was obtained from all participants, and the study was registered at the Chinese Clinical Trial Registry (ChiCTR2200062483). Patients with hip/knee osteoarthritis scheduled for total hip/knee replacement were recruited from Peking University Third Hospital (Beijing, China) from April 2023 to December 2023. Patients who declined participation in the study or experienced delayed surgery were excluded. Patients with neuromuscular disorders (such as muscular dystrophy, spinal muscular atrophy, amyotrophic lateral sclerosis, or polyneuropathy) were also excluded from the study, based on their medical history and clinical presentation. Additionally, patients with significant symptoms related to upper limb were excluded, as these symptoms could affect dimensions and stiffness of the biceps brachii (BB), which were key outcome measurements in this study. The basic information collected included patient demographics such as age, sex, weight, height, body mass index (BMI), as well as preoperative laboratory findings including hemoglobin, lymphocyte count, neutrophil count, serum albumin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). To assess the nutritional status of patients prior to surgery, the geriatric nutritional risk index (GNRI) and prognostic nutritional index (PNI) were calculated using corresponding formulas [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUS and Shear Wave Elastography (SWE) Examination\u003c/h3\u003e\n\u003cp\u003eUS and SWE examinations were performed one day before the surgery by two experienced radiologists with more than 10 years of experience in musculoskeletal US and 5 years of experience in SWE. To minimize bias, the two radiologists conducting all ultrasound examinations in this study underwent standardized training based on the study protocol. Following the standardized training, the two radiologists collaboratively examined 10 subjects to further reinforce their learning. All grayscale and SWE images and measurements were obtained using a single US system (Aixplorer; SuperSonic Imagine, Aix en Provence, France), equipped with 4\u0026ndash;15 MHz and 4\u0026ndash;10 MHz liner transducers. Although the majority of US based sarcopenia studies have been performed on the quadriceps muscle and/or biceps brachii [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], considering the effect of the primary disease (hip/knee osteoarthritis) on the lower limb muscles, only biliteral biceps brachii (BB) were measured. The patient is seated in front of the examiner with forearm externally rotated to bring the muscle forward (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The muscle was measured in the transverse plane at the midpoint between the coracoid process and the elbow crease. The thickness, perimeter and area of BB muscle were measured on both dominant side and non-dominant side (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Trapezoidal extension may be used when the boundary extended beyond the field of view. In order to standardize various muscle mass composition to body habitus, these values were divided by BMI for normalization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emeasuring the thickness, perimeter and area of BB muscle at transverse plane. B After grey scale evaluation, SWE was performed at longitudinal plane when the BB muscle is relaxed. C Following the relaxed position assessments, the patient was instructed to hold a 2-kg dumbbell in their hand, with the arm positioned away from the knee or bed. SWE was performed for BB muscle in a tensed state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter grey scale evaluation, SWE was performed by using the 4\u0026ndash;10 MHz linear transducer with minimum pressure. The system reports SWE readings in both Shear Wave Velocity (SWV) (meters per second) and Young modulus (kilopascals) as surrogates for tissue stiffness. SWV was used for analysis due to the anisotropy of muscle. Participants were asked to relax the upper limbs, and SWE was performed in the longitudinal plane for its better reproducibility [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e](Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). During image acquisition, the probe was applied to the skin with minimum pressure and the state immobilization was maintained for several seconds to ensure the acquisition of stable SWE images. The frequency was switched to penetration mode. A rectangular elasticity box was fixed at a size of 2 cm\u0026times;1.5cm, and a circular region of interest (Quantification BOX, Q-BOX) with a fixed diameter of 6mm was used to calculate muscle elasticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). SWE images free from random inconsistent artifactual color patterns were selected for measurement. Q-BOX was put at the most homogeneously elastic area of the muscle, away from fascia or bone. Five reliable SWE images were obtained, and the median of measurements was used as the result, in order to provide a comparatively unbiased estimation of muscle stiffness. Following the relaxed position assessments, the patient was instructed to hold a 2-kg dumbbell in their hand, with the arm positioned away from the knee or bed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Elasticity of contracted BB muscle was measured with the same method employed during the relaxed position (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePrefrailty and MCI Assessments\u003c/h3\u003e\n\u003cp\u003ePrefrailty and MCI were assessed by a geriatric physician with over 10 years of experience. Prefrailty was diagnosed using the FRAIL scale [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which consists of 5 self-report questions. One point is given for any affirmative response, with a total score raging from 0 to 5 points. Patients with 0 point were assigned to the non-frailty group, while those 1 to 2 points were assigned to the prefrailty group. MCI was assessed based on Montreal Cognitive Assessment (MoCA) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Clinical Dementia Rating Scale (CDR) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Participants were considered eligible for MCI if they had a below normal score on both tests. The assessments of frailty and MCI were conducted after the US and SWE examinations, one day prior to surgery.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe sample size was calculated based on previous research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] using PASS 11. A random sample of 48 subjects from the prefrail population and 48 subjects from the non-frail population produced a two-sided 95.0% confidence interval with a width of 0.220, assuming a sample AUC value of 0.650.\u003c/p\u003e \u003cp\u003eWe performed statistical analysis using SPSS software (version 25.0, IBM). Categorical variables were described using frequencies and percentages, while continuous variables were presented as mean and standard deviation (SD). Independent-samples t-test was used to compare quantitative values such as muscle dimensions and stiffness, between the prefrail and non-frail group, as well as the MCI and non-MCI groups. Binary multivariate logistic regression analyses were performed to identify multivariate predictors of prefrailty and MCI. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance of various US parameters to predict prefrailty and MCI. Areas under the ROC curves and 95% CIs were calculated in each ROC curve analysis. The optimal cutoff values were determined using the Youden index. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered indicative of a statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrefrailty and MCI Diagnosis in Study Participants\u003c/h2\u003e \u003cp\u003eA total of 121 consecutive patients were included, with a median age of 71 years (IQR 68\u0026ndash;73 years, 94 women). Based on the FRAIL scale, prefrailty was identified in 65 patients (53.7%), and no patients met the criteria for frailty (score\u0026thinsp;\u0026ge;\u0026thinsp;3). MCI was identified in 51 patients (42.1%). Demographics and clinical parameters of non-frail and prefrail groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The proportion of females is higher in the prefrail group compared to the non-frail group. Additionally, patients with prefrailty had lower hemoglobin levels. No significant differences were observed in other demographic or clinical parameters between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical parameters between Non-frail and Prefrail groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-frail (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrefrail (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCI (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data in bold represent statistically significant differences.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictive value of ultrasound measurement for Prefrailty and MCI\u003c/h3\u003e\n\u003cp\u003eUltrasound (US) measurements revealed significant differences in biceps brachii (BB) thickness and area between prefrail and non-prefrail groups on both dominant and non-dominant sides (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These differences were observed in both absolute values and relative values (normalized by BMI). BB perimeter showed a significant difference between groups on the dominant side only. US stiffness measurements did not reveal any significant differences between prefrail and non-prefrail groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference of ultrasound measurement for non-frail and prefrail group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-frail (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrefrail (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of BB on dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of contracted BB on dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on non-dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on non-dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on non-dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on non-dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on non-dominant side (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on non-dominant side (cm\u003csup\u003e2\u003c/sup\u003e) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0. 08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0. 06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of BB on non-dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0. 96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0. 83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of contracted BB on non-dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data in bold represent statistically significant differences.\u003c/p\u003e \u003cp\u003e*After divided by BMI for normalization.\u003c/p\u003e \u003cp\u003eUS measurements of BB dimensions and stiffness revealed no statistically significant differences between the MCI and non-MCI groups, neither before nor after normalization for BMI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consequently, no further analysis using ROC curves to predict MCI was performed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference of ultrasound measurement for MCI and non-MCI group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-MCI (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of BB on dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of contracted BB on dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on non-dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on non-dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on non-dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on non-dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on non-dominant side (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of BB on non-dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWV of contracted BB on non-dominant side (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*After dividing BMI for normalization.\u003c/p\u003e \u003cp\u003eThe diagnostic performance and area under the curve (AUC) of statistically significant US features in predicting prefrailty is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All AUC values were concentrated within a narrow range (0.616\u0026ndash;0.664) with similar accuracy (58.1%-66.1%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of statistically significant US measurements in predicting prefrailty patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOptimal cut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003cp\u003e(0.557\u0026ndash;0.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.9%\u003c/p\u003e \u003cp\u003e(23.5%-46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.2%\u003c/p\u003e \u003cp\u003e(78.5%-95.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.5%\u003c/p\u003e \u003cp\u003e(50.1%-67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003cp\u003e(0.565\u0026ndash;0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.9%\u003c/p\u003e \u003cp\u003e(46.8%-70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.8%\u003c/p\u003e \u003cp\u003e(57.8%-81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.4%\u003c/p\u003e \u003cp\u003e(55.6%-72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003cp\u003e(0.520\u0026ndash;0.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.3%\u003c/p\u003e \u003cp\u003e(52.2%-74.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.2%\u003c/p\u003e \u003cp\u003e(53.2%-77.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.1%\u003c/p\u003e \u003cp\u003e(56.4%-73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter of BB on dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003cp\u003e(0.514\u0026ndash;0.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.3%\u003c/p\u003e \u003cp\u003e(52.2%-74.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003cp\u003e(46.9%-71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003cp\u003e(53.4%-70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003cp\u003e(0.548\u0026ndash;0.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.9%\u003c/p\u003e \u003cp\u003e(46.8%-70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.2%\u003c/p\u003e \u003cp\u003e(56.3%-80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.7%\u003c/p\u003e \u003cp\u003e(54.8%-71.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on dominant side (cm\u003csup\u003e2\u003c/sup\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003cp\u003e(0.564\u0026ndash;0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.1%\u003c/p\u003e \u003cp\u003e(44.8%-68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.8%\u003c/p\u003e \u003cp\u003e(57.8%-81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.4%\u003c/p\u003e \u003cp\u003e(54.6%-71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on non-dominant side (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003cp\u003e(0.533\u0026ndash;0.732)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.9%\u003c/p\u003e \u003cp\u003e(23.5%-46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.2%\u003c/p\u003e \u003cp\u003e(74.9%-93.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003cp\u003e(48.9%-66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of BB on non-dominant side (cm)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003cp\u003e(0.537\u0026ndash;0.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.4%\u003c/p\u003e \u003cp\u003e(43.3%-66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.8%\u003c/p\u003e \u003cp\u003e(57.8%-81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.5%\u003c/p\u003e \u003cp\u003e(53.7%-70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on non-dominant side (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003cp\u003e(0.517\u0026ndash;0.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.4%\u003c/p\u003e \u003cp\u003e(59.4%-80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.3%\u003c/p\u003e \u003cp\u003e(40.0%-64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.5%\u003c/p\u003e \u003cp\u003e(53.7%-70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of BB on non-dominant side (cm\u003csup\u003e2\u003c/sup\u003e) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003cp\u003e(0.564\u0026ndash;0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.1%\u003c/p\u003e \u003cp\u003e(54.0%-76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.2%\u003c/p\u003e \u003cp\u003e(53.2%-77.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.1%\u003c/p\u003e \u003cp\u003e(56.9%-74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*After dividing BMI for normalization.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrefrailty and mild cognitive impairment (MCI) are well-established risk factors associated with increased morbidity and mortality after surgery. However, the use of ultrasound (US) for preoperative identification of these conditions remains under-investigated in the literature. To address this gap, our pilot study aims to explore the potential value of US and elastography measurements in predicting prefrailty. Notably, prefrailty and frailty show a stronger association with low skeletal muscle mass (sarcopenia) compared to MCI [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous research has demonstrated the promise of US measurements of quadriceps depth in discriminating frail from non-frail surgical patients. Furthermore, these measurements have also been linked to adverse outcomes, such as prolonged hospitalization, increased morbidity, and non-home discharge [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, our study population specifically focused on patients with hip or knee osteoarthritis, a condition that can affect lower limb muscle mass. Consequently, we opted to measure bilateral biceps brachii muscle dimensions, as they are less likely to be directly impacted by osteoarthritis in the lower limbs. Consistent with prior research, our study found that individuals with prefrailty exhibited reduced muscle dimensions, particularly on the dominant side. However, in contrast to studies solely focused on discriminating between frail and non-frail participants, our study found limited discriminatory ability of the US method in distinguishing prefrailty from non-prefrailty within this population. A potential explanation for the discrepancy can be attributed to our study design. We exclusively recruited patients, whereas the previous study included both patients and volunteers. The inherent health differences between patients and healthy volunteers are likely to be more pronounced than variations within a patient population. This could explain why US effectively identified reduced muscle dimensions in our prefrail patients, while encountering challenges in distinguishing prefrail from non-prefrail individuals in a clinical setting.\u003c/p\u003e \u003cp\u003eFrailty and sarcopenia are characterized by declines in both muscle quantity and quality. Shear wave elastography (SWE), by measuring muscle stiffness, offers promise for detecting these conditions due to its ability to assess muscle quality alongside traditional measures of muscle size [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Janczyk EM et al. conducted a systematic review to evaluate the diagnostic utility of sonoelastography for sarcopenia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nine studies employed shear wave elastography (SWE), and one utilized one-strain elastography. However, due to significant heterogeneity among the included studies, the authors were unable to draw definitive conclusions regarding the effectiveness of elastography in assessing sarcopenia. We aimed to determine whether SWE could also serve as a potential tool for identifying prefrailty patients, given the significant association between sarcopenia and frailty syndrome [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We hypothesized that there would be a difference in muscle stiffness between the prefrail and non-prefrail groups; however, our analysis did not reveal any statistically significant differences. These findings suggest that SWE of bilateral BB muscles may not be a reliable tool for identifying prefrailty in this patient population.\u003c/p\u003e \u003cp\u003eIn addition to investigating prefrailty, we explored the potential of US to predict MCI in this patient population undergoing total hip/knee replacement surgery. We hypothesized that individuals diagnosed with MCI would exhibit lower muscle volume and stiffness, possibly attributed to fat infiltration, in comparison to those without MCI, given the established link between sarcopenia and both conditions. However, our investigation did not reveal any significant differences between the two groups, suggesting that grayscale US and SWE may not be reliable tools for predicting or differentiating MCI from non-MCI in this context.\u003c/p\u003e \u003cp\u003eOur study had a few limitations. Although previous studies have reported SWE is a reliable and repeatable method for measuring the SWV of muscle and tendon [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], including in both pre- and post-contraction states [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], inter-and intra-observer variability of greyscale US and SWE was not evaluated in this study. Two radiologists performed all US exams in this study, which may have potentially introduced unrecognized bias and variability. Second, our investigation did not encompass the assessment of the impact of US measurements on secondary outcomes, such as unplanned ICU admission and hospital stay duration. The omission of secondary outcomes precludes a comprehensive evaluation of the effectiveness and value of US in predicting prefrailty. Third, we selected only patients who underwent total hip/knee replacement surgeries, thereby excluding those who received other types of surgeries. This exclusion introduces a selection bias, limiting the generalizability of our findings to the broader patient population undergoing various surgical procedures. Fourth, even though US demonstrated promising potential for identifying prefrailty in our study, its clinical application is currently hindered by the absence of standardized cutoff values for different age groups, genders, and anatomical locations. This lack of standardization limits the validation of US measurements for routine clinical use.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePreoperative US measurements of BB dimensions demonstrated its predictive value for prefrailty in patients undergoing total hip or knee replacement surgery. However, SWE was found to be insufficient in distinguishing between prefrail and non-frail patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eultrasound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMild cognitive impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eshear-wave elastography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiceps brachii\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egeriatric nutritional risk index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePNI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprognostic nutritional index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShear Wave Velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMontreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Dementia Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by approved by Peking University Third Hospital Medical Science Research Ethics Committee (IRB00006761-M2022303) on May 24\u003csup\u003eth\u003c/sup\u003e, 2022.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by Ministry of Science and Technology of the People\u0026apos;s Republic of China (STI2030-Major Projects+2021ZD0204300), Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD2202), the Fundamental Research Funds for the Central Universities (Clinical Medicine Plus X - Young Scholars Project of Peking University, PKU2024LCXQ029).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eY.H., Y.L., X.G., and H.T. designed the study protocol. X.W. and X.L. contributed materials and analysis tools. H.X., J.H., Z.M. and Q.X. sampled the data and performed the analyses. H.X. and M.K. were major contributors in writing the manuscript. Y.H. supervised the study and revised the manuscript. Y.H. and Y.L. were the main contributors to the writing in correspondence with the other authors. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394:1365\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkonen JN, Eriksson JG, Salonen MK, Kajantie E, Arponen O, Haapanen MJ. The utilization of specialized healthcare services among frail older adults in the Helsinki Birth Cohort Study. Ann Med. 2021;53:1875\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen RC. Mild Cognitive Impairment. Continuum (Minneap Minn). 2016;22:404\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanford AM. Mild Cognitive Impairment. Clin Geriatr Med. 2017;33:325\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilotta F, Lauretta MP, Borozdina A, Mizikov VM, Rosa G. Postoperative delirium: risk factors, diagnosis and perioperative care. Minerva Anestesiol. 2013;79:1066\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrabold B, Metterlein T. Postoperative delirium: risk factors, prevention, and treatment. J Cardiothorac Vasc Anesth. 2014;28:1352\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Kiesswetter E, Drey M, Sieber CC. Nutrition, frailty, and sarcopenia. Aging Clin Exp Res. 2017;29:43\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLigthart-Melis GC, Luiking YC, Kakourou A, Cederholm T, Maier AB, de van der Schueren MAE, Frailty, Sarcopenia, and, Frequently M. (Co-)occur in Hospitalized Older Adults: A Systematic Review and Meta-analysis. J Am Med Dir Assoc 2020;21:1216-28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Xiao M, Leng L, et al. A systematic review and meta-analysis of the prevalence and correlation of mild cognitive impairment in sarcopenia. J Cachexia Sarcopenia Muscle. 2023;14:45\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng TC, Chen WL, Wu LW, Chang YW, Kao TW. Sarcopenia and cognitive impairment: A systematic review and meta-analysis. Clin Nutr. 2020;39:2695\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabett Cipolli G, Sanches Yassuda M, Aprahamian I. Sarcopenia Is Associated with Cognitive Impairment in Older Adults: A Systematic Review and Meta-Analysis. J Nutr Health Aging. 2019;23:525\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang KV, Hsu TH, Wu WT, Huang KC, Han DS. Association Between Sarcopenia and Cognitive Impairment: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2016;17:1164e7. e15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStringer HJ, Wilson D. The Role of Ultrasound as a Diagnostic Tool for Sarcopenia. J Frailty Aging. 2018;7:258\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkisas S, Bastijns S, Baudry S, et al. Application of ultrasound for muscle assessment in sarcopenia: 2020 SARCUS update. Eur Geriatr Med. 2021;12:45\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiovannini S, Brau F, Forino R et al. Sarcopenia: Diagnosis and Management, State of the Art and Contribution of Ultrasound. J Clin Med 2021;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvanoski S, Vasilevska Nikodinovska V. Future Ultrasound Biomarkers for Sarcopenia: Elastography, Contrast-Enhanced Ultrasound, and Speed of Sound Ultrasound Imaging. Semin Musculoskelet Radiol. 2020;24:194\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastijns S, De Cock AM, Vandewoude M, Perkisas S. Usability and Pitfalls of Shear-Wave Elastography for Evaluation of Muscle Quality and Its Potential in Assessing Sarcopenia: A Review. Ultrasound Med Biol. 2020;46:2891\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanczyk EM, Champigny N, Michel E, et al. Sonoelastography to Assess Muscular Stiffness Among Older Adults and its Use for the Diagnosis of Sarcopenia: A Systematic Review. Ultraschall Med. 2021;42:634\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHug F, Tucker K, Gennisson JL, Tanter M, Nordez A. Elastography for Muscle Biomechanics: Toward the Estimation of Individual Muscle Force. Exerc Sport Sci Rev. 2015;43:125\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82:777\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang N, Gu H, Ni Y, Wei X, Zheng S. Prognostic and clinicopathological significance of the Prognostic Nutritional Index in patients with gastrointestinal stromal tumours undergoing surgery: a meta-analysis. BMJ Open. 2022;12:e064577.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang KV, Yang KC, Wu WT, Huang KC, Han DS. Association between metabolic syndrome and limb muscle quantity and quality in older adults: a pilot ultrasound study. Diabetes Metab Syndr Obes. 2019;12:1821\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerri S, Mercatelli D, Aparisi Gomez MP, et al. Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia. Quant Imaging Med Surg. 2018;8:60\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortez CD, Hermitte L, Ramain A, Mesmann C, Lefort T, Pialat JB. Ultrasound shear wave velocity in skeletal muscle: A reproducibility study. Diagn Interv Imaging. 2016;97:71\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAprahamian I, Cezar NOC, Izbicki R, et al. Screening for Frailty With the FRAIL Scale: A Comparison With the Phenotype Criteria. J Am Med Dir Assoc. 2017;18:592\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoo J, Yu R, Wong M, Yeung F, Wong M, Lum C. Frailty Screening in the Community Using the FRAIL Scale. J Am Med Dir Assoc. 2015;16:412\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43:2412\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanales C, Mazor E, Coy H, et al. Preoperative Point-of-Care Ultrasound to Identify Frailty and Predict Postoperative Outcomes: A Diagnostic Accuracy Study. Anesthesiology. 2022;136:268\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen-Menachem E, Ashes C, Lepar G et al. Smaller rectus femoris size measured by ultrasound is associated with poorer outcomes after cardiac surgery. J Thorac Cardiovasc Surg 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNascimento CM, Ingles M, Salvador-Pascual A, Cominetti MR, Gomez-Cabrera MC, Vina J. Sarcopenia, frailty and their prevention by exercise. Free Radic Biol Med. 2019;132:42\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTas S, Onur MR, Yilmaz S, Soylu AR, Korkusuz F. Shear Wave Elastography Is a Reliable and Repeatable Method for Measuring the Elastic Modulus of the Rectus Femoris Muscle and Patellar Tendon. J Ultrasound Med. 2017;36:565\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatta T, Giambini H, Uehara K, et al. Quantitative assessment of rotator cuff muscle elasticity: Reliability and feasibility of shear wave elastography. J Biomech. 2015;48:3853\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDulgheriu IT, Solomon C, Muntean DD et al. Shear-Wave Elastography and Viscosity PLUS for the Assessment of Peripheric Muscles in Healthy Subjects: A Pre- and Post-Contraction Study. Diagnostics (Basel) 2022;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ultrasound, Prefrailty, Hip/knee replacement","lastPublishedDoi":"10.21203/rs.3.rs-5304280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5304280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo identify prefrailty in patients undergoing total hip or knee replacement using preoperative ultrasound measurements of muscle dimensions and stiffness, with the goal of detecting high-risk prefrailty patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective cohort study, patients who underwent total hip/knee replacement were enrolled. Preoperative dimensions and stiffness of the biceps brachii (BB) were assessed using grey scale ultrasound (US) and shear wave elastography (SWE). Patients were preoperatively assessed for prefrailty based on FRAIL scale.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, a total of 121 consecutive patients [median age 71 years, (IQR 68\u0026ndash;73 years), 94 women] were included. Sixty-five patients (53.7%) had prefrailty. The proportion of females is higher in the prefrail group compared to the non-frail group (86.2% vs. 67.9%, P\u0026thinsp;=\u0026thinsp;0.023). The hemoglobin value in prefrail group was lower than that in non-frail group (130.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 g/L vs. 136.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 g/L, P\u0026thinsp;=\u0026thinsp;0.031). US measurements revealed significant differences in BB thickness and area between prefrail and non-prefrail groups on both dominant and non-dominant sides. These differences were observed in both absolute values and relative values (normalized by BMI) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BB perimeter showed a significant difference between groups on the dominant side only (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The AUC of BB thickness on dominant side (after normalization by dividing by BMI) was 0.664 (0.565\u0026ndash;0.762), which was the largest among all US variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePreoperative US measurements of BB dimensions demonstrated its predictive value for prefrailty in patients undergoing total hip or knee replacement surgery. However, SWE was found to be insufficient in distinguishing between prefrail and non-frail patients.\u003c/p\u003e","manuscriptTitle":"Preoperative ultrasound to identify prefrailty in patients undergoing total hip/knee replacement: A single-center, prospective, cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 11:28:07","doi":"10.21203/rs.3.rs-5304280/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"daefd397-9d02-4e33-9ff3-f1fceb244d6d","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T05:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 11:28:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5304280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5304280","identity":"rs-5304280","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}