Multimodal Ultrasound for Early Diagnosis and Prediction of ICU-Acquired Weakness

Multimodal Ultrasound for Early Diagnosis and Prediction of ICU-Acquired Weakness | 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 Multimodal Ultrasound for Early Diagnosis and Prediction of ICU-Acquired Weakness Wang Meiqi, Chen Daonan, Wang Yu, zhang chenchen, Wang Ruilan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7583201/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background ICU-acquired weakness (ICU-AW) is a common long-term complication in critically ill patients whose diagnostic criteria are based on muscle strength scores and neuro-electrophysiological examination(e.g., electromyography,EMG). ICU-AW can lead to prolonged duration of mechanical ventilation, weaning difficulty, increased hospitalization costs, reduced long-term quality of life, and significant muscle atrophy, usually occurring on day 7 to 10 of hospitalization, so early recognition and intervention are critical for prognosis. However, current diagnostic is limited by variations in physician expertise, patient cooperation, the invasiveness and complexity of EMG, and environmental interference. Ultrasound, a simple, noninvasive, and safe imaging technique, has become critical in evaluating various diseases. This study aimed to investigate characteristics of multimodal ultrasound (two-dimensional and elastic ultrasound) in ICU-AW patients at different stages, and to identify potential diagnostic and predictive indicators. Methods This study enrolled 100 ICU inpatients from October 2023 to August 2024. The ultrasound parameters—cross-sectional area, muscle thickness, pinnate angle of the quadriceps, and subcutaneous fat thickness of both lower limbs—were recorded on Days 1, 5, and 10 of hospitalization. Muscle strength scores and/or EMG were performed on Day 10.The baseline, clinical and ultrasound data of the enrolled patients were analyzed and compared, the potential diagnoses and predictors were selected, and the model value was evaluated through the measures of diagnostic accuracy and the area under the ROC curve. Results A total of 100 patients were included in this study, including 43 patients underwent three complete ultrasound examinations,23 ICU-AW patients and 20 non-ICU-AW patients. (1) Univariate analysis showed that on the Day10, the cross-sectional area of rectus femoris muscle and the size of the feather angle of the rectus intermediate muscle in the ICU-AW group were significantly reduced compared with those in the non-ICU-AW group, which was statistically significant. (2) The change rate of left/right rectus femoris cross-sectional area (25.01%/29.66%, AUC = 0.87/0.89) and the change rate of pinnate angle of left/right vastus intermedius muscle (27.48%/27.56%, AUC = 0.81/0.84) were the best cutoff values. (3) The binary logistic regression model (based on age and pinnate angle of the femoral intermediate muscle on Day 5) had higher predictive performance (AUC = 0.889, sensitivity = 0.818, specificity = 0.813). Conclusion Using multimodal ultrasound measures, we developed a convenient, simple, and safe diagnostic index (the rate of change in ultrasound parameters between Day 1 and Day 10), confirming its potential as a standardized and efficient quantitative tool. In addition, we used binary logistic regression to establish a highly predictive model that may inform clinical decision-making and early intervention in ICU-AW. ICU-acquired weakness Critical illness myopathy Muscular ultrasound Multimodal Logistic regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction ICU-acquired weakness (ICU-AW) is a clinical syndrome that typically occurs in patients with prolonged ICU stays, mechanical ventilation, sepsis, or multi-organ dysfunction syndrome (MODS) [ 1 – 3 ] .The main clinical manifestations include diffuse and symmetrical limb weakness, muscle atrophy, and neuromuscular dysfunction. The tendon reflex is generally reduced or absent, while cranial nerve function is preserved [ 4 ] . As patients stay longer, the muscles continue to atrophy, causing the muscle strength and tone to decline.ICU-acquired weakness includes critical myopathy (CIM), critical neuropathy (CIP), and critical neuromyopathy (CIPNM), with most patients experiencing both myogenic and neurogenic injuries [ 5 ] .Based on the findings of Fenzi [ 6 ] regarding E-selectin expression, microvascular leakage, and changes in the microvascular environment in the peripheral neurovascular endothelium of certain ICU-AW patients, Bolton [ 7 ] hypothesized that ICU-acquired weakness may be associated with neuronal damage and axonal degeneration caused by microcirculation disturbances in critically ill patients. Hyperglycemia induces mitochondrial dysfunction and microcirculatory disorders; however, appropriate glycemic control can reduce the risk of severe polyneuromuscular disease, as well as the duration of mechanical ventilation and hospital stay.Therefore, active control of blood glucose can reduce the risk of severe polyneuromuscular disease and the duration of mechanical ventilation and hospital stay. Prolonged mechanical ventilation, extensive sedation, and use of muscle relaxants are also major contributing factors in the development of ICU-AW [ 8 ] . The incidence of ICU-AW has been reported to be approximately 50% [ 9 ] , this figure increases to 52–67% in patients receiving mechanical ventilation for more than 48 hours and can exceed 80% in critically ill patients with sepsis and multi-organ failure [ 10 ] , and it is worth noting that more than 40% of survivors with ICU-acquired weakness still have gait abnormalities or limitations in activities of daily living within five years of discharge .About 35% patients with psychiatric comorbidities such as anxiety and post-traumatic stress disorder (PTSD) have an average annual increase in costs of $ 2.3 to $ 41,000 per patient, which greatly affects the normal life of patients and their families after discharge [ 11 – 13 ] . With the advancement of life support technologies such as extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT), the improvement of the survival rate of critically ill patients has made the long-term management of ICU-acquired weakness a new clinical problem. In the early stages of the course of the disease, it is necessary to consciously prevent the occurrence of ICU-acquired weakness, which has a great impact on long-term life. The ESICM expert panel emphasized the need for an ICU-led, multidisciplinary collaborative model to initiate neurological examination as early as possible during hospitalization to guide management and predict long-term prognosis [ 14 ] . Commonly used diagnostic methods of ICU-AW are electromyography, nerve conduction examination, muscle biopsy and the Medical Research Council (MRC) score. However, these tests are often limited by the patient's level of consciousness,the operator's level of expertise and interference with ICU instruments making those patients with ICU-AW often have obvious clinical symptoms and severe muscle atrophy by the time of these gold standard diagnosis, so it is important to recognize the occurrence of this disease early. Ultrasound is a widely accepted, noninvasive,bedside diagnostic tool that is widely used in the diagnosis of a variety of clinical diseases and can also be used to monitor muscle changes in ICU patients. Recent advances in musculoskeletal ultrasound have positioned it as a promising alternative for monitoring muscle dynamics in ICU settings. Prior studies have demonstrated the utility of single ultrasound parameters, such as muscle thickness or cross-sectional area (CSA), in detecting early muscle atrophy [ 15 , 16 ] . However, reliance on isolated metrics risks oversimplification, as ICU-AW involves multifaceted pathophysiological processes including myofiber degeneration, connective tissue remodeling, and altered muscle stiffness that may not be fully captured by a single indicator. For instance, CSA reduction reflects atrophy but lacks sensitivity to microstructural changes (e.g., fibrosis or edema), while shear wave elastography (SWE) quantifies tissue stiffness but may overlook volumetric loss [ 17 , 18 ] . Multimodal ultrasound, integrating structural (CSA, thickness), architectural (pinnate angle), and biomechanical (SWE) parameters, offers a holistic assessment of muscle integrity. This approach leverages complementary data to improve diagnostic accuracy, reduce observer variability, and enable earlier detection of heterogeneous pathological changes. A large number of studies on ICU-AW have focused on building a prediction model with lower efficiency than expected, and few studies have determined a cutoff value for the diagnosis of ICU-AW. [ 19 – 26 ] This may be due to the following reasons: (1) previous studies were not large enough and were single-center studies; (2) Previous studies only used two-dimensional ultrasound to monitor muscle changes, and did not use new technologies such as elastography and contrast-enhanced ultrasound to evaluate changes in muscle structure and function in multiple dimensions. (3) Most of previous studies only used univariate analysis to predict ICU-AW and did not further analyze the confounding effect of patients' clinical indicators on prediction.Our study aimed to determine a diagnostic cutoff value and validate the accuracy of ultrasound as a new diagnostic tool. Meanwhile,we also wanted to created a superior model base on binary logistic regression to increase prediction efficiency. Methods Design and Ethical Approval: The main study comprised two separate analyses, both conducted on ICU patients admitted to Shanghai General Hospital between October 2023 and July 2024. Within 24 hours of admission, all patients underwent standardized musculoskeletal ultrasonography (US) using a high-resolution linear array transducer. The following parameters were systematically recorded for the rectus femoris muscle: (1) thickness (TH,cm), (2) cross-sectional area (CSA, cm²), (3) pennation angle (PA,°), (4) shear wave elastic modulus (SWE,kPa), (5) shear wave velocity (SWV,m/s), (6) thickness of subcutaneous fat in both lower extremities(FTH,mm). All operations were performed by the same operator, and image interpretation and measurement were performed by three senior physicians, and the average value was taken as the final measurement.Subsequently, the above ultrasound measurements were repeated on day 5 and 10. On day 10, the patient was evaluated by a senior physician for electromyography to confirm the diagnosis of ICU-AW and were divided into the ICU-AW and non-ICU-AW groups according to the EMG results. The first investigation was a prospective diagnostic trial designed to establish a novel diagnostic approach for ICU-AW. Electromyography served as the diagnostic gold standard, while the rate of change in ultrasonographic parameters (from day 1 to day 10) was proposed as a potential biomarker. Given the lack of validated cutoff values in existing ultrasonographic diagnostic criteria, this study aimed to determine preliminary thresholds using receiver operating characteristic (ROC) curve analysis. The second investigation used the baseline data of patients, ultrasound and clinical data on day5 as predictors, and based on the binary logistic regression model to screen out the characteristic predictors and established a high predictive power prediction model. This study was approved by the local ethics committee of Shanghai General Hospital (identifier:【2023】228 ). Written informed consent was obtained from all participants or their legal representatives prior to inclusion in the study. All data were anonymized prior to analysis. Inclusion and Exclusion Criteria: Patients were eligible for inclusion if they had an expected ICU stay of more than two days, were between 18 and 90 years of age, and informed consent was required for participation. Exclusion criteria included the presence of primary motor dysfunction or a confirmed neuromuscular disease, as well as the diagnosis of such conditions during hospitalization. Patients with a history of trauma or surgery involving the lumbar spine, pelvis, or lower limbs, as well as those with limb defects, were also excluded. Pregnant women and individuals with conditions preventing ultrasound examination, such as trauma, redness, or edema at the measurement site, were not eligible. Furthermore, patients with advanced or terminal illness were excluded from the study. Diagnosis of ICU-AW: ICU-acquired weakness (ICU-AW) was diagnosed on day 10 based on EMG findings. EMG criteria included bilateral symmetrical neuromuscular injury, defined as a reduction in compound muscle action potential (CMAP) amplitude exceeding 2 standard deviations below the normal range.Neuropathic involvement was characterized by a weakened or absent response to direct electrical stimulation and a prolonged CMAP duration. In contrast, myopathic involvement presented with a normal response to direct electrical stimulation but a decreased CMAP amplitude. Needle EMG demonstrated nonspecific abnormal spontaneous activity, including fibrillation potentials or positive sharp waves, along with reduced duration and amplitude of motor unit potentials. Ultrasound Protocol: Ultrasound Protocol: Femoral rectus muscle thickness, cross-sectional area,shear wave elastography vastus Intermedius pinnate angle and thickness of subcutaneous fat in the lower extremities were measured in the ICU patients on days 1, 5, and 10 respectively. Each patient was assessed by the same operator and each parameter was measured three times, and the average was calculated. A clinical ultrasound system (VINNO 10P , China) equipped with a 10–12 MHz linear array transducer was used to measure these indicators. The measurement position was at the midpoint of the connection between the anterior superior iliac spine and upper margin of the patella, and the measurement depth was 4 cm. Two-dimensional ultrasound: The operator minimizes the degree of compression on the measuring point to reduce the measurement error caused by too much or too little compression. During the first measurement, the operator should make a conspicuous mark on the patient's lower limb that is not easy to erase to ensure the consistency of the subsequent measurement position. In the two-dimensional ultrasound image of the rectus femoris cross-section, the hypoechoic fusiform abdomen is surrounded by a hyperechoic fascia with a clear boundary. We measure the cross-sectional area of the rectus femoris muscle by delineating the hyperechoic fascial edge [ 27 ] . Subsequently, we rotated the probe 90° to obtain a two-dimensional ultrasound image of the longitudinal section of the rectus femoris, which showed that the hypoechoic area between the two hyperechoic fascia was the rectus femoris muscle, and the rectus femoris thickness could be measured by measuring the distance between the two hyperechoic fascia [ 28 ] . In the same section, the hyperechoic muscle fibers arranged in a bundle between the abdomen of the hypoechoic muscle can be seen by sliding the probe left and right, and the angle formed between them and the deep fascia is called the pinnate angle [ 29 ] . This angle is a reflection of muscle strength, as the larger the pinnate angle, the greater the number of fascicles per unit volume and the greater the ability of the muscle to generate force [ 30 ] . The thickness of subcutaneous fat in both lower extremities is measured at the same point as the quadriceps muscle, and in the longitudinal section, it is a layer of tissue with a slightly higher echo layer at the muscle level. Figure 1 (A,B,C)shows the longitudinal and cross-sectional views of the rectus femoris muscle, the thickness of the rectus femoris muscle and the pinnate angle of the vastus intermedius muscle can be measured in the longitudinal section, and the cross-sectional area of the rectus femoris muscle can be measured in the cross-section, and the ultrasound changes of the same patient on the 1st, 5th, and 10th days of hospitalization are shown in the figure. Note: The distance marked in the red line in a is the thickness of the subcutaneous fat (FTH) of the lower extremity, and the distance marked in the red line is the thickness of the rectus femoris muscle (RF-TH). c The distance marked on the red line is the thickness of the intermediaus femoris muscle (VI-TH); The area circled in Fig. 2 d is the cross-sectional area of the rectus femoris muscle (RF-CSA), and the red line in Fig. 3 shows the pinnate angle (VI-PA) of the intermediaus femoris muscle. Shear wave elastography: We first found the rectus femoris muscle on the B-mode ultrasonic image, then switched to the shear wave mode, and selected three regions of interest (ROI) in the sampling box with a diameter of 0.25 cm. Finally, the average value of the shear wave velocity (SWV) and the average value of the shear wave elastic modulus (E) were calculated. Demographic and Baseline Data Collection: We collected the following demographic and baseline characteristics: age, gender, body weight, body height and length at ICU admission, admission type, admission diagnosis, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and maximal total Sequential Organ Failure Assessment (SOFA) score. Clinical data collection: We collected clinical data on pre-existing polyneuropathy or myopathy, risk factors for polyneuropathy before ICU admission (diabetes mellitus, alcohol abuse, chemotherapy, and kidney failure), days with mechanical ventilation, ICU length of stay, and ICU mortality. In addition, we collected biochemical indicators related to nutrition and metabolism, such as albumin, creatinine, urea nitrogen, uric acid, blood glucose, blood potassium, and blood sodium, on days 1, day5 and day10, synchronizing with the ultrasound indicators. Sample size estimation: This was a prospective observational cohort study, and we included ICU patients who met the inclusion and exclusion criteria at the Shanghai General Hospital from October 2023 to July 2024. Sample size was calculated based on an expected ICU-AW incidence of 30%, with 80% power (β = 0.2) and α = 0.05 to detect a 20% difference in ultrasound parameters between groups, yielding a minimum requirement of 43 patients (PASS11). Statistical analysis: Statistical Package for the Social Sciences version 26.0 (IBM) software was used for statistical analysis. The normality of the distribution of variables was assessed using the Kolmogorov-Smirnov test. Normally distributed data are expressed as mean and standard deviations. Non-normally distributed data are presented as medians with interquartile range (IQR). The Student t-test was used for normally distributed data and the Mann-Whitney test was used for non-normally distributed data. The P value was less than 0.05 for statistical significance. For the diagnostic model, we plot the ROC curve, calculate the AUC value, and evaluate the accuracy of the diagnosis with sensitivity, specificity, positive predictive value, and negative predictive value, and in the construction of the prediction model based on binary logistic regression, we plot the ROC curve to calculate the AUC value, sensitivity, and specificity to evaluate the prediction performance. Results Demographic characteristics: From October 2023 to August 2024, we collected a total of 188 patients admitted to the ICU of Shanghai First People's Hospital, and 100 patients who met the inclusion criteria and signed the informed consent form were included in this study. A total of 88 patients with a hospital stay of less than 3 days, central nervous system lesions such as cerebral infarction and cerebral hemorrhage, and no informed consent were excluded. Figure 3 illustrates the complete screening process, in which 43 patients completed three complete ultrasonography of the lower extremity muscles. On day 10, 23 patients (53.5%) were diagnosed with ICU-AW and 20 patients (46.5%) were diagnosed with non-ICU-AW. We included the basic demographic characteristics of the 43 patients who passed the screening and some clinical indicators on the first day of ICU admission, and performed statistical analyses, as shown in Table 1 . Table 1 Patients’Demographic and Clinical characteristics ICU-AW(n = 23) Non-ICU-AW(n = 20) P Age 73(63 ~ 80) 56(29.25 ~ 66.75) 0.001 Gender Men(65%),Women(35%) Men(80%),Women(20%) 0.076 BMI 22.89(19.9 ~ 26.03) 22.28(21.06 ~ 24.52) 0.981 SOFA 12(4 ~ 14) 6(4.25 ~ 12.75) 0.328 APACHEII 14(8 ~ 17.5) 5(4 ~ 15.75) 0.035 creatinine 113.55(71.95 ~ 141.1) 115.85(56.55 ~ 240.2) 0.865 Urea nitrogen 11.3(6.5 ~ 14.75) 7.22(4.4 ~ 13.58) 0.158 uric acid 287.5(217.23 ~ 442.2) 325.25(156.25 ~ 497.5) 0.609 Blood sodium 139.52(135.36 ~ 141.37) 137.8(135.25 ~ 143.44) 0.865 Potassium in the blood 3.98 ± 0.58 3.82 ± 0.51 0.357 blood sugar 8.7(7.19 ~ 11.15) 8.95(6.93 ~ 9.83) 0.503 albumin 29.94 ± 7.46 31.12 ± 5.28 0.559 Diagnostic category at admission, n (%) malignancy 2(8.7%) 1(5%) Respiratory system 10(43.5%) 8(40%) Cardiovascular system 3(13%) 0(0%) Endocrine system 1(4.3%) 1(5%) Hematologic system 0(0%) 2(10%) Digestive system 4(17.4%) 4(20%) Sepsis 4(17.4%) 3(15%) Table 1 shows the demographic characteristics of the two groups of patients (ICU-AW and non-ICU-AW), and the incidence of ICU-AW did not differ significantly between genders, BMI, and SOFA scores, but was statistically significant in terms of APACHEII scores and age. Thus, age at admission and APCHE-II score may be important factors influencing the onset of ICU-acquired weakness.In the ICU-acquired frailty group, patients with respiratory diseases (43.5%) accounted for the largest proportion, followed by sepsis (17.4%), digestive system diseases (17.4%), cardiovascular system diseases (13%), malignant tumors (8.7%), and endocrine system diseases (4.3%). Among them, patients with ICU-acquired frailty with respiratory diseases were given sedation and analgesic ventilator-assisted ventilation, while patients with non-ICU-acquired frailty with respiratory diseases were given non-invasive ventilators and high-flow respiratory support. It can be seen that prolonged invasive mechanical ventilation and sepsis are high-risk factors for the development of ICU-AW, which is consistent with the results reported in the previous literature. We compared the differences in the quadriceps ultrasound on the first day of all patients on the first day, and found that there was no statistically significant difference in the initial characteristics of rectus ultrasound on the first day of ICU admission between the two groups (as shown in Table 1 ),we can also see that the CSA, TH, PA, SWV, SWE, and FTH in the ICU-AW group were slightly lower than those in the non-ICU-AW group. Table 2 Baseline of quadriceps ultrasound on Day1 ICU-AW(n = 23) No ICU-AW(n = 20) P-value CSA(right) 1.31 ± 0.51 1.57 ± 0.55 0.116 CSA(left) 1.23 ± 0.47 1.48 ± 0.54 0.117 CSA(mean) 1.27 ± 0.47 1.52 ± 0.5 0.098 TH(right) 1.05 ± 0.34 1.22 ± 0.34 0.098 TH(left) 1.02 ± 0.33 1.18 ± 0.3 0.096 TH(mean) 1.03 ± 0.32 1.2 ± 0.3 0.086 SWV(right) 3.33 ± 0.77 3.83 ± 1.19 0.125 SWV(left) 3.53 ± 1.26 3.64 ± 0.93 0.515 SWV(mean) 3.43 ± 0.89 3.74 ± 0.98 0.328 SWE(right) 32.35(22.77 ~ 44.22) 49.16(30.05 ~ 65.47) 0.051 SWE(left) 32.59(23.69 ~ 46.72) 41.37(22.66 ~ 63.97) 0.274 SWE(mean) 36.44(25.94 ~ 44.52) 42.11(33.04 ~ 65.24) 0.101 PA(right) 9.81 ± 0.98 10.5 ± 1.3 0.063 PA(left) 9.77 ± 1.32 10.3 ± 1.11 0.157 PA(mean) 9.79 ± 1.08 10.4 ± 1.03 0.066 FTH(right) 1.21 ± 0.31 1.37 ± 0.18 0.075 FTH(left) 1.25 ± 0.27 1.32 ± 0.21 0.529 FTH(mean) 1.25 ± 0.27 1.34 ± 0.18 0.205 Diagnosis of ICU-acquired weakness: Table 3 Receiver Operating Characteristic curves of changes in CSA,TH,PA,E,SWV, and FTH of muscles on Day10 Muscle AUC SE P 95% CI for ROC-AUC Lower Upper Cutoff RF-RCSA/% 0.891 0.048 < 0.0001 0.778 0.985 29.66% RF-LCSA/% 0.874 0.055 < 0.0001 0.7660 0.9819 25.01% RF-ACSA/% 0.926 0.04 < 0.0001 0.847 1 24.72% RF-RTH/% 0.742 0.078 0.0066 0.5902 0.8945 21.37% RF-LTH/% 0.657 0.084 0.0796 0.4929 0.8202 31.08% RF-ATH/% 0.676 0.088 0.067 0.504 0.848 21.2% VI-RPA/% 0.815 0.063 0.0004 0.6913 0.9391 27.38% VI-LPA/% 0.84 0.064 0.0001 0.7149 0.9655 27.56% VI-APA/% 0.85 0.071 0.001 0.668 0.946 28.25% RF-RE/% 0.548 0.083 0.583 0.3843 0.7110 79.48% RF-LE/% 0.505 0.081 0.9547 0.3452 0.6646 104.4% RF-AE/% 0.528 0.096 0.767 0.34 0.716 8.19% RF-RSWV/% 0.502 0.086 0.985 0.3323 0.6710 26.97% RF-LSWV /% 0.53 0.083 0.7331 0.3676 0.6916 5.395% RF-ASWV /% 0.472 0.095 0.767 0.284 0.659 36.08% RFTH/% 0.58 0.089 0.368 0.4052 0.7557 26.97% LFTH/% 0.736 0.08 0.0082 0.5795 0.8922 16.78% AFTH/% 0.668 0.088 0.081 0.496 0.839 22.88% Notes: RF-RCSA, right rectus femoris cross-sectional area; RF-LCSA, left rectus femoris cross-sectional area; RF-ACSA, mean cross-sectional area of bilateral rectus femoris; RF-RTH, right rectus femoris thickness; RF-LTH, left rectus femoris thickness; RF-ATH, mean of bilateral rectus femoris muscle thickness; VI-RPA, right vastus intermediaus pinnate angle; VI-LPA, left vastus intermediaus pinnate angle; VI-APA, mean of both femoral intermediate muscle pinnate angles; RF-RE, elastic modulus of shear wave of right rectus femoris; RF-LE, elastic modulus of shear wave of the left rectus femoris; RF-AE, mean elastic modulus of bilateral rectus femoris shear wave; RF-RSWV, right rectus femoris shear wave velocity; RF-LSWV, left rectus femoris shear wave velocity; RF-ASWV, mean velocity of bilateral rectus femoris muscle; RFTH, subcutaneous fat thickness of the right lower extremity; LFTH, subcutaneous fat thickness of the left lower extremity; AFTH, mean subcutaneous fat thickness in both lower extremities. Note CSA% represents the rate of change of CSA, TH% represents the rate of change of TH, PA% represents the rate of change of PA, E% represents the rate of change of CSA, SWV% represents the rate of change of SWV, and FTH% represents the rate of change of FTH. From the analysis of Table 3 , CSA of rectus femoris muscle (right: AUC = 0.891, 95% CI = 0.778 ~ 0.985, P < 0.0001; left: AUC = 0.874, 95% CI = 0.766 ~ 0.982, P < 0.0001; average: AUC = 0.926, 95% CI = 0.847 ~ 0.1, P < 0.0001) and PA of the vastus intermediaus muscle (right: AUC = 0.815, P = 0.0004; left: AUC = 0.840, P = 0.0001; average: AUC = 0.85, P = 0.001) From the above analysis, it can be preliminarily concluded that ICU-AW can be diagnosed when the CSA of the rectus femoris muscle is reduced (left: 25.01%, right: 29.66%, average: 24.72%) and the PA of the femoral intermediar muscle is reduced (left: 27.48%, right: 27.56%, average: 28.25%). Table 4 Diagnosis accuracy of changes of RF-CSA, VI-PA from day 1 to day 10 Muscle AUC Sensitivity Specificity Positive predictive value Negative predictive value RF-RCSA/% 0.891 0.86 0.77 0.78 0.85 RF-LCSA/% 0.874 0.83 0.84 0.87 0.8 RF-ACSA/% 0.926 0.87 0.85 0.87 0.85 VI-RPA/% 0.815 0.83 0.68 0.65 0.85 VI-LPA/% 0.84 0.86 0.81 0.83 0.85 VI-APA/% 0.85 0.89 0.78 0.77 0.9 RF-LCSA + VI-LPA/% 0.891 1 0.8 0.78 1 RF-RCSA + VI-RPA/% 0.711 0.86 0.62 0.52 0.9 RF-ACSA + VI-APA/% 0.826 1 0.71 0.65 1 RF-LCSA/VI-LPA/% 0.782 0.75 0.87 0.91 0.65 RF-RCSA/VI-RPA/% 0.857 0.84 0.89 0.91 0.8 RF-ACSA/VI-APA/% 0.853 0.815 0.938 0.96 0.75 Notes: RF-RCSA, right rectus femoris cross-sectional area; RF-LCSA, left rectus femoris cross-sectional area; RF-ACSA, mean cross-sectional area of bilateral rectus femoris; VI-RPA, right vastus intermedius pinnate angle; VI-LPA, left vastus intermedius pinnate angle; VI-APA, right femoral intermediate muscle pinnate angle; "+", which means that the two diagnostic conditions of the rectus femoris cross-sectional area and the femoral intermediate pinnate angle are met at the same time;"/", indicating that either of the diagnostic criteria of the rectus femoris cross-sectional area and the femoral intermediate muscle pinnate angle is satisfied. From the results presented in Table 4 , we can conclude that the mean diagnostic efficiency of the bilateral middle femoral and pinnate angles is the highest (AUC = 0.926, sensitivity = 0.86, specificity = 0.77), which can be used as an independent indicator for the diagnosis of ICU-AW. By combining the CSA of the rectus femoris muscle and the PA of vastus intermedius muscle we found that the negative predictive value of the diagnosis was significantly improved when both diagnostic indicators were met (left: AUC = 0.891, sensitivity = 1, specificity = 0.8, positive predictive value = 0.78, negative predictive value = 1; right: AUC = 0.711, sensitivity = 0.86, specificity = 0.62, positive predictive value = 0.52, negative predictive value = 0.9; average: AUC=). 0.826, sensitivity = 1, specificity = 0.71, positive predictive value = 0.65, negative predictive value = 1), especially the index on the left side reflected a higher AUC value, sensitivity and specificity. This indicator is of high value for excluding people with ICU-AW. When either of the two indicators was met, the positive predictive value of the diagnosis was significantly improved (left: AUC = 0.782, sensitivity = 0.75, specificity = 0.87, positive predictive = 0.91, negative = 0.8; right: AUC = 0.857, sensitivity = 0.84, specificity = 0.89, positive = 0.91, negative = 0.8; average: AUC = 0.853, sensitivity = 0.815, specificity = 0.938, positive predictive value = 0.96, negative predictive value = 0.75), especially the indicators of the right and bilateral mean showed high AUC value, sensitivity and specificity. This indicator is of great significance for early screening of high-risk groups for ICU-AW. Predictive Modeling for the Identification of ICU-AW: In this part of the study, 18 characteristics such as basic data (age, gender, APCHEII score, SOFA score, BMI), clinical indicators and ultrasound measurement indicators on day 5 were included in this part of the study, and SPSS26.0 software was used for statistical analysis. The global likelihood ratio test of the model was carried out by Omnibus test, and p < 0.05 indicated that the model was generally meaningful. Table 5 Omnibus Tests of model Coefficients and Goodness of Fit Step2 Χ 2 dF Sig. Step 6.021 1 .014 Block 21.305 2 .000 Model 21.305 2 .000 Model summary Step 2 -2 Log - Likelihood 30.423a Cox&Snell R Square .429 Nagelkerke R Square .577 a Because the parameter estimate changed by less than .001, the estimate was terminated at the 5th iteration. Horsmer-Lemeshaw test Step 2 Χ 2 9.663 Df 7 Sig. .208 Firstly, the Omnibus test results show that the model is generally meaningful (p 0.4), and the significance of the Hosmer-Lemesho test (H-L test) was 0.208 (p > 0.05), indicating that the goodness-of-fit of the model was good. Table 6 Multivariate logistic regression analysis Note: PA5 represents the mean of pinnate angles on day 5 . variable B SE Waldχ 95% CI for ROC-AUC Lower Upper Exp(B) Sig. Age .098 .039 6.222 1.021 1.190 1.103 .013 PA5 − .669 .312 4.612 .278 .943 .512 .032 constant − .415 3.765 .012 .66 .912 Classification table prediction Measured group Non-ICU-AW ICU-AW Correct percentage Step2 group Non-ICU-AW ICU-AW 13 4 3 18 81.3 81.8 Overall percentage 81.6 Table 7 Evaluation of the prediction performance of the prediction model ROC-AUC SE P 95% CI for ROC-AUC Lower Upper Sensitivity Specificity 0.889 0.065 0.000 0.762 1 0.818 0.813 Logistic regression model analysis showed that age (OR = 1.103, 95% CI: 1.021 ~ 1.19, p = 0.013) and femoral intermediate muscle pinnate angle on day 5 (OR = 0.512, 95% CI: 0.278 ~ 0.943, p = 0.032) were statistically significant for predicting ICU-acquired frailty, and the binary logistic regression equation was obtained: Y=-0.415 0.066×0.098X Age -0.669×X PA5 It can be concluded that the risk of ICU-AW increases by 9.8% for every 1 year of age of the prediction model, and the risk of ICU-AW decreases by 66.9% for every 1° increase in the pinnate angle, and the size of the pinnae angle is an important factor affecting the occurrence of ICU-AW. Discussion The present study underscores the potential of multimodal ultrasound as a non-invasive, bedside tool for the early diagnosis and prediction of ICU-AW. Our findings demonstrate that the rate of change in CSA of rectus femoris and PA of vastus intermedius between days 1 and 10 of ICU admission serves as a robust diagnostic marker for ICU-AW, with high accuracy (AUC = 0.887–0.926). These structural and architectural parameters reflect rapid muscle atrophy and biomechanical alterations, aligning with previous studies that highlighted the utility of ultrasound in detecting early muscle wasting in critically ill patients. Notably, the integration of CSA and PA metrics improved diagnostic specificity and negative predictive value, suggesting that multimodal ultrasound captures complementary aspects of muscle degeneration, such as volumetric loss and microstructural remodeling, which are not fully assessed by isolated parameters. The predictive model incorporating age and PA on day5 further advances clinical utility by enabling early risk stratification. Older age (cutoff > 68.5 years) and reduced PA on day 5 were identified as independent predictors of ICU-AW, consistent with prior evidence linking advanced age and prolonged immobility to neuromuscular dysfunction. The model's high sensitivity (81.8%) and specificity (81.3%) highlight its potential to guide preemptive interventions, such as early mobilization or nutritional support, in high-risk patients. This aligns with expert recommendations advocating multidisciplinary, ICU-led protocols to mitigate long-term disability. Despite promising results, several limitations warrant consideration. The single-center design and modest sample size (n = 43) may limit generalizability, and the exclusion of patients with comorbidities or severe edema introduces selection bias [ 31 ] . While shear wave elastography (SWE) revealed trends toward reduced muscle stiffness in ICU-AW patients, statistical significance was not achieved, possibly due to age-related variability in elastographic measurements [ 32 ] .Future studies with age-stratified cohorts are needed to clarify SWE's role in assessing muscle quality. Additionally, the reliance on operator-dependent ultrasound measurements underscores the need for standardized protocols and automated image analysis tools to reduce inter-observer variability. The integration of artificial intelligence (AI) for automated quantification of ultrasound parameters could enhance diagnostic precision and scalability [ 33 – 35 ] . Emerging technologies, such as AI-driven shear wave analysis or contrast-enhanced ultrasound, may further elucidate microvascular and connective tissue changes in ICU-AW. Multicenter validation of the proposed diagnostic thresholds and predictive model is essential to confirm their robustness across diverse ICU populations [ 36 – 38 ] . In conclusion, this study establishes multimodal ultrasound as a practical and accurate modality for diagnosing ICU-AW and provides a predictive framework to identify high-risk patients. By facilitating early intervention, these tools may reduce the burden of prolonged mechanical ventilation, rehabilitation delays, and long-term disability associated with ICU-AW. Future research should focus on protocol standardization, technological innovation, and large-scale validation to optimize clinical translation [ 39 , 40 ] .With the development of more and more new technologies of musculoskeletal ultrasound and the innovation of ultrasound machines, if you want to make good use of these new technologies and use ultrasound to monitor muscle changes more accurately, it is necessary to develop a standardized ultrasound evaluation process, so that more doctors can learn systematically and standardly, so as to improve image quality. The evaluation process referred to in this study provides a reliable approach to the early identification of ICU-acquired weakness, which in turn reminds clinicians to provide early rehabilitation for patients at potential risk. For some patients with severe edema and obesity, we need to further explore and find new ways to reduce the interference of these factors on imaging. In this era of rapid development of big data, the application of artificial intelligence is believed to realize automatic image analysis in the future, so as to help intensive care doctors identify patients with ICU-acquired frailty more quickly and accurately. Conclusion In this study, the CSA(left/right) decreased by 25.01%/29.66% (AUC = 0.874/0.891, P < 0.05) and PA (left/right) decreased by 27.48%/27.56% (AUC = 0.815/0.84) as diagnostic cut-off values. By combining the CSA of the rectus femoris muscle and PA of verasimus intermedias, it can be found that when the two diagnostic indicators are met at the same time, the negative predictive value of the diagnosis is significantly improved.When either of the two indicators was met, the positive predictive value of the diagnosis was significantly improved.The dynamic changes of the above ultrasound indicators can intuitively reflect muscle atrophy and functional deterioration, and these changes are closely related to the occurrence of ICU-acquired frailty, which provides a reliable tool for bedside non-invasive assessment. We also developed a logistic regression model to predict the occurrence of ICU-AW (AUC = 0.889, sensitivity = 0.818, specificity = 0.813). Multiple variables, such as age and PA were included in the model. By combining these two variables, we have plotted nomograms to make individualized predictions of ICU-AW more intuitively. Declarations Conflict of Interest Statement: The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article. Funding: This work was supported by: 1.Noncommunicable Chronic Diseases-National Science and Technology Major Project (NO. 2023ZD0506502) 2.the Key Supporting Discipline of Shanghai Healthcare System (NO. 2023ZDFC0102) 3.the National Natural Science Foundation of China (Grant No. 82402583) 4.the Clinical Research Special Project of the Shanghai Municipal Health Commission (Grant No. 20244Y0015). 5.National Clinical Key Specialty Construction Project(Z155080000004) Author Contribution Author W.M.Q., X.H., C.D.N. and W.R.L. designed the study.Author W.M.Q. and W.Y. AND Z.C.C. supervised data collection. Author W.M.Q. and M.S.Y.analyzed the data. Author W.M.Q. drafted the manuscript. All authors critically revised the manuscript and approved the final version. 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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-7583201","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515121417,"identity":"246c4824-735c-4e2f-9eb2-e80a108f932f","order_by":0,"name":"Wang Meiqi","email":"","orcid":"","institution":"Shanghai General Hospital of Shanghai Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Meiqi","suffix":""},{"id":515121418,"identity":"faaf38ef-1a0d-4c86-be08-0f4082fa667e","order_by":1,"name":"Chen Daonan","email":"","orcid":"","institution":"Shanghai General Hospital of Shanghai 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18:07:35","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155407,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/22172bb5a86feb11c8705594.html"},{"id":91737706,"identity":"8196b849-5a79-42e0-a473-9465c22f3c4f","added_by":"auto","created_at":"2025-09-19 18:07:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211370,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/0a23daec553311a1fc1bb9ac.png"},{"id":91737707,"identity":"22644933-82a1-40e9-9597-500f41569a0a","added_by":"auto","created_at":"2025-09-19 18:07:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271183,"visible":true,"origin":"","legend":"\u003cp\u003erectus femoris shear wave elastography\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/db49e6a651fd1a03f9f8a9d0.png"},{"id":91737704,"identity":"453b33f5-fdfa-48d6-a952-b7957947f898","added_by":"auto","created_at":"2025-09-19 18:07:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28605,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of screening and scheduling patients\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/b2712072a90e98427fa29248.jpg"},{"id":91737708,"identity":"55b3a046-9561-4fa7-8fb6-e163684f584c","added_by":"auto","created_at":"2025-09-19 18:07:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117596,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of changes in CSA,TH,PA,E,SWV, and FTH of muscles on Day10\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/19f0e81d8468dffa15655a97.png"},{"id":91740861,"identity":"2174b24c-b343-4ef1-933f-c51ae40738eb","added_by":"auto","created_at":"2025-09-19 18:39:35","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the prediction model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/f35b87d3f6bd10330f3ecd72.jpeg"},{"id":91816809,"identity":"fae1a87e-098b-4fd9-86b4-aa53cedd17be","added_by":"auto","created_at":"2025-09-22 06:52:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1752025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7583201/v1/d3f6cdcd-c5aa-44d0-87e4-de2bbe890334.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimodal Ultrasound for Early Diagnosis and Prediction of ICU-Acquired Weakness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eICU-acquired weakness (ICU-AW) is a clinical syndrome that typically occurs in patients with prolonged ICU stays, mechanical ventilation, sepsis, or multi-organ dysfunction syndrome (MODS) \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.The main clinical manifestations include diffuse and symmetrical limb weakness, muscle atrophy, and neuromuscular dysfunction. The tendon reflex is generally reduced or absent, while cranial nerve function is preserved\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. As patients stay longer, the muscles continue to atrophy, causing the muscle strength and tone to decline.ICU-acquired weakness includes critical myopathy (CIM), critical neuropathy (CIP), and critical neuromyopathy (CIPNM), with most patients experiencing both myogenic and neurogenic injuries\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.Based on the findings of Fenzi \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e regarding E-selectin expression, microvascular leakage, and changes in the microvascular environment in the peripheral neurovascular endothelium of certain ICU-AW patients, Bolton\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e hypothesized that ICU-acquired weakness may be associated with neuronal damage and axonal degeneration caused by microcirculation disturbances in critically ill patients. Hyperglycemia induces mitochondrial dysfunction and microcirculatory disorders; however, appropriate glycemic control can reduce the risk of severe polyneuromuscular disease, as well as the duration of mechanical ventilation and hospital stay.Therefore, active control of blood glucose can reduce the risk of severe polyneuromuscular disease and the duration of mechanical ventilation and hospital stay. Prolonged mechanical ventilation, extensive sedation, and use of muscle relaxants are also major contributing factors in the development of ICU-AW\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The incidence of ICU-AW has been reported to be approximately 50%\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, this figure increases to 52\u0026ndash;67% in patients receiving mechanical ventilation for more than 48 hours and can exceed 80% in critically ill patients with sepsis and multi-organ failure\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, and it is worth noting that more than 40% of survivors with ICU-acquired weakness still have gait abnormalities or limitations in activities of daily living within five years of discharge .About 35% patients with psychiatric comorbidities such as anxiety and post-traumatic stress disorder (PTSD) have an average annual increase in costs of \u003cspan\u003e$\u003c/span\u003e2.3 to \u003cspan\u003e$\u003c/span\u003e41,000 per patient, which greatly affects the normal life of patients and their families after discharge\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. With the advancement of life support technologies such as extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT), the improvement of the survival rate of critically ill patients has made the long-term management of ICU-acquired weakness a new clinical problem. In the early stages of the course of the disease, it is necessary to consciously prevent the occurrence of ICU-acquired weakness, which has a great impact on long-term life. The ESICM expert panel emphasized the need for an ICU-led, multidisciplinary collaborative model to initiate neurological examination as early as possible during hospitalization to guide management and predict long-term prognosis \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCommonly used diagnostic methods of ICU-AW are electromyography, nerve conduction examination, muscle biopsy and the Medical Research Council (MRC) score. However, these tests are often limited by the patient's level of consciousness,the operator's level of expertise and interference with ICU instruments making those patients with ICU-AW often have obvious clinical symptoms and severe muscle atrophy by the time of these gold standard diagnosis, so it is important to recognize the occurrence of this disease early.\u003c/p\u003e\u003cp\u003eUltrasound is a widely accepted, noninvasive,bedside diagnostic tool that is widely used in the diagnosis of a variety of clinical diseases and can also be used to monitor muscle changes in ICU patients. Recent advances in musculoskeletal ultrasound have positioned it as a promising alternative for monitoring muscle dynamics in ICU settings. Prior studies have demonstrated the utility of single ultrasound parameters, such as muscle thickness or cross-sectional area (CSA), in detecting early muscle atrophy \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, reliance on isolated metrics risks oversimplification, as ICU-AW involves multifaceted pathophysiological processes including myofiber degeneration, connective tissue remodeling, and altered muscle stiffness that may not be fully captured by a single indicator. For instance, CSA reduction reflects atrophy but lacks sensitivity to microstructural changes (e.g., fibrosis or edema), while shear wave elastography (SWE) quantifies tissue stiffness but may overlook volumetric loss \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Multimodal ultrasound, integrating structural (CSA, thickness), architectural (pinnate angle), and biomechanical (SWE) parameters, offers a holistic assessment of muscle integrity. This approach leverages complementary data to improve diagnostic accuracy, reduce observer variability, and enable earlier detection of heterogeneous pathological changes.\u003c/p\u003e\u003cp\u003eA large number of studies on ICU-AW have focused on building a prediction model with lower efficiency than expected, and few studies have determined a cutoff value for the diagnosis of ICU-AW. \u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003eThis may be due to the following reasons: (1) previous studies were not large enough and were single-center studies; (2) Previous studies only used two-dimensional ultrasound to monitor muscle changes, and did not use new technologies such as elastography and contrast-enhanced ultrasound to evaluate changes in muscle structure and function in multiple dimensions. (3) Most of previous studies only used univariate analysis to predict ICU-AW and did not further analyze the confounding effect of patients' clinical indicators on prediction.Our study aimed to determine a diagnostic cutoff value and validate the accuracy of ultrasound as a new diagnostic tool. Meanwhile,we also wanted to created a superior model base on binary logistic regression to increase prediction efficiency.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eDesign and Ethical Approval:\u003c/h2\u003e\n \u003cp\u003eThe main study comprised two separate analyses, both conducted on ICU patients admitted to Shanghai General Hospital between October 2023 and July 2024.\u003c/p\u003e\n \u003cp\u003eWithin 24 hours of admission, all patients underwent standardized musculoskeletal ultrasonography (US) using a high-resolution linear array transducer. The following parameters were systematically recorded for the rectus femoris muscle: (1) thickness (TH,cm), (2) cross-sectional area (CSA, cm\u0026sup2;), (3) pennation angle (PA,\u0026deg;), (4) shear wave elastic modulus (SWE,kPa), (5) shear wave velocity (SWV,m/s), (6) thickness of subcutaneous fat in both lower extremities(FTH,mm). All operations were performed by the same operator, and image interpretation and measurement were performed by three senior physicians, and the average value was taken as the final measurement.Subsequently, the above ultrasound measurements were repeated on day 5 and 10. On day 10, the patient was evaluated by a senior physician for electromyography to confirm the diagnosis of ICU-AW and were divided into the ICU-AW and non-ICU-AW groups according to the EMG results.\u003c/p\u003e\n \u003cp\u003eThe first investigation was a prospective diagnostic trial designed to establish a novel diagnostic approach for ICU-AW. Electromyography served as the diagnostic gold standard, while the rate of change in ultrasonographic parameters (from day 1 to day 10) was proposed as a potential biomarker. Given the lack of validated cutoff values in existing ultrasonographic diagnostic criteria, this study aimed to determine preliminary thresholds using receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\n \u003cp\u003eThe second investigation used the baseline data of patients, ultrasound and clinical data on day5 as predictors, and based on the binary logistic regression model to screen out the characteristic predictors and established a high predictive power prediction model.\u003c/p\u003e\n \u003cp\u003eThis study was approved by the local ethics committee of Shanghai General Hospital (identifier:【2023】228 ). Written informed consent was obtained from all participants or their legal representatives prior to inclusion in the study. All data were anonymized prior to analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria:\u003c/h3\u003e\n\u003cp\u003ePatients were eligible for inclusion if they had an expected ICU stay of more than two days, were between 18 and 90 years of age, and informed consent was required for participation.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included the presence of primary motor dysfunction or a confirmed neuromuscular disease, as well as the diagnosis of such conditions during hospitalization. Patients with a history of trauma or surgery involving the lumbar spine, pelvis, or lower limbs, as well as those with limb defects, were also excluded. Pregnant women and individuals with conditions preventing ultrasound examination, such as trauma, redness, or edema at the measurement site, were not eligible. Furthermore, patients with advanced or terminal illness were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eDiagnosis of ICU-AW:\u003c/h3\u003e\n\u003cp\u003eICU-acquired weakness (ICU-AW) was diagnosed on day 10 based on EMG findings. EMG criteria included bilateral symmetrical neuromuscular injury, defined as a reduction in compound muscle action potential (CMAP) amplitude exceeding 2 standard deviations below the normal range.Neuropathic involvement was characterized by a weakened or absent response to direct electrical stimulation and a prolonged CMAP duration. In contrast, myopathic involvement presented with a normal response to direct electrical stimulation but a decreased CMAP amplitude. Needle EMG demonstrated nonspecific abnormal spontaneous activity, including fibrillation potentials or positive sharp waves, along with reduced duration and amplitude of motor unit potentials.\u003c/p\u003e\n\u003ch3\u003eUltrasound Protocol:\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eUltrasound Protocol:\u003c/div\u003e\n\u003cp\u003eFemoral rectus muscle thickness, cross-sectional area,shear wave elastography vastus Intermedius pinnate angle and thickness of subcutaneous fat in the lower extremities were measured in the ICU patients on days 1, 5, and 10 respectively. Each patient was assessed by the same operator and each parameter was measured three times, and the average was calculated. A clinical ultrasound system (VINNO\u003csup\u003e10P\u003c/sup\u003e, China) equipped with a 10\u0026ndash;12 MHz linear array transducer was used to measure these indicators. The measurement position was at the midpoint of the connection between the anterior superior iliac spine and upper margin of the patella, and the measurement depth was 4 cm.\u003c/p\u003e\n\u003ch3\u003eTwo-dimensional ultrasound:\u003c/h3\u003e\n\u003cp\u003eThe operator minimizes the degree of compression on the measuring point to reduce the measurement error caused by too much or too little compression. During the first measurement, the operator should make a conspicuous mark on the patient\u0026apos;s lower limb that is not easy to erase to ensure the consistency of the subsequent measurement position. In the two-dimensional ultrasound image of the rectus femoris cross-section, the hypoechoic fusiform abdomen is surrounded by a hyperechoic fascia with a clear boundary. We measure the cross-sectional area of the rectus femoris muscle by delineating the hyperechoic fascial edge\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Subsequently, we rotated the probe 90\u0026deg; to obtain a two-dimensional ultrasound image of the longitudinal section of the rectus femoris, which showed that the hypoechoic area between the two hyperechoic fascia was the rectus femoris muscle, and the rectus femoris thickness could be measured by measuring the distance between the two hyperechoic fascia\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In the same section, the hyperechoic muscle fibers arranged in a bundle between the abdomen of the hypoechoic muscle can be seen by sliding the probe left and right, and the angle formed between them and the deep fascia is called the pinnate angle\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This angle is a reflection of muscle strength, as the larger the pinnate angle, the greater the number of fascicles per unit volume and the greater the ability of the muscle to generate force\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The thickness of subcutaneous fat in both lower extremities is measured at the same point as the quadriceps muscle, and in the longitudinal section, it is a layer of tissue with a slightly higher echo layer at the muscle level. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(A,B,C)shows the longitudinal and cross-sectional views of the rectus femoris muscle, the thickness of the rectus femoris muscle and the pinnate angle of the vastus intermedius muscle can be measured in the longitudinal section, and the cross-sectional area of the rectus femoris muscle can be measured in the cross-section, and the ultrasound changes of the same patient on the 1st, 5th, and 10th days of hospitalization are shown in the figure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eThe distance marked in the red line in a is the thickness of the subcutaneous fat (FTH) of the lower extremity, and the distance marked in the red line is the thickness of the rectus femoris muscle (RF-TH). c The distance marked on the red line is the thickness of the intermediaus femoris muscle (VI-TH); The area circled in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed is the cross-sectional area of the rectus femoris muscle (RF-CSA), and the red line in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the pinnate angle (VI-PA) of the intermediaus femoris muscle.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eShear wave elastography:\u003c/h2\u003e\n \u003cp\u003eWe first found the rectus femoris muscle on the B-mode ultrasonic image, then switched to the shear wave mode, and selected three regions of interest (ROI) in the sampling box with a diameter of 0.25 cm. Finally, the average value of the shear wave velocity (SWV) and the average value of the shear wave elastic modulus (E) were calculated.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDemographic and Baseline Data Collection:\u003c/h3\u003e\n\u003cp\u003eWe collected the following demographic and baseline characteristics: age, gender, body weight, body height and length at ICU admission, admission type, admission diagnosis, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and maximal total Sequential Organ Failure Assessment (SOFA) score.\u003c/p\u003e\n\u003ch3\u003eClinical data collection:\u003c/h3\u003e\n\u003cp\u003eWe collected clinical data on pre-existing polyneuropathy or myopathy, risk factors for polyneuropathy before ICU admission (diabetes mellitus, alcohol abuse, chemotherapy, and kidney failure), days with mechanical ventilation, ICU length of stay, and ICU mortality. In addition, we collected biochemical indicators related to nutrition and metabolism, such as albumin, creatinine, urea nitrogen, uric acid, blood glucose, blood potassium, and blood sodium, on days 1, day5 and day10, synchronizing with the ultrasound indicators.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSample size estimation:\u003c/h2\u003e\n \u003cp\u003eThis was a prospective observational cohort study, and we included ICU patients who met the inclusion and exclusion criteria at the Shanghai General Hospital from October 2023 to July 2024. Sample size was calculated based on an expected ICU-AW incidence of 30%, with 80% power (\u0026beta;\u0026thinsp;=\u0026thinsp;0.2) and \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05 to detect a 20% difference in ultrasound parameters between groups, yielding a minimum requirement of 43 patients (PASS11).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis:\u003c/h2\u003e\n \u003cp\u003eStatistical Package for the Social Sciences version 26.0 (IBM) software was used for statistical analysis. The normality of the distribution of variables was assessed using the Kolmogorov-Smirnov test. Normally distributed data are expressed as mean and standard deviations. Non-normally distributed data are presented as medians with interquartile range (IQR). The Student t-test was used for normally distributed data and the Mann-Whitney test was used for non-normally distributed data. The P value was less than 0.05 for statistical significance. For the diagnostic model, we plot the ROC curve, calculate the AUC value, and evaluate the accuracy of the diagnosis with sensitivity, specificity, positive predictive value, and negative predictive value, and in the construction of the prediction model based on binary logistic regression, we plot the ROC curve to calculate the AUC value, sensitivity, and specificity to evaluate the prediction performance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic characteristics:\u003c/h2\u003e\n \u003cp\u003eFrom October 2023 to August 2024, we collected a total of 188 patients admitted to the ICU of Shanghai First People\u0026apos;s Hospital, and 100 patients who met the inclusion criteria and signed the informed consent form were included in this study. A total of 88 patients with a hospital stay of less than 3 days, central nervous system lesions such as cerebral infarction and cerebral hemorrhage, and no informed consent were excluded. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the complete screening process, in which 43 patients completed three complete ultrasonography of the lower extremity muscles. On day 10, 23 patients (53.5%) were diagnosed with ICU-AW and 20 patients (46.5%) were diagnosed with non-ICU-AW.\u003c/p\u003e\n \u003cp\u003eWe included the basic demographic characteristics of the 43 patients who passed the screening and some clinical indicators on the first day of ICU admission, and performed statistical analyses, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatients\u0026rsquo;Demographic and Clinical characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICU-AW(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-ICU-AW(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73(63\u0026thinsp;~\u0026thinsp;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(29.25\u0026thinsp;~\u0026thinsp;66.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen(65%),Women(35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen(80%),Women(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.89(19.9\u0026thinsp;~\u0026thinsp;26.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.28(21.06\u0026thinsp;~\u0026thinsp;24.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(4\u0026thinsp;~\u0026thinsp;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(4.25\u0026thinsp;~\u0026thinsp;12.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPACHEII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(8\u0026thinsp;~\u0026thinsp;17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(4\u0026thinsp;~\u0026thinsp;15.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.55(71.95\u0026thinsp;~\u0026thinsp;141.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.85(56.55\u0026thinsp;~\u0026thinsp;240.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrea nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3(6.5\u0026thinsp;~\u0026thinsp;14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22(4.4\u0026thinsp;~\u0026thinsp;13.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003euric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287.5(217.23\u0026thinsp;~\u0026thinsp;442.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325.25(156.25\u0026thinsp;~\u0026thinsp;497.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood sodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.52(135.36\u0026thinsp;~\u0026thinsp;141.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.8(135.25\u0026thinsp;~\u0026thinsp;143.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium in the blood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eblood sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.7(7.19\u0026thinsp;~\u0026thinsp;11.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.95(6.93\u0026thinsp;~\u0026thinsp;9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ealbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.94\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic category at admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiovascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrine system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematologic system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics of the two groups of patients (ICU-AW and non-ICU-AW), and the incidence of ICU-AW did not differ significantly between genders, BMI, and SOFA scores, but was statistically significant in terms of APACHEII scores and age. Thus, age at admission and APCHE-II score may be important factors influencing the onset of ICU-acquired weakness.In the ICU-acquired frailty group, patients with respiratory diseases (43.5%) accounted for the largest proportion, followed by sepsis (17.4%), digestive system diseases (17.4%), cardiovascular system diseases (13%), malignant tumors (8.7%), and endocrine system diseases (4.3%). Among them, patients with ICU-acquired frailty with respiratory diseases were given sedation and analgesic ventilator-assisted ventilation, while patients with non-ICU-acquired frailty with respiratory diseases were given non-invasive ventilators and high-flow respiratory support. It can be seen that prolonged invasive mechanical ventilation and sepsis are high-risk factors for the development of ICU-AW, which is consistent with the results reported in the previous literature.\u003c/p\u003e\n \u003cp\u003eWe compared the differences in the quadriceps ultrasound on the first day of all patients on the first day, and found that there was no statistically significant difference in the initial characteristics of rectus ultrasound on the first day of ICU admission between the two groups (as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e),we can also see that the CSA, TH, PA, SWV, SWE, and FTH in the ICU-AW group were slightly lower than those in the non-ICU-AW group.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline of quadriceps ultrasound on Day1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICU-AW(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo ICU-AW(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSA(right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSA(left)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSA(mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH(right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH(left)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH(mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWV(right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWV(left)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWV(mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWE(right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.35(22.77\u0026thinsp;~\u0026thinsp;44.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.16(30.05\u0026thinsp;~\u0026thinsp;65.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWE(left)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.59(23.69\u0026thinsp;~\u0026thinsp;46.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.37(22.66\u0026thinsp;~\u0026thinsp;63.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWE(mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.44(25.94\u0026thinsp;~\u0026thinsp;44.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.11(33.04\u0026thinsp;~\u0026thinsp;65.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA(right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA(left)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA(mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTH(right)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTH(left)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFTH(mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDiagnosis of ICU-acquired weakness:\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReceiver Operating Characteristic curves of changes in CSA,TH,PA,E,SWV, and FTH of muscles on Day10\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMuscle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI for ROC-AUC\u003c/p\u003e\n \u003cp\u003eLower Upper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCutoff\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RCSA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LCSA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-ACSA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RTH/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LTH/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-ATH/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVI-RPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVI-LPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVI-APA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RE/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LE/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-AE/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RSWV/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LSWV /%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.395%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-ASWV /%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRFTH/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLFTH/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFTH/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNotes: RF-RCSA, right rectus femoris cross-sectional area; RF-LCSA, left rectus femoris cross-sectional area; RF-ACSA, mean cross-sectional area of bilateral rectus femoris; RF-RTH, right rectus femoris thickness; RF-LTH, left rectus femoris thickness; RF-ATH, mean of bilateral rectus femoris muscle thickness; VI-RPA, right vastus intermediaus pinnate angle; VI-LPA, left vastus intermediaus pinnate angle; VI-APA, mean of both femoral intermediate muscle pinnate angles; RF-RE, elastic modulus of shear wave of right rectus femoris; RF-LE, elastic modulus of shear wave of the left rectus femoris; RF-AE, mean elastic modulus of bilateral rectus femoris shear wave; RF-RSWV, right rectus femoris shear wave velocity; RF-LSWV, left rectus femoris shear wave velocity; RF-ASWV, mean velocity of bilateral rectus femoris muscle; RFTH, subcutaneous fat thickness of the right lower extremity; LFTH, subcutaneous fat thickness of the left lower extremity; AFTH, mean subcutaneous fat thickness in both lower extremities.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u0026nbsp;\u003c/strong\u003eCSA% represents the rate of change of CSA, TH% represents the rate of change of TH, PA% represents the rate of change of PA, E% represents the rate of change of CSA, SWV% represents the rate of change of SWV, and FTH% represents the rate of change of FTH.\u003c/p\u003e\n \u003cp\u003eFrom the analysis of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, CSA of rectus femoris muscle (right: AUC\u0026thinsp;=\u0026thinsp;0.891, 95% CI\u0026thinsp;=\u0026thinsp;0.778\u0026thinsp;~\u0026thinsp;0.985, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; left: AUC\u0026thinsp;=\u0026thinsp;0.874, 95% CI\u0026thinsp;=\u0026thinsp;0.766\u0026thinsp;~\u0026thinsp;0.982, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; average: AUC\u0026thinsp;=\u0026thinsp;0.926, 95% CI\u0026thinsp;=\u0026thinsp;0.847\u0026thinsp;~\u0026thinsp;0.1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and PA of the vastus intermediaus muscle (right: AUC\u0026thinsp;=\u0026thinsp;0.815, P\u0026thinsp;=\u0026thinsp;0.0004; left: AUC\u0026thinsp;=\u0026thinsp;0.840, P\u0026thinsp;=\u0026thinsp;0.0001; average: AUC\u0026thinsp;=\u0026thinsp;0.85, P\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e\n \u003cp\u003eFrom the above analysis, it can be preliminarily concluded that ICU-AW can be diagnosed when the CSA of the rectus femoris muscle is reduced (left: 25.01%, right: 29.66%, average: 24.72%) and the PA of the femoral intermediar muscle is reduced (left: 27.48%, right: 27.56%, average: 28.25%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnosis accuracy of changes of RF-CSA, VI-PA from day 1 to day 10\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMuscle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RCSA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LCSA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-ACSA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVI-RPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVI-LPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVI-APA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LCSA\u0026thinsp;+\u0026thinsp;VI-LPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RCSA\u0026thinsp;+\u0026thinsp;VI-RPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-ACSA\u0026thinsp;+\u0026thinsp;VI-APA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-LCSA/VI-LPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-RCSA/VI-RPA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF-ACSA/VI-APA/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNotes: RF-RCSA, right rectus femoris cross-sectional area; RF-LCSA, left rectus femoris cross-sectional area; RF-ACSA, mean cross-sectional area of bilateral rectus femoris; VI-RPA, right vastus intermedius pinnate angle; VI-LPA, left vastus intermedius pinnate angle; VI-APA, right femoral intermediate muscle pinnate angle; \u0026quot;+\u0026quot;, which means that the two diagnostic conditions of the rectus femoris cross-sectional area and the femoral intermediate pinnate angle are met at the same time;\u0026quot;/\u0026quot;, indicating that either of the diagnostic criteria of the rectus femoris cross-sectional area and the femoral intermediate muscle pinnate angle is satisfied.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom the results presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, we can conclude that the mean diagnostic efficiency of the bilateral middle femoral and pinnate angles is the highest (AUC\u0026thinsp;=\u0026thinsp;0.926, sensitivity\u0026thinsp;=\u0026thinsp;0.86, specificity\u0026thinsp;=\u0026thinsp;0.77), which can be used as an independent indicator for the diagnosis of ICU-AW.\u003c/p\u003e\n \u003cp\u003eBy combining the CSA of the rectus femoris muscle and the PA of vastus intermedius muscle we found that the negative predictive value of the diagnosis was significantly improved when both diagnostic indicators were met (left: AUC\u0026thinsp;=\u0026thinsp;0.891, sensitivity\u0026thinsp;=\u0026thinsp;1, specificity\u0026thinsp;=\u0026thinsp;0.8, positive predictive value\u0026thinsp;=\u0026thinsp;0.78, negative predictive value\u0026thinsp;=\u0026thinsp;1; right: AUC\u0026thinsp;=\u0026thinsp;0.711, sensitivity\u0026thinsp;=\u0026thinsp;0.86, specificity\u0026thinsp;=\u0026thinsp;0.62, positive predictive value\u0026thinsp;=\u0026thinsp;0.52, negative predictive value\u0026thinsp;=\u0026thinsp;0.9; average: AUC=). 0.826, sensitivity\u0026thinsp;=\u0026thinsp;1, specificity\u0026thinsp;=\u0026thinsp;0.71, positive predictive value\u0026thinsp;=\u0026thinsp;0.65, negative predictive value\u0026thinsp;=\u0026thinsp;1), especially the index on the left side reflected a higher AUC value, sensitivity and specificity. This indicator is of high value for excluding people with ICU-AW.\u003c/p\u003e\n \u003cp\u003eWhen either of the two indicators was met, the positive predictive value of the diagnosis was significantly improved (left: AUC\u0026thinsp;=\u0026thinsp;0.782, sensitivity\u0026thinsp;=\u0026thinsp;0.75, specificity\u0026thinsp;=\u0026thinsp;0.87, positive predictive\u0026thinsp;=\u0026thinsp;0.91, negative\u0026thinsp;=\u0026thinsp;0.8; right: AUC\u0026thinsp;=\u0026thinsp;0.857, sensitivity\u0026thinsp;=\u0026thinsp;0.84, specificity\u0026thinsp;=\u0026thinsp;0.89, positive\u0026thinsp;=\u0026thinsp;0.91, negative\u0026thinsp;=\u0026thinsp;0.8; average: AUC\u0026thinsp;=\u0026thinsp;0.853, sensitivity\u0026thinsp;=\u0026thinsp;0.815, specificity\u0026thinsp;=\u0026thinsp;0.938, positive predictive value\u0026thinsp;=\u0026thinsp;0.96, negative predictive value\u0026thinsp;=\u0026thinsp;0.75), especially the indicators of the right and bilateral mean showed high AUC value, sensitivity and specificity. This indicator is of great significance for early screening of high-risk groups for ICU-AW.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive Modeling for the Identification of ICU-AW:\u003c/h2\u003e\n \u003cp\u003eIn this part of the study, 18 characteristics such as basic data (age, gender, APCHEII score, SOFA score, BMI), clinical indicators and ultrasound measurement indicators on day 5 were included in this part of the study, and SPSS26.0 software was used for statistical analysis. The global likelihood ratio test of the model was carried out by Omnibus test, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated that the model was generally meaningful.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOmnibus Tests of model Coefficients and Goodness of Fit\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStep2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel summary\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2 Log - Likelihood\u003c/p\u003e\n \u003cp\u003e30.423a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCox\u0026amp;Snell R Square\u003c/p\u003e\n \u003cp\u003e.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNagelkerke R Square\u003c/p\u003e\n \u003cp\u003e.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ea Because the parameter estimate changed by less than .001, the estimate was terminated at the 5th iteration.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eHorsmer-Lemeshaw test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003cp\u003e.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFirstly, the Omnibus test results show that the model is generally meaningful (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Then, the goodness-of-fit of the regression model was evaluated to reflect the construction effect of the model, and the value of the Negorko R-square test was 0.577 (\u0026gt;\u0026thinsp;0.4), and the significance of the Hosmer-Lemesho test (H-L test) was 0.208 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the goodness-of-fit of the model was good.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate logistic regression analysis\u003c/strong\u003e Note: PA5 represents the mean of pinnate angles on day 5 .\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald\u0026chi;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI for ROC-AUC\u003c/p\u003e\n \u003cp\u003eLower Upper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExp(B)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.021 1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.278 .943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eClassification table\u003c/p\u003e\n \u003cp\u003eprediction\u003c/p\u003e\n \u003cp\u003eMeasured group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-ICU-AW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU-AW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrect percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStep2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003egroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-ICU-AW\u003c/p\u003e\n \u003cp\u003eICU-AW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall percentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEvaluation of the prediction performance of the prediction model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROC-AUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI for ROC-AUC\u003c/p\u003e\n \u003cp\u003eLower Upper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eLogistic regression model analysis showed that age (OR\u0026thinsp;=\u0026thinsp;1.103, 95% CI: 1.021\u0026thinsp;~\u0026thinsp;1.19, p\u0026thinsp;=\u0026thinsp;0.013) and femoral intermediate muscle pinnate angle on day 5 (OR\u0026thinsp;=\u0026thinsp;0.512, 95% CI: 0.278\u0026thinsp;~\u0026thinsp;0.943, p\u0026thinsp;=\u0026thinsp;0.032) were statistically significant for predicting ICU-acquired frailty, and the binary logistic regression equation was obtained:\u003c/p\u003e\n \u003cp\u003eY=-0.415 0.066\u0026times;0.098X \u003csub\u003eAge\u003c/sub\u003e-0.669\u0026times;X\u003csub\u003ePA5\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eIt can be concluded that the risk of ICU-AW increases by 9.8% for every 1 year of age of the prediction model, and the risk of ICU-AW decreases by 66.9% for every 1\u0026deg; increase in the pinnate angle, and the size of the pinnae angle is an important factor affecting the occurrence of ICU-AW.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study underscores the potential of multimodal ultrasound as a non-invasive, bedside tool for the early diagnosis and prediction of ICU-AW. Our findings demonstrate that the rate of change in CSA of rectus femoris and PA of vastus intermedius between days 1 and 10 of ICU admission serves as a robust diagnostic marker for ICU-AW, with high accuracy (AUC\u0026thinsp;=\u0026thinsp;0.887\u0026ndash;0.926). These structural and architectural parameters reflect rapid muscle atrophy and biomechanical alterations, aligning with previous studies that highlighted the utility of ultrasound in detecting early muscle wasting in critically ill patients.\u003c/p\u003e\u003cp\u003eNotably, the integration of CSA and PA metrics improved diagnostic specificity and negative predictive value, suggesting that multimodal ultrasound captures complementary aspects of muscle degeneration, such as volumetric loss and microstructural remodeling, which are not fully assessed by isolated parameters.\u003c/p\u003e\u003cp\u003eThe predictive model incorporating age and PA on day5 further advances clinical utility by enabling early risk stratification. Older age (cutoff\u0026thinsp;\u0026gt;\u0026thinsp;68.5 years) and reduced PA on day 5 were identified as independent predictors of ICU-AW, consistent with prior evidence linking advanced age and prolonged immobility to neuromuscular dysfunction.\u003c/p\u003e\u003cp\u003eThe model's high sensitivity (81.8%) and specificity (81.3%) highlight its potential to guide preemptive interventions, such as early mobilization or nutritional support, in high-risk patients. This aligns with expert recommendations advocating multidisciplinary, ICU-led protocols to mitigate long-term disability.\u003c/p\u003e\u003cp\u003eDespite promising results, several limitations warrant consideration. The single-center design and modest sample size (n\u0026thinsp;=\u0026thinsp;43) may limit generalizability, and the exclusion of patients with comorbidities or severe edema introduces selection bias\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. While shear wave elastography (SWE) revealed trends toward reduced muscle stiffness in ICU-AW patients, statistical significance was not achieved, possibly due to age-related variability in elastographic measurements\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.Future studies with age-stratified cohorts are needed to clarify SWE's role in assessing muscle quality. Additionally, the reliance on operator-dependent ultrasound measurements underscores the need for standardized protocols and automated image analysis tools to reduce inter-observer variability.\u003c/p\u003e\u003cp\u003eThe integration of artificial intelligence (AI) for automated quantification of ultrasound parameters could enhance diagnostic precision and scalability\u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Emerging technologies, such as AI-driven shear wave analysis or contrast-enhanced ultrasound, may further elucidate microvascular and connective tissue changes in ICU-AW. Multicenter validation of the proposed diagnostic thresholds and predictive model is essential to confirm their robustness across diverse ICU populations\u003csup\u003e[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn conclusion, this study establishes multimodal ultrasound as a practical and accurate modality for diagnosing ICU-AW and provides a predictive framework to identify high-risk patients. By facilitating early intervention, these tools may reduce the burden of prolonged mechanical ventilation, rehabilitation delays, and long-term disability associated with ICU-AW. Future research should focus on protocol standardization, technological innovation, and large-scale validation to optimize clinical translation\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.With the development of more and more new technologies of musculoskeletal ultrasound and the innovation of ultrasound machines, if you want to make good use of these new technologies and use ultrasound to monitor muscle changes more accurately, it is necessary to develop a standardized ultrasound evaluation process, so that more doctors can learn systematically and standardly, so as to improve image quality. The evaluation process referred to in this study provides a reliable approach to the early identification of ICU-acquired weakness, which in turn reminds clinicians to provide early rehabilitation for patients at potential risk. For some patients with severe edema and obesity, we need to further explore and find new ways to reduce the interference of these factors on imaging. In this era of rapid development of big data, the application of artificial intelligence is believed to realize automatic image analysis in the future, so as to help intensive care doctors identify patients with ICU-acquired frailty more quickly and accurately.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the CSA(left/right) decreased by 25.01%/29.66% (AUC\u0026thinsp;=\u0026thinsp;0.874/0.891, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and PA (left/right) decreased by 27.48%/27.56% (AUC\u0026thinsp;=\u0026thinsp;0.815/0.84) as diagnostic cut-off values. By combining the CSA of the rectus femoris muscle and PA of verasimus intermedias, it can be found that when the two diagnostic indicators are met at the same time, the negative predictive value of the diagnosis is significantly improved.When either of the two indicators was met, the positive predictive value of the diagnosis was significantly improved.The dynamic changes of the above ultrasound indicators can intuitively reflect muscle atrophy and functional deterioration, and these changes are closely related to the occurrence of ICU-acquired frailty, which provides a reliable tool for bedside non-invasive assessment.\u003c/p\u003e\u003cp\u003eWe also developed a logistic regression model to predict the occurrence of ICU-AW (AUC\u0026thinsp;=\u0026thinsp;0.889, sensitivity\u0026thinsp;=\u0026thinsp;0.818, specificity\u0026thinsp;=\u0026thinsp;0.813). Multiple variables, such as age and PA were included in the model. By combining these two variables, we have plotted nomograms to make individualized predictions of ICU-AW more intuitively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest Statement:\u003c/h2\u003e\u003cp\u003eThe authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis work was supported by:\u003c/p\u003e\u003cp\u003e1.Noncommunicable Chronic Diseases-National Science and Technology Major Project (NO. 2023ZD0506502)\u003c/p\u003e\u003cp\u003e2.the Key Supporting Discipline of Shanghai Healthcare System (NO. 2023ZDFC0102)\u003c/p\u003e\u003cp\u003e3.the National Natural Science Foundation of China (Grant No. 82402583)\u003c/p\u003e\u003cp\u003e4.the Clinical Research Special Project of the Shanghai Municipal Health Commission (Grant No. 20244Y0015).\u003c/p\u003e\u003cp\u003e5.National Clinical Key Specialty Construction Project(Z155080000004)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor W.M.Q., X.H., C.D.N. and W.R.L. designed the study.Author W.M.Q. and W.Y. AND Z.C.C. supervised data collection. Author W.M.Q. and M.S.Y.analyzed the data. Author W.M.Q. drafted the manuscript. All authors critically revised the manuscript and approved the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDue to ethical restrictions, the raw data cannot be made publicly available. However, de-identified data may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParry SM, Puthucheary ZA. The impact of extended bed rest on the musculoskeletal system in the critical care environment. Extrem Physiol Med. 2015;4:16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan E, Cheek F, Chlan L, et al. An official American Thoracic Society Clinical Practice guideline: the diagnosis of intensive care unit-acquired weakness in adults[J]. 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Shear-Wave Elastography: Basic Physics and Musculoskeletal Applications. Radiographics 2017 May-Jun;37(3):855\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavis LC, Baumer TG, Bey MJ, Holsbeeck MV. Clinical utilization of shear wave elastography in the musculoskeletal system. Ultrasonography. 2019;38(1):2\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14366/usg.18039\u003c/span\u003e\u003cspan address=\"10.14366/usg.18039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2018 Aug 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuthucheary ZA, Rawal J, McPhail M, et al. Acute skeletal muscle wasting in critical illness. JAMA. 2013;310(15):1591\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePardo E, El Behi H. Boizeau P.Reliability of ultrasound measurements of quadriceps muscle thickness in critically ill patients. BMC Anesthesiol. 2018;18(1):205.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWitteveen E, Sommers J, Wieske L, et al. Diagnostic accuracy of quantitative neuromuscular ultrasound for the diagnosis of intensive care unit-acquired weakness:a cross-sectional observational study. Ann Intensive Care. 2017;7(1):40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakanishi N, Oto J, Tsutsumi R, et al. Upper and lower limb muscle atrophy in critically ill patients: an observational ultrasonography study. Intensive Care Med. 2018;44(2):263\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang W, Wu J, Gu Q, et al. Changes in muscle ultrasound for the diagnosis of intensive care unit acquired weakness in critically ill patients. Sci Rep. 2021;11(1):18280.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNaoi T, Morita M, Koyama K, et al. Upper Arm Muscular Echogenicity Predicts Intensive Care Unit-acquired Weakness in Critically Ill Patients. Prog Rehabil Med. 2022;7:20220034.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaolo F, Valentina G, Silvia C, et al. The possible predictive value of muscle ultrasound in the diagnosis of ICUAW in long-term critically ill patients. J Crit Care. 2022;71:154104.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlawitter F, Walter U, Patejdl R, et al. Sonographic Evaluation of Muscle Echogenicity for the Detection of Intensive Care Unit-Acquired Weakness:A Pilot Single-Center Prospective Cohort Study. Diagnostics (Basel). 2022;12(6):1378.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLima KM, da Matta TT, de Oliveira LF. Reliability of the rectus femoris muscle cross-sectional area measurements by ultrasonography. 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Acute pulmonary embolism: impact of selection bias in prospective diagnostic studies. ANTELOPE Study Group. Advances in New Technologies Evaluating the Localization of Pulmonary Embolism. Thromb Haemost. 2001;85(4):604\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang A, Cloutier G, Szeverenyi NM, Sirlin CB. Ultrasound Elastography and MR Elastography for Assessing Liver Fibrosis: Part 2, Diagnostic Performance, Confounders, and Future Directions. AJR Am J Roentgenol. 2015;205(1):33\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou B, Yang X, Curran WJ, Liu T. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. J Ultrasound Med. 2022;41(6):1329\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark SH. Artificial intelligence for ultrasonography: unique opportunities and challenges. Ultrasonography. 2021;40(1):3\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14366/usg.20078\u003c/span\u003e\u003cspan address=\"10.14366/usg.20078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Nov 3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsterwalder J, Polyzogopoulou E, Hoffmann B. Point-of-Care Ultrasound-History, Current and Evolving Clinical Concepts in Emergency Medicine. Med (Kaunas). 2023;59(12):2179.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWitteveen E, Wieske L, Sommers J, Spijkstra JJ, de Waard MC, Endeman H, Rijkenberg S, de Ruijter W, Sleeswijk M, Verhamme C, Schultz MJ, van Schaik IN, Horn J. Early Prediction of Intensive Care Unit-Acquired Weakness: A Multicenter External Validation Study. J Intensive Care Med. 2020;35(6):595\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoogaard M, Pickkers P, Slooter AJ, Kuiper MA, Spronk PE, van der Voort PH, van der Hoeven JG, Donders R, van Achterberg T, Schoonhoven L. Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study. BMJ. 2012;344:e420.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018;8(1):e017833.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRadabaugh HL, Ferguson AR, Bramlett HM, Dietrich WD. Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation. Neurotherapeutics. 2023;20(6):1433\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmidi Y. Translational researches require effective protocols for knowledge and technology transfer and integration. Bioimpacts. 2011;1(2):71\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ICU-acquired weakness, Critical illness myopathy, Muscular ultrasound, Multimodal, Logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-7583201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7583201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eICU-acquired weakness (ICU-AW) is a common long-term complication in critically ill patients whose diagnostic criteria are based on muscle strength scores and neuro-electrophysiological examination(e.g., electromyography,EMG). ICU-AW can lead to prolonged duration of mechanical ventilation, weaning difficulty, increased hospitalization costs, reduced long-term quality of life, and significant muscle atrophy, usually occurring on day 7 to 10 of hospitalization, so early recognition and intervention are critical for prognosis. However, current diagnostic is limited by variations in physician expertise, patient cooperation, the invasiveness and complexity of EMG, and environmental interference. Ultrasound, a simple, noninvasive, and safe imaging technique, has become critical in evaluating various diseases. This study aimed to investigate characteristics of multimodal ultrasound (two-dimensional and elastic ultrasound) in ICU-AW patients at different stages, and to identify potential diagnostic and predictive indicators.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study enrolled 100 ICU inpatients from October 2023 to August 2024. The ultrasound parameters\u0026mdash;cross-sectional area, muscle thickness, pinnate angle of the quadriceps, and subcutaneous fat thickness of both lower limbs\u0026mdash;were recorded on Days 1, 5, and 10 of hospitalization. Muscle strength scores and/or EMG were performed on Day 10.The baseline, clinical and ultrasound data of the enrolled patients were analyzed and compared, the potential diagnoses and predictors were selected, and the model value was evaluated through the measures of diagnostic accuracy and the area under the ROC curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 100 patients were included in this study, including 43 patients underwent three complete ultrasound examinations,23 ICU-AW patients and 20 non-ICU-AW patients. (1) Univariate analysis showed that on the Day10, the cross-sectional area of rectus femoris muscle and the size of the feather angle of the rectus intermediate muscle in the ICU-AW group were significantly reduced compared with those in the non-ICU-AW group, which was statistically significant. (2) The change rate of left/right rectus femoris cross-sectional area (25.01%/29.66%, AUC\u0026thinsp;=\u0026thinsp;0.87/0.89) and the change rate of pinnate angle of left/right vastus intermedius muscle (27.48%/27.56%, AUC\u0026thinsp;=\u0026thinsp;0.81/0.84) were the best cutoff values. (3) The binary logistic regression model (based on age and pinnate angle of the femoral intermediate muscle on Day 5) had higher predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.889, sensitivity\u0026thinsp;=\u0026thinsp;0.818, specificity\u0026thinsp;=\u0026thinsp;0.813).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eUsing multimodal ultrasound measures, we developed a convenient, simple, and safe diagnostic index (the rate of change in ultrasound parameters between Day 1 and Day 10), confirming its potential as a standardized and efficient quantitative tool. In addition, we used binary logistic regression to establish a highly predictive model that may inform clinical decision-making and early intervention in ICU-AW.\u003c/p\u003e","manuscriptTitle":"Multimodal Ultrasound for Early Diagnosis and Prediction of ICU-Acquired Weakness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 18:07:30","doi":"10.21203/rs.3.rs-7583201/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T10:35:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T19:53:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116055853557105230131921964587466116419","date":"2025-09-28T18:47:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212793452662583054923431731408020160169","date":"2025-09-15T07:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T06:39:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T03:55:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T03:54:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Clinical Monitoring and Computing","date":"2025-09-10T12:59:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1c083dc1-3c7f-40ae-9019-9e2198919978","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T09:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 18:07:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7583201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7583201","identity":"rs-7583201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}