Psoas muscle index predicts osteoporosis and fracture risk in individuals with degenerative spinal disease

Development and validation of a radiomics-based model for predicting osteoporosis in patients with lumbar compression fractures

BACKGROUND

Osteoporosis, a metabolic bone disorder, markedly elevates fracture risks, with vertebral compression fractures being predominant. Antiosteoporotic treatments for patients with osteoporotic vertebral compression fractures (OVCF) lessen both the occurrence of subsequent fractures and associated pain. Thus, diagnosing osteoporosis in OVCF patients is vital.

PURPOSE

The aim of this study was to develop a predictive radiographic model using T1 sequence MRI images to accurately determine whether patients with lumbar spine compression fractures also have osteoporosis.

STUDY DESIGN

Retrospective cohort study.

PATIENT SAMPLE

Patients over 45 years of age diagnosed with a fresh lumbar compression fracture.

OUTCOME MEASURES

Diagnostic accuracy of the model (area under the ROC curve).

METHODS

The study retrospectively collected clinical and imaging data (MRI and DEXA) from hospitalized lumbar compression fracture patients (L1-L4) aged 45 years or older between January 2021 and June 2023. Using the pyradiomics package in Python, features from the lumbar compression fracture vertebral region of interest (ROI) were extracted. Downscaling of the extracted features was performed using the Mann-Whitney U test and the least absolute shrinkage selection operator (LASSO) algorithm. Subsequently, six machine learning models (Naive Bayes, Support Vector Machine [SVM], Decision Tree, Random Forest, Extreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LightGBM]) were employed to train and validate these features in predicting osteoporosis comorbidity in OVCF patients.

RESULTS

A total of 128 participants, 79 in the osteoporotic group and 49 in the non-osteoporotic group, met the study's inclusion and exclusion criteria. From the T1 sequence MRI images, 1906 imaging features were extracted in both groups. Utilizing the Mann-Whitney U test, 365 radiologic features were selected out of the initial 1,906. Ultimately, the lasso algorithm identified 14 significant radiological features. These features, incorporated into six conventional machine learning algorithms, demonstrated successful prediction of osteoporosis in the validation set. The NaiveBayes model yielded an area under the receiver operating characteristic curve (AUC) of 0.84, sensitivity of 0.87, specificity of 0.70, and accuracy of 0.81.

CONCLUSIONS

A NaiveBayes machine learning algorithm can predict osteoporosis in OVCF patients using t1-sequence MRI images of lumbar compression fractures. This approach aims to obviate the necessity for further osteoporosis assessments, diminish patient exposure to radiation, and bolster the clinical care of patients with OVCF.

Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study

Background

Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study.

Methods

A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis.

Results

The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001).

Conclusion

SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

The psoas muscle index as a useful predictor of total hip arthroplasty outcomes

INTRODUCTION

The aim of this study is to assess the association between the psoas muscle index (PMI) and total hip arthroplasty (THA) outcomes. This is a critical issue as sarcopenia has been associated with poor patient satisfaction post-THA.

MATERIALS AND METHODS

This was a retrospective case-control study of 205 THAs, with a mean follow-up of 3.6 (range, 2.0-5.5) years. Age, sex, serum immune markers, spinopelvic parameters, PMI (quantified as the cross-sectional area of the psoas, bilaterally, at L3 divided by the individual's height squared), and patient-reported outcomes were compared between patients 'with' (n = 118) and 'without' (n = 87) achievement of a minimum clinically important difference (MCID) improvement in the EuroQol 5-Dimension (EQ-5D), post-THA. Logistic regression and receiver operating characteristic curve analyses were used to identify predictive factors.

RESULTS

A ≥ MCID improvement in the EQ-5D was associated with the PMI (odds ratio, 0.75; 95% confidence interval, 0.63-0.91; P = 0.028), prognostic nutritional index (odds ratio, 0.85; 95% confidence interval, 0.45-0.94; P = 0.043), and age (odds ratio, 1.09; 95% confidence interval, 1.01-1.18; P = 0.044). After adjusting the PMI threshold to 4.0 cm2/m2 for females and 6.4 cm2/m2 for males, there were significant differences in serum factors (P = 0.041 for albumin and P = 0.016 for a prognostic nutritional index < 40), MCID (P < 0.001 for EQ-5D, P < 0.001 for low back pain, and P = 0.008 for the Hip Disability and Osteoarthritis Outcome Score Joint Replacement score), patient satisfaction (P = 0.003), and T1 pelvic angle (P = 0.030).

CONCLUSION

The PMI, which is associated with nutritional status and global sagittal spinal deformity, does predict THA outcomes. Therefore, it can be useful when discussing THA expectations with patients.

Heterogeneity in regional changes in body composition induced by androgen deprivation therapy in prostate cancer patients: potential impact on bone health-the BLADE study

BACKGROUND

It is not clear whether changes in body composition induced by androgen deprivation therapy (ADT) in prostate cancer (PC) patients are uniform or vary in the different body districts and whether regional lean body mass (LBM) and fat body mass (FBM) could have an impact on bone health.

OBJECTIVE

To prospectively evaluate the regional changes in LBM and FBM in PC patients submitted to degarelix; to explore the relationship of regional body composition and bone mineral density (BMD) and bone turnover markers.

DESIGN, SETTING, AND PARTICIPANTS

29 consecutive non metastatic PC patients enrolled from 2017 to 2019. FBM, LBM and bone mineral density (BMD) evaluated by dual-energy x-ray absorptiometry (DXA) at baseline and after 12-month of ADT. Alkaline phosphate (ALP) and C-terminal telopeptide of type I collagen (CTX) assessed at baseline, 6 and 12 months.

INTERVENTION

All patients underwent degarelix administration.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

T-test or sign test and Pearson or Spearman test for continuous variables were used when indicated.

RESULTS AND LIMITATIONS

Median percent increase in FBM ranged from + 14.5% in trunk to + 25.4% in the left leg after degarelix. LBM changes varied from + 2% in the trunk to - 4.9% in the right arm. LBM in both arms and legs and their variations after degarelix directly correlated with ALP and inversely correlated with CTX. Lean mass of limbs, trunk and legs significantly correlated with BMD of the hip, lean mass of the trunk significantly correlated with spine BMD. These are post-hoc analysis of a prospective study and this is the main limitation.

CONCLUSIONS

an heterogeneous change in body composition among body district is observed after ADT and bone turnover is influenced by lean mass and its variation. A supervised physical activity is crucial to maintain general physical performance and preserving bone health.

Comparison Between the Clinical Effect of Percutaneous Kyphoplasty for Osteoporosis Vertebral Compression Fracture Patient with or Without Sarcopenia: A Retrospective Cohort Study

Background

Sarcopenia and osteoporosis vertebral compression fractures (OVCF) are common diseases that increase with age. This study aimed to investigate the effects of sarcopenia on OVCF patients after percutaneous kyphoplasty (PKP).

Methods

Data of 101 patients who were treated with single-level PKP between January 2021 and March 2022 at Ningbo No.6 Hospital were enrolled. Forty-five OVCF patients with sarcopenia who met our inclusion criteria were included in the Sarcopenia-PKP group (SPKP group), and 56 patients in the Normal-PKP group (NPKP group). All clinical and radiological data were collected from medical records. Baseline characteristics, operation-related parameters (operation time, time to ambulation, hospital stay, surgery segment), clinical outcomes (visual analog score [VAS], Oswestry Disability Index [ODI], Japanese Orthopaedic Association Scores [JOA] of lumber), radiological outcomes (vertebral anterior height rate and local kyphosis angle), Macnab score, and complications were evaluated and compared.

Results

There were no significant differences in age, sex, surgical segment preoperative VAS score, ODI, or JOA between the two groups (P > 0.05). The SPKP group had a significantly lower body mass index (BMI), bone mineral density (BMD), and smooth muscle index (SMI) than the NPKP group (P < 0.05). Significantly longer hospital stays and time to ambulation in SPKP group than NPKP group (3.7±0.8 vs 3.4±0.5 and 2.0±0.8 vs 1.6±0.5, P < 0.05). In SPKP group, significantly better clinical outcomes at 6- and 12-months follow-up were observed in NPKP group than SPKP group (P < 0.05), and NPKP group showed significantly better in vertebral anterior height rates than SPKP group after 6-month follow-up (P < 0.05). Moreover, there were significantly more cases of complications in the SPKP group (P < 0.05).

Conclusion

Sarcopenia could reduce the clinical effect of percutaneous kyphoplasty, and furthermore. Related studies are needed to verify the effect of sarcopenia on OVCF patients.

Impact of obesity on complications and surgical outcomes after adult degenerative scoliosis spine surgery

OBJECTIVE

To compare perioperative outcomes of obese versus non-obese adult patients who underwent degenerative scoliosis spine surgery.

METHODS

235 patients who underwent thoracolumbar adult spinal deformity (ASD) surgery (≥4 levels) were identified and categorized into two cohorts based on their body mass indices (BMI): obese (BMI ≥30 kg/m²; n = 81) and non-obese (BMI <30 kg/m²; n = 154). Preoperative (demographics, co-morbidities, American Society of Anesthesiologists (ASA) score and modified frailty indices (mFI-5 and mFI-11)), intraoperative (estimated blood loss (EBL) and anesthesia duration), and postoperative (complication rates, Oswestry Disability Index (ODI) scores, discharge destination, readmission rates, and survival) characteristics were analyzed by student's t, chi-squared, and Mann-Whitney U tests.

RESULTS

Obese patients were more likely to be Black/African-American (p < 0.05, OR:4.11, 95% CI:1.20-14.10), diabetic (p < 0.05, OR:10.18, 95% CI:4.38-23.68) and had higher ASA (p < .01) and psoas muscle indices (p < 0.0001). Furthermore, they had greater pre- and post-operative ODI scores (p < 0.05) with elevated mFI-5 (p < 0.0001) and mFI-11 (p < 0.01). Intraoperatively, obese patients were under anesthesia for longer time periods (p < 0.05) with higher EBL (p < 0.05). Postoperatively, while they were more likely to have complications (OR:1.77, 95% CI:1.01 - 3.08), had increased postop days to initiate walking (p < .05) and were less likely to be discharged home (OR:0.55, 95% CI:0.31-0.99), no differences were found in change in ODI scores or readmission rates between the two cohorts.

CONCLUSIONS

Obesity increases pre-operative risk factors including ASA, frailty and co-morbidities leading to longer operations, increased EBL, higher complications and decreased discharge to home. Pre-operative assessment and systematic measures should be taken to improve peri-operative outcomes.

A preliminary study on degenerate characteristics of lumbar and abdominal muscles in middle-aged and elderly people with varying bone mass

BACKGROUND

With the wide application of QCT in the clinical assessment of osteoporosis and sarcopenia, the characteristics of musculoskeletal degeneration in middle-aged and elderly people need to be further revealed. We aimed to investigate the degenerate characteristics of lumbar and abdominal muscles in middle-aged and elderly people with varying bone mass.

METHODS

A total of 430 patients aged 40-88 years were divided into normal, osteopenia, and osteoporosis groups according to quantitative computed tomography (QCT) criteria. The skeletal muscular mass indexes (SMIs) of five muscles [abdominal wall muscles (AWM), rectus abdominis (RA), psoas major muscle (PMM), posterior vertebral muscles (PVM), and paravertebral muscles (PM)] included in lumbar and abdominal muscles were measured by QCT. Differences in SMIs among three groups, as well as the correlation between SMIs and volumetric bone mineral density (vBMD) were analyzed. The areas under the curves (AUCs) for SMIs for prediction of low bone mass and osteoporosis were calculated.

RESULTS

In male group, SMIs of RA and PM in osteopenia group were significantly lower than those in the normal group (P = 0.001 and 0.023, respectively). In female group, only SMI of RA in osteopenia group was significantly lower than that in the normal group (P = 0.007). SMI of RA was positively correlated with vBMD with the highest coefficients in male and female groups (r = 0.309 and 0.444, respectively). SMIs of AWM and RA had higher AUCs varying from 0.613 to 0.737 for prediction of low bone mass and osteoporosis in both genders.

CONCLUSIONS

The changes of SMIs of the lumbar and abdominal muscles in patients with varying bone mass are asynchronous. SMI of RA is expected to be a promising imaging marker for predicting abnormal bone mass.

TRIAL REGISTRATION

ChiCTR1900024511 (Registered 13-07-2019).

Based on CT at the third lumbar spine level, the skeletal muscle index and psoas muscle index can predict osteoporosis

Background

With the increasing number of studies on osteoporosis and muscle adipose tissue, existing studies have shown that skeletal muscle tissue and adipose tissue are closely related to osteoporosis by dual-energy x-ray absorptiometry (DXA) measurement. However, few studies have explored whether the skeletal muscle and adipose tissue index measured at the lumbar spine 3 (L3) level are closely related to bone mineral density (BMD) and can even predict osteoporosis. Therefore, this study aimed to prove whether skeletal muscle and adipose tissue index measured by computed tomography (CT) images based on a single layer are closely related to BMD.

Methods

A total of 180 participants were enrolled in this study to obtain skeletal muscle index (SMI), psoas muscle index (PMI), subcutaneous fat index (SFI), visceral fat index (VFI), and the visceral-to-subcutaneous ratio of the fat area (VSR) at L3 levels and divide them into osteoporotic and normal groups based on the T-score of DXA. Spearman rank correlation was used to analyze the correlation between SMI, PMI, SFI, VFI, VSR, and BMD. Similarly, spearman rank correlation was also used to analyze the correlation between SMI, PMI, SFI, VFI, VSR, and the fracture risk assessment tool (FRAX). Receiver operating characteristic (ROC) was used to analyze the efficacy of SMI, PMI, SFI, VFI, and VSR in predicting osteoporosis.

Results

BMD of L1-4 was closely correlated with SMI, PMI, VFI and VSR (r = 0.199 p = 0.008, r = 0.422 p < 0.001, r = 0.253 p = 0.001, r = 0.310 p < 0.001). BMD of the femoral neck was only correlated with PMI and SFI (r = 0.268 p < 0.001, r = − 0.164 p-0.028). FRAX (major osteoporotic fracture) was only closely related to PMI (r = − 0.397 p < 0.001). FRAX (hip fracture) was closely related to SMI and PMI (r = − 0.183 p = 0.014, r = − 0.353 p < 0.001). Besides, FRAX (major osteoporotic fracture and hip fracture) did not correlate with VFI, SFI, and VSR. SMI and PMI were statistically significant, with the area under the curve (AUC) of 0.400 (95% confidence interval 0.312-0.488 p = 0.024) and 0.327 (95% confidence interval 0.244-0.410 p < 0.001), respectively. VFI, SFI, and VSR were not statistically significant in predicting osteoporosis.

Conclusions

This study demonstrated that L3-based muscle index could assist clinicians in the diagnosis of osteoporosis to a certain extent, and PMI is superior to SMI in the diagnosis of osteoporosis. In addition, VFI, SFI, and VSR do not help clinicians to diagnose osteoporosis well.

Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study

BACKGROUND

With rapid economic development, the world's average life expectancy is increasing, leading to the increasing prevalence of osteoporosis worldwide. However, due to the complexity and high cost of dual-energy x-ray absorptiometry (DXA) examination, DXA has not been widely used to diagnose osteoporosis. In addition, studies have shown that the psoas index measured at the third lumbar spine (L3) level is closely related to bone mineral density (BMD) and has an excellent predictive effect on osteoporosis. Therefore, this study developed a variety of machine learning (ML) models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography (CT) to predict osteoporosis.

METHODS

Medical professionals collected the CT images and the clinical characteristics data of patients over 40 years old who underwent DXA and abdominal CT examination in the Second Affiliated Hospital of Wenzhou Medical University database from January 2017 to January 2021. Using 3D Slicer software based on horizontal CT images of the L3, the specialist delineated three layers of the region of interest (ROI) along the bilateral psoas muscle edges. The PyRadiomics package in Python was used to extract the features of ROI. Then Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension of the extracted features. Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis.

RESULTS

A total of 172 participants met the inclusion and exclusion criteria for the study. 82 participants were enrolled in the osteoporosis group, and 90 were in the non-osteoporosis group. Moreover, the two groups had no significant differences in age, BMI, sex, smoking, drinking, hypertension, and diabetes. Besides, 826 radiomic features were obtained from unenhanced abdominal CT images of osteoporotic and non-osteoporotic patients. Five hundred fifty radiomic features were screened out of 826 by the Mann-Whitney U test. Finally, 16 significant radiomic features were obtained by the LASSO algorithm. These 16 radiomic features were incorporated into six traditional machine learning models (GBM, GNB, LR, RF, SVM, and XGB). All six machine learning models could predict osteoporosis well in the validation set, with the area under the receiver operating characteristic (AUROC) values greater than or equal to 0.8. GBM is more effective in predicting osteoporosis, whose AUROC was 0.86, sensitivity 0.70, specificity 0.92, and accuracy 0.81 in validation sets.

CONCLUSION

We developed six machine learning models to predict osteoporosis based on psoas muscle images of abdominal CT, and the GBM model had the best predictive performance. GBM model can better help clinicians to diagnose osteoporosis and provide timely anti-osteoporosis treatment for patients. In the future, the research team will strive to include participants from multiple institutions to conduct external validation of the ML model of this study.

Correlation of Psoas Muscle Index with Fragility Vertebral Fracture: A Retrospective Cross-Sectional Study of Middle-Aged and Elderly Women

OBJECTIVE

To investigate the correlation of psoas muscle index (PMI) with fragility vertebral fracture.

METHODS

A total of 184 middle-aged and elderly women were included in the study. We measured the bilateral psoas muscle area on the picture archiving and communication system (PACS) from computed tomography images and calculated PMI. We observed lateral radiographs of the thoracolumbar spine and assessed vertebral fractures using the Genant semiquantitative method. The T-score, bone mineral density (BMD) of the lumbar (L)1-4, femoral neck, and trochanter were measured by dual-energyX-ray absorptiometry (DXA). The data was collected and then statistically analyzed.

RESULTS

The PMI of the nonosteoporosis group was higher than that of the osteoporosis group (P value = 0.006). Height in the nonosteoporosis group was higher than that in the osteoporosis group (P value = 0.013). Weight, body mass index (BMI), left psoas muscle area, BMD of the L1-4, femoral neck, femoral trochanter, and T-score in the nonosteoporosis group were higher than those in the osteoporosis group (P value <0.001). The right psoas muscle area in the nonosteoporosis group was higher than that in the osteoporosis group (P value = 0.008). The incidence of combined thoracolumbar fracture was significantly higher in the osteoporosis group than that in the nonosteoporosis group (P value <0.001). For nonosteoporosis subjects, the PMI of the vertebral fracture group was lower than that of the nonvertebral fracture group (P value = 0.034).

CONCLUSIONS

A decrease in height, weight, BMI, bilateral psoas muscle area, and PMI is associated with osteoporosis. Combined thoracolumbar fractures are more common in osteoporosis. Sarcopenia may be an independent risk factor for nonosteoporotic vertebral fractures.