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OBJECTIVE: This study endeavors to examine the feasibility of predicting the clinical outcomes of patients suffering from peripheral artery disease (PAD) who undergo endovascular intervention, by employing the Syngo iFlow technology. METHODS: Retrospectively enrolling 76 patients from December 2021 to May 2023, yielding a total of 77 affected limbs, this study employs clinical outcomes (improvement or otherwise) as the gold standard. Two physicians conducted visual assessments on both DSA and iFlow images to gauge patient improvement and assessed inter-observer consistency for each image modality. The Time to Peak (TTP) of regions of interest (ROI) at the femoral head, knee joint, and ankle joint was measured. Differences in pre- and post-procedure TTP were juxtaposed, and statistically significant parameter cutoff values were identified via ROC analysis. Employing these cutoffs for TTP classification, multivariate logistic regression and the C-statistic were utilized to assess the predictive value of distinct parameters for clinical success. RESULTS: Endovascular procedure exhibited technical and clinical success rates of 82.58 and 75.32%, respectively. Diagnostic performance of iFlow image visual assessment surpassed that of DSA images. Inter-observer agreement for iFlow and DSA image evaluations was equivalent (κ = 0.48 vs 0.50). Post-classification using cutoff values, multivariate logistic regression demonstrated the statistical significance of ankle joint TTP in post-procedure iFlow images of the endovascular procedure for clinical success evaluation (OR 7.21; 95% CI 1.68, 35.21; P = 0.010), with a C-statistic of 0.612. CONCLUSION: Syngo iFlow color-encoded imagery holds practical value in assessing the technical success of post-endovascular procedures, offering comprehensive lower limb arterial perfusion visualization. Its quantifiable parameters exhibit promising potential for prognosticating clinical success.
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Procedimientos Endovasculares , Enfermedad Arterial Periférica , Humanos , Estudios de Factibilidad , Estudios Retrospectivos , Enfermedad Arterial Periférica/diagnóstico por imagen , Enfermedad Arterial Periférica/terapia , Procedimientos Endovasculares/efectos adversos , Hemodinámica , Resultado del TratamientoRESUMEN
Purpose: To develop an individualized predictive model for postoperative recurrent lumbar disc herniation (PRLDH) in patients undergoing percutaneous endoscopic transforaminal discectomy (PETD) by considering postoperative activity factors. Patients and Methods: Retrospectively collected data from 612 LDH patients who underwent PETD in our institution from January 2017 to June 2023. They were divided into a training group (429 cases) and a validation group (183 cases). Lasso regression (Model 1) and random forest (Model 2) were applied for variable selection in the training group. The two models were compared in terms of discrimination (the area under curve, AUC), calibration (calibration curve), and clinical utility (decision curve analysis, DCA). Akaike information criterion (AIC) was used for model comparison, and internal validation employed 1000 times Bootstrap + 10-fold cross-validation. Finally, a Nomogram was constructed to display the results and uploaded to the web version. Results: Among 612 treated LDH patients, 66 (10.78%) developed PRLDH. Model 1, superior in AUC, calibration, DCA, and AIC over Model 2, was chosen as the predictive model. Logistic regression in the training group identified BMI, smoking, activity level score, time to first ambulation, diabetes, Modic change, and Pfirrmann grade as independent predictors of PRLDH. Model 1 exhibited a training group AUC of 0.813 (95% CI 0.753-0.872) and a validation group AUC of 0.868 (95% CI 0.773-0.962). At a Youden index of 0.50, sensitivity was 0.73, specificity was 0.77. Internal validation (1000 times Bootstrap + 10-fold cross-validation) for the training group showed accuracy of 0.889, kappa consistency of 0.112, and AUC of 0.757. The Hosmer-Lemeshow goodness-of-fit tests indicated good discriminative ability for Model 1 in both the training (χ2=2.895, P=0.941) and validation groups (χ2=8.197, P=0.414). The DCA and Nomogram are accessible at https://sofarnomogram.shinyapps.io/PRLDHNom/. Conclusion: The Nomogram predictive model, developed based on postoperative activity factors in this study, demonstrates excellent predictive capability, facilitating risk assessment for the occurrence of PRLDH after PETD.
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Objective: Prior research underscores the significance of paraspinal muscles in maintaining spinal stability. This study aims to investigate the predictive value of paraspinal muscle parameters for the occurrence of new vertebral compression fractures (NVCF) following percutaneous vertebroplasty (PVP) or percutaneous kyphoplasty (PKP) in patients with osteoporotic vertebral compression fractures (OVCF). Methods: Retrospectively collected data from October 2019 to February 2021 (internal validation, n = 235) and March 2021 to November 2021 (external validation, n = 105) for patients with OVCF treated with PVP/PKP at our institution. They were randomly divided into training (188 cases) and validation groups (47 cases) at an 8:2 ratio. Lasso regression and multivariable logistic regression identified independent risk factors in the training set, and a Nomogram model was developed. Accuracy was assessed using receiver operating characteristic curves (ROC), calibration was evaluated with calibration curves and the Hosmer-Lemeshow test, and clinical utility was analyzed using decision curve analysis (DCA) and clinical impact curve (CIC). Results: Surgical approach, spinal computed tomography (CT) values, and multifidus skeletal muscle index (SMI) are independent predictors of postoperative NVCF in OVCF patients. A Nomogram model, based on the identified predictors, was developed and uploaded online. Internal validation results showed area under the curve (AUC) values of 0.801, 0.664, and 0.832 for the training set, validation set, and external validation, respectively. Hosmer-Lemeshow goodness-of-fit tests (χ2 = 7.311-14.474, p = 0.070-0.504) and calibration curves indicated good consistency between observed and predicted values. DCA and CIC demonstrated clinical net benefit within risk thresholds of 0.06-0.84, 0.12-0.23, and 0.01-0.27. At specificity 1.00-0.80, the partial AUC (0.106) exceeded that at sensitivity 1.00-0.80 (0.062). Conclusion: Compared to the spinal CT value, the multifidus SMI has certain potential in predicting the occurrence of NVCF. Additionally, the Nomogram model of this study has a greater negative predictive value.
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INTRODUCTION: Lower limb venous anomalies, including duplicated veins, are common and have significant impacts on the outcomes and efficacy of venous surgery. Digital subtraction angiography (DSA) guided venography, serving as the tertiary diagnostic option for venous disorders, offers valuable informations to clinical practitioners. PATIENTS AND METHODS: A retrospective study was conducted on 195 patients with suspected venous disease, evaluating 259 limbs with venography imaging. Two experienced interventional vascularists evaluated the images to determine the incidence and characteristics of variances in the femoral, popliteal, great saphenous, and small saphenous veins. Moreover, blood samples were collected to assess the safety of the venography procedure by monitoring changes in renal function. RESULT: Duplication variations were found in the lower limb veins, with the highest prevalence in the femoral vein (11.28%, 22/195), followed by the great saphenous vein (4.1%, 8/195), and the popliteal vein (1.54%, 3/195). No severe contrast agent allergies or postoperative complications were reported. No statistically significant differences were found in creatinine and urea levels pre- and post-operation for patients without duplication variations, those with duplication of the great saphenous, femoral, or popliteal vein (P < .05). CONCLUSION: DSA-guided venography is effective in identifying venous variations in lower limb disease. DFV is the most common recurrent vein, while DPV is the least. Adequate preparation ensures safety, high spatial resolution, dynamic imaging, and low tissue interference.
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Enfermedades Vasculares , Insuficiencia Venosa , Humanos , Flebografía/métodos , Estudios Retrospectivos , Angiografía de Substracción Digital , Resultado del Tratamiento , Extremidad Inferior/irrigación sanguínea , Vena Femoral/diagnóstico por imagen , Vena Safena/diagnóstico por imagenRESUMEN
Purpose: Previous studies highlight paraspinal muscles' significance in spinal stability. This study aims to assess paraspinal muscle predictiveness for postoperative recurrent lumbar disc herniation (PRLDH) after lumbar disc herniation patients undergo percutaneous endoscopic transforaminal discectomy (PETD). Patients and Methods: Retrospectively collected data from 232 patients undergoing PETD treatment at our institution between January 2020 and January 2023, randomly allocated into training (60%) and validation (40%) groups. Utilizing Lasso regression and multivariable logistic regression, independent risk factors were identified in the training set to construct a Nomogram model. Internal validation employed Enhanced Bootstrap, with Area Under the ROC Curve (AUC) assessing accuracy. Calibration was evaluated through calibration curves and the Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis (DCA) and clinical impact curve (CIC) were employed for clinical utility analysis. Results: Diabetes, Modic changes, and ipsilesional multifidus muscle skeletal muscle index (SMI) were independent predictive factors for PRLDH following PETD (P<0.05). Developed Nomogram model based on selected predictors, uploaded to a web page. AUC for training: 0.921 (95% CI 0.872-0.970), validation: 0.900 (95% CI 0.828-0.972), respectively. The Hosmer-Lemeshow test yielded χ 2=5.638/6.259, P=0.688/0.618, and calibration curves exhibited good fit between observed and predicted values. DCA and CIC demonstrate clinical net benefit for both models at risk thresholds of 0.02-1.00 and 0.02-0.80. Conclusion: The Nomogram predictive model developed based on paraspinal muscle parameters in this study demonstrates excellent predictive capability and aids in personalized risk assessment for PRLDH following PETD.