Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective:To compare the clinical efficacy of Minimally Invasive Surgery (MIS) and traditional Posterior Spinal Fusion (PSF) in treating children with paralytic scoliosis with pelvic obliquity (PSPO) following spinal cord injury.Methods:A retrospective analysis was conducted on the data of 25 patients with PSPO who underwent surgical treatment at the Drum Tower Hospital affiliated with Nanjing University Medical School from January 2017 to June 2023. The cohort included 4 males and 21 females, aged 12.3±2.8 years (range 9-14 years). Patients were divided into the MIS group (12 cases) and the PSF group (13 cases). Radiological parameters were measured preoperatively, postoperatively, and at the last follow-up. Surgical time, intraoperative blood loss, intraoperative blood transfusion volume, length of hospital stay, total hospitalization costs, and complications were recorded. The Scoliosis Research Society questionnaires-22 (SRS-22) Chinese version were used to assess patient satisfaction and efficacy.Results:There were no statistically significant differences between the MIS and PSF groups in age, gender, Risser sign, preoperative Cobb angle for scoliosis, pelvic tilt angle, or local kyphosis angle ( P>0.05). The MIS group demonstrated surgical time of 176±30 minutes, intraoperative blood loss of 300±70 ml, blood transfusion volume of 280±175 ml, and total hospitalization costs of 87'800± 13'300 yuan, all of which were lower than PSF group, with values of 280±91 minutes, 1'433±116 ml, 1'351±996 ml, and 14'8400±26'100 yuan, respectively. These differences were statistically significant ( t=3.789, P=0.001; t=29.328, P<0.001; t=3.667, P=0.001; t=7.271, P<0.001). In the MIS group, preoperative, postoperative, and last follow-up Cobb angles were 79.11°±6.74°, 35.86°±4.98°, and 36.27°±4.84° respectively; pelvic tilt angles were 24.79°±5.58°, 9.18°±3.32°, and 8.79°±2.94°; local kyphosis angles were 38.84°±4.18°, 12.96°±4.87°, and 11.43°±6.08°, respectively. Postoperative and last follow-up angles were significantly reduced compared to preoperative values, with statistically significant differences ( P<0.05). In the PSF group, preoperative, postoperative, and last follow-up Cobb angles were 82.06°±9.26°, 34.75°±5.14°, and 35.15°±5.04° respectively; pelvic tilt angles were 26.60°±6.21°, 10.12°±3.21°, and 9.91°±2.97°; local kyphosis angles were 40.92°±7.04°, 10.92°±7.26°, and 14.02°±5.58°, respectively. Differences from preoperative to postoperative measurements were statistically significant ( P<0.05). At the last follow-up, both groups showed no significant loss of scoliosis correction, and there were no statistically significant differences between the groups postoperatively or at the last follow-up ( P>0.05). In the MIS group, one case of superficial surgical site infection and one case of postoperative atelectasis occurred. In the PSF group, two cases of deep surgical site infection, one case of poor screw placement, and two cases were transferred to the ICU postoperatively due to excessive intraoperative bleeding. Preoperative SRS-22 total scores were 2.0±0.6 for PSF and 2.1±0.4 for MIS. Postoperative SRS-22 total scores (excluding satisfaction) were 3.0±0.5 for PSF and 2.9±0.3 for MIS. The within-group differences from preoperative to postoperative were statistically significant ( P<0.05), while the between-group differences from preoperative to postoperative were not statistically significant ( P>0.05). Conclusion:Compared to the PSF technique, MIS can shorten surgery time, reduce intraoperative blood loss and perioperative complications, and decrease hospitalization costs. MIS can achieve similar early clinical efficacy.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Objective:To compare the clinical efficacy of Minimally Invasive Surgery (MIS) and traditional Posterior Spinal Fusion (PSF) in treating children with paralytic scoliosis with pelvic obliquity (PSPO) following spinal cord injury.Methods:A retrospective analysis was conducted on the data of 25 patients with PSPO who underwent surgical treatment at the Drum Tower Hospital affiliated with Nanjing University Medical School from January 2017 to June 2023. The cohort included 4 males and 21 females, aged 12.3±2.8 years (range 9-14 years). Patients were divided into the MIS group (12 cases) and the PSF group (13 cases). Radiological parameters were measured preoperatively, postoperatively, and at the last follow-up. Surgical time, intraoperative blood loss, intraoperative blood transfusion volume, length of hospital stay, total hospitalization costs, and complications were recorded. The Scoliosis Research Society questionnaires-22 (SRS-22) Chinese version were used to assess patient satisfaction and efficacy.Results:There were no statistically significant differences between the MIS and PSF groups in age, gender, Risser sign, preoperative Cobb angle for scoliosis, pelvic tilt angle, or local kyphosis angle ( P>0.05). The MIS group demonstrated surgical time of 176±30 minutes, intraoperative blood loss of 300±70 ml, blood transfusion volume of 280±175 ml, and total hospitalization costs of 87'800± 13'300 yuan, all of which were lower than PSF group, with values of 280±91 minutes, 1'433±116 ml, 1'351±996 ml, and 14'8400±26'100 yuan, respectively. These differences were statistically significant ( t=3.789, P=0.001; t=29.328, P<0.001; t=3.667, P=0.001; t=7.271, P<0.001). In the MIS group, preoperative, postoperative, and last follow-up Cobb angles were 79.11°±6.74°, 35.86°±4.98°, and 36.27°±4.84° respectively; pelvic tilt angles were 24.79°±5.58°, 9.18°±3.32°, and 8.79°±2.94°; local kyphosis angles were 38.84°±4.18°, 12.96°±4.87°, and 11.43°±6.08°, respectively. Postoperative and last follow-up angles were significantly reduced compared to preoperative values, with statistically significant differences ( P<0.05). In the PSF group, preoperative, postoperative, and last follow-up Cobb angles were 82.06°±9.26°, 34.75°±5.14°, and 35.15°±5.04° respectively; pelvic tilt angles were 26.60°±6.21°, 10.12°±3.21°, and 9.91°±2.97°; local kyphosis angles were 40.92°±7.04°, 10.92°±7.26°, and 14.02°±5.58°, respectively. Differences from preoperative to postoperative measurements were statistically significant ( P<0.05). At the last follow-up, both groups showed no significant loss of scoliosis correction, and there were no statistically significant differences between the groups postoperatively or at the last follow-up ( P>0.05). In the MIS group, one case of superficial surgical site infection and one case of postoperative atelectasis occurred. In the PSF group, two cases of deep surgical site infection, one case of poor screw placement, and two cases were transferred to the ICU postoperatively due to excessive intraoperative bleeding. Preoperative SRS-22 total scores were 2.0±0.6 for PSF and 2.1±0.4 for MIS. Postoperative SRS-22 total scores (excluding satisfaction) were 3.0±0.5 for PSF and 2.9±0.3 for MIS. The within-group differences from preoperative to postoperative were statistically significant ( P<0.05), while the between-group differences from preoperative to postoperative were not statistically significant ( P>0.05). Conclusion:Compared to the PSF technique, MIS can shorten surgery time, reduce intraoperative blood loss and perioperative complications, and decrease hospitalization costs. MIS can achieve similar early clinical efficacy.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Humans ; Transcatheter Aortic Valve Replacement ; Aortic Valve/surgery* ; Stents ; Aortic Valve Stenosis/surgery* ; Treatment Outcome ; Heart Valve Prosthesis

Humans ; Transcatheter Aortic Valve Replacement ; Aortic Valve/surgery* ; Stents ; Aortic Valve Stenosis/surgery* ; Treatment Outcome ; Heart Valve Prosthesis

Objective: To evaluate the safety and efficacy of transcatheter aortic valve implantation (TAVI) with the novel Prizvalve^® system in treating severe aortic stenosis. Methods: This is a single-center, prospective, single-arm, observational study. A total of 11 patients with severe aortic stenosis with high risk or inappropriate for conventional surgical aortic valve replacement (SAVR) were included, and TAVI was achieved with the Prizvalve^® system between March 2021 and May 2021 in West China Hospital. Transthoracic echocardiography (TTE) was performed immediately after prosthesis implantation to evaluate mean transaortic gradient and maximal transaortic velocity. The device success rate was calculated, which was defined as (1) the device being delivered via the access, deployed, implanted and withdrawn, (2) mean transaortic gradient<20 mmHg (1 mmHg=0.133 kPa) or a maximal transaortic velocity<3 m/s post TAVI, and without severe aortic regurgitation or paravalvular leak post TAVI. TTE was performed at 30 days after the surgery, and all-cause mortality as well as the major cardiovascular adverse events (including acute myocardial infarction, disabling hemorrhagic or ischemic stroke) up to 30 days post TAVI were analyzed. Results: The age of 11 included patients were (78.1±6.3) years, with 8 males. A total of 10 patients were with NYHA functional class Ⅲ or Ⅳ. Devices were delivered via the access, deployed, implanted and withdrawn successfully in all patients. Post-implant mean transaortic gradient was (7.55±4.08) mmHg and maximal transaortic velocity was (1.78±0.44) m/s, and both decreased significantly as compared to baseline levels (both P<0.05). No severe aortic regurgitation or paravalvular leak was observed post TAVI. Device success was achieved in all the 11 patients. No patient died or experienced major cardiovascular adverse events up to 30 days post TAVI. Mean transaortic gradient was (9.45±5.07) mmHg and maximal transaortic velocity was (2.05±0.42) m/s at 30 days post TAVI, which were similar as the values measured immediately post TAVI (both P>0.05). Conclusions: TAVI with the Prizvalve^® system is a feasible and relatively safe procedure for patients with severe aortic stenosis and at high risk or inappropriate for SAVR. Further clinical studies could be launched to obtain more clinical experience with Prizvalve^® system.
Aged ; Aged, 80 and over ; Aortic Valve ; Aortic Valve Stenosis/surgery* ; Heart Valve Prosthesis ; Heart Valve Prosthesis Implantation ; Humans ; Male ; Prospective Studies ; Transcatheter Aortic Valve Replacement/methods* ; Treatment Outcome

Aortic Valve/surgery* ; Aortic Valve Stenosis/surgery* ; Humans ; Transcatheter Aortic Valve Replacement