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Yang F, Feng Y, Sun P, Traverso A, Dekker A, Zhang B, Huang Z, Wang Z, Yan D. Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study. J Bone Oncol 2024; 47:100614. [PMID: 38975332 PMCID: PMC11225658 DOI: 10.1016/j.jbo.2024.100614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/05/2024] [Accepted: 06/01/2024] [Indexed: 07/09/2024] Open
Abstract
Objective To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. Methods 183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. Results LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. Conclusion Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.
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Affiliation(s)
- Fan Yang
- Department of Radiation, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
| | - Ying Feng
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Pengfei Sun
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bin Zhang
- Department of Radiation, Peking University Shougang Hospital, Beijing 100144, China
| | - Zhen Huang
- Department of Bone Oncology, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
| | - Zhixiang Wang
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Dong Yan
- Department of Radiation, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
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Wang J, Zhang F, Dong S, Yang Y, Gao F, Liu G, Zhang P, Wang X, Du X, Tian Z. Apatinib plus chemotherapy for non-metastatic osteosarcoma: a retrospective cohort study. Front Oncol 2023; 13:1227461. [PMID: 38023239 PMCID: PMC10679406 DOI: 10.3389/fonc.2023.1227461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Background Effective adjuvant therapy for osteosarcoma is necessary for improved outcomes. Previous studies demonstrated that apatinib plus doxorubicin-based chemotherapy may improve the efficacy of neoadjuvant therapy. This study aimed to clarify the effectiveness and safety of apatinib plus doxorubicin and cisplatin (AP) as neoadjuvant therapy for osteosarcoma. Methods The clinical data of osteosarcoma patients who underwent neoadjuvant therapy and surgery between August 2016 and April 2022 were retrospectively collected and analyzed. Patients were divided into two groups: the apatinib plus AP (apatinib + AP) group and the methotrexate, doxorubicin, and cisplatin (MAP) group. Results This study included 42 patients with nonmetastatic osteosarcoma (19 and 23 patients in the apatinib + AP and MAP groups, respectively). The 1- and 2-year disease-free survival rates in the apatinib + AP group were higher than those in the MAP group, but the difference was not significant (P=0.165 and 0.283, respectively). Some adverse events were significantly more common in the apatinib + AP group than in the MAP group, including oral mucositis (grades 3 and 4) (52.6% vs. 17.4%, respectively, P=0.023), limb edema (47.4% vs. 17.4%, respectively, P=0.049), hand-foot syndrome (31.6% vs. 0%, respectively, P=0.005), proteinuria (26.3% vs. 0%, respectively, P=0.014), hypertension (21.1% vs. 0%, respectively, P=0.035), and hypothyroidism (21.1% vs. 0%, respectively, P=0.035). No drug-related deaths occurred. There was no statistically significant difference in the incidence of postoperative complications between the groups (P>0.05). Conclusion The present study suggests that apatinib + AP may be a promising candidate for neoadjuvant therapy for osteosarcoma, warranting further validation in prospective randomized controlled clinical trials with long-term follow-up.
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Affiliation(s)
- Jiaqiang Wang
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Fan Zhang
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Shuping Dong
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Yang Yang
- Modern Educational Technology Center, Henan University of Economics and Law, Zhengzhou, Henan, China
| | - Fangfang Gao
- Department of Medical Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Guancong Liu
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Peng Zhang
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Xin Wang
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Xinhui Du
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Zhichao Tian
- Department of Orthopedics, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
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Fu Y, Yu Y, Zhou Y, Li T, Xie Y, Wang Y, Ran Q, Chen Y, Fan X. The fibrinogen-albumin ratio as a novel prognostic factor for elderly patients with osteosarcoma. Medicine (Baltimore) 2023; 102:e34926. [PMID: 37682137 PMCID: PMC10489207 DOI: 10.1097/md.0000000000034926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/03/2023] [Indexed: 09/09/2023] Open
Abstract
To analyze the prognostic value of fibrinogen-albumin ratio (FAR) in predicting the overall survival in elderly osteosarcoma patients. One hundred nineteen elderly osteosarcoma patients (> 40 years old) from 2 centers were retrospectively reviewed and analyzed. The cutoff values of the biomarker were calculated via receiver operating characteristic curves, and the cohort was divided into high FAR group and low FAR group. The association between the FAR and clinical-pathological parameters was analyzed. And the prognosis of elderly osteosarcoma patients and the potential risk factors were analyzed using Kaplan-Meier method and Cox proportional hazards model. Finally, a clinical nomogram was constructed, and its predictive capacity was verified. According to receiver operating characteristic results, the cutoff value for FAR was 0.098, and the enrolled patients were divided into the low FAR group and high FAR group. The FAR was significantly correlated with several clinical-pathological characteristics, including age, tumor size, tumor stage, recurrence, and metastasis. Moreover, the multivariate Cox analyses results showed that the FAR, pathological fracture, and metastasis were independent risk factors for overall survival in elderly osteosarcoma patients. The predictive nomogram was subsequently constructed, representing satisfactory predictive performance for prognosis in elderly patients with osteosarcoma. The FAR value is a promising indicator for elderly osteosarcoma patients, which is correlated with the various clinical characteristics and prognosis. A clinical nomogram integrating FAR and other clinical indicators is a convenient and available tool to assess the prognosis and manage the individualized and precise treatment of elderly patients with osteosarcoma.
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Affiliation(s)
- Yang Fu
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yang Yu
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yi Zhou
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Tong Li
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yizhou Xie
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yehui Wang
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Qiang Ran
- Department of Orthopaedics, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Yiming Chen
- Department of Radiology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Xiaohong Fan
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
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Basoli S, Cosentino M, Traversari M, Manfrini M, Tsukamoto S, Mavrogenis AF, Bordini B, Donati DM, Errani C. The Prognostic Value of Serum Biomarkers for Survival of Children with Osteosarcoma of the Extremities. Curr Oncol 2023; 30:7043-7054. [PMID: 37504371 PMCID: PMC10378558 DOI: 10.3390/curroncol30070511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/28/2023] [Accepted: 07/21/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Osteosarcoma is a highly aggressive malignant bone tumor that affects mainly adolescents and young adults. We analyzed serum biomarkers for their prognostic significance in children with osteosarcoma. METHODS In this retrospective study, we investigated the prognostic factors in 210 children who were treated for appendicular osteosarcoma, including patient age and sex, tumor site and size (≥8 cm or <8 cm), presence of metastasis, chemotherapy-induced tumor necrosis, serum levels of alkaline phosphatase (AP), C-reactive protein, serum hemoglobin, lactate dehydrogenase, erythrocyte sedimentation rate (ESR), leukocyte counts, platelet count, and neutrophil-lymphocyte ratio. RESULTS A multivariate Cox regression model showed that high level of AP [HR of 1.73; 95% CI, 1.02 to 2.94], poor chemotherapy-induced tumor necrosis [HR of 2.40; 95% CI, 1.41 to 4.08] and presence of metastases at presentation [HR of 3.71; 95% CI, 2.19 to 6.29] were associated with poor prognosis at 5 years (p < 0.05). Inadequate surgical margins [HR 11.28; 95% CI, 1.37 to 92.79] and high levels of ESR [HR 3.58; 95% CI, 1.29 to 9.98] showed a greater risk of local recurrence at 5 years follow-up (p < 0.05). CONCLUSIONS AP and ESR can identify osteosarcoma-diagnosed children with a greater risk of death and local recurrence, respectively.
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Affiliation(s)
- Stefano Basoli
- Clinica Ortopedica e Traumatologica III a Prevalente Indirizzo Oncologico, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
| | - Monica Cosentino
- Laboratorio di Tecnologia Medica, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
| | - Matteo Traversari
- Clinica Ortopedica e Traumatologica III a Prevalente Indirizzo Oncologico, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
| | - Marco Manfrini
- Clinica Ortopedica e Traumatologica III a Prevalente Indirizzo Oncologico, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
| | - Shinji Tsukamoto
- Department of Orthopaedic Surgery, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Nara, Japan
| | - Andreas F Mavrogenis
- First Department of Orthopaedics, School of Medicine, National and Kapodistrian University of Athens, 41 Ventouri Street, Holargos, 15562 Athens, Greece
| | - Barbara Bordini
- Laboratorio di Tecnologia Medica, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
| | - Davide Maria Donati
- Clinica Ortopedica e Traumatologica III a Prevalente Indirizzo Oncologico, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
| | - Costantino Errani
- Clinica Ortopedica e Traumatologica III a Prevalente Indirizzo Oncologico, IRCCS Istituto Ortopedico Rizzoli, Via Pupilli 1, 40136 Bologna, Italy
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Yin P, Zhong J, Liu Y, Liu T, Sun C, Liu X, Cui J, Chen L, Hong N. Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma. BMC Med Imaging 2023; 23:40. [PMID: 36959569 PMCID: PMC10037898 DOI: 10.1186/s12880-023-00991-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 03/06/2023] [Indexed: 03/25/2023] Open
Abstract
OBJECTIVES Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. METHODS 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. RESULTS The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (tradscore = -5.829, χ2ALP = 97.137, tsize = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784). CONCLUSION The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.
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Affiliation(s)
- Ping Yin
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China
| | - Junwen Zhong
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China
| | - Ying Liu
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China
| | - Tao Liu
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China
| | - Chao Sun
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China
| | - Xiaoming Liu
- Department of Research and Development, United Imaging Intelligence (Beijing) Co.,Ltd, Yongteng North Road, Haidian District, Beijing, 100089, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co.,Ltd, Yongteng North Road, Haidian District, Beijing, 100089, China
| | - Lei Chen
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China.
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Wang Z, Lu H, Wu Y, Ren S, Diaty DM, Fu Y, Zou Y, Zhang L, Wang Z, Wang F, Li S, Huo X, Yu W, Xu J, Ye Z. Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Affiliation(s)
- Zhan Wang
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
- Orthopedics Research Institute of Zhejiang University Hangzhou China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province Hangzhou China
- Clinical Research Center of Motor System Disease of Zhejiang Province Hangzhou China
| | - Haoda Lu
- Institute for AI in Medicine School of Artificial Intelligence, Nanjing University of Information Science & Technology Nanjing China
| | - Yan Wu
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
- Orthopedics Research Institute of Zhejiang University Hangzhou China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province Hangzhou China
- Clinical Research Center of Motor System Disease of Zhejiang Province Hangzhou China
| | - Shihong Ren
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
| | - Diarra mohamed Diaty
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
- Orthopedics Research Institute of Zhejiang University Hangzhou China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province Hangzhou China
- Clinical Research Center of Motor System Disease of Zhejiang Province Hangzhou China
| | - Yanbiao Fu
- Department of Pathology The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
| | - Yi Zou
- Department of Pathology The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
| | - Lingling Zhang
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
- Orthopedics Research Institute of Zhejiang University Hangzhou China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province Hangzhou China
- Clinical Research Center of Motor System Disease of Zhejiang Province Hangzhou China
| | - Zenan Wang
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
| | - Fangqian Wang
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
| | - Shu Li
- Department of Hematology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Xinmi Huo
- Bioinformatics Institute (BII) Agency for Science, Technology and Research (A*STAR) Singapore Singapore
| | - Weimiao Yu
- Bioinformatics Institute (BII) Agency for Science, Technology and Research (A*STAR) Singapore Singapore
| | - Jun Xu
- Institute for AI in Medicine School of Artificial Intelligence, Nanjing University of Information Science & Technology Nanjing China
| | - Zhaoming Ye
- Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China
- Orthopedics Research Institute of Zhejiang University Hangzhou China
- Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province Hangzhou China
- Clinical Research Center of Motor System Disease of Zhejiang Province Hangzhou China
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Chen L, Liu C, Ye Z, Huang S, Liang T, Li H, Chen J, Chen W, Guo H, Chen T, Yao Y, Jiang J, Sun X, Yi M, Liao S, Yu C, Wu S, Fan B, Zhan X. Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram. Surg Infect (Larchmt) 2022; 23:564-575. [PMID: 35723640 PMCID: PMC9398487 DOI: 10.1089/sur.2022.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram. Patients and Methods: We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression. The logistic regression analysis and receiver operating characteristic (ROC) curve analysis were further used to obtain statistically significant parameters. These parameters were then used to construct a nomogram. The C-index, ROC curve, and decision curve analysis (DCA) were used to assess the predictive ability and accuracy of the nomogram, whereas internal verification was used to calculate the C-index by bootstrapping with 1,000 resamples. Results: A total of 394 patients with spinal tuberculosis surgery were included in the study, of whom 76 patients had surgical site infections whereas 318 patients did not. The predicted risk of surgical site infection in the nomogram ranged between 0.01 and 0.98. Both the value of the C-index of the nomogram (95% confidence interval [CI], 0.62–0.76) and the area under the curve (AUC) were found to be 0.69. The net benefit of the model ranged between 0.01 and 0.99. In contrast, the C-index calculated by the internal verification method of the nomogram was found to be 0.68. Conclusions: The risk factors predicting surgical site infection after spinal tuberculosis surgery included albumin, lesion segment, operation time, and incision length.
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Affiliation(s)
- Liyi Chen
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Chong Liu
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Zhen Ye
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Shengsheng Huang
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Tuo Liang
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Hao Li
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Jiarui Chen
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Wuhua Chen
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Hao Guo
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Tianyou Chen
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Yuanlin Yao
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Jie Jiang
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Xuhua Sun
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Ming Yi
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Shian Liao
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Chaojie Yu
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Shaofeng Wu
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Binguang Fan
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
| | - Xinli Zhan
- Spine and Osteopathy Ward, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi Province, China
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