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Jadresic MC, Baker JF. Predicting complications of spine surgery: external validation of three models. Spine J 2022; 22:1801-1810. [PMID: 35870799 DOI: 10.1016/j.spinee.2022.07.092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/24/2022] [Accepted: 07/14/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation and comparison of these tools is critical prior to clinical use. No model for adverse events after spine surgery has undergone decision curve analysis. PURPOSE External validation, comparison, and decision curve analysis of 3 previously described models [SpineSage, Risk Assessment Tool (RAT), National Surgical Quality Improvement Program Risk Calculator (NSQIP)] for predicting 30-day postoperative complications after spine surgery STUDY DESIGN: Retrospective cohort study. PATIENT SAMPLE Three hundred fifteen patients who underwent spine surgery at a tertiary academic surgical center in New Zealand between January 2019 and April 2020. OUTCOME MEASURES As defined by each risk prediction tool and objectively using the Comprehensive Complication Index. METHODS We retrospectively reviewed risk of postoperative complication was calculated for each patient according to the 3 models. Overall model fit, calibration, discrimination, and decision curve analysis for each model were assessed in line with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. RESULTS 100 (35%) patients experienced complications. SpineSage and RAT were well calibrated, NSQIP systematically underestimated risk. Area under the curve was greatest for SpineSage (0.75) compared with the NSQIP (0.72) and the RAT (0.69). Decision curve analysis showed SpineSage resulted in greatest net benefit across all risk thresholds. CONCLUSIONS Of the models studied, SpineSage most accurately predicted risk and can be expected to perform better than a strategy of treating all patients if patient or surgeon deem complication risk >10% significant. NSQIP may not be suitable for the clinical use in our local population.
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Affiliation(s)
- Martin Coia Jadresic
- Department of Orthopaedic Surgery, Waikato Hospital, Hamilton, 3204, New Zealand.
| | - Joseph F Baker
- Department of Orthopaedic Surgery, Waikato Hospital, Hamilton, 3204, New Zealand; Department of Surgery, University of Auckland, Auckland, New Zealand
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Li Y, Wu JH, Li CP, Liu BN, Tian XY, Qiu H, Hao CY, Lv A. Multidimensional characteristics, prognostic role, and preoperative prediction of peritoneal sarcomatosis in retroperitoneal sarcoma. Front Oncol 2022; 12:950418. [DOI: 10.3389/fonc.2022.950418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 10/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundPeritoneal sarcomatosis (PS) could occur in patients with retroperitoneal sarcomas (RPS). This study aimed to expand the understanding of PS on its characteristics and prognostic role, and develop a nomogram to predict its occurrence preoperatively.MethodsData of 211 consecutive patients with RPS who underwent surgical treatment between 2011 and 2019 was retrospectively reviewed. First, the clinicopathological characteristics of PS were summarized and analyzed. Second, the disease-specific survival (DSS) and recurrence-free survival (RFS) of patients were analyzed to evaluate the prognostic role of PS. Third, preoperative imaging, nearly the only way to detect PS preoperatively, was combined with other screened risk factors to develop a nomogram. The performance of the nomogram was assessed.ResultsAmong the 211 patients, 49 (23.2%) patients had PS with an incidence of 13.0% in the primary patients and 35.4% in the recurrent patients. The highest incidence of PS occurred in dedifferentiated liposarcoma (25.3%) and undifferentiated pleomorphic sarcoma (25.0%). The diagnostic sensitivity of the preoperative imaging was 71.4% and its specificity was 92.6%. The maximum standardized uptake value (SUVmax) was elevated in patients with PS (P<0.001). IHC staining for liposarcoma revealed that the expression of VEGFR-2 was significantly higher in the PS group than that in the non-PS group (P = 0.008). Survival analysis (n =196) showed significantly worse DSS in the PS group than in non-PS group (median: 16.0 months vs. not reached, P < 0.001). In addition, PS was proven as one of the most significant prognostic predictors of both DSS and RFS by random survival forest algorithm. A nomogram to predict PS status was developed based on preoperative imaging combined with four risk factors including the presentation status (primary vs. recurrent), ascites, SUVmax, and tumor size. The nomogram significantly improved the diagnostic sensitivity compared to preoperative imaging alone (44/49, 89.8% vs. 35/49, 71.4%). The C-statistics of the nomogram was 0.932, and similar C-statistics (0.886) was achieved at internal cross-validation.ConclusionPS is a significant prognostic indicator for RPS, and it occurs more often in recurrent RPS and in RPS with higher malignant tendency. The proposed nomogram is effective to predict PS preoperatively.
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Qiao J, Zhang X, Du M, Wang P, Xin J. 18F-FDG PET/CT radiomics nomogram for predicting occult lymph node metastasis of non-small cell lung cancer. Front Oncol 2022; 12:974934. [PMID: 36249026 PMCID: PMC9554943 DOI: 10.3389/fonc.2022.974934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/12/2022] [Indexed: 11/29/2022] Open
Abstract
Purpose To investigate the ability of a PET/CT-based radiomics nomogram to predict occult lymph node metastasis in patients with clinical stage N0 non-small cell lung cancer (NSCLC). Materials and methods This retrospective study included 228 patients with surgically confirmed NSCLC (training set, 159 patients; testing set, 69 patients). ITKsnap3.8.0 was used for image(CT and PET images) segmentation, AK version 3.2.0 was used for radiomics feature extraction, and Python3.7.0 was used for radiomics feature screening. A radiomics model for predicting occult lymph node metastasis was established using a logistic regression algorithm. A nomogram was constructed by combining radiomics scores with selected clinical predictors. Receiver operating characteristic (ROC) curves were used to verify the performance of the radiomics model and nomogram in the training and testing sets. Results The radiomics nomogram comprising six selected features achieved good prediction efficiency, including radiomics characteristics and tumor location information (central or peripheral), which demonstrated good calibration and discrimination ability in the training (area under the ROC curve [AUC] = 0.884, 95% confidence interval [CI]: 0.826-0.941) and testing (AUC = 0.881, 95% CI: 0.8031-0.959) sets. Clinical decision curves demonstrated that the nomogram was clinically useful. Conclusion The PET/CT-based radiomics nomogram is a noninvasive tool for predicting occult lymph node metastasis in NSCLC.
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Affiliation(s)
- Jianyi Qiao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ming Du
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Pengyuan Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jun Xin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
- *Correspondence: Jun Xin,
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Shi S, Pan X, Zhang L, Wang X, Zhuang Y, Lin X, Shi S, Zheng J, Lin W. An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data. Front Genet 2022; 13:979529. [PMID: 36159979 PMCID: PMC9490444 DOI: 10.3389/fgene.2022.979529] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/10/2022] [Indexed: 12/02/2022] Open
Abstract
Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method. Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis. Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.
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Affiliation(s)
- Songchang Shi
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Jinshan Hospital, Fujian Provincial Hospital, Fuzhou, China
| | - Xiaobin Pan
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Jinshan Hospital, Fujian Provincial Hospital, Fuzhou, China
| | - Lihui Zhang
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Jinshan Hospital, Fujian Provincial Hospital, Fuzhou, China
| | - Xincai Wang
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Jinshan Hospital, Fujian Provincial Hospital, Fuzhou, China
| | - Yingfeng Zhuang
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Jinshan Hospital, Fujian Provincial Hospital, Fuzhou, China
| | - Xingsheng Lin
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Jinshan Hospital, Fujian Provincial Hospital, Fuzhou, China
| | - Songjing Shi
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
| | - Jianzhang Zheng
- Department of Orthopedics, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
| | - Wei Lin
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
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Huang Y, Huang S, Wang Y, Li Y, Gui Y, Huang C. A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning. Front Physiol 2022; 13:937546. [PMID: 36187785 PMCID: PMC9520324 DOI: 10.3389/fphys.2022.937546] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/23/2022] [Indexed: 11/18/2022] Open
Abstract
The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.
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Affiliation(s)
- Yuanqi Huang
- Research and Communication Center for Exercise and Health, Xiamen University of Technology, Xiamen, China
- School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China
| | - Shengqi Huang
- School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China
| | - Yukun Wang
- School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China
| | - Yurong Li
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
| | - Yuheng Gui
- Fujian Provincial Basketball and Volleyball Centre, Fuzhou, China
| | - Caihua Huang
- Research and Communication Center for Exercise and Health, Xiamen University of Technology, Xiamen, China
- *Correspondence: Caihua Huang,
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Yao Z, Zhang H, Zhang X, Zhang Z, Jie J, Xie K, Li F, Tan W. Identification of tumor microenvironment-related signature for predicting prognosis and immunotherapy response in patients with bladder cancer. Front Genet 2022; 13:923768. [PMID: 36147509 PMCID: PMC9485450 DOI: 10.3389/fgene.2022.923768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
The tumor microenvironment (TME) not only provides fertile soil for tumor growth and development but also widely involves immune evasion as well as the resistance towards therapeutic response. Accumulating interest has been attracted from the biological function of TME to its effects on patient outcomes and treatment efficacy. However, the relationship between the TME-related gene expression profiles and the prognosis of bladder cancer (BLCA) remains unclear. The TME-related genes expression data of BLCA were collected from The Cancer Genome Atlas (TCGA) database. NFM algorithm was used to identify the distinct molecular pattern based on the significantly different TME-related genes. LASSO regression and Cox regression analyses were conducted to identify TME-related gene markers related to the prognosis of BLCA and to establish a prognostic model. The predictive efficacy of the risk model was verified through integrated bioinformatics analyses. Herein, 10 TME-related genes (PFKFB4, P4HB, OR2B6, OCIAD2, OAS1, KCNJ15, AHNAK, RAC3, EMP1, and PRKY) were identified to construct the prognostic model. The established risk scores were able to predict outcomes at 1, 3, and 5 years with greater accuracy than previously known models. Moreover, the risk score was closely associated with immune cell infiltration and the immunoregulatory genes including T cell exhaustion markers. Notably, the predictive power of the model in immunotherapy sensitivity was verified when it was applied to patients with metastatic urothelial carcinoma (mUC) undergoing immunotherapy. In conclusion, TME risk score can function as an independent prognostic biomarker and a predictor for evaluating immunotherapy response in BLCA patients, which provides recommendations for improving patients’ response to immunotherapy and promoting personalized tumor immunotherapy in the future.
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Affiliation(s)
| | | | | | | | | | | | - Fei Li
- *Correspondence: Fei Li, ; Wanlong Tan,
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Jeong HW, Kim M, Choi HG, Park SY, Lee YS. Development of a machine learning model to predict lateral hinge fractures by analyzing patient factors before open wedge high tibial osteotomy. Knee Surg Sports Traumatol Arthrosc 2022:10.1007/s00167-022-07137-6. [PMID: 36036269 DOI: 10.1007/s00167-022-07137-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/19/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Several methods have been developed to prevent lateral hinge fractures (LHFs), using only classic statistical models. Machine learning is under the spotlight because of its ability to analyze various weights and model nonlinear relationships. The purpose of this study was to create a machine learning model that predicts LHF with high predictive performance. METHODS Data were collected from a total of 439 knees with medial osteoarthritis (OA) treated with Medial open wedge high tibial osteotomy (MOW-HTO) from March 2014 to February 2020. The patient data included age, sex, height, and weight. Preoperative, determined, and modifiable factors were categorized using X-ray and CT data to create ensemble models with better predictive performance. Among the 57 ensemble models, which is the total number of possible combinations with six models, the model with the highest area under curve (AUC) or F1-score was selected as the final ensemble model. Gain feature importance analysis and the Shapley additive explanations (SHAP) feature explanation were performed on the best models. RESULTS The ensemble model with the highest AUC was a combination of a light gradient boosting machine (LGBM) and multilayer perceptron (MLP) (AUC = 0.992). The ensemble model with the highest F1-score was the model that combined logistic regression (LR) and MLP (F1-score = 0.765). Distance X was the most predictive feature in the results of both model interpretation analyses. CONCLUSION Two types of ensemble models, LGBM with MLP and LR with MLP, were developed as machine learning models to predict LHF with high predictive performance. Using these models, surgeons can identify important features to prevent LHF and establish strategies by adjusting modifiable factors. STUDY DESIGN Retrospective cohort study.
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Affiliation(s)
- Ho Won Jeong
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-707, South Korea
| | - Myeongju Kim
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital Healthcare Innovation Park, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, South Korea
| | - Han Gyeol Choi
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-707, South Korea
| | - Seong Yun Park
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-707, South Korea
| | - Yong Seuk Lee
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 463-707, South Korea.
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Zhu J, Lu Q, Liang T, Li H, Zhou C, Wu S, Chen T, Chen J, Deng G, Yao Y, Liao S, Yu C, Huang S, Sun X, Chen L, Chen W, Ye Z, Guo H, Chen W, Jiang W, Fan B, Tao X, Zhan X, Liu C. Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis. Rheumatol Ther 2022; 9:1377-1397. [PMID: 35932360 PMCID: PMC9510083 DOI: 10.1007/s40744-022-00481-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/21/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible. Methods We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort. Results Seven factors—erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)—were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%. Conclusion Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies. Supplementary Information The online version contains supplementary material available at 10.1007/s40744-022-00481-6. AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.
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Affiliation(s)
- Jichong Zhu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Qing Lu
- The First Affiliated Hospital of Guangxi, University of Science and Technology, Liuzhou, 540000, People's Republic of China
| | - Tuo Liang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Hao Li
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Chenxin Zhou
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Shaofeng Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Tianyou Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Jiarui Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Guobing Deng
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Yuanlin Yao
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Shian Liao
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Chaojie Yu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Shengsheng Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Xuhua Sun
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Liyi Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Wenkang Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Zhen Ye
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Hao Guo
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Wuhua Chen
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Wenyong Jiang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Binguang Fan
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Xiang Tao
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Xinli Zhan
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
| | - Chong Liu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
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Yan X, Wang L, Liang C, Zhang H, Zhao Y, Zhang H, Yu H, Di J. Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea. Front Neurosci 2022; 16:936946. [PMID: 35992917 PMCID: PMC9390335 DOI: 10.3389/fnins.2022.936946] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/13/2022] [Indexed: 11/15/2022] Open
Abstract
Background OSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model. Method We collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility. Results There are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ2 = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility. Conclusion The prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.
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Affiliation(s)
- Xiangru Yan
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Liying Wang
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Chunguang Liang
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
- *Correspondence: Chunguang Liang,
| | - Huiying Zhang
- Sleep Monitoring Center, The First Hospital of Jinzhou Medical University, Jinzhou, China
| | - Ying Zhao
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Hui Zhang
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Haitao Yu
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Jinna Di
- Respiratory Medicine, The Third Hospital of Jinzhou Medical University, Jinzhou, China
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Li R, Xue M, Ma Z, Qu C, Wang K, Zhang Y, Yue W, Zhang H, Tian H. Construction and validation of a nomogram for predicting prolonged air leak after minimally invasive pulmonary resection. World J Surg Oncol 2022; 20:249. [PMID: 35922824 PMCID: PMC9347096 DOI: 10.1186/s12957-022-02716-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background Prolonged air leak (PAL) remains one of the most frequent postoperative complications after pulmonary resection. This study aimed to develop a predictive nomogram to estimate the risk of PAL for individual patients after minimally invasive pulmonary resection. Methods Patients who underwent minimally invasive pulmonary resection for either benign or malignant lung tumors between January 2020 and December 2021 were included. All eligible patients were randomly assigned to the training cohort or validation cohort at a 3:1 ratio. Univariate and multivariate logistic regression were performed to identify independent risk factors. All independent risk factors were incorporated to establish a predictive model and nomogram, and a web-based dynamic nomogram was then built based on the logistic regression model. Nomogram discrimination was assessed using the receiver operating characteristic (ROC) curve. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curves. The nomogram was also evaluated for clinical utility by the decision curve analysis (DCA). Results A total of 2213 patients were finally enrolled in this study, among whom, 341 cases (15.4%) were confirmed to have PAL. The following eight independent risk factors were identified through logistic regression: age, body mass index (BMI), smoking history, percentage of the predicted value for forced expiratory volume in 1 second (FEV1% predicted), surgical procedure, surgical range, operation side, operation duration. The area under the ROC curve (AUC) was 0.7315 [95% confidence interval (CI): 0.6979–0.7651] for the training cohort and 0.7325 (95% CI: 0.6743–0.7906) for the validation cohort. The P values of the Hosmer-Lemeshow test were 0.388 and 0.577 for the training and validation cohorts, respectively, with well-fitted calibration curves. The DCA demonstrated that the nomogram was clinically useful. An operation interface on a web page (https://lirongyangql.shinyapps.io/PAL_DynNom/) was built to improve the clinical utility of the nomogram. Conclusion The nomogram achieved good predictive performance for PAL after minimally invasive pulmonary resection. Patients at high risk of PAL could be identified using this nomogram, and thus some preventive measures could be adopted in advance. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-022-02716-w.
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Affiliation(s)
- Rongyang Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Mengchao Xue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Zheng Ma
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Chenghao Qu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Kun Wang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Yu Zhang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Weiming Yue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Huiying Zhang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250000, Shandong, China.
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Gao Y, Liu X, Wang L, Wang S, Yu Y, Ding Y, Wang J, Ao H. Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting. Front Cardiovasc Med 2022; 9:881881. [PMID: 35966564 PMCID: PMC9366116 DOI: 10.3389/fcvm.2022.881881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesPostoperative major bleeding is a common problem in patients undergoing cardiac surgery and is associated with poor outcomes. We evaluated the performance of machine learning (ML) methods to predict postoperative major bleeding.MethodsA total of 1,045 patients who underwent isolated coronary artery bypass graft surgery (CABG) were enrolled. Their datasets were assigned randomly to training (70%) or a testing set (30%). The primary outcome was major bleeding defined as the universal definition of perioperative bleeding (UDPB) classes 3–4. We constructed a reference logistic regression (LR) model using known predictors. We also developed several modern ML algorithms. In the test set, we compared the area under the receiver operating characteristic curves (AUCs) of these ML algorithms with the reference LR model results, and the TRUST and WILL-BLEED risk score. Calibration analysis was undertaken using the calibration belt method.ResultsThe prevalence of postoperative major bleeding was 7.1% (74/1,045). For major bleeds, the conditional inference random forest (CIRF) model showed the highest AUC [0.831 (0.732–0.930)], and the stochastic gradient boosting (SGBT) and random forest models demonstrated the next best results [0.820 (0.742–0.899) and 0.810 (0.719–0.902)]. The AUCs of all ML models were higher than [0.629 (0.517–0.641) and 0.557 (0.449–0.665)], as achieved by TRUST and WILL-BLEED, respectively.ConclusionML methods successfully predicted major bleeding after cardiac surgery, with greater performance compared with previous scoring models. Modern ML models may enhance the identification of high-risk major bleeding subpopulations.
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Affiliation(s)
- Yuchen Gao
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaojie Liu
- Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lijuan Wang
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sudena Wang
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Yu
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yao Ding
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingcan Wang
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hushan Ao
- Department of Anesthesiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Hushan Ao,
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Koh KA, Montgomery AE, O'Brien RW, Kennedy CJ, Luedtke A, Sampson NA, Gildea SM, Hwang I, King AJ, Petriceks AH, Petukhova MV, Stein MB, Ursano RJ, Kessler RC. Predicting Homelessness Among U.S. Army Soldiers No Longer on Active Duty. Am J Prev Med 2022; 63:13-23. [PMID: 35725125 PMCID: PMC9219110 DOI: 10.1016/j.amepre.2021.12.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 12/23/2022]
Abstract
INTRODUCTION The ability to predict and prevent homelessness has been an elusive goal. The purpose of this study was to develop a prediction model that identified U.S. Army soldiers at high risk of becoming homeless after transitioning to civilian life based on information available before the time of this transition. METHODS The prospective cohort study consisted of observations from 16,589 soldiers who were separated or deactivated from service and who had previously participated in 1 of 3 baseline surveys of the Army Study to Assess Risk and Resilience in Servicemembers in 2011-2014. A machine learning model was developed in a 70% training sample and evaluated in the remaining 30% test sample to predict self-reported homelessness in 1 of 2 Longitudinal Study surveys administered in 2016-2018 and 2018-2019. Predictors included survey, administrative, and geospatial variables available before separation/deactivation. Analysis was conducted in November 2020-May 2021. RESULTS The 12-month prevalence of homelessness was 2.9% (SE=0.2%) in the total Longitudinal Study sample. The area under the receiver operating characteristic curve in the test sample was 0.78 (SE=0.02) for homelessness. The 4 highest ventiles (top 20%) of predicted risk included 61% of respondents with homelessness. Self-reported lifetime histories of depression, trauma of having a loved one murdered, and post-traumatic stress disorder were the 3 strongest predictors of homelessness. CONCLUSIONS A prediction model for homelessness can accurately target soldiers for preventive intervention before transition to civilian life.
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Affiliation(s)
- Katherine A Koh
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Boston Health Care for the Homeless Program, Boston, Massachusetts.
| | - Ann Elizabeth Montgomery
- Department of Health Behavior, School of Public Health, The University of Alabama at Birmingham, Birmingham, Alabama; VA Health Care System, Birmingham, U.S. Department of Veteran Affairs, Birmingham, Alabama
| | - Robert W O'Brien
- VA Health Services Research and Development Service, Washington, District of Columbia
| | - Chris J Kennedy
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, Washington; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Nancy A Sampson
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Sarah M Gildea
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Irving Hwang
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Andrew J King
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | | | - Maria V Petukhova
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Family Medicine & Public Health, University of California San Diego, San Diego, California
| | - Robert J Ursano
- Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
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Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders. Anesthesiology 2022; 137:55-66. [PMID: 35147666 PMCID: PMC9177553 DOI: 10.1097/aln.0000000000004139] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders. METHODS The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020. RESULTS Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit. CONCLUSIONS A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach. EDITOR’S PERSPECTIVE
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Zhang Z, Liu Y, Ma G, Su J. A Nomogram Model for Evaluating the Risk of Lymph Node Metastasis in cT2-cT4N0M0 Gastric Cancer Population. Med Sci Monit 2022; 28:e935696. [PMID: 35527384 PMCID: PMC9102730 DOI: 10.12659/msm.935696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/15/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy is an important treatment for advanced gastric cancer, but it has been unclear whether neoadjuvant chemotherapy is closely related to lymph node metastasis. Therefore, based on the disease characteristics of the cT2-cT4N0M0 gastric cancer population, this study established a nomogram prediction model of lymph node metastasis risk in this gastric cancer population to help clinicians optimize clinical decision-making. MATERIAL AND METHODS We analyzed the data of 336 patients with advanced gastric cancer with CT imaging stage of cT2-cT4N0M0 admitted to the Third Department of the Fourth Hospital of Hebei Medical University from 2015 to 2021. Combined with the results of univariate and multivariate logistic regression analysis, 7 indicators were selected to establish a nomogram prediction model. The calibration curves, ROC curves, and decision curves were drawn against the nomogram model using R language. RESULTS The results showed that the AUC value of the model and the external validation data set were 0.925 and 0.911, respectively. The P value of the Hosmer-Lemeshow test for the internal validation dataset was 0.082, and the P value of Hosmer-Lemeshow test for the external validation dataset was 0.076.The decision curve results showed that when the threshold probability was 0.1-0.9, this model could benefit patients by predicting the risk of lymph node metastasis in patients with advanced gastric cancer, and formulating appropriate treatment schemes accordingly. CONCLUSIONS This nomogram has shown good discrimination and fit, and can also be combined with imaging examination to screen the populations suitable for neoadjuvant chemotherapy, avoid the risk of misdiagnosis of N staging to the greatest extent, and to assist clinicians to optimize clinical decision-making.
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115
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Dang X, Xiong G, Fan C, He Y, Sun G, Wang S, Liu Y, Zhang L, Bao Y, Xu J, Du H, Deng D, Chen S, Li Y, Gong X, Wu Y, Wu J, Lin X, Qiao F, Zeng W, Feng L, Liu H. Systematic external evaluation of four preoperative risk prediction models for severe postpartum hemorrhage in patients with placenta previa: a multicenter retrospective study. J Gynecol Obstet Hum Reprod 2022; 51:102333. [DOI: 10.1016/j.jogoh.2022.102333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/19/2022] [Accepted: 02/02/2022] [Indexed: 10/19/2022]
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Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X, Tang K. Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front Oncol 2021; 11:740111. [PMID: 34765549 PMCID: PMC8576566 DOI: 10.3389/fonc.2021.740111] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/07/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives The aim of this study was to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting peritoneal metastasis (PM) of gastric cancer (GC). Methods In this study, a total of 355 patients (109PM+, 246PM-) who underwent preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images were retrospectively analyzed. According to a 7:3 ratio, patients were randomly divided into a training set and a validation set. Radiomics features and metabolic parameters data were extracted from PET images. The radiomics features were selected by logistic regression after using maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator (LASSO) method. The radiomics models were based on the rest of these features. The performance of the models was determined by their discrimination, calibration, and clinical usefulness in the training and validation sets. Results After dimensionality reduction, 12 radiomics feature parameters were obtained to construct radiomics signatures. According to the results of the multivariate logistic regression analysis, only carbohydrate antigen 125 (CA125), maximum standardized uptake value (SUVmax), and the radiomics signature showed statistically significant differences between patients (P<0.05). A radiomics model was developed based on the logistic analyses with an AUC of 0.86 in the training cohort and 0.87 in the validation cohort. The clinical prediction model based on CA125 and SUVmax was 0.76 in the training set and 0.69 in the validation set. The comprehensive model, which contained a rad-score and the clinical factor (CA125) as well as the metabolic parameter (SUVmax), showed promising performance with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort, respectively. The calibration curve showed the actual rate of the nomogram-predicted probability of peritoneal metastasis. Decision curve analysis (DCA) also demonstrated the good clinical utility of the radiomics nomogram. Conclusions The comprehensive model based on the rad-score and other factors (SUVmax, CA125) can provide a novel tool for predicting peritoneal metastasis of gastric cancer patients preoperatively.
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Affiliation(s)
- Beihui Xue
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jia Jiang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lei Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Sunjie Wu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xuan Zheng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiangwu Zheng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Ziobrowski HN, Kennedy CJ, Ustun B, House SL, Beaudoin FL, An X, Zeng D, Bollen KA, Petukhova M, Sampson NA, Puac-Polanco V, Lee S, Koenen KC, Ressler KJ, McLean SA, Kessler RC, Stevens JS, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Germine LT, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Lyons MS, Murty VP, McGrath ME, Pascual JL, Seamon MJ, Datner EM, Chang AM, Pearson C, Peak DA, Jambaulikar G, Merchant RC, Domeier RM, Rathlev NK, O'Neil BJ, Sergot P, Sanchez LD, Bruce SE, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Sheridan JF, Harte SE, Elliott JM, van Rooij SJH. Development and Validation of a Model to Predict Posttraumatic Stress Disorder and Major Depression After a Motor Vehicle Collision. JAMA Psychiatry 2021; 78:1228-1237. [PMID: 34468741 PMCID: PMC8411364 DOI: 10.1001/jamapsychiatry.2021.2427] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
IMPORTANCE A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions. OBJECTIVES To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later. DESIGN, SETTING, AND PARTICIPANTS The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021. MAIN OUTCOMES AND MEASURES The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE. RESULTS A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms. CONCLUSIONS AND RELEVANCE The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.
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Affiliation(s)
| | - Chris J. Kennedy
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Berk Ustun
- Halıcıoğlu Data Science Institute, University of California, San Diego
| | - Stacey L. House
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Francesca L. Beaudoin
- Department of Emergency Medicine & Department of Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, Rhode Island
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Kenneth A. Bollen
- Department of Psychology and Neuroscience & Department of Sociology, University of North Carolina at Chapel Hill
| | - Maria Petukhova
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Nancy A. Sampson
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Victor Puac-Polanco
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Sue Lee
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Karestan C. Koenen
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Kerry J. Ressler
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts,Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts
| | - Samuel A. McLean
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill,Department of Emergency Medicine, University of North Carolina at Chapel Hill
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | | | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California, San Francisco
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.,Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill
| | - Laura T Germine
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, Massachusetts.,The Many Brains Project, Belmont, Massachusetts
| | - Scott L Rauch
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, Massachusetts.,Department of Psychiatry, McLean Hospital, Belmont, Massachusetts
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, New Jersey
| | - Brittany E Punches
- Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio.,College of Nursing, University of Cincinnati, Cincinnati, Ohio.,Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Michael S Lyons
- Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio.,Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Vishnu P Murty
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, Massachusetts
| | - Jose L Pascual
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia.,Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Mark J Seamon
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Elizabeth M Datner
- Department of Emergency Medicine, Einstein Healthcare Network, Philadelphia, Pennsylvania.,Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, Pennsylvania
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Detroit, Michigan
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston
| | | | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, Michigan
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit, Michigan
| | - Paulina Sergot
- McGovern Medical School, University of Texas Health Science Center, Houston
| | - Leon D Sanchez
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.,Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri, St Louis
| | - Robert H Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, Veterans Affairs Connecticut Healthcare System, West Haven.,Department of Psychiatry, Yale School of Medicine, West Haven, Connecticut
| | - Jutta Joormann
- Department of Psychology, Yale University, West Haven, Connecticut
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University, St Louis, Missouri
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.,Division of Depression and Anxiety, McLean Hospital, Belmont, Massachusetts.,Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - John F Sheridan
- Department of Biosciences and Neuroscience, Wexner Medical Center, The Ohio State University, Columbus.,Institute for Behavioral Medicine Research, Wexner Medical Center, The Ohio State University, Columbus
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor.,Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor
| | - James M Elliott
- Kolling Institute of Medical Research, University of Sydney, St Leonards, New South Wales, Australia.,Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, New South Wales, Australia.,Department of Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
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Affiliation(s)
- Brook I Martin
- Departments of Orthopaedics and Population Health Sciences, University of Utah, Salt Lake City, UT, USA.
| | - Christopher M Bono
- Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, USA; The Spine Journal, North American Spine Society, 7075 Veterans Boulevard, Burr Ridge, IL, USA
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