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Qiu J, Yan M, Wang H, Liu Z, Wang G, Wu X, Gao Q, Hu H, Chen J, Dai Y. Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study. Front Med (Lausanne) 2023; 10:1202486. [PMID: 37601775 PMCID: PMC10433756 DOI: 10.3389/fmed.2023.1202486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/12/2023] [Indexed: 08/22/2023] Open
Abstract
Obstructive To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics. Methods A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models. Results 1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models. Conclusion Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.
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Affiliation(s)
- Junliang Qiu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Minbo Yan
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Haojie Wang
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zicheng Liu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Guojie Wang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xianbo Wu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Qindong Gao
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Hongji Hu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Junyong Chen
- Department of Urology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, Guangdong, China
| | - Yingbo Dai
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
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Anastasiadis A, Koudonas A, Langas G, Tsiakaras S, Memmos D, Mykoniatis I, Symeonidis EN, Tsiptsios D, Savvides E, Vakalopoulos I, Dimitriadis G, de la Rosette J. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian J Urol 2023; 10:258-274. [PMID: 37538159 PMCID: PMC10394286 DOI: 10.1016/j.ajur.2023.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/23/2022] [Accepted: 02/10/2023] [Indexed: 08/05/2023] Open
Abstract
Objective To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease. Methods A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language. Results A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. Conclusion AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
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Affiliation(s)
- Anastasios Anastasiadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Antonios Koudonas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Langas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Stavros Tsiakaras
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Memmos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Ioannis Mykoniatis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Evangelos N. Symeonidis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Tsiptsios
- Neurology Department, Democritus University of Thrace, Alexandroupolis, Greece
| | | | - Ioannis Vakalopoulos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Dimitriadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Jean de la Rosette
- Department of Urology, Istanbul Medipol Mega University Hospital, Istanbul, Turkey
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Wang Z, Yang G, Wang X, Cao Y, Jiao W, Niu H. A combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy. Urolithiasis 2023; 51:37. [PMID: 36745218 DOI: 10.1007/s00240-023-01405-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 02/07/2023]
Abstract
The aim of this study was to develop a CT-based radiomics and clinical variable diagnostic model for the preoperative prediction of uric acid calculi. In this retrospective study, 370 patients with urolithiasis who underwent preoperative urinary CT scans were enrolled. The CT images of each patient were manually segmented, and radiomics features were extracted. Sixteen radiomics features were selected by one-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to model the selected features, and the model with the best performance was selected. Multivariate logistic regression was used to screen out significant clinical variables, and the radiomics features and clinical variables were combined to construct a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), etc., were used to evaluate the diagnostic performance of the model. Among the three machine learning models, the LR model had the best performance and good robustness of the dataset. Therefore, the LR model was used to construct the nomogram. The AUCs of the nomogram model in the training set and validation set were 0.878 and 0.867, respectively, which were significantly higher than those of the radiomics model and the clinical feature model. The CT-based radiomics model based has good performance in distinguishing uric acid stones from nonuric acid stones, and the nomogram model has the best diagnostic performance among the three models. This model can provide an effective reference for clinical decision-making.
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Affiliation(s)
- Zijie Wang
- Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China
| | - Guangjie Yang
- PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xinning Wang
- Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China
| | - Yuanchao Cao
- Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China
| | - Wei Jiao
- Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China.
| | - Haitao Niu
- Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China.
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Kaviani P, Primak A, Bizzo B, Ebrahimian S, Saini S, Dreyer KJ, Kalra MK. Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT. Jpn J Radiol 2023; 41:194-200. [PMID: 36331701 DOI: 10.1007/s11604-022-01349-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE Knowledge of kidney stone composition can help in patient management; urine composition analysis and dual-energy CT are frequently used to assess stone type. We assessed if threshold-based stone segmentation and radiomics can determine the composition of kidney stones from single-energy, non-contrast abdomen-pelvis CT. METHODS With IRB approval, we identified 218 consecutive patients (mean age 64 ± 13 years; male:female 138:80) with the presence of kidney stones on non-contrast, abdomen-pelvis CT and surgical or biochemical proof of their stone composition. CT examinations were performed on one of the seven multidetector-row scanners from four vendors (GE, Philips, Siemens, Toshiba). Deidentified CT images were processed with a radiomics prototype (Frontier, Siemens Healthineers) to segment the entire kidney volumes with an AI-based organ segmentation tool. We applied a threshold of 130 HU to isolate stones in the segmented kidneys and to estimate radiomics over the segmented stone volume. A coinvestigator verified kidney stone segmentation and adjusted the volume of interest to include the entire stone volume when necessary. We applied multiple logistic regression tests with precision recall plots to obtain area under the curve (AUC) using a built-in R statistical program. RESULTS The threshold-based stone segmentation successfully isolated kidney stones (uric acid: n = 102 patients, calcium oxalate/phosphate: n = 116 patients) in all patients. Radiomics differentiated between calcium and uric acid stones with an AUC of 0.78 (p < 0.01, 95% CI 0.73-0.83), 0.79 sensitivity, and 0.90 specificity regardless of CT vendors (GE CT: AUC = 0.82, p < 0.01, 95% CI 0.740-0896; Siemens CT: AUC = 0.77, 95% CI 0.700-0.846, p < 0.01). CONCLUSION Automated threshold-based stone segmentation and radiomics can differentiate between calcium oxalate/phosphate and urate stones from non-contrast, single-energy abdomen CT.
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Affiliation(s)
- Parisa Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA
| | - Andrew Primak
- Siemens Medical Solutions USA Inc, Malvern, PA, 19355, USA
| | - Bernardo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA.,MGH and BWH Center for Clinical Data Science, Boston, USA
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA
| | - Sanjay Saini
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA
| | - Keith J Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA.,MGH and BWH Center for Clinical Data Science, Boston, USA.,Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Boston, MA, 02114, USA
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA.
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Wang Y, Feng S. A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes. Medicine (Baltimore) 2022; 101:e30578. [PMID: 36181047 PMCID: PMC9524964 DOI: 10.1097/md.0000000000030578] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
To establish a prediction model for the 30-day mortality in sepsis patients. The data of 1185 sepsis patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) and all participants were randomly divided into the training set (n = 829) and the testing set (n = 356). The model was established in the training set and verified in the testing set. After standardization of the data, age, gender, input, output, and variables with statistical difference between the survival group and the death group in the training set were involved in the extreme gradient boosting (XGBoost) model. Subgroup analysis was performed concerning age and gender in the testing set. In the XGBoost model with variables related to intravenous (IV) fluid management and electrolytes for the 30-day mortality of sepsis patients, the area under the curve (AUC) was 0.868 (95% confidence interval [CI]: 0.867-0.869) in the training set and 0.781 (95% CI: 0.779-0.782) in the testing set. The sensitivity was 0.815 (95% CI: 0.774-0.857) in the training set and 0.755 (95% CI: 0.686-0.825) in the testing set. The specificity was 0.761 (95% CI: 0.723-0.798) in the training set, and 0.737 (95% CI: 0.677-0.797) in the testing set. In the XGBoost forest model without variables related to IV fluid management and electrolytes for the 30-day mortality of sepsis patients, in the training set, the AUC was 0.830 (95% CI: 0.829-0.831), the sensitivity was 0.717 (95% CI: 0.669-0.765), the specificity was 0.797 (95% CI: 0.762-0.833), and the accuracy was 0.765 (95% CI: 0.736-0.794). In the testing set, the AUC was 0.751 (95% CI: 0.750-0.753), the sensitivity was 0.612 (95% CI: 0.533-0.691), the specificity was 0.756 (95% CI: 0.698-0.814), and the accuracy was 0.697(95% CI: 0.649-0.744). The prediction model including variables associated with IV fluids and electrolytes had good predictive value for the 30-day mortality of sepsis patients.
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Affiliation(s)
- Yan Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Songqiao Feng
- Department of Critical Care Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Songqiao Feng, Department of Critical Care Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China (e-mail: )
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Lim EJ, Castellani D, So WZ, Fong KY, Li JQ, Tiong HY, Gadzhiev N, Heng CT, Teoh JYC, Naik N, Ghani K, Sarica K, De La Rosette J, Somani B, Gauhar V. Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions. J Clin Med 2022; 11:jcm11175151. [PMID: 36079078 PMCID: PMC9457189 DOI: 10.3390/jcm11175151] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 08/29/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of various urological conditions. Urolithiasis is a common benign condition with a high incidence and recurrence rate. The purpose of this scoping review is to evaluate the current evidence of the application of radiomics in urolithiasis, especially its utility in diagnostics and therapeutics. An electronic literature search on radiomics in the setting of urolithiasis was conducted on PubMed, EMBASE, and Scopus from inception to 21 March 2022. A total of 7 studies were included. Radiomics has been successfully applied in the field of urolithiasis to differentiate phleboliths from calculi and classify stone types and composition pre-operatively. More importantly, it has also been utilized to predict outcomes and complications after endourological procedures. Although radiomics in urolithiasis is still in its infancy, it has the potential for large-scale implementation. Its greatest potential lies in the correlation with conventional established diagnostic and therapeutic factors.
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Affiliation(s)
- Ee Jean Lim
- Department of Urology, Singapore General Hospital, Singapore 169608, Singapore
- Correspondence: ; Tel.: +65-6321-4693
| | - Daniele Castellani
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica Delle Marche, 60126 Ancona, Italy
| | - Wei Zheng So
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Khi Yung Fong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Jing Qiu Li
- Department of Urology, Singapore General Hospital, Singapore 169608, Singapore
| | - Ho Yee Tiong
- Department of Urology, National University Hospital, Singapore 119074, Singapore
| | - Nariman Gadzhiev
- Department of Urology, Saint-Petersburg State University Hospital, 199034 St. Petersburg, Russia
| | - Chin Tiong Heng
- Department of Urology, Ng Teng Fong Hospital, Singapore 609606, Singapore
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Khurshid Ghani
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kemal Sarica
- Department of Urology, Biruni University, 34010 Istanbul, Turkey
| | - Jean De La Rosette
- Istanbul Medipol University, TEM Avrupa Otoyolu Goztepe Cikisi No: 1, 34010 Istanbul, Turkey
| | - Bhaskar Somani
- Department of Urology, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Vineet Gauhar
- Department of Urology, Ng Teng Fong Hospital, Singapore 609606, Singapore
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Yu Q, Liu J, Lin H, Lei P, Fan B. Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith. Int J Clin Pract 2022; 2022:5478908. [PMID: 36474549 PMCID: PMC9678460 DOI: 10.1155/2022/5478908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/24/2022] [Accepted: 11/05/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. METHOD Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. RESULTS The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model. CONCLUSIONS The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.
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Affiliation(s)
- Qiuyue Yu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550000, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
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Chen L, Chen S. Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge. BMC Pulm Med 2021; 21:320. [PMID: 34654406 PMCID: PMC8518323 DOI: 10.1186/s12890-021-01692-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/20/2021] [Indexed: 12/12/2022] Open
Abstract
Background To investigate the risk factors and construct a logistic model and an extreme gradient boosting (XGBoost) model to compare the predictive performances for readmission in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients within one year. Methods In total, 636 patients with AECOPD were recruited and divided into readmission group (n = 449) and non-readmission group (n = 187). Backward stepwise regression method was used to analyze the risk factors for readmission. Data were divided into training set and testing set at a ratio of 7:3. Variables with statistical significance were included in the logistic model and variables with P < 0.1 were included in the XGBoost model, and receiver operator characteristic (ROC) curves were plotted. Results Patients with acute exacerbations within the previous 1 year [odds ratio (OR) = 4.086, 95% confidence interval (CI) 2.723–6.133, P < 0.001), long-acting β agonist (LABA) application (OR = 4.550, 95% CI 1.587–13.042, P = 0.005), inhaled corticosteroids (ICS) application (OR = 0.227, 95% CI 0.076–0.672, P = 0.007), glutamic-pyruvic transaminase (ALT) level (OR = 0.985, 95% CI 0.971–0.999, P = 0.042), and total CAT score (OR = 1.091, 95% CI 1.048–1.136, P < 0.001) were associated with the risk of readmission. The AUC value of the logistic model was 0.743 (95% CI 0.692–0.795) in the training set and 0.699 (95% CI 0.617–0.780) in the testing set. The AUC value of XGBoost model was 0.814 (95% CI 0.812–0.815) in the training set and 0.722 (95% CI 0.720–0.725) in the testing set. Conclusions The XGBoost model showed a better predictive value in predicting the risk of readmission within one year in the AECOPD patients than the logistic regression model. The findings of our study might help identify patients with a high risk of readmission within one year and provide timely treatment to prevent the reoccurrence of AECOPD. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-021-01692-3.
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Affiliation(s)
- Lili Chen
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Nanjing Medical University, Jiangjiayuan 121#, Gulou District, Nanjing, 210000, Jiangsu, China
| | - Shiping Chen
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Nanjing Medical University, Jiangjiayuan 121#, Gulou District, Nanjing, 210000, Jiangsu, China.
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