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Wang Y, Zhao Y, Shi C, Li J, Huang X. Development and Validation of a Nomogram to Predict the Risk of Special Uterine Leiomyoma Pathological Types or Leiomyosarcoma in Postmenopausal Women: A Retrospective Study. Risk Manag Healthc Policy 2024; 17:1669-1685. [PMID: 38919406 PMCID: PMC11198023 DOI: 10.2147/rmhp.s461773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
Purpose The aim of this study was to investigate the risk factors of postmenopausal special uterine leiomyoma pathological types or leiomyosarcoma and to develop a nomogram for clinical risk assessment, ultimately to reduce unnecessary surgical interventions and corresponding economic expenses. Methods A total of 707 patients with complete information were enrolled from 1 August 2012 to 1 August 2022. Univariate and multivariate logistic regression models were used to analyse the association between variables and special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. A nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients was developed and validated by bootstrap resampling. The calibration curve was used to assess the accuracy of the model and receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were compared with the clinical experience model. Results The increasing trend after menopause, the diameter of the largest uterine fibroid, serum carcinoembryonic antigen 125 concentration, Serum neutrophil to lymphocyte ratio, and Serum phosphorus ion concentration were independent risk factors for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. We developed a user-friendly nomogram which showed good diagnostic performance (AUC=0.724). The model was consistent and the calibration curve of our cohort was close to the ideal diagonal line. DCA indicated that the model has potential value for clinical application. Furthermore, our model was superior to the previous clinical experience model in terms of ROC and DCA. Conclusion We have developed a prediction nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. This nomogram could serve as an important warning signal and evaluation method for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients.
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Affiliation(s)
- Yaping Wang
- Zhejiang University, Womens Hospital, Sch Med, Department Obstet & Gynecol, Hangzhou, Zhejiang, People’s Republic of China
| | - Yiyi Zhao
- Zhejiang University, Womens Hospital, Sch Med, Department Obstet & Gynecol, Hangzhou, Zhejiang, People’s Republic of China
| | - Chaolu Shi
- Cixi maternity&health Care Hospital, Department Obstet & Gynecol Ningbo, Ningbo, Zhejiang, People’s Republic of China
| | - Juanqing Li
- Zhejiang University, Womens Hospital, Sch Med, Department Obstet & Gynecol, Hangzhou, Zhejiang, People’s Republic of China
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China
| | - Xiufeng Huang
- Zhejiang University, Womens Hospital, Sch Med, Department Obstet & Gynecol, Hangzhou, Zhejiang, People’s Republic of China
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China
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Wei Z, Wang X, Feng H, Ji F, Bai D, Dong X, Huang W. Isothermal nucleic acid amplification technology for rapid detection of virus. Crit Rev Biotechnol 2022; 43:415-432. [PMID: 35156471 DOI: 10.1080/07388551.2022.2030295] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
While the research field and industrial market of in vitro diagnosis (IVD) thrived during and post the COVID-19 pandemic, the development of isothermal nucleic acid amplification test (INAAT) based rapid diagnosis was engendered in a global wised large measure as a problem-solving exercise. This review systematically analyzed the recent advances of INAAT strategies with practical case for the real-world scenario virus detection applications. With the qualities that make INAAT systems useful for making diagnosis relevant decisions, the key performance indicators and the cost-effectiveness of enzyme-assisted methods and enzyme-free methods were compared. The modularity of nucleic acid amplification reactions that can lead to thresholding signal amplifications using INAAT reagents and their methodology design were examined, alongside the potential application with rapid test platform/device integration. Given that clinical practitioners are, by and large, unaware of many the isothermal nucleic acid test advances. This review could bridge the arcane research field of different INAAT systems and signal output modalities with end-users in clinic when choosing suitable test kits and/or methods for rapid virus detection.
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Affiliation(s)
- Zhenting Wei
- Frontiers Science Center for Flexible Electronics (FSCFE), Institute of Flexible Electronics (IFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Xi'an Key Laboratory of Special Medicine and Health Engineering, Northwestern Polytechnical University, Xi'an, China
- North Sichuan Medical College, Nanchong, China
| | - Xiaowen Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Institute of Flexible Electronics (IFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Xi'an Key Laboratory of Special Medicine and Health Engineering, Northwestern Polytechnical University, Xi'an, China
- North Sichuan Medical College, Nanchong, China
| | - Huhu Feng
- Frontiers Science Center for Flexible Electronics (FSCFE), Institute of Flexible Electronics (IFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Xi'an Key Laboratory of Special Medicine and Health Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Fanpu Ji
- Department of Infectious Diseases, The 2nd Hospital of Xi'an Jiaotong University, Nanchong, China
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The 2nd Hospital of Xi'an Jiaotong University, Nanchong, China
- Division of Gastroenterology and Hepatology, Stanford University Medical Center, Nanchong, China
| | - Dan Bai
- Frontiers Science Center for Flexible Electronics (FSCFE), Institute of Flexible Electronics (IFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Xi'an Key Laboratory of Special Medicine and Health Engineering, Northwestern Polytechnical University, Xi'an, China
- Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Northwestern Polytechnical University, Nanchong, China
| | - Xiaoping Dong
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Nanchong, China
- State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Nanchong, China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics (FSCFE), Institute of Flexible Electronics (IFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Xi'an Key Laboratory of Special Medicine and Health Engineering, Northwestern Polytechnical University, Xi'an, China
- Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Northwestern Polytechnical University, Nanchong, China
- Institute of Advanced Materials (IAM), Nanjing Tech University, Nanchong, China
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Ma X, Wang H, Huang J, Geng Y, Jiang S, Zhou Q, Chen X, Hu H, Li W, Zhou C, Gao X, Peng N, Deng Y. A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients. BMC Infect Dis 2020; 20:899. [PMID: 33256643 PMCID: PMC7702207 DOI: 10.1186/s12879-020-05614-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 11/11/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. METHODS COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ2-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. RESULTS A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). CONCLUSIONS A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.
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Affiliation(s)
- Xiaojun Ma
- Department of Infectious Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong Province, China
| | - Huifang Wang
- Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China
| | - Junwei Huang
- Departments of Respiratory and Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Zhongshan 2nd Road NO.106, Guangzhou, 510080, Guangdong, China
| | - Yan Geng
- Department of Digestive, NO. 923 Hospital of Joint Service Supporting Force, Nanning, 530021, Guangxi, China
| | - Shuqi Jiang
- School of Medicine, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, 510006, Guangdong, China
| | - Qiuping Zhou
- School of Medicine, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, 510006, Guangdong, China
| | - Xuan Chen
- Shantou University Medical College, 243 Daxue Road, Shantou, 5105063, Guangdong, China
| | - Hongping Hu
- Department of Emergency, Wuhan Hankou Hospital, 2273 Jiefang Avenue, Wuhan, 430010, Hubei, China
| | - Weifeng Li
- Department of Emergency and Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chengbin Zhou
- Department of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China
| | - Xinglin Gao
- Departments of Respiratory and Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Zhongshan 2nd Road NO.106, Guangzhou, 510080, Guangdong, China
| | - Na Peng
- Department of Critical Care Medicine, General Hospital of Southern Theater Command of PLA, Guangzhou, 510010, Guangdong, China.
- China Department of Critical Care Medicine, Huo Shenshan Hospital of Wuhan, Wuhan, 430199, Hubei, China.
| | - Yiyu Deng
- Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China.
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