A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer
Abstract Background Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high‐risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patient...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | Thoracic Cancer |
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| Online Access: | https://doi.org/10.1111/1759-7714.15219 |
| _version_ | 1797335296401997824 |
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| author | Yan Wang Qiuyue Li Yanjun Zhou Yiting Dong Jinping Li Tao Liang |
| author_facet | Yan Wang Qiuyue Li Yanjun Zhou Yiting Dong Jinping Li Tao Liang |
| author_sort | Yan Wang |
| collection | DOAJ |
| description | Abstract Background Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high‐risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management. Methods Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748). Results Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D‐dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation. Conclusion VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models. |
| first_indexed | 2024-03-08T08:35:55Z |
| format | Article |
| id | doaj.art-0e061140c81f45ff8d979c281b7a10ce |
| institution | Directory Open Access Journal |
| issn | 1759-7706 1759-7714 |
| language | English |
| last_indexed | 2024-03-08T08:35:55Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Thoracic Cancer |
| spelling | doaj.art-0e061140c81f45ff8d979c281b7a10ce2024-02-02T01:11:27ZengWileyThoracic Cancer1759-77061759-77142024-02-0115427728510.1111/1759-7714.15219A systematic review of risk prediction model of venous thromboembolism for patients with lung cancerYan Wang0Qiuyue Li1Yanjun Zhou2Yiting Dong3Jinping Li4Tao Liang5State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaSchool of Nursing Peking Union Medical College Beijing ChinaDepartment of Nursing, Beijing Children's Hospital Capital Medical University, National Center for Children's Health Beijing ChinaState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing ChinaSchool of Nursing Peking Union Medical College Beijing ChinaAbstract Background Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high‐risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management. Methods Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748). Results Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D‐dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation. Conclusion VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.https://doi.org/10.1111/1759-7714.15219lung cancerrisk prediction modelsystematic reviewvenous thromboembolism |
| spellingShingle | Yan Wang Qiuyue Li Yanjun Zhou Yiting Dong Jinping Li Tao Liang A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer Thoracic Cancer lung cancer risk prediction model systematic review venous thromboembolism |
| title | A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer |
| title_full | A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer |
| title_fullStr | A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer |
| title_full_unstemmed | A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer |
| title_short | A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer |
| title_sort | systematic review of risk prediction model of venous thromboembolism for patients with lung cancer |
| topic | lung cancer risk prediction model systematic review venous thromboembolism |
| url | https://doi.org/10.1111/1759-7714.15219 |
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