A novel simple risk model to predict the prognosis of patients with paraquat poisoning
Abstract To identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into tr...
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Nature Portfolio
2021
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oai:doaj.org-article:b23193d9201a41959bcaf72a4ec934ca2021-12-02T15:12:55ZA novel simple risk model to predict the prognosis of patients with paraquat poisoning10.1038/s41598-020-80371-52045-2322https://doaj.org/article/b23193d9201a41959bcaf72a4ec934ca2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80371-5https://doaj.org/toc/2045-2322Abstract To identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into training (n = 609) and test (n = 304) samples. Another two independent cohorts were used as validation samples for a different time (n = 207) and site (n = 79). Risk factors were identified using a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated using a latent class analysis. The prediction score was developed based on the training sample and was evaluated using the testing and validation samples. Eight factors, including age, ingestion volume, creatine kinase-MB [CK-MB], platelet [PLT], white blood cell [WBC], neutrophil counts [N], gamma-glutamyl transferase [GGT], and serum creatinine [Cr] were identified as independent risk indicators of in-hospital death events. The risk model had C statistics of 0.895 (95% CI 0.855–0.928), 0.891 (95% CI 0.848–0.932), and 0.829 (95% CI 0.455–1.000), and predictive ranges of 4.6–98.2%, 2.3–94.9%, and 0–12.5% for the test, validation_time, and validation_site samples, respectively. In the training sample, the risk model classified 18.4%, 59.9%, and 21.7% of patients into the high-, average-, and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. We developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This risk scoring system could be helpful for identifying high-risk patients and reducing mortality due to PQ poisoning.Yanxia GaoLiwen LiuTiegang LiDing YuanYibo WangZhigao XuLinlin HouYan ZhangGuoyu DuanChanghua SunLu CheSujuan LiPei SunYi LiZhigang RenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) |
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EN |
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Medicine R Science Q |
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Medicine R Science Q Yanxia Gao Liwen Liu Tiegang Li Ding Yuan Yibo Wang Zhigao Xu Linlin Hou Yan Zhang Guoyu Duan Changhua Sun Lu Che Sujuan Li Pei Sun Yi Li Zhigang Ren A novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| description |
Abstract To identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into training (n = 609) and test (n = 304) samples. Another two independent cohorts were used as validation samples for a different time (n = 207) and site (n = 79). Risk factors were identified using a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated using a latent class analysis. The prediction score was developed based on the training sample and was evaluated using the testing and validation samples. Eight factors, including age, ingestion volume, creatine kinase-MB [CK-MB], platelet [PLT], white blood cell [WBC], neutrophil counts [N], gamma-glutamyl transferase [GGT], and serum creatinine [Cr] were identified as independent risk indicators of in-hospital death events. The risk model had C statistics of 0.895 (95% CI 0.855–0.928), 0.891 (95% CI 0.848–0.932), and 0.829 (95% CI 0.455–1.000), and predictive ranges of 4.6–98.2%, 2.3–94.9%, and 0–12.5% for the test, validation_time, and validation_site samples, respectively. In the training sample, the risk model classified 18.4%, 59.9%, and 21.7% of patients into the high-, average-, and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. We developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This risk scoring system could be helpful for identifying high-risk patients and reducing mortality due to PQ poisoning. |
| format |
article |
| author |
Yanxia Gao Liwen Liu Tiegang Li Ding Yuan Yibo Wang Zhigao Xu Linlin Hou Yan Zhang Guoyu Duan Changhua Sun Lu Che Sujuan Li Pei Sun Yi Li Zhigang Ren |
| author_facet |
Yanxia Gao Liwen Liu Tiegang Li Ding Yuan Yibo Wang Zhigao Xu Linlin Hou Yan Zhang Guoyu Duan Changhua Sun Lu Che Sujuan Li Pei Sun Yi Li Zhigang Ren |
| author_sort |
Yanxia Gao |
| title |
A novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| title_short |
A novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| title_full |
A novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| title_fullStr |
A novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| title_full_unstemmed |
A novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| title_sort |
novel simple risk model to predict the prognosis of patients with paraquat poisoning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/b23193d9201a41959bcaf72a4ec934ca |
| work_keys_str_mv |
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