@article{ref1,
title="A novel simple risk model to predict the prognosis of patients with paraquat poisoning",
journal="Scientific reports",
year="2021",
author="Liu, Liwen and Gao, Yanxia and Ren, Zhigang and Li, Yi and Sun, Pei and Li, Sujuan and Che, Lu and Sun, Changhua and Duan, Guoyu and Zhang, Yan and Hou, Linlin and Xu, Zhigao and Wang, Yibo and Yuan, Ding and Li, Tiegang",
volume="11",
number="1",
pages="e237-e237",
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.<p /> <p>Language: en</p>",
language="en",
issn="2045-2322",
doi="10.1038/s41598-020-80371-5",
url="http://dx.doi.org/10.1038/s41598-020-80371-5"
}