TY - JOUR
PY - 2021//
TI - A novel simple risk model to predict the prognosis of patients with paraquat poisoning
JO - Scientific reports
A1 - Liu, Liwen
A1 - Gao, Yanxia
A1 - Ren, Zhigang
A1 - Li, Yi
A1 - Sun, Pei
A1 - Li, Sujuan
A1 - Che, Lu
A1 - Sun, Changhua
A1 - Duan, Guoyu
A1 - Zhang, Yan
A1 - Hou, Linlin
A1 - Xu, Zhigao
A1 - Wang, Yibo
A1 - Yuan, Ding
A1 - Li, Tiegang
SP - e237
EP - e237
VL - 11
IS - 1
N2 - 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. Language: en
LA - en
SN - 2045-2322
UR - http://dx.doi.org/10.1038/s41598-020-80371-5
ID - ref1
ER -