Abstract
Background: Acute paraquat (PQ) poisoning is characterized by multi-organ failure and lacking effective therapies. Therefore, identifying risk factors and developing model that could predict early prognosis for patients with PQ poisoning is of great importance. Methods: This was a retrospective cohort study employed with patients suffered from acute PQ poisoning (n=1199). Patients (n=913) with PQ poisoning from 2011 to 2018 were randomly divided into 2 mutually exclusive groups: training (609 patients) and test (304 patients). Another 2 external cohorts containing 207 cases from Zhengzhou 2019 were used as validation from different time and 79 from Shenyang as validation from different site. Risk factors were identified by a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated by a latent class analysis. The prediction score of this model was developed based on the training sample and was further evaluated using the testing and validation samples. Findings: Eight risk factors including age, ingestion volume, 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 a C statistic 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 a predictive range of 4.6%-98.2%, 2.3%-94.9% and 0%-12.5% for the test, validation_time and validation_site group, respectively. In the training group, 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. Interpretation: Eight risk factors were identified in this study. And we developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This simple and reliable risk score system could be helpful in recognizing high-risk patients and reducing in-hospital death rate due to PQ poisoning. Funding Statement: This work was supported by the National Natural Science Foundation of China (81701893 and 81600506), National S&T Major Project of China (2018ZX10301201-008), Key Scientific Research Projects of Higher Education Institutions in Henan Province (20A320046 and 20A320056), Joint Construction Project of Henan Province Medical S&T Research (SB201901006). Declaration of Interests: All authors declare no conflict of interest. Ethics Approval Statement: The Institutional Review Board of the First Affiliated Hospital of Zhengzhou University (2017-XY-002) approved this study and exemption of the patient’s consent.
Highlights
As a non-selective contact herbicide, paraquat (PQ) is environmental harmless and predominantly used in developing agricultural countries [1, 2]
Several prognostic score systems have been reported to predict the clinical outcomes of patients with PQ poisoning, the major ones being the Acute Physiology and Chronic Health Evaluation II (APACHE II) score [7], Sequential Organ Failure Assessment (SOFA) score [8], the severity index of PQ poisoning (SIPP) [9], Poisoning Severity Score (PSS) [10] and some equations and nomograms based on large cohort study [11,12,13]
To facilitate the calculation of risk score, we examined the nonlinear relationship of each continuous factor with the outcome and categorized it by a cut-off point taking into account both in-hospital death rates and sample sizes (Additional file: Figure S)
Summary
As a non-selective contact herbicide, paraquat (PQ) is environmental harmless and predominantly used in developing agricultural countries [1, 2]. Several prognostic score systems have been reported to predict the clinical outcomes of patients with PQ poisoning, the major ones being the Acute Physiology and Chronic Health Evaluation II (APACHE II) score [7], Sequential Organ Failure Assessment (SOFA) score [8], the severity index of PQ poisoning (SIPP) [9], Poisoning Severity Score (PSS) [10] and some equations and nomograms based on large cohort study [11,12,13]. Most of them are more suitable for critically ill patients instead of minimally poisoned or early-stage patients who showed mild symptoms. It is highly possible for these score systems failed to predict mortality and conduct risk assessment for PQ poisoned patients instantly because of their difficult calculation or unavailable laboratory tests, which cannot meet emergency work demand. Identifying risk factors and developing model that could predict early prognosis for patients with PQ poisoning is of great importance
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