A novel simple risk model to predict the prognosis of patients with paraquat poisoning

Yanxia Gao,Zhigao Xu,Guoyu Duan,Tiegang Li,Ding Yuan,Linlin Hou,Yan Zhang,Pei Sun,Zhigang Ren,Yi Li,Changhua Sun,Yibo Wang,Lu Che,Sujuan Li,Liwen Liu

doi:10.1038/s41598-020-80371-5

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.

Highlights

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)
Several systems have been reported to predict the prognosis of patients with PQ poisoning, including the Acute Physiology and Chronic Health Evaluation II (APACHE II) s core[7], Sequential Organ Failure Assessment (SOFA) s core[8], the Severity Index of PQ Poisoning (SIPP)[9], Poisoning Severity Score (PSS)[10], and several equations and nomograms based on large cohort studies[11,12,13]
As described p reviously[15], to facilitate the use of the selected risk factors and the risk model, we developed a simple risk score for each patient based on the regression coefficients estimated from the risk model with the training sample

Summary

Introduction

Several systems have been reported to predict the prognosis of patients with PQ poisoning, including the Acute Physiology and Chronic Health Evaluation II (APACHE II) s core[7], Sequential Organ Failure Assessment (SOFA) s core[8], the Severity Index of PQ Poisoning (SIPP)[9], Poisoning Severity Score (PSS)[10], and several equations and nomograms based on large cohort studies[11,12,13] Most of these models are suitable for critically ill patients rather than patients with minimal exposure or early-stage patients with mild symptoms. This study describes a well-designed and easy to administer tool for predicting in-hospital death of PQ poisoning patients using a combination of simple and clinically relevant variables

Methods

Results

Discussion

Conclusion