Construction of a risk prediction model of diquat poisoning based on clinical indicators.

Abstract

This study aims to explore the clinical characteristics and prognostic factors in patients with diquat (DQ) poisoning and to develop a clinical risk assessment model to improve diagnosis and treatment strategies. Data from 60 patients with DQ poisoning, including basic characteristics, poisoning severity, and inflammatory response indicators, were collected. The plasma concentration of DQ was measured using liquid chromatography-mass spectrometry. The included patients were categorized into survival and death groups based on their 30-day outcomes. Fisher's exact test was used to identify statistically significant clinical indicators (p < .05), and logistic regression within a generalized linear model (GLM) framework was employed to analyze these indicators alongside the severity index of diquat poisoning (SIDP), followed by the construction of a prognostic model. The performance of the model was evaluated through receiver operating characteristic (ROC) analysis, and the accuracy of the model was assessed. Additionally, two independent sample Wilcoxon tests compared the clinical indicators between high-risk and low-risk groups. Fisher's exact test identified significant differences in variables such as oral drug dosage (ODD), time from poisoning to admission (TFPTA), state of consciousness (SOC), Glasgow Coma Scale (GCS), white blood cells (WBC), myoglobin (Myo), high-flow nasal cannula (HFNC), invasive mechanical ventilation (IMV), acute kidney injury (AKI), and acute lung injury (ALI) (p < .05) between the survival and death groups. The GLM-based risk assessment model demonstrated high predictive accuracy, with an area under the ROC curve (AUC) of 0.97 (SE 0.017, 95% CI 0.939-1.001), indicating potent prognostic capability. The Wilcoxon test revealed that ODD, Myo, SIDP, aspartate transferase (AST), creatine kinase (CK), hemoglobin (Hb), cardiac troponin (cTnT), and serum creatinine (Cr) levels were significantly higher in the high-risk group. The clinical risk assessment model effectively predicts the prognosis of patients with DQ poisoning, aiding clinicians in personalizing treatment strategies to improve patient outcomes.