Citation

Wang S, Han W, Sun T, Wang H, Zhang Z, Li H. J. Integr. Neurosci. 2023; 22(6): e165.

Copyright

(Copyright © 2023, Imperial College Press)

DOI

10.31083/j.jin2206165

PMID

38176918

Abstract

BACKGROUND: Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a severe complication that can arise from acute carbon monoxide poisoning (ACOP). This study aims to identify the independent risk factors associated with DEACMP and to develop a nomogram to predict the probability of developing DEACMP.

METHODS: The data of patients diagnosed with ACOP between September 2015 and June 2021 were analyzed retrospectively. The patients were divided into the two groups: the DEACMP group and the non-DEACMP group. Univariate analysis and multivariate logistic regression analysis were conducted to identify the independent risk factors for DEACMP. Subsequently, a nomogram was constructed to predict the probability of DEACMP.

RESULTS: The study included 122 patients, out of whom 30 (24.6%) developed DEACMP. The multivariate logistic regression analysis revealed that acute high-signal lesions on diffusion-weighted imaging (DWI), duration of carbon monoxide (CO) exposure, and Glasgow Coma Scale (GCS) score were independent risk factors for DEACMP (Odds Ratio = 6.230, 1.323, 0.714, p < 0.05). Based on these indicators, a predictive nomogram was constructed.

CONCLUSIONS: This study constructed a nomogram for predicting DEACMP using high-signal lesions on DWI and clinical indicators. The nomogram may serve as a dependable tool to differentiate high-risk patients and enable the provision of personalized treatment to lower the incidence of DEACMP.

Language: en

Keywords

calibration curve; delayed encephalopathy after acute carbon monoxide poisoning; diffusion weight imaging; multivariate logistic regression; nomogram; prediction model