IMR Press / JIN / Volume 22 / Issue 6 / DOI: 10.31083/j.jin2206165
Open Access Original Research
Development of a Nomogram Based on Diffusion-Weighted Imaging and Clinical Information to Predict Delayed Encephalopathy after Acute Carbon Monoxide Poisoning
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1 Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China
2 Department of Medical Imaging, Yan'an People's Hospital of Shaanxi Province, 716000 Yan'an, Shaanxi, China
3 School of Future Technology, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China
*Correspondence: li9717@stu.xjtu.edu.cn (Haining Li)
These authors contributed equally.
J. Integr. Neurosci. 2023, 22(6), 165; https://doi.org/10.31083/j.jin2206165
Submitted: 20 February 2023 | Revised: 20 March 2023 | Accepted: 6 April 2023 | Published: 23 November 2023
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
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.

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