Establishment and application of severity assessment system for patients with delayed encephalopathy caused by carbon...

To identify the factors related to the severity of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) and establishment of a clinical nomogram assessment model.

Clinical data of 200 patients with DEACMP admitted to the First Hospital of Yulin from January 2019 to December 2022 were retrospectively analyzed. The patients were classified into severe and non-severe groups according to the severity of the disease. Clinical data was collected from both groups. Logistic regression was applied to analyze the risk factors for disease severity of DEACMP patients. The risk prediction model of the nomogram was constructed by incorporating risk factors, and its effectiveness was verified. Model differentiation performance was evaluated using the Respondent Operating Characteristic (ROC) Curve. Model calibration curve was adopted for fitting the situation of evaluation. The consistency of the model was evaluated by Hosmer-Lemeshow (H-L) analysis.

Age, coma time out of exposure, creatine kinase (CK), caspase, and red blood cell distribution width (RDW) were the risk factors for the severe DEACMP. A nomogram prediction model was built based on the above indicators. The area under the curve (AUC) of the model in predicting severe DEACMP was 0.961 (95% CI: 0.934-0.988) and 0.929 (95% CI: 0.841-1) in the training and test sets, respectively. The H-L test showed good goodness of fit (χ² = 4.468, P = 0.813). The calibration curve showed a good agreement between the predicted values of the nomogram and the actual observed values.

Age, coma time out of exposure, CK, caspase, and RDW were significantly correlated with the severity of DEACMP patients. The nomogram prediction model incorporating the five indicators has certain clinical reference value for predicting the severe DEACMP and could be used as an accurate and rapid clinical assessment tool.