A scoring system with high predictive performance for poor outcomes in acute carbon monoxide poisoning
Abstract Carbon monoxide (CO) poisoning causes significant mortality and hypoxic brain injury. Hyperbaric oxygen therapy (HBOT) may reduce delayed neurological sequelae, but poor outcomes persist. A model for predicting outcomes early after hospital admission is crucial for guiding care and early re...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-98162-1 |
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| Summary: | Abstract Carbon monoxide (CO) poisoning causes significant mortality and hypoxic brain injury. Hyperbaric oxygen therapy (HBOT) may reduce delayed neurological sequelae, but poor outcomes persist. A model for predicting outcomes early after hospital admission is crucial for guiding care and early rehabilitation. In this study, we aimed to develop a clinical scoring model to predict poor outcomes in acute CO poisoning cases. The study included 176 patients aged ≥ 15 years with acute CO poisoning who were transported for HBOT between 2012 and 2023, after excluding those aged < 15 years and those in cardiac arrest on arrival. Acute CO poisoning was defined as CO exposure or COHb > 5% (> 10% for smokers). HBOT involved ≥ 1 session at 2.8 absolute atmospheres for 60 min. Predictors of poor outcomes included age, GCS < 13, burns and low C-reactive protein levels. The ABCG score (age, burns, CRP, GCS) demonstrated strong discriminative ability, with an area under the ROC curve of 0.917, sensitivity of 0.852 and specificity of 0.828. The ABCG score accurately predicts poor outcomes in acute CO poisoning and supports early intervention and treatment planning. External validation and broader application are needed for clinical adoption. |
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| ISSN: | 2045-2322 |