A Prospective Comparative Study between Three Chemical Markers for Predicting Delayed Neurological Sequelae in Patients with Acute Carbon Monoxide Poisoning of Poison Control Center in Minia University Hospital.

Document Type : Original Article

Authors

1 Department of Forensic medicine and Toxicology.Faculty of Medicine- Minia University, Minia, Egypt.

2 Department of Clinical Pathology.Faculty of Medicine- Minia University, Minia, Egypt.

Abstract

Carbon monoxide poisoning (CO) is a major public health problem. Brain is the most sensitive organ to hypoxia induced by CO poisoning. Delayed Neurological Sequelae (DNS) is considered to be a delayed onset of neuropsychiatric symptoms after apparent recovery from acute CO poisoning. Therefore, this study was aimed to make a prospective comparative study between three markers (serum glutathione reductase, S100b protein and serum neurone- specific enolase) to predict the occurrence of DNS. This study was performed on 57 adult patients with acute CO poisoning. The markers were measured after arrival and the patients were divided into two groups: the DNS group (8 patients) & the non –DNS group (49 patients). There was a statistical difference between the two groups in terms of significant increase in loss of consciousness, syncope, dizziness, ECG changes, pneumonia, carboxyhemoglobin level, creatine phosphokinase, creatine phosphokinase-MB, troponin I, S100b protein, neurone-specific enolase in DNS grouped patiens and significant decrease in glasgow coma scale and glutathione reductase in DNS group. The cut off value of glutathione reductase was ≤ 30 U/L with a percentage of accuracy 94. 74.  The cut off value of S100b protein was > 18.94 Pg/ L with 98.25 % percentage of accuracy, while, the cut off value of neurone-specific enolase was > 30.49 ng/ml and its accuracy was 96.49 %. All these cut off values predicted the occurrence of DNS. SO, it is concluded that serum S100b protein may represent the most reliable chemical marker for the prediction of DNS after acute CO poisoning by logistic regression analysis.  

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