Carbon Monoxide-induced Coma: Prognostic Factors

Overview

The primary objective of the study is to determine prognostic factors for hospital-mortality following carbon monoxide (CO)-induced coma. The secondary objective is to determine prognostic factors of CO related cognitive sequelae, at the time of hospital discharge.

Carbon monoxide is a leading cause of poison related lethality in France. Moreover, survivors may develop severe neuro-cognitive sequelae. Few studies sought to determine prognostic factors related to CO induced coma. Coma is defined as a Glasgow coma score of \< 8. Neurological sequels will be considered persistent when present at hospital discharge. This is a retrospective observational study. All comatose patients with Glasgow coma score \< 8 due to carbon monoxide poisoning, treated by hyperbaric oxygen therapy in a tertiary hospital in the Île de France area, will be included in the study. Clinical, biological, iconographic and electrophysiological data collected from patient's medical files between January 2000 to December 2018 will be analysed retrospectively. Statistics Base-line characteristics will be summarized using tabulated Statistics, namely median \[interquartile range, IQR\] or percentages (\*"Statistics") unless specified. Exact Fisher tests will be used to compare distribution of binary outcomes, while nonparametric Wilcoxon rank sum tests will be used for comparison of continuous variables. Logistic regression models will be used to measure the strength of association of the variables with the outcomes, by odds ratio (OR), either unadjusted or adjusted in multivariable models. Multivariate imputation by chained equations (MICE) will be used as the method of dealing with missing data in covariates. It operates under the assumption that given the variables used in the imputation procedure, the missing data are Missing At Random (MAR), which means that the probability that a value is missing depends only on observed values and not on unobserved values. A total of m = 30 imputed datasets will be generated, with Rubin's rules applied thereafter to provide mean estimates of the ORs. The package mice implemented the MICE procedure within R. All tests are 2-sided with p-values of 0.05 or less denoting statistical significance. Statistical analysis will be performed on R 3.5.1

Conditions

Eligibility

Locations (1)

Outcomes