Carbon Monoxide-induced Coma: Prognostic Factors

NCT ID: NCT03926494

Last Updated: 2020-07-07

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

212 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-05-01

Study Completion Date

2019-06-30

Brief Summary

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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.

Detailed Description

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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

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Coma Carbon Monoxide Poisoning

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* All comatose patients (Glasgow coma score \<8) due to carbon monoxide poisoning, treated by hyperbaric oxygen therapy in a tertiary hospital in Île de France area;
* Aged ≥ 18 years;
* Hospitalized in hyperbaric medicine unity of Raymond Poincaré hospital in Garches.

Exclusion Criteria

* Pregnant woman;
* Patient present clinical signes of stroke and without CO detection.

In case of unusable data, patient will be excluded from study.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Assistance Publique - Hôpitaux de Paris

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Paris Meng, MD

Role: PRINCIPAL_INVESTIGATOR

Intensive Care Unit, Raymond Poincaré hospital

Djillali ANNANE, MD, PhD

Role: STUDY_DIRECTOR

Intensive Care Unit, Raymond Poincaré hospital

Locations

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Intensive Care Unit, Raymond Poincaré hospital

Garches, , France

Site Status

Countries

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France

References

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Resche-Rigon M, White IR. Multiple imputation by chained equations for systematically and sporadically missing multilevel data. Stat Methods Med Res. 2018 Jun;27(6):1634-1649. doi: 10.1177/0962280216666564. Epub 2016 Sep 19.

Reference Type BACKGROUND
PMID: 27647809 (View on PubMed)

Related Links

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https://www.jstatsoft.org/article/view/v045i03

Mice : Multivariate Imputation by Chained Equations in R.

https://www.scirp.org/(S(351jmbntvnsjt1aadkposzje))/reference/ReferencesPapers.aspx?ReferenceID=1614964

R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing

https://www.r-project.org/

R Development Core Team (2008)

Other Identifiers

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18PMG-COMA-CO

Identifier Type: -

Identifier Source: org_study_id

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