Identifying Life-Threatening Admissions for Drug Dependence or Abuse (ILIADDA): Derivation and Validation of a Model. View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Tri-Long Nguyen, Thierry Boudemaghe, Géraldine Leguelinel-Blache, Céline Eiden, Jean-Marie Kinowski, Yannick Le Manach, Hélène Peyrière, Paul Landais

ABSTRACT

Given that drug abuse and dependence are common reasons for hospitalization, we aimed to derive and validate a model allowing early identification of life-threatening hospital admissions for drug dependence or abuse. Using the French National Hospital Discharge Data Base, we extracted 66,101 acute inpatient stays for substance abuse, dependence, mental disorders or poisoning associated with medicines or illicit drugs intake, recorded between January 1st, 2009 and December 31st, 2014. We split our study cohort at the center level to create a derivation cohort and a validation cohort. We developed a multivariate logistic model including patient's age, sex, entrance mode and diagnosis as predictors of a composite primary outcome of in-hospital death or ICU admission. A total of 2,747 (4.2%) patients died or were admitted to ICU. The risk of death or ICU admission was mainly associated with the consumption of opioids, followed by cocaine and other narcotics. Particularly, methadone poisoning was associated with a substantial risk (OR: 35.70, 95% CI [26.94-47.32], P < 0.001). In the validation cohort, our model achieved good predictive properties in terms of calibration and discrimination (c-statistic: 0.847). This allows an accurate identification of life-threatening admissions in drug users to support an early and appropriate management. More... »

PAGES

44428

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep44428

DOI

http://dx.doi.org/10.1038/srep44428

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084132318

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28290530


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