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

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

doi:10.1038/srep44428

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.

Highlights

From January 2009 to December 2014, we reported a total of 66,101 acute admissions for drug abuse or dependence in 956 centers (Fig. 1)
There was a substantial increase in the number of hospital admissions, which was associated with a widespread geographical distribution (Fig. 2)
We report the derivation and validation of a model, which demonstrates an accurate prediction of death or intensive care unit (ICU) admission in hospitalized drug users

Summary

Introduction

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. 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. Illicit drug use and dependence are important contributors to the global burden of disease, accounting for 20 million disability-adjusted life years[1]. This burden is mainly associated with opioid dependence and increases in highest incomes countries[1]. Admissions to acute care hospitals for drug abuse or dependence are common and likely associated with complications

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