title: Analysis of potential drug-drug interactions in medical intensive care unit patients.
abstract: OBJECTIVE
  To describe the frequency and type of potential drug-drug interactions (pDDIs) in a general intensive care unit (ICU) and to make recommendations to improve the management of these pDDIs.
  
  
  METHODS
  Retrospective observational study.
  
  
  METHODS
  General ICU of a tertiary care hospital.
  
  
  METHODS
  All patients admitted for more than 24 hours between May 2009 and December 2010 who were prescribed at least one medication.
  
  
  RESULTS
  Based on the G-Standaard, the Dutch national drug database, pDDIs were identified and classified into categories of potential clinical outcome and management advice. In total, 35,784 medication episodes were identified, resulting in 2887 pDDIs (8.1%). These 2887 pDDIs occurred in 1659 patients for a mean frequency of 1.7 (95% confidence interval [CI] 1.6-1.9) pDDIs per patient. Overall, 54% of the patients experienced at least one pDDI with pDDIs present during 27% of all ICU admission days. All pDDIs could be reconstructed using 81 of the 358 (23%) relevant unique pDDI pairs described in the G-Standaard. The most frequently occurring potential clinical consequence was an increased risk of side effects or toxicity (91% of the pDDIs) such as electrolyte disturbances and masking of hypoglycemia. The most important advised management strategy was monitoring (81%), consisting of monitoring of laboratory values (52%), clinical monitoring of toxicity or effectiveness (48%), or monitoring of physical parameters such as electrocardiogram and blood pressure (11%).
  
  
  CONCLUSIONS
  Potential drug-drug interactions occur in 54% of all ICU patients, which is two times more than the rate seen in patients on general wards. A limited set of 20 pDDI pairs is responsible for more than 90% of all pDDIs. Therefore, it is worthwhile to develop guidelines for the management of these specific pDDIs. As the vast majority of the interactions can be managed by monitoring, advanced clinical decision support systems linking laboratory data to prescription data may be an effective risk management strategy.
authors.affiliation: Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, the Netherlands.
authors.forename: Esther V
authors.initials: EV
authors.lastname: Uijtendaal
authors.forename: Lieke L M
authors.initials: LL
authors.lastname: van Harssel
authors.forename: Gerard W K
authors.initials: GW
authors.lastname: Hugenholtz
authors.forename: Emile M
authors.initials: EM
authors.lastname: Kuck
authors.forename: Jeannette E F
authors.initials: JE
authors.lastname: Zwart-van Rijkom
authors.forename: Olaf L
authors.initials: OL
authors.lastname: Cremer
authors.forename: Toine C G
authors.initials: TC
authors.lastname: Egberts
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual / Q000639:trends; D004347:Drug Interactions / Q000502:physiology; D016903:Drug Monitoring / Q000379:methods / Q000592:standards* / Q000639:trends*; D005260:Female; D006760:Hospitalization / Q000639:trends*; D006801:Humans; D007362:Intensive Care Units / Q000592:standards* / Q000639:trends*; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D012189:Retrospective Studies; D012307:Risk Factors; D055815:Young Adult
keywords: adverse drug event; drug interaction database; intensive care unit; medication safety; potential drug-drug interaction; risk management