title: An individual patient data meta-analysis on factors associated with adverse drug events in surgical and non-surgical inpatients.
abstract: OBJECTIVE
  The incidence of adverse drug events (ADEs) in surgical and non-surgical patients may differ. This individual patient data meta-analysis (IPDMA) identifies patient characteristics and types of medication most associated with patients experiencing ADEs and suggests target areas for reducing harm and implementing focused interventions.
  
  
  METHODS
  Authors of eligible studies on preventable ADEs (pADEs) were approached for collaboration. For assessment of differences among (non-)surgical patients and identification of associated factors descriptive statistics, Pearson chi-square, Poisson and logistic regression analyses were performed. For identification of high risk drugs (HRDs), a model was developed based on frequency, severity and preventability of medication related to ADEs.
  
  
  RESULTS
  Included were 5367 patients from four studies. Patients aged ≥ 77 years experienced more ADEs and pADEs compared with patients aged ≤ 52 years (odds ratios (OR) 2.12 (95% CI 1.70, 2.65) and 2.55 (95% CI 1.70, 3.84), respectively, both P < 0.05). Polypharmacy on admission also increased the risk of ADEs (OR 1.21 (95% CI 1.03, 1.44), P < 0.05) and pADEs (OR 1.85 (95% CI 1.34, 2.56), P < 0.05). pADEs were associated with more severe harm than non-preventable ADEs (54% vs. 32%, P < 0.05). The top five HRDs were antibiotics, sedatives, anticoagulants, diuretics and antihypertensives. Events associated with HRDs included diarrhoea or constipation, abnormal liver function test and central nervous system events. Most pADEs resulted from prescribing errors (90%).
  
  
  CONCLUSIONS
  Elderly patients with polypharmacy on admission and receiving antibiotics, sedatives, anticoagulants, diuretics or antihypertensives were more prone to experiencing ADEs. Efficiency in prevention of ADEs may be improved by targeted vigilance systems for alertness of physicians and pharmacists.
authors.affiliation: Department of Surgery, Academic Medical Center, Amsterdam, The Netherlands.
authors.forename: Eveline B
authors.initials: EB
authors.lastname: Boeker
authors.forename: Kim
authors.initials: K
authors.lastname: Ram
authors.forename: Joanna E
authors.initials: JE
authors.lastname: Klopotowska
authors.forename: Monica
authors.initials: M
authors.lastname: de Boer
authors.forename: Montse Tuset
authors.initials: MT
authors.lastname: Creus
authors.forename: Ana L
authors.initials: AL
authors.lastname: de Andrés
authors.forename: Mio
authors.initials: M
authors.lastname: Sakuma
authors.forename: Takeshi
authors.initials: T
authors.lastname: Morimoto
authors.forename: Marja A
authors.initials: MA
authors.lastname: Boermeester
authors.forename: Marcel G W
authors.initials: MG
authors.lastname: Dijkgraaf
mesh_terms: D000367:Age Factors; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D017558:Elective Surgical Procedures / Q000009:adverse effects / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D019338:Polypharmacy
keywords: adverse drug events; epidemiology; medication safety; non-surgical patient; surgical patient