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Deriving Measures of Intensive Care Unit Antimicrobial Use from Computerized Pharmacy Data: Methods, Validation and Overcoming Barriers
Filetype[PDF - 309.35 KB]


Details:
  • Pubmed ID:
    21515978
  • Pubmed Central ID:
    PMC3678980
  • Funding:
    1 U01 CI000328/CI/NCPDCID CDC HHS/United States
    1 U01 CI000333-01/CI/NCPDCID CDC HHS/United States
    1 U01 CI000334-0/CI/NCPDCID CDC HHS/United States
    CI000327/CI/NCPDCID CDC HHS/United States
    KL2 TR000450/TR/NCATS NIH HHS/United States
    TL1 RR024995/RR/NCRR NIH HHS/United States
    TL1RR024995/RR/NCRR NIH HHS/United States
    UL1 TR000448/TR/NCATS NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Objective

    To outline methods for deriving and validating intensive care unit (ICU) antimicrobial utilization (AU) measures from computerized data and to describe programming problems that emerged.

    Design

    Retrospective evaluation of computerized pharmacy and administrative data.

    Setting

    ICUs from four academic medical centers over 36 months.

    Interventions

    Investigators separately developed and validated programming code to report AU measures in selected ICUs. Antibacterial and antifungal drugs for systemic administration were categorized and expressed as antimicrobial days (each day that each antimicrobial drug was given to each patient) and patient-days on antimicrobials (each day that any antimicrobial drug was given to each patient). Monthly rates were compiled and analyzed centrally with ICU patient-days as the denominator. Results were validated against data collected from manual medical record review. Frequent discussion among investigators aided identification and correction of programming problems.

    Results

    AU data were successfully programmed though a reiterative process of computer code revision. After identifying and resolving major programming errors, comparison of computerized patient-level data with data collected by manual medical record review revealed discrepancies in antimicrobial days and patient-days on antimicrobials ranging from <1% to 17.7%. The hospital for which numerator data were derived from electronic medication administration records had the least discrepant results.

    Conclusions

    Computerized AU measures can be derived feasibly, but threats to validity must be sought and corrected. The magnitude of discrepancies between computerized AU data and a gold standard based on manual chart review varies, with electronic medication administration records providing maximal accuracy.