Solitary blood cultures as a quality assurance indicator

Qual Assur Util Rev. 1991 Winter;6(4):132-7. doi: 10.1177/0885713x9100600406.

Abstract

For patients with suspected bacteremia, at least two separate blood cultures are recommended to achieve maximum sensitivity and to properly interpret results. Since a single blood collection may signify an improper procedure with serious consequences if the diagnosis of blood stream infection is missed, we investigated this problem with studies at three teaching hospitals (A, B, and C) and by a survey of 38 other hospitals. The incidence of solitary blood cultures ranged from 1 to 99% (median 26%) at the surveyed institutions. Among the cases investigated at hospitals B and C, between 10 and 30% of solitary blood cultures were not clinically indicated, while most of the others were caused by the physician not knowing that one culture was insufficient or by failure to complete the diagnostic plan. Focused concurrent intervention at hospital B was associated with reductions in solitary blood cultures from 40.0 to 24.6% (p = 0.045) and a decline in those not indicated from 38.1 to 12.5% (p = 0.192). Global educational efforts at hospital A were associated with a decrease in solitary blood culture rates from 52 to 37% (p = 0.016). These results show that blood culture practice varies widely among institutions in spite of consensus recommendations for proper specimen collections. We estimate that, nationwide, up to 18,000 etiologic diagnoses of bacteremia are missed annually because of this problem. Monitoring institutional solitary blood cultures is recommended as a test utilization indicator and as the basis for improving blood culture practice.

MeSH terms

  • Bacteremia / blood
  • Bacteremia / diagnosis*
  • Blood Specimen Collection / standards*
  • Blood Specimen Collection / statistics & numerical data
  • Data Collection
  • Evaluation Studies as Topic
  • Forms and Records Control
  • Hospitals, Teaching / standards
  • Humans
  • Laboratories, Hospital / standards*
  • Models, Statistical
  • Quality Assurance, Health Care / standards*
  • United States