Evaluating laboratory usage in the intensive care unit: patient and institutional characteristics that influence frequency of blood sampling.
OBJECTIVE
To develop a predictive equation to estimate the frequency of blood drawing for intensive care unit (ICU) laboratory tests and to evaluate variations in ICU blood sampling practices after adjusting for patient and institutional factors.


METHODS
Prospective, inception, cohort study.


METHODS
Forty-two ICUs in 40 hospitals, including 20 teaching and 17 nonteaching ICUs.


METHODS
A consecutive sample of 17,440 ICU admissions, in which 14,043 blood samples were drawn for laboratory testing on ICU days 2 to 7.


METHODS
None.


RESULTS
Patient demographic, physiologic, and treatment data were obtained on ICU day 1; the type and number of blood samples for laboratory testing were recorded on ICU days 1 to 7. In the 42 ICUs, a mean of 16.2 blood samples were drawn for tests on ICU days 2 to 7, but varied between 23 samples in the teaching ICUs and 9.9 samples in nonteaching ICUs. Using only ICU day 1 patient data, we predicted the subsequent number of samples drawn on ICU day 2 (R2 = .26 across individual patients) and on ICU days 2 to 7 (R2 = .26 across individual patients). The most important determinants of the number of blood samples drawn on ICU days 2 to 7 were the ICU day 1 Acute Physiology Score and admission diagnosis. After controlling for patient variables, hospital teaching status, number of beds, and location in the East and South were significantly (p < .05) associated with increased blood sampling on ICU day 2 and on ICU days 2 to 7. More frequent use of an arterial cannula and mechanical ventilation were also associated with increased blood sampling on subsequent days.


CONCLUSIONS
The ability to adjust for patient and institutional variables and to predict the number of blood samples drawn for laboratory tests can allow ICUs to compare their practices with those of other units. When integrated into a continuous quality improvement process, this information can be used to identify and focus on opportunities for improving blood conservation and reducing excessive diagnostic testing.
Zimmerman
J E
JE
Department of Anesthesiology, George Washington University Medical Center, Washington, DC, USA.
Seneff
M G
MG
Sun
X
X
Wagner
D P
DP
Knaus
W A
WA
D018806:APACHE; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003363:Cost Control; D003422:Critical Care / Q000379:methods* / Q000706:statistics & numerical data; D003953:Diagnosis-Related Groups; D006403:Hematologic Tests / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000191:economics / Q000706:statistics & numerical data*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012720:Severity of Illness Index; D014481:United States