Tighter precision target required for lactate testing in patients with lactic acidosis

Abstract

As such, evaluations of lactate measurements must use tighter allowable error limits.

Lactate point-of-care testing for acidosis: Cross-comparison of two devices with routine laboratory results

“…Both POCT devices were evaluated for analytical imprecision by comparing the measured CV of internal quality controls with the CV provided by the company, the analytical imprecision and the Total Allowable Error (TEa) [18] , [19] . Lactate concentrations, measured with the StatStrip-Lactate, showed a CV of 5.5% (mean: 6.2 mmol/L), which is lower than the CV stated by the company and the target analytical imprecision (13.6%) for lactate, as recently published [16] . Lactate and pH measured with the iSTAT-CG4+ showed very low CV’s of 1.1% (mean: 6.94 mmol/L) and 0.1% (mean: 7.05), respectively.…”

“…Both POCT lactate devices studied showed good analytical performance and met the recently published tight precision target for testing patients with lactate acidosis [16] .…”

“…For lactate, as recently published, the imprecision is strongly dependent on the patient population. It was found that for patients with lactic acidosis, the imprecision was about half of that compared to normal individuals [16] . We implemented this tighter precision target and found for both POCT devices that this new target was met.…”

“…The possibilities of lactate measurement with POCT devices at the bedside of patients in the ED and Critical Care are also explored in the literature [13] , [14] , [15] . Interestingly, for patients with metabolic acidosis, biological variability of lactate was shown to be about half that of healthy individuals, which is clearly important for validation of lactate POCT devices [16] .…”

The use of serial outpatient complete blood count (CBC) results to derive biologic variation: a new tool to gauge the acceptability of hematology testing

“…Table 1 summarizes published biological variation of CBC components expressed as a relative standard deviation [also known as coefficient of variation (CV b )]. We have been using a disruptive, data-mining approach to determine the biologic variation [4,5]. Our method does not require any additional laboratory testing.…”