Techs at the BryantGH labs began using the six-part differential, which includes the IG count, in 2006.

In any given day, techs at the twin labs at BryanLGH Medical Center run about 300 complete blood count (CBC) tests. Faced with the stark realities of limited staffing and budget resources, the techs at the not-for-profit medical center, located in Lincoln, Neb, know that any improvement in turnaround time can make a big difference for clinicians and patients alike.

Performing frequent manual differentials and smear review tests counting immature granulocytes (IGs) is a labor-intensive process, and it had become taxing on techs at both of BryanLGH’s locations: BryanLGH East and BryanLGH West. Looking for a sustainable solution to their problem, they turned to their existing automation vendor.

Since the techs were already working with XE series analyzers from Sysmex, Mundelein, Ill, they explored the company’s IG parameter to help relieve them of the burdens of numerous, labor-intensive manual tests on normal samples. Sysmex’s XE-2100™ automated hematology analyzer provides an extended six-part differential via patented technology that separates the white blood cells into distinct cell clusters. The IG parameter, which is a combination of neutrophilic metamyelocytes, myelocytes, and promyelocytes, is one of those six parameters.

While the presence of IGs in peripheral blood is not necessarily a bad thing, it is an abnormal finding, explains Judy A. Miller, who has served as hematology section coordinator for both BryanLGH hospitals since 2001. “It may indicate a bacterial infection, or it may be an indication of myeloproliferative disorder, which is seen in certain types of leukemia,” she says.

Before the IG parameter was implemented, a manual differential had to be performed on all CBC samples that contained IGs. “When you do a manual differential, you have to prepare a slide, stain it, take it to a microscope, and the tech has to sit down at the microscope and then do a thorough review of the slide,” Miller says. “It’s very time-consuming. Most of the time we tabulate a 100-cell differential, but sometimes we’ll do a 200-cell to classify what kinds of white cells comprise the total white blood cell count.”

Naturally, the potential to reduce the number of manual differentials by adding an automated platform was well received. However, before they could start using the parameter to replace manual testing, the techs first had to complete a lengthy validation study that consisted of comparing the lab’s manual differentials with the automated IG count, establishing its reference ranges, and verifying the manufacturer’s stability claims.

Additionally, the techs were charged with communicating the changes to the clinical staff, which they did by mailing a memo explaining the changes in methodology and by including the same information in a medical staff newsletter distributed by the hospital.

When the techs completed their communication and validation procedures, they worked closely with the hospital’s laboratory information system (LIS) and hospital information system teams to ensure the smooth flow of information once they began using the parameter to replace manual differentials.

Since the IG parameter’s implementation, techs run samples through the analyzer first, and if the count reads less than 5%, they can move along to other tasks as results automatically report through the lab’s LIS. If the count is greater than 5%, however, the sample is flagged and the techs must perform a manual confirmation. “We do that because it’s not so much that we don’t trust the accuracy of the IG parameter, but because we want to ensure there are not other abnormalities that we would want to report such as toxic changes to the neutrophils,” Miller says.

Exact Science

Like at most labs, Miller strives to maintain a high level of accuracy while keeping the demands on her staff reasonable. It therefore comes as no surprise that Miller regards the IG parameter’s precision (repeatability) as an invaluable asset. The validation study performed during the parameter’s implementation process in Miller’s labs was the perfect litmus test for examining the accuracy the labs could expect with the new parameter, compared with the traditional manual differential method.

During the study, Miller took a patient sample that had roughly 7% IGs and ran it 10 times on the XE analyzer. The same sample was then distributed to 10 techs, who each performed a 200-cell manual differential test on it. On automated differentials from the analyzer, the IG counts ranged from 6% to 7% while the manual differential showed IG counts that ranged anywhere from 0% to 10%. “That is really due to the inherent inaccuracy of the manual differential. The analyzer counts 32,000 cells, while we count one or two hundred,” Miller explains. “The fact that the analyzer counts at 32,000 cell differentials means that you’re greatly improving your precision, and that’s a great benefit in terms of a better-quality result for the physician and for the patient.”

The marked decrease in manual differential tests performed is not an indication that manual tests are on their way out. In addition to checking samples with IG counts greater than 5%, techs must also manually check a sample if the analyzer has flagged abnormal cell populations. “Abnormal cells will not fall within the area on the scattergram as normal cells,” Miller says. “The analyzer is very good at flagging samples with abnormal cells present that need to be reviewed.”

Significant Savings

Second to the greatly improved accuracy, Miller was most concerned with improving lab efficiency by decreasing the amount of time-consuming manual tests that had to be run each day. Postimplementation, the labs have seen a sizeable reduction in manual testing. At the BryanLGH West lab, manual differentials reviewed for IGs dropped 44% and the smear reviews dropped by 12%. BryanLGH East experienced a similar boon in time savings, with manual differentials for IGs tumbling 42% and smear reviews falling 32% of their preimplementation levels. “It’s significant,” Miller says. “And at the same time on those samples that have a reportable automated IG count we gained accuracy, so it’s a win-win.”

Miller was also impressed that techs could jump right in and start working with the parameter almost immediately. “There’s essentially no training involved at all, other than communicating changing in flagging and the technical information they got regarding the parameter itself,” she says.

The labs are now in the process of implementing two new parameters into their XE system, following the same algorithm they did when they implemented the IG parameter, and they are revisiting the validation procedures.

“I think we’re all aware of the projected shortage of med techs in the future, so we’re constantly challenged to improve our productivity and our efficiency,” Miller says. “As with most labs, we’re challenged to do more with less. This frees our techs to do other things and really helps ease some of the strain of the workload.”

Stephen Noonoo is associate editor of CLP.