Streamlining blood counts with a microcomputer Essay

The blood count is one of the most widely performed laboratorytests, however we define it.

Years ago, a complete blood countconsisted of several manual procedures such as a red cell count andhematocrit, and usually a peripheral blood film evaluation anddifferential white count. Today, the CBC usually includes a panel of upto nine discrete measurements. Thanks to the development of rapid,precise, and accurate hematology instruments, labs now perform moreblood counts than ever. Changing reimbursement incentives, however, have pressured themedical establishment to scrutinize costs and benefits of every aspectof patient care, blood counts included. In the current setting,we’re unlikely to find funds for more and better testing; in fact,we may be able to add new procedures only by deleting others. The bloodcount, with its multiple components, offers potential savings without areduction in quality of service. In our hematology labs, a microcomputer helps us identify andeliminate unnecessary blood film reviews. The computer”reads” data directly from our automated hematology analyzer,flags specimens that need review, and cues technologists on possiblecauses for the abnormality.

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Here’s how we developed the system. We began with a re-examination of the basics: Why do physiciansorder blood counts, and what do they hope to learn from the results? Isa rapid normal or abnormal result sufficient when the test is used as ascreening procedure? We expect the blood count to indicate certainbasic conditions in peripheral blood cells, as shown in Figure 1. Ifthese expectations are reasonable, then we must determine how oursophisticated instruments can help examine specimens most efficiently. Optimal instrument use rests on the principle that a specimen is”normal” it selected quantitative and qualitative parameterslie within a prescribed set of ranges, indicating no need for additionalstudies. These studies, such as the stained blood film review anddifferential leukocyte count, cost considerable time, labor, and money.By eliminating them when not clinically indicated, we can trim costs andmake better use of technologist time. The decision sapling in FigureII–the algorithm is too simple to qualify as a decisiontree–illustrates this line of reasoning.

On that basis, we laid the scientific groundwork for our study.Hematology instruments produce whole sets of quantitative andqualitative data on a single blood specimen within one minute aftersampling. By qualitative data, we mean the histograms automaticallygenerated by all larger instrments.

Quantitative data analysis used toemploy strict statistical methods, such as determining normal ranges bythe mean, plus or minus 2 standard deviations. As the inadequacies ofthis simple method became clear, percentile determinations came intowider use. We have pursued a somewhat different tack in our studies over thelast few years, by attempting to set clinically useful limits for bloodfilm reviews.

We evaluated various arbitrary limits on quantitative CBCdata to insure that no significant findings from the automated analysiswould be missed. By comparing individual determinations to eachpatient’s entire set of test results, we arrived at the flagginglimits shown in Figure III. As we’ll see, the personal computerhandles the task of flagging out-of-range results. Two other parameters have proved quite sensitive to hematologic abnormalities. The first, the multivariate reference range, originatedin clinical chemistry as a statistical technique designed to simplityanalysis of large amounts of data, such as chemistry panels. This method merges all eight quantitative CBC results anddetermines their relative normality by the size of the resulting number.The multivariate statistic is far more sensitive than histogram analysisto the presence of circulating normoblasts, for instance, and hasflagged such specimens almost unerringly. The second parameter is the histogram distance, or HD.

So far wehave worked primarily with the white cell histogram because of itsgreater importance. We ran 250 normal blood specimens and stored theirwhite cell histograms on the microcomputer. Next, the computerdetermined the mean and variability of all these curves and calculated aChi square distance at four carefully selected points–shown in FigureIV, using the formula: [sigma] (observed — expected).sup.2.

/expected To obtain the HD, the computer compares the patient’shistogram to the reference curve at all four points. Specimens thatdiffer significantly are flagged for further study. Our system was designed to maximize the capabilities of laboratorystaff members as well as instruments. In addition to extending ourautomation, it frees skilled technologists from the tedium of routineblood film evaluations, allowing them to concentrate on those specimensthat really need attention.

For this reason, we have avoided attemptsto computerize blood evaluation beyond the parameters of the CBC. Nowlet’s take a closer look at how our computer puts theory intoaction. We interfaced a multichannel automated hematology analyzer, theOrtho ELT-8/ds with Data Handler, and an Apple IIe personal computer,including disk drive and CRT, and equipped with a serial input/outputcard. The interfacing process required some real detective work sincethe complexity of the instrument’s software made access difficult.

Finally, after much research and effort, we were able to capture allrelevant data and display it on the CRT in real time. Figure V depictshow the two systems interconnect. With expert advice, othermultichannel instruments and microcomputers can probably be interfacedand programmed in a similar manner, although our experience is limitedto this system.

When a specimen’s index parameters on the automated analyzerfall outside any of the predetermined limits, the computer flags thevariant value with a flashing arrow. These single and double flagsproduce a footnote-like CRT display of appropriate prompts (Figure VI).These prompts give the operator useful information for blood filmexamination, and can be custom-programmed for any laboratory’spatient population. Of course, few if any laboratory tests are 100 per cent sensitiveand 100 per cent specific.

We have deliberately set the flagging limitsto point out all abnormal specimens. As a result, the computer includessome false-positive specimens for review, but we also lower the risk ofmissing a significant abnormality. Our latest review shows aspecificity of 76 per cent, with a predictive value of 86 per cent forpositive screens. We tabulated the sensitivity of the individual flags–that is, howoften each flag yields an abnormal result (Figure VII). The morecomplex statistics, like histogram distance, are abnormal in about halfof all specimens. Out-of-range hemoglobin, on the other hand, isflagged in a far lower percentage of cases, but this and other lessfrequent flags often provide the most direct clues to abnormalities. In a review of 3,500 cases, we identified clear flagging patterns associated with various clinicalconditions, also shown in Figure VII.

We determined possible causesbased on observation and experience. The next phase of our studyconfirmed the correlation of non-flagged ABCs with the clinicalcondition on a case-by-case basis. At this point, it’s difficult to measure how the system hasaffected the laboratory’s workload. Orders for differential countshave decreased by some 50 per cent, but some of this drop may be due toa change in ordering protocols that allows physicians to order anautomated blood count alone, without differential. In any case, anincreasing proportion of CBCs are being ordered as screening countsonly, with the option for further review left up to the laboratory. Our system now functions in three sections of the hematology labsystem.

We were careful to introduce and develop the new method in anevolutionary rather than a revolutionary way. Technologists haveaccepted it well, once they realize that the computer helps them usetheir time and skills most effectively. Most important, our personal computer allows us to cut unnecessarytesting without compromising patient care.

In light of prospectivepayment, that’s a strategy with considerable implications for thefuture.


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