Manual Differential Cell Counts Help Predict Bacterial Infection A Multivariate Analysis

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1 Hematopathology / LEFT SHIFT MODEL PREDICTS INFECTION Manual Differential Cell Counts Help Predict Bacterial Infection A Multivariate Analysis Michael J. Wile, MD, 1 Louis D. Homer, MD, PhD, 2 Stede Gaehler, MT(ASCP), 3 Shirley Phillips, PhD, 3 and Juan Millan, MD 1 Key Words: Neutrophils; Band neutrophils; Immature neutrophil; Leukocyte differential count; Blood cell count; Manual differential count; Absolute neutrophil count; CBC count; Left shift Abstract We developed logistic regression models that combine information from the automated CBC and manual 100-cell differential counts to predict bacterial infection. The logistic models were fitted from a case group of 116 patients with proven bacterial infection and a control group of 930 presumably uninfected outpatients. A 4-variable, 15-parameter model, which includes automated absolute neutrophil, manual band, and manual immature granulocyte counts, performed best with a receiver operating characteristic (ROC) curve area of 89%. A more practical 2-variable model including automated absolute neutrophil and manual band counts performed almost as well with an ROC curve area of 86%. The automated neutrophil count only model is less informative with an ROC curve area of 78%. The combined information from automated and manual differential cell counts more accurately predicts bacterial infection than automated counting alone. Despite these modest improvements, the high cost of manual differential cell counts dictates careful patient selection. The supplemental information gained from manual differential counts is most useful for patients with low to normal neutrophil counts (8,000/µL [ /L] or less). Further studies are indicated to determine the characteristic patient populations deriving maximal benefit from this information. The manual slide differential count is a costly and laborintensive test that clinicians often order as a part of a CBC count without full understanding of its use and limitations. Some problems of the 100-cell differential include imprecision, variation owing to nonhomogeneous distribution of leukocytes on the blood film, and interobserver variation in cell identification. 1,2 These problems make the slide differential count a poor method for precise quantification of leukocyte subclasses. Despite its shortcomings, the 100-cell manual differential count is the most practical method for assessing left shift. The most common reason for performing the leukocyte differential count is to assess neutrophil left shift as an aid in the diagnosis and management of acute infectious or inflammatory conditions, especially bacterial infection. The validity of this practice has been questioned because of low specificity, accuracy, and precision of band counts. 3-8 Automated hematology analyzers offer automated, accurate, and precise differential counts of the 5 major subclasses of leukocytes. There are many flow cytometric blood cell analyzers available to perform routine complete leukocyte differential counts and to flag abnormal specimens for possible microscopic review. The analyzers flag specimens with excessive left shift, but they are unable to distinguish specific cells in the developmental sequence of granulocytes. 9 The absolute neutrophil number and percentage are the instrument s best substitutes. Our clinical colleagues desire more information about left shift because they believe it permits them to make treatment decisions in the face of limited information. Numerous publications point to the advantages of automated differential counts. 1,2,10-13 These reports state that manual band counts add no diagnostically useful information. 644 Am J Clin Pathol 2001;115: American Society of Clinical Pathologists

2 Hematopathology / ORIGINAL ARTICLE They suggest that laboratory efficiency could be improved by relying more on instruments that perform accurate total leukocyte and absolute neutrophil counts for the diagnosis of infection and relying less on manual differential counts. We found contradictory claims in only 1 report stating that the presence of a left shift, defined as more than 6% bands, in 44 patients with a normal total leukocyte count had a 100% correlation with an infection or an acute inflammatory disease. 14 Our clinical colleagues suggest that information on band counts be used in conjunction with the absolute neutrophil count rather than as a substitute. We were interested in testing the value of band counts as a supplement rather than as a replacement for absolute neutrophil counts. Many clinicians make discharge and medication decisions based on all available information indicating presence or absence of infection. Previous studies have focused on substituting the absolute neutrophil count for the band count. 1,2,10-13 The present study focused on the supplemental information gained by adding manual band counts and immature granulocyte counts to the absolute neutrophil count. We tested the value of this supplemental information by using multivariate logistic modeling and receiver operating characteristic (ROC) curves Materials and Methods We selected 116 patients with documented infections by querying the laboratory information system. The patients ranged in age from 2 weeks to 93 years (mean, 50.0 years) and had total leukocyte counts between 400 and 50,000/µL ( /L). Cultures from various sites were positive for neutrophil-evoking bacterial organisms Table 1. The control group was 930 outpatients with total leukocyte counts ranging from 900 to 33,000/µL ( /L) without known infections. They ranged in age from 1 to 99 years (mean, 43.7 years). Results from routinely performed 100-cell microscopic leukocyte differential counts performed within 24 hours of the positive culture were used to develop a multivariate logistic regression model. Automated 5-part differential counts were performed on the Toa Sysmex NE-8000 (Baxter, McGraw Park, IL). One hundred cell manual differential counts were performed on flagged cases according to College of American Pathologists standards. Neutrophilic bands and immature granulocytes were identified under oil immersion microscopy at 500, using criteria advocated by the College of American Pathologists Hematology Survey Subcommittee. 18 We queried the laboratory information system using Discern Explorer (Cerner, Kansas City, MO), a standard computer query language (SQL) derivative. We wrote the computer code to identify cases with positive cultures from the microbiology database. The computer code contains several SQL joins, which provide additional information on these patients including CBC count results, manual differential cell count results, historic information, and information about the physician ordering the test. We ran the query program nightly between October 6, 1999, and January 28, 2000, and pooled the results in a database. The initial data set included several thousand cases. We screened the initial data set using Microsoft Excel 2000 (Microsoft, Redmond, WA) to eliminate cases with incomplete information. We defined complete cases as cases with a positive culture and a complete CBC count with a 100-cell manual differential count. We reviewed the electronic medical records on approximately 500 complete cases and eliminated cases with other reasons for neutrophilia such as surgery, trauma, and myocardial infarction. Our microbiologist screened the cases to ensure that the organisms and culture types were consistent with bacterial organisms that typically evoke neutrophilia. Our remaining case group consisted of 116 patients. Table 1 Source and Organisms Cultured From Infected Patients No. of Positive Source Cultures Organism (No. of Cases) Body fluid 6 Bacteroides fragilis (1), Klebsiella pneumoniae (1), Staphylococcus aureus (1), Streptococcus agalactiae (1), Streptococcus pneumoniae (2) Blood 49 B fragilis (2), Escherichia coli (12), Enterobacter cloacae (1), Enterococcus faecalis (2), K pneumoniae (3), Moraxella catarrhalis (1), Pseudomonas aeruginosa (2), Salmonella species (1), S aureus (6), S agalactiae (1), S pneumoniae (16), Streptococcus anginosus (2) Cerebrospinal fluid 1 S pneumoniae (1) Exudate 12 S aureus (9), S anginosus (2), Streptococcus milleri (1) Respiratory tract 38 E coli (2), E cloacae (1), Haemophilus influenzae (13), K pneumoniae (1), M catarrhalis (1), Neisseria meningitidis (1), P aeruginosa (3), Serratia liquefaciens (1), S aureus (1), S pneumoniae (13), Streptococcus pyogenes (1) Stool 4 Campylobacter species (1), Salmonella species (2), Shigella species (1) Throat 6 S pyogenes (4), Bordetella pertussis (1), S pneumoniae (1) American Society of Clinical Pathologists Am J Clin Pathol 2001;115:

3 Wile et al / LEFT SHIFT MODEL PREDICTS INFECTION We obtained the control group using a similar SQL query code. The initial data set included several thousand outpatients with a blood specimen obtained for a CBC count between October 7, 1999, and December 8, We downloaded the initial data set into Microsoft Excel 2000 and eliminated all cases with incomplete information. We defined complete cases as cases with a complete CBC count and 100-cell manual differential count. The remaining 930 cases made up the control group. In selection of the control group, we assumed that outpatient status without clinical symptomatology implied negative cultures. With these data, we fitted a logistic regression model 15 ; the independent variables were the absolute neutrophil count, band count, patient age, and immature granulocyte count. More descriptive detail about this model is available in the Appendix. The software for maximizing the likelihood and producing ROC plots was written in S programming language. Additional analyses were done using S-plus (Mathsoft, Seattle, WA). Results The most general model fitted includes absolute neutrophil count, band count, immature granulocytes, age, and their squares and cross-products. This 15-parameter model fits better than a 3-parameter model involving just a constant term, coefficients for neutrophil count, and band count (likelihood ratio chi square = ; P < ). In Figure 1 and Table 2, we compare the ROC curves for the 15-parameter model (model 3) with a model including band count and neutrophil count (model 2) and a third model using only the automated absolute neutrophil count (model 1). A good predictor will have a large area under its ROC curve. For our most comprehensive 15-parameter model, the area is 89% with an estimated SE of 2%. For the neutrophil and band model, the area is 86% with a 2% SE, and for the neutrophil-only model, the area is 78%. The overall logistic regression model is improved by the use of all variables, their squares, and cross-products. ROC curves suggest that while adding the band count to the model makes a statistically and clinically significant improvement, further extension of the model makes only modest changes in clinical prediction, even though the changes are statistically significant. We also assessed goodness of fit of model 2 by stratifying the data into high, low, and normal neutrophil counts and calculated the chi-square statistic comparing observed positive cultures with predicted in each of these partitions (chi square = ; P =.78) and found reasonable agreement between the predicted and actual number of positive cultures in each group Table 3. A similar test partitioning Sensitivity Specificity Figure 1 Receiver operating characteristic curve for models 1, 2, and 3. Model 3 is the 15-parameter model that includes absolute neutrophils, bands, immature granulocytes, their sums, cross-products, and a constant. This model fills 89% of available area. Model 2 includes automated absolute neutrophil count, manual band count, and a constant. This model fills 86% of the available area. Model 1 includes the automated absolute neutrophil count. This model fills 78% of the available area. Table 2 Comparison of Receiver Operating Characteristic (ROC) Areas for Models 1, 2, and 3 No. of ROC Area Model Variable Parameters (%) * 1 Automated absolute neutrophil 2 78 count 2 Automated absolute neutrophil 3 86 count; manual band count 3 Automated absolute neutrophil count; manual band count; manual immature granulocyte count; patient age * ROC area is the area under the receiver operator characteristic curve. patients into low band count groups (<12% [0.12]) and high band count groups also found reasonable agreement (chi square = ; P =.38). Hosmer and Lemeshow 15 suggest a goodness of fit test comparing observed with predicted events after ordering patients by predicted risk and grouping them in 10 or 20 groups of equal size. This goodness of fit test indicated that model 2 offered a reasonable description of the data (chi square = ; P =.15). We found that high band counts and high neutrophil counts predicted positive cultures. Individual z tests (normal approximation) applied to the coefficients of both bands and neutrophils proved statistically significant (P < ). 646 Am J Clin Pathol 2001;115: American Society of Clinical Pathologists

4 Hematopathology / ORIGINAL ARTICLE Table 3 Comparison of Predicted With Observed Positive Cultures * No. Observed No. Predicted Total Percentage Positive Neutrophils Low (<1,500/µL [< /L]) Normal (1,500-8,000/µL [ /L]) High (>8,000/µL [> /L) Bands Low (<12% [0.12]) High (12% [0.12] or more) * Observed and predicted refer to cultures positive for bacterial infection. Total refers to total positive and negative cultures. The logistic also was evaluated without the band counts. The log likelihood for this regression was (model 1) compared with (model 2) when both variables were included. The likelihood ratio chi-square statistic calculated from these values was or 66.4 with 1 df (P < ). The degradation in fit when band information was removed from model 3 was even more significant. Coefficients and SDs of the coefficients are given in a table in the Appendix. The combined variables improved the prediction as illustrated by plotting band count against neutrophil count. In Figure 2, we plot culture-positive cases, outpatient control cases, and the isoprobability line of the logistic regression (probability of 20% of having a positive culture). Of the 930 control cases, 877 lie to the left of that line (specificity 94%), while 67 of 116 positive cases lie to the right (sensitivity 58%). By moving the isoprobability line toward the origin, we could obtain an improved sensitivity in exchange for a loss in specificity as illustrated in the ROC curves shown in Figure 1. Normally, in calculating a sensitivity and specificity for neutrophil counts or for band counts, one selects a horizontal cutoff value (threshold). No horizontal or vertical line separates infection-positive cases from outpatients as well as a diagonal line. For example, if we attempt to classify patients using a cutoff of /L neutrophils, we can achieve a sensitivity of 72% and specificity of 78%, but the predictions arranged by range of neutrophil count are grossly inadequate. The high and middle groups have more than 300 predicted positive cultures, vastly overestimating the 99 actually found in those groups, while none of the 17 positive cultures in the low neutrophil range is correctly predicted. By using a band count of 3 as cutoff, we have a sensitivity of 75% with a specificity of 83% but an excessive number of predicted positive cultures in all categories of neutrophil count. No matter what band count threshold we choose, there are problems with predictions for individual patients. Many publications have focused on these problems 1,2,10-13 and dismissed the additional information from band counts and immature granulocytes as unnecessary. Neutrophils (x 10 9 /L) Discussion % Bands Figure 2 Plot of absolute neutrophil count vs percentage band count with isoprobability line. A diagonal isoprobability line (model 2) better separates infection-positive cases (+) from control cases (circles) than any horizontal line. This multivariate logistic model provides diagonal separation isoprobability lines, whereas horizontal isoprobability lines result from the single-variable model. Despite numerous publications 1-3,10-13 refuting the manual band count as an indicator of infection, we continue to receive numerous requests for manual differential blood cell counts. We undertook the present study to answer the question: Are working (100-cell) manual differential blood American Society of Clinical Pathologists Am J Clin Pathol 2001;115:

5 Wile et al / LEFT SHIFT MODEL PREDICTS INFECTION counts useful supplements to absolute neutrophil counts for predicting infection? Manual differential cell counts make a significant contribution to our multivariate logistic regression model. Model 2 uses the absolute neutrophil count and the percentage band count to help predict infection. This 2-variable model produces an ROC curve with 86% of the available area falling under the ROC curve. If we use information from the automated differential count alone, we generate a 1- variable model with an ROC area of 78%. We can obtain another view of this issue by analyzing the accuracy of information available for clinical decision making on an individual patient basis. When we compare the accuracy of information of model 2 and model 1, we find that 38.8% of the patients with positive cultures have more accurate information for clinical decision making when model 2 is used, and only 13.8% of cases have less accurate information. Likewise, 32.5% of control cases would have more accurate information from model 2 compared with model 1, and only 5.9% would have less accurate information. Details of these calculations are given in the Appendix. The supplemental information provided by band count is most useful when the neutrophil count is low to normal (8,000/µL [ /L] or less). As the neutrophil count rises above normal, the probability of infection becomes so large that additional information from band count adds little supplemental value. Multivariate logistic regression models resemble the way manual and automated differential counts are used by clinicians to treat patients because clinicians examine the test results from manual and automated differential counts together. Typically, they make decisions about infection using multiple sources of information and without using discrete cutoffs or thresholds for single variables. The present study raises some possibilities for further investigation of manual differential cell counting. One possibility is to collect a repository of cases large enough to allow for further segmentation beyond the segments chosen for our study (low, normal, and high neutrophil counts). From this information, we could derive a set of clinical guidelines to help curtail unnecessary band counting and increase its use when the information would be helpful. Another possibility would be to create even more sophisticated multivariate models that include even more variables, such as temperature and sedimentation rate. Factor analysis of such models may prove covariance between band count and some unstudied variables. In this case, the new variables might supplant band counting. Physicians typically use more information than we entered in our model, and it may well be that if one were to take this larger set of information into account, diagnoses would rarely change. Such a study could be done only prospectively, because it would be important to clearly identify the population in question. Combining information from absolute neutrophil counts and the manual band count can provide better diagnosis of infection, especially at low to normal absolute neutrophil counts, and evaluation of the merits of doing manual counts should consider the joint use of information rather than merely assessing the merits of each test as a solitary and independent procedure. Additional studies to further elucidate the populations deriving maximal benefit from band counting are indicated. Appendix Logistic Model The logistic equation often is used to describe the risk of an event such as death or a positive culture or some other event. The probability of that event, P e, is calculated from P e 1 + exp( H) H is an expression with the variables thought to affect P. Specifically we use for H, H = b 1 + b 2 N + b 3 B H = (Absolute Neutrophil Count) (Band Neutrophil Count) where N represents the total neutrophil count 10 9 /L and B the total band counts of 100 cells counted. With this notation, a positive coefficient for N or B indicates that high values predict an increased probability of the event (a positive culture). The expression for H may be extended to include additional variables and quadratic terms and crossproducts. We obtained the following estimates of the parameters for the neutrophil + band model: Constant term (B1) = 3.92 ± 0.23 (SD) Neutrophil count coefficient = ± (SD) Band count coefficient = ± (SD) Coefficients of Models 1, 2 and 3 The variables of the model were absolute neutrophil count (N), band count (B), preband count (PB), and age of the patient (A). P values reported as 0 were all less than Table 4. Statistical Analysis of Clinical Decision Making If we take the difference between the 2-variable P e and the neutrophil-only P e and divide it by the SE of that difference, patient-by-patient we derive a z score for each patient. A z score of 1.96 or 1.96 represents a statistically significant shift at P <.05. By this measure, the addition of band counts to the regression made a statistically large increase in 648 Am J Clin Pathol 2001;115: American Society of Clinical Pathologists

6 Hematopathology / ORIGINAL ARTICLE Table 4 Model Parameters Model Variable Coefficient SD z P 1 Intercept N Intercept N B Intercept N B PB A N N N B N PB N A B B B PB B A PB PB PB A A A P e in 45 of 116 patients with positive cultures while appropriately reducing P e in 302 of 930 outpatients. Thus, the inclusion of band counts provides more secure decisions in a large number of cases. In a few instances (16/116 for the positive culture group and 55/930 for the outpatient group), less accurate decisions could be made by adding the band count information to the neutrophil count. From the 1 Cascade Pathology Group, Legacy Portland Hospitals, Department of Pathology, Emanuel Hospital and Health Center; 2 Research Department, Legacy Holliday Park Medical Center Clinical Research and Technology Center; and 3 Department of Pathology, Legacy Portland Hospitals, Emanuel Hospital and Health Center, Portland, OR. Supported by the Cascade Pathology Group, Portland, OR. Address reprint requests to Dr Wile: Department of Pathology, Legacy Emanuel Hospital and Health Center, 2801 N Gantenbein Ave, Portland, OR References 1. Novak RW. The beleaguered band count. Clin Lab Med. 1993;14: Dutcher TF. Leukocyte differentials: are they worth the effort? Clin Lab Med. 1984;4: Buttarello M, Gadotti M, Lorenz C, et al. Evaluation of four automated hematology analyzers: a comparative study of differential counts (imprecision and inaccuracy). Am J Clin Pathol. 1992;97: Cornbleet PJ, Myrick D, Judkins S, et al. Evaluation of the Cell DYN 3000 differential. Am J Clin Pathol. 1992;98: Cornbleet PJ, Myrick D, Levy R. Evaluation of the Coulter STKS five-part differential. Am J Clin Pathol. 1993;99: Gulati GL, Hyun BH, Ashton JK. Advances in the past decade in automated hematology. Am J Clin Pathol. 1992;98(suppl 1):S11-S Krause JR. The automated white blood cell differential: a current perspective. Hematol Oncol Clin North Am. 1994; 8: Cornbleet PJ, Thorpe G, Myrick D. Evaluation of instrument flagging of left shift using College of American Pathologists reference band identification criteria. Lab Hematol. 1995;1: Marigold A, Westengard J, Dutcher T. Band neutrophil counts are unnecessary for the diagnosis of infection in patients with normal total leukocyte counts. Am J Clin Pathol. 1994;102: Rimarenko S, Castella A, Salzberg MR, et al. Evaluation of the automated leukocyte differential count in emergency department patients. Am J Emerg Med. 1987;5: Shapiro M, Martin F, Greenfield S. The complete blood count and leukocyte differential count: an approach to the rational application. Ann Intern Med. 1987;106: Krause J. Automated differentials in the hematology laboratory. Am J Clin Pathol. 1990;93(suppl 1):S11-S Wenz B, Gennis P, Canova C, et al. The clinical utility of the leukocyte differential in emergency medicine. Am J Clin Pathol. 1986;86: Mathy K, Koeptke JA. The clinical usefulness of segmented vs stab neutrophil criteria for differential leukocyte counts. Am J Clin Pathol. 1974;61: Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley and Sons; 1989:6-9, Hanley, JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143: Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148: College of American Pathologists. College of American Pathologists Survey Manual. Northfield, IL: College of American Pathologists; 1994: American Society of Clinical Pathologists Am J Clin Pathol 2001;115: