The Association between White Blood Cell ... - Wiley Online Library

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October 2004, Vol. 11, No. 10

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The Association between White Blood Cell Count and Acute Myocardial Infarction In-hospital Mortality: Findings from the National Registry of Myocardial Infarction Mary Grzybowski, PhD, MPH, Robert D. Welch, MD, Lori Parsons, BS, Chiadi E. Ndumele, MD, Edmond Chen, MD, Robert Zalenski, MD, Hal V. Barron, MD Abstract Objectives: Although cross-sectional and prospective studies have shown that the white blood cell (WBC) count is associated with long-term mortality for patients with ischemic heart disease, the role of the WBC count as an independent predictor of short-term mortality in patients with acute myocardial infarction (AMI) has not been examined as extensively. The objective of this study was to determine whether the WBC count is associated with inhospital mortality for patients with ischemic heart disease after controlling for potential confounders. Methods: From July 31, 2000, to July 31, 2001, the National Registry of Myocardial Infarction 4 enrolled 186,727 AMI patients. A total of 115,273 patients were included in the analysis. Results: WBC counts were subdivided into intervals of 1,000/mL, and in-hospital mortality rates were determined for each interval. The distribution revealed a J-shaped curve. Patients with WBC counts .5,000/mL were subdivided into quartiles, whereas patients with WBC counts ,5,000/mL were assigned to a separate category labeled ‘‘subquartile’’ and were analyzed separately. A linear increase in inhospital mortality by WBC count quartile was found. The unadjusted odds ratio (OR) for the fourth versus the first

quartile showed strong associations with in-hospital mortality among the entire population and by gender: 4.09 (95% confidence interval [95% CI] = 3.83 to 4.73) for all patients, 4.31 (95% CI = 3.93 to 4.73) for men, and 3.65 (95% CI = 3.32 to 4.01) for women. Following adjustment for covariates, the magnitude of the ORs attenuated, but the ORs remained highly significant (OR, 2.71 [95% CI = 2.53 to 2.90] for all patients; OR, 2.87 [95% CI = 2.59 to 3.19] for men; OR, 2.61 [95% CI = 2.36 to 2.99] for women). Reperfused patients had consistently lower in-hospital mortality rates for all patients and by gender (p , 0.0001). Conclusions: The WBC count is an independent predictor of in-hospital AMI mortality and may be useful in assessing the prognosis of AMI in conjunction with other early risk-stratification factors. Whether elevated WBC count is a marker of the inflammatory process or is a direct risk factor for AMI remains unclear. Given the simplicity and availability of the WBC count, the authors conclude that the WBC count should be used in conjunction with other ancillary tests to assess the prognosis of a patient with AMI. Key words: WBC count; in-hospital mortality; epidemiology; myocardial infarction; predictor. ACADEMIC EMERGENCY MEDICINE 2004; 11:1049–1060.

Elevated white blood cell (WBC) counts typically indicate infection and inflammation but also play a role in vascular injury and atherogenesis,1–3 the

development of an atherosclerotic plaque rupture, and thrombosis.2,4–6 This suggests that elevated WBC counts may both serve as a biomarker for, and be a risk factor for, cardiac disease. A number of prospective epidemiologic studies have demonstrated that an elevated WBC count is associated with risk factors for coronary heart disease,7–9 acute myocardial infarction (AMI),9–13 coronary artery disease and related events,14–16 all-cause mortality,11,17,18 and long-term mortality in patients with known coronary artery disease.14 While an elevated WBC count has been shown to be an independent predictor of long-term cardiac mortality and all-cause mortality, there have been few studies19–21 examining the association between elevated WBC count and early death. The purpose of this study was to determine whether the WBC count at presentation is associated with in-hospital mortality in AMI patients after controlling for demographics,

From the Department of Emergency Medicine, Wayne State University School of Medicine (MG, RDW, RZ), Detroit, MI; Ovation Research Group (LP), Seattle, WA; Department of Medical Affairs, Genentech Inc. (EC, HVB), South San Francisco, CA; Brigham and Women’s Hospital (CEN), Boston, MA; and Departments of Epidemiology and Biostatistics and Medicine, University of California (HVB), San Francisco, CA. Received January 16, 2004; revision received April 15, 2004; accepted June 8, 2004. The National Registry of Myocardial Infarction is supported by Genentech. Drs. Zalenski and Barron are paid consultants to Genentech. Address for correspondence and reprints: Mary Grzybowski, PhD, MPH, Wayne State University School of Medicine, 6G University Health Center, 4201 St. Antoine, Detroit, MI 48201. Fax: 313-9937703; e-mail: [email protected]. doi:10.1197/j.aem.2004.06.005

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medical history, presenting clinical characteristics as well as medications given within 24 hours of presentation, and diagnostic and therapeutic procedures administered during hospitalization.

METHODS Study Design. The National Registry of Myocardial Infarction (NRMI)22 is an observational study of hospitalized patients with confirmed AMI that commenced in 1990. NRMI collects data in order to describe national AMI presentation, treatment interventions, practice patterns, and patient outcomes. Since its inception, NRMI has launched four multicenter observational studies. The study population for the current investigation was from the NRMI 4 database. Data for the current study were collected from 1,189 hospitals between July 31, 2000, and July 31, 2001, in all 50 states in the United States. All study site coordinators were formally trained and given a manual of operations to correctly abstract the data from the medical records, inclusive of electrocardiogram readings, and transcribe the data onto case report forms. The case report forms were then forwarded to StatProbe, Inc. (Lexington, KY), where the data underwent double data entry or electronic scanning as well as multiple electronic checks for accuracy. Any unrecorded fields or inconsistencies were flagged and returned to the site for clarification and correction. NRMI collection procedures have been previously reported.23 Individual sites were responsible for compliance with institutional review board protocols regarding the collection and use of data for research purposes. Study Population. Adult AMI patients eligible for study inclusion (n = 186,727) were obtained from the NRMI 4 database between July 31, 2000, and July 31, 2001. Hospital participation varied by geographic location, ranging from 10.9% in the East to 30.9% in the South (Table 1). The diagnosis of myocardial infarction was based on a patient history suggestive of AMI accompanied by at least one of the following: 1) a total creatine kinase (CK) or CK-MB value greater than or equal to twice the upper limit of normal (normal as defined by an individual hospital’s laboratory definition; 2) electrocardiographic evidence indicative an AMI, including new Q-wave and ST elevation; and 3) in the event the CK, CK-MB, or electrocardiogram data were unavailable/inconclusive, alternative cardiac markers, nuclear medicine testing, echocardiographic evidence, or autopsy evidence indicative of AMI. Additionally, a discharge diagnosis of AMI (International Classification of Diseases, Ninth Revision, Clinical Modification code 410.X1) must have been present to qualify for entry into the NRMI 4. Exclusion criteria included the following: 1) patients who were transferred to other

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WBC COUNT AND MI MORTALITY

TABLE 1. Distribution of National Registry of Myocardial Infarction 4 Final Study Population by Geographic Regions (n = 115,273) U.S. Census Region East (10.9%) New England Middle Atlantic South (30.9%) East South Central West South Central South Atlantic Midwest (30.7%) East North Central West North Central West (27.5%) Mountain Pacific

Percent 3.5 7.4 6.4 8.2 16.3 20.7 10.0 8.0 19.5

health care facilities because the outcome results were not consistently available at nonstudy sites, 2) patients who presented with cardiogenic shock or had missing heart failure data, 3) patients who did not have the WBC count documented in the medical chart, and 4) patients who did not have a primary diagnosis of AMI. The latter criterion eliminated patients with other primary illnesses that may have resulted in altered WBC counts. Data Collection and Analysis. Independent variables included WBC count at admission, demographics, medical history, presenting clinical characteristics, medications administered within 24 hours of presentation, diagnostic studies used, and therapeutic procedures. Age, systolic blood pressure, heart rate, and WBC count were analyzed as both continuous and categorical variables. Values for continuous variables outside the following ranges were set to missing: WBC count equal to 0 or .30 (103/mL), systolic blood pressure ,1 mm Hg or .250 mm Hg, and heart rate ,1 beat/min or .220 beats/min. For the multivariate regression, all independent variables were categorical (yes or no); elder age was defined as older than 75 years, systolic hypotension as ,90 mm Hg, and tachycardia as .100 beats/min. Race was collapsed into a dichotomous variable (white or nonwhite). Heart failure was classified as evidence of clinical heart failure (yes [Killip classes II and III] or no [Killip class I]). The dependent variable was inhospital mortality. The collected data were first examined to determine differences between patients who did and did not meet eligibility for inclusion. Chi-square tests were used to determine frequency differences in selected characteristics between the groups.24 White blood cell counts were subdivided into intervals of 1,000/mL, and in-hospital mortality rates were determined for each interval. The distribution revealed a J-shaped curve (Figure 1). In-hospital

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Figure 1. In-hospital mortality by white blood cell count (1,000/mL).

mortality was 28.6% in the lowest interval (0.1–1.0 3 103/mL), decreased to 3.7% when the WBC count was .5–6 3 103/mL, and peaked at 34.8% when the WBC count was .25–26 3 103/mL. Patients with WBC counts .5,000/mL were subdivided into quartiles, whereas patients with WBC counts ,5,000/mL were assigned to a separate category labeled ‘‘subquartile.’’ Classifying patients in this manner ensured that the first quartile, which had the least mortality, would serve as the appropriate reference group for comparing the odds of death. Four indicator variables were created to assess comparisons for the subquartile and quartiles 2, 3, and 4 relative to quartile 1. Baseline differences between quartiles were compared using analysis of variance and chi-square tests for continuous variables and categorical variables, respectively.24 The Cochran–Mantel–Haenszel test was used to determine proportional trends for selected independent variables and in-hospital mortality by WBC count quartile. Chi-square and trend tests excluded patients with WBC counts in the subquartile group because they may have represented a special subpopulation (e.g., patients in the subquartile group had the highest proportion of active malignancy history). We also evaluated the association between reperfusion status and in-hospital mortality by WBC count quartile before and after adjusting for age, gender, and race. Maximum likelihood estimates of the unadjusted and adjusted odds ratios (ORs) were calculated using simple and multivariate logistic regression, respectively, to estimate the association between in-hospital mortality and WBC count quartile indicator variables. Variables considered for entry in the multivariate models were based on the significance of bivariate associations and empirical knowledge. Although geographic variations in the treatment of AMI have been demonstrated in the literature,25,26 we a priori chose not to adjust for geographic region because we suspected that region would be highly correlated with several of the predictor variables. Variables were entered into the models in successive covariatespecific blocks: demographics (age, gender, and race), medical history (chronic renal failure, previous AMI, hypertension, cerebrovascular disease, congestive

heart failure, active malignancy, current smoking status, chronic obstructive pulmonary disease, diabetes, stroke, and prior coronary artery bypass graft [CABG] surgery and percutaneous coronary intervention [PCI]), presenting clinical characteristics (evidence of clinical heart failure, systolic hypotension, tachycardia, ST elevation or left bundle branch block; infarct location [anterior/septal or neither]), transfer status (having been transferred from a different institution), medications administered within 24 hours of arrival (aspirin, beta-blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, and glycoprotein IIa/IIIb inhibitors), initial hospital diagnostic test (cardiac catheterization), and hospital therapeutic procedures (initial reperfusion and nonprimary CABG surgery and PCI). The c statistics for covariate-specific fitted models were computed to indicate the ability to discriminate between in-hospital death and survival. Because of reports of genderspecific differences regarding demographics, medical history, and treatment in AMI patients, adjusted inhospital mortality rates were calculated for all patients and then by gender using logistic regression.27–33 Logistic regression is appropriate in estimating the association between a binary dependent variable and a primary independent variable while adjusting for at least one potential confounder. Logistic regression requires only minimal assumptions of the distribution properties of the independent variables and allows for estimation of the OR via exponentiation of the b coefficient.34 Appropriate regression diagnostics were performed to confirm the validity of the multivariate models and included testing for outliers and assessing the predicted outcomes by actual outcomes via classification tables.34,35 All reported p-values are two-tailed. An a of 0.05 was considered significant. Due to the extremely large number of patients in this investigation, care must be used in interpreting the clinical importance of all p-values. All statistical analyses were performed using SAS version 8.2 (SAS Institute, Inc., Cary, NC).36

RESULTS Of 186,727 eligible study patients, a total of 71,454 patients (38.3%) were excluded from analysis for several reasons. We excluded 38,479 patients who were transferred from NRMI-participating hospitals to other health care facilities, 4,967 patients who presented with cardiogenic shock or had missing heart failure data, 6,204 patients who did not have WBC count data, and 21,804 patients who did not have a primary diagnosis of AMI. Therefore, 115,273 patients were included in this analysis. Mean WBC counts, gender distributions, or race distributions did not differ between patients who were and were not included in the final study

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population. Patients included were more likely to be elders than those excluded (35.9% vs. 38.4%; OR, 1.11; 95% confidence interval [95% CI] = 1.09 to 1.13) and more likely to have been transferred to an NRMIparticipating hospital than those excluded (14.3% vs. 26.7%; OR, 2.18; 95% CI = 1.13 to 2.24). Those included were also more likely to have a medical history of myocardial infarction (25.1% vs. 22.7%; OR, 1.14; 95% CI = 1.11 to 1.16). Fewer systolic hypotensive patients were included in the analysis (3.4% vs. 4.8%; OR, 0.70; 95% CI = 0.66 to 0.73). The mean (6SD) age of the final study population was 68.8 (614.3) years, and the mean (6SD) WBC count was 10.4 (64.0) 3 103/mL. The mean (6SD) heart rate was 86.3 (623.7) beats/min, and the mean (6SD) systolic blood pressure was 144.1 (632.0) mm Hg. Most patients included in the study were white (84%), and almost 60% were men. The overall inhospital mortality rate was 8.7% (95% CI = 8.53% to 8.86%). The in-hospital mortality rate was higher

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among women than men (10.8% vs. 7.3%; OR, 1.55; 95% CI = 1.49 to 1.61). Overall significant differences were found for all characteristics by WBC count quartile (Tables 2 and 3). Trend tests showed that the proportion of patients increased by WBC count quartile for patients with the following demographic and medical history characteristics (Table 2): white race, smoking, and history of chronic illnesses, including congestive heart failure, chronic obstructive pulmonary disease, chronic renal insufficiency, diabetes, peripheral vascular disease, and stroke. Inverse associations were found between the following characteristics and WBC count quartile: male gender and history of chronic illnesses, including angina, hypercholesterolemia, hypertension, myocardial infarction, and history of CABG surgery and PCI. Mean heart rate increased from the first to the fourth WBC count quartile (82.3 to 92.8 beats/min; p , 0.0001), whereas mean systolic blood pressure

TABLE 2. Demographic and Medical History Characteristics by White Blood Cell Count Quartile

Characteristic Demographics Mean age, yr (SD) Age 75 years or older Male gender White race Medical history Cigarette smoker Active malignancy Angina Congestive heart failure Chronic obstructive pulmonary disease Cerebrovascular disease Chronic renal insufficiency Diabetes Hypercholesterolemia Hypertension Myocardial infarction Peripheral vascular disease Stroke History of therapeutic procedures Coronary artery bypass graft surgery Percutaneous coronary intervention

Subquartile All Quartiles ,5.0 3 103/ (n = 115,273) mL (n = 3,544)

Quartile 1 Quartile 2 5.0–7.7 3 103/ 7.8–9.7 3 103/ mL (n = 27,268) mL (n = 28,446)

Quartile 3 9.8–12.4 3 103/ mL (n = 28,217)

Quartile 4 $12.5 3 103/ mL (n = 27,798)

pvalue

68.8 (14.3)

70.6 (13.5)

69.6 (13.6)

68.5 (14.0)

67.8 (14.5)

69.0 (15.1)

,0.0001

44,244 (38.4) 68,813 (59.7) 96,797 (84.0)

1,508 (42.6) 2,154 (60.8) 2,628 (74.2)

10,799 (39.6) 17,091 (62.7) 22,258 (81.7)

10,479 (36.8) 17,395 (61.2) 23,949 (84.3)

10,071 (35.7) 16,951 (60.1) 24,043 (85.3)

11,387 (41.0) 15,222 (54.8) 23,919 (86.1)

,0.0001 ,0.0001 ,0.0001

29,848 (25.9)

497 (14.0)

4,801 (17.6)

7,111 (25.0)

8,758 (31.0)

8,681 (31.2)

,0.0001

2,545 (2.2) 14,633 (12.7)

248 (7.0) 521 (14.7)

564 (2.1) 4,020 (14.7)

514 (1.8) 3,812 (13.4)

514 (1.8) 3,391 (12.0)

705 (2.5) 2,889 (10.4)

,0.0001 ,0.0001

19,399 (16.8)

595 (16.8)

3,949 (14.5)

4,322 (15.2)

4,662 (16.5)

5,871 (21.1)

,0.0001

17,633 (15.3)

453 (12.8)

3,417 (12.5)

3,936 (13.8)

4,320 (15.3)

5,507 (19.8)

,0.0001

5,922 (5.1)

177 (5.0)

1,353 (5.0)

1,397 (4.9)

1,410 (5.0)

1,585 (5.7)

,0.0001

11,279 34,242 40,497 69,449 28,903

(9.8) (29.7) (35.1) (60.2) (25.1)

369 953 1,148 2,178 986

(10.4) (26.9) (32.4) (61.5) (27.8)

2,372 7,443 10,399 16,670 7,392

(8.7) (27.3) (38.1) (61.1) (27.1)

2,503 8,142 10,428 17,194 7,173

(8.8) (28.6) (36.7) (60.4) (25.2)

2,632 8,502 10,086 16,867 6,845

(9.3) (30.1) (35.7) (59.8) (24.3)

3,403 9,202 8,436 16,540 6,507

(12.2) (33.1) (30.3) (59.5) (23.4)

,0.0001 ,0.0001 ,0.0001 ,0.001 ,0.0001

9,963 (8.6) 11,319 (9.8)

297 (8.4) 329 (9.3)

2,092 (7.7) 2,385 (8.7)

2,326 (8.2) 2,624 (9.2)

2,433 (8.6) 2,693 (9.5)

2,815 (10.1) 3,289 (11.8)

,0.0001 ,0.0001

16,571 (14.4)

611 (17.2)

4,896 (18.0)

4,339 (15.3)

3,597 (12.7)

3,128 (11.3)

,0.0001

13,938 (12.1)

499 (14.1)

3,922 (14.4)

3,631 (12.8)

3,239 (11.5)

2,647 (9.5)

,0.0001

Data are presented as the number (percent) unless otherwise indicated. p-value for overall difference represents differences by quartiles only (excludes subquartile). All tests for trend were highly significant (p , 0.0001) except age 75 years or older, active malignancy, and cerebrovascular disease.

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TABLE 3. Clinical Presenting Characteristics, Medications Administered within 24 Hours, Initial Diagnostic Tests Administered, and Therapeutic Procedures by White Blood Cell Count Quartile

Characteristic Clinical presentation Heart rate .100 beats/min Systolic blood pressure ,90 mm Hg Killip class II/III ECG reading Normal ST elevation ST depression T-wave inversion Nonspecific ST/ T waves Q wave LBBB (new/unknown) LBBB (old) RBBB Other ECG reading Anterior/septal infarct Transferred in Medications administered Aspirin Angiotensinconverting enzyme inhibitors Antiarrhythmics Beta-blockers Calcium channel blockers Digoxin Diuretics Glycoprotein IIa/IIIb inhibitors Lipid-lowering agent Statins Initial diagnostics Cardiac catheterization Initial reperfusion therapy* Thrombolytics PCI (with/without stent) Immediate CABG surgery Nonprimary therapeutic procedures PCI CABG surgery

Quartile 1 Quartile 2 Quartile 3 Quartile 4 Subquartile 5.0–7.7 3 103/ 7.8–9.7 3 103/ 9.8–12.4 3 103/ $12.5 3 103/ All Quartiles ,5.0 3 103/ (n = 115,273) mL (n = 3,544) mL (n = 27,268) mL (n = 28,446) mL (n = 28,217) mL (n = 27,798) p-value

26,486 (23.3)

643 (18.3)

4,366 (16.2)

5,311 (18.9)

6,513 (23.4)

9,653 (35.3)

,0.0001

3,843 (3.4) 27,109 (23.5)

126 (3.6) 648 (18.3)

583 (2.2) 4,629 (17.0)

733 (2.6) 5,542 (19.5)

876 (3.1) 6,578 (23.3)

1,525 (5.6) 9,712 (34.9)

,0.0001 ,0.0001

8,463 37,625 33,705 16,318

(7.4) (32.8) (29.4) (14.2)

390 734 833 517

(11.1) (20.8) (23.6) (14.7)

2,632 6,837 6,983 4,145

(9.7) (25.2) (25.7) (15.3)

2,249 8,949 8,102 4,194

(7.9) (31.6) (28.6) (14.8)

1,901 10,333 8,773 3,925

(6.8) (36.8) (31.2) (14.0)

1,291 10,772 9,014 3,537

(4.7) (38.9) (32.6) (12.8)

,0.0001 ,0.0001 ,0.0001 ,0.0001

36,549 10,041 4,491 2,622 7,145 22,153

(31.9) (8.8) (3.9) (2.3) (6.2) (19.3)

1,316 181 121 76 234 818

(37.3) (5.1) (3.4) (2.2) (6.6) (23.2)

9,715 1,764 840 629 1,772 5,298

(35.8) (6.5) (3.1) (2.3) (6.5) (19.5)

9,255 2,330 1,001 593 1,685 5,256

(32.7) (8.2) (3.5) (2.1) (6.0) (18.6)

8,456 2,753 1,042 632 1,683 5,153

(30.1) (9.8) (3.7) (2.2) (6.0) (18.3)

7,807 3,013 1,487 692 1,771 5,628

(28.2) (10.9) (5.4) (2.5) (6.4) (20.3)

,0.0001 ,0.0001 ,0.0001 ,0.05 ,0.01 ,0.0001

24,483 (21.2) 30,775 (26.7)

621 (17.5) 855 (24.1)

4,866 (17.9) 7,231 (26.5)

5,721 (20.1) 7,888 (27.7)

6,398 (22.7) 7,817 (27.7)

6,877 (24.7) 6,984 (25.1)

,0.0001 ,0.0001

97,654 (84.7)

2,911 (82.1)

23,551 (86.4)

24,652 (86.7)

24,190 (85.7)

22,350 (80.4)

,0.0001

39,632 (34.4) 10,160 (8.8) 77,451 (67.2)

1,179 (33.3) 216 (6.1) 2,343 (66.1)

9,253 (33.9) 1,796 (6.6) 18,868 (69.2)

9,888 (34.8) 2,302 (8.1) 19,952 (70.1)

9,884 (35.0) 2,711 (9.6) 19,270 (68.3)

9,428 (33.9) 3,135 (11.3) 17,018 (61.2)

,0.01 ,0.0001 ,0.0001

16,196 (14.1) 12,536 (10.9) 35,868 (31.1)

549 (15.5) 372 (10.5) 1,091 (28.8)

4,084 (15.0) 2,538 (9.3) 7,176 (26.3)

3,912 (13.8) 2,803 (9.9) 7,859 (27.6)

3,811 (13.5) 3,035 (10.8) 8,762 (31.1)

3,840 (13.8) 3,788 (13.6) 11,052 (39.8)

,0.0001 ,0.0001 ,0.0001

28,217 (24.5)

640 (18.1)

6,553 (24.0)

7,267 (25.5)

7,536 (26.7)

6,221 (22.4)

,0.0001

5,286 (4.6) 26,802 (23.3)

183 (5.2) 777 (21.9)

1,441 (5.3) 6,914 (25.4)

1,371 (4.8) 6,961 (24.5)

1,241 (4.4) 6,764 (24.0)

1,050 (3.8) 5,386 (19.4)

,0.0001 ,0.0001

42,105 (36.5)

1,252 (35.3)

10,927 (40.1)

11,036 (38.8)

10,386 (36.8)

8,504 (30.6)

,0.0001

30,954 (27.0) 17,752 (15.4)

573 (16.3) 321 (9.1)

5,928 (21.8) 4,195 (15.5)

7,704 (27.2) 4,338 (15.3)

8,694 (30.9) 5,006 (18.1)

8,055 (29.1) 17,752 (15.5)

,0.0001

12,596 (10.9)

245 (7.0)

2,516 (9.3)

3,217 (11.4)

3,526 (12.6)

3,092 (11.2)

7 (0.2)

127 (0.5)

146 (0.5)

160 (0.6)

152 (0.5)

819 (23.1) 302 (8.5)

7,386 (27.1) 3,267 (12.0)

7,699 (27.1) 3,478 (12.2)

7,375 (26.1) 3,098 (11.0)

5,660 (20.4) 2,496 (9.0)

592 (0.5)

28,939 (25.1) 12,641 (11.0)

,0.0001 ,0.0001

Data are presented as number (percent). p-value for overall difference represents differences by quartiles only (excludes subquartile). All tests for trend were highly significant (p , 0.0001) except old LBBB, RBBB, other ECG reading, angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and glycoprotein IIa/IIIb inhibitors. CABG = coronary artery bypass graft; ECG = electrocardiogram; LBBB = left bundle branch block; PCI = percutaneous coronary intervention; RBBB = right bundle branch block. *Includes at least one reperfusion strategy: thrombolytics, PCI, and immediate CABG.

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decreased (from 147.8 to 139.1 mm Hg; p , 0.0001). Table 3 includes the associations between presenting clinical characteristics, medications administered within 24 hours of arrival, and diagnostic tests and therapeutic procedures used during hospitalization. There were positive linear trends in the proportion of patients and WBC count quartiles for the following presenting clinical characteristics: tachycardia, systolic hypotension, clinical signs of heart failure, and electrocardiogram readings including ST depression, Q wave, new/unknown left bundle branch block, and evidence of an anterior/septal myocardial infarction. Inverse linear trends were found among patients presenting with normal electrocardiogram readings, T-wave inversions, and nonspecific ST/T waves. With regard to medications administered, positive linear trends were noted in patients given antiarrhythmics, digoxin, and diuretics, whereas inverse trends were observed in those given aspirin, lipid-lowering agents, and statins. Initial reperfusion was positively associated with WBC count quartile. Inverse linear trends were also found between WBC count quartile and patients having had a diagnostic cardiac catheterization and nonprimary therapeutic procedures, including CABG surgery and PCI during hospitalization. In-hospital mortality increased as WBC count quartile increased among all patients, by gender, and by initial reperfusion status (Table 4). Lower prevalences of in-hospital mortality were consistently found in male patients for all quartiles and the subquartile. In-hospital mortality in the subquartile group generally was in the midrange between quartiles 2 and 3. Compared with patients receiving initial reperfusion,

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those not receiving reperfusion were more likely to die while hospitalized (1.84 [95% CI = 1.74 to 1.94] for all patients, 2.24 [95% CI = 2.08 to 2.24] for men, and 1.27 [95% CI = 1.8 to 3.7] for women). Men, compared with women, were 1.8 times more likely to undergo initial reperfusion (95% CI of OR = 1.76 to 1.86). Among reperfused patients, men were more likely to survive to discharge (OR, 2.33; 95% CI = 2.13 to 2.58). Because NRMI results have shown that outcomes of reperfusion,37 coronary angiography,37 and immediate mechanical reperfusion38 differ by age and gender, and given our findings of significant differences in age, gender, and race by WBC count quartile, we calculated the ORs for in-hospital mortality and reperfusion status before and after controlling for age, gender, and race for all quartiles and the subquartile. Figure 2 clearly illustrates that, compared with patients receiving initial reperfusion, patients not receiving reperfusion were significantly more likely to die, except for those in the subquartile group (OR, 1.22; 95% CI = 0.88 to 1.8). Although the strength of the associations decreased following adjustment, the ORs retained their statistical significance, except for patients in quartile 1. Both unadjusted and adjusted analyses showed that the ORs increased in magnitude from WBC count quartile 1 to 4. To investigate the independent association of high WBC count and in-hospital mortality after controlling for potential confounding factors, multivariate models were developed for WBC count quartile (4 vs. 1) alone, for WBC count adjusting for covariate-specific blocks, and for WBC count adjusting for therapeutic procedures. Results are presented for the entire data

TABLE 4. In-hospital Mortality among All Patients and by Gender, Initial Reperfusion Status, and White Blood Cell Count Quartile Characteristic In-hospital mortality (n = 115,273) Gender Male (n = 68,813) Female (n = 46,460) Initial reperfusion status No (n = 83,775) Male (n = 46,920) Female (n = 36,855) Yes (n = 30,954) Male (n = 21,561) Female (n = 9,393)

All Subquartile Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quartiles ,5.0 3 103/mL 5.0–7.7 3 103/mL 7.8–9.7 3 103/mL 9.8–12.4 3 103/mL $12.5 3 103/mL p-value 8.7

7.1

4.4

6.2

8.4

15.9

,0.0001

7.3

6.3

3.6

5.2

7.3

13.9

,0.0001

10.8

8.4

5.8

7.7

10.2

18.4

,0.0001

9.9

7.4

4.7

7.0

10.0

18.6

,0.0001

8.7

7.0

4.0

6.3

9.2

17.6

,0.0001

11.3

8.0

5.8

8.0

10.9

19.6

,0.0001

5.6

5.9

3.5

4.0

5.0

9.3

,0.0001

4.1

3.6

2.4

2.9

3.8

7.0

,0.0001

9.1

12.3

6.2

6.6

8.0

14.0

,0.0001

All p-values for trend chi-square are ,0.0001.

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Figure 2. Unadjusted (filled circle) and adjusted (open diamond) odds ratios representing the relationship between reperfusion status and in-hospital mortality (n = 115,273).

set and by gender (Table 5). As expected, the strength of the association attenuated as covariates were added to the models. For all patients, male patients, and female patients, the greatest reductions in the ORs occurred after adding presenting clinical characteristics (22.9%, 26.6%, and 18.3%, respectively). Relative to those in the first quartile, there was a 2.7-fold increase in the odds of death during hospitalization for all patients in the fourth quartile. Men had a slightly higher OR (9.1% higher) for in-hospital mortality than women (ORs of 2.87 and 2.61, respectively) after controlling for covariates. Table 6 presents the unadjusted and adjusted ORs for in-hospital mortality for each quartile and subquartile relative to the first quartile. Covariates from the final population-specific multivariate model (described in Table 5) were used in adjusting in-hospital mortality for each population. For example, covariates from the final model for men in Table 5 were used to adjust in-hospital mortality ORs in Table 6. Again, after adjustment, the greatest reductions in OR among all patients, male patients, and female patients were in

those in the highest quartile relative to the lowest quartile (33.7%, 33.4%, and 28.5%, respectively) followed by patients in the WBC count subquartile group (19.3%, 9.9%, and 8.7%, respectively). The magnitudes of association remained higher in male patients, with the exception of the OR assessing the subquartile effect on in-hospital mortality relative to the first quartile.

DISCUSSION In this study, we evaluated the association between WBC count at admission and in-hospital mortality following AMI in 115,273 patients at 1,189 hospitals in the United States. We observed a strong association between WBC count and in-hospital mortality of AMI patients. After adjustment for patient demographics, medical history, presenting clinical characteristics, and treatment factors, patients in the higher WBC count quartile remained with significantly increased odds of death. This association was independent of age, gender, and reperfusion status, with increasing

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TABLE 5. Adjusted Odds Ratio for In-hospital Mortality (Fourth WBC Quartile vs. First WBC Quartile)

Mode Unadjusted Adjusted for Adjusted for Adjusted for Adjusted for Adjusted for Adjusted for

demographics above and medical history above and presenting clinical characteristics above and medications administered within 24 hours above and diagnostic procedures above and therapeutic procedures

All patients* (n = 115,169) OR (95% CI) 4.09 4.17 3.98 3.07 2.85 2.78 2.71

(3.83, (3.90, (3.72, (2.86, (2.65, (2.58, (2.53,

4.37) 4.46) 4.26) 3.29) 3.07) 2.99) 2.90)

Meny (n = 68,742) OR (95% CI) 4.31 4.62 4.39 3.22 3.02 2.94 2.87

(3.93, (4.20, (3.99, (2.91, (2.72, (2.65, (2.59,

4.73) 5.07) 4.84) 3.56) 3.34) 3.25) 3.19)

Womenz (n = 46,427) OR (95% CI) 3.65 3.75 3.60 2.94 2.71 2.65 2.61

(3.32, (3.41, (3.27, (2.66, (2.45, (2.39, (2.36,

4.01) 4.12) 4.00) 3.25) 3.00) 2.93) 2.99)

Only patients with complete data were included in multivariate logistic regression models. *The model adjusted for demographics included WBC, age 75 years or older, and gender (c statistic = 0.72). The model adjusted for medical history included smoking, chronic renal failure, cerebrovascular disease, active malignancy, percutaneous coronary intervention, chronic obstructive pulmonary disease, stroke, diabetes, and congestive heart failure (c statistic = 0.75). The model adjusted for presenting clinical characteristics included systolic blood pressure ,90 mm Hg, heart rate .100 beats/min, ST elevation or left bundle branch block, and anterior/septal myocardial infarction location (c statistic = 0.78). The model adjusted for medications administered within 24 hours included angiotensin-converting enzyme inhibitors, aspirin, beta-blockers, calcium channel blockers, and GP IIa/IIIb inhibitors (c statistic = 0.81). The model adjusted for diagnostic procedures included cardiac catheterization (c statistic = 0.81). The model adjusted for therapeutic procedures included initial reperfusion, nonprimary percutaneous coronary intervention, and nonprimary coronary artery bypass graft surgery (c statistic = 0.82). yThe model adjusted for demographics included WBC, age 75 years or older, and gender (c statistic = 0.73). The model adjusted for medical history included smoking, myocardial infarction, chronic renal failure, cerebrovascular disease, active malignancy, percutaneous coronary intervention, chronic obstructive pulmonary disease, stroke, and congestive heart failure (c statistic = 0.77). The model adjusted for presenting clinical characteristics included systolic blood pressure ,90 mm Hg, heart rate .100 beats/min, ST elevation or left bundle branch block, and anterior/septal myocardial infarction location (c statistic = 0.81). The model adjusted for medications administered within 24 hours included angiotensin-converting enzyme inhibitors, aspirin, beta-blockers, calcium channel blockers, and GP IIa/IIIb inhibitors (c statistic = 0.83). The model adjusted for diagnostic procedures included cardiac catheterization (c statistic = 0.84). The model adjusted for therapeutic procedures included initial reperfusion, nonprimary cardiac catheterization, nonprimary percutaneous coronary intervention, and nonprimary coronary artery bypass graft surgery (c statistic = 0.89). zThe model adjusted for demographics included WBC, age 75 years or older, and gender (c statistic = 0.69). The model adjusted for medical history included smoking, myocardial infarction, chronic renal failure, cerebrovascular disease, active malignancy, stroke, and congestive heart failure (c statistic = 0.71). The model adjusted for presenting clinical characteristics included systolic blood pressure ,90 mm Hg, heart rate .100 beats/min, ST elevation or left bundle branch block, and anterior/septal myocardial infarction location (c statistic = 0.75). The model adjusted for medications administered within 24 hours included angiotensin-converting enzyme inhibitors, aspirin, beta-blockers, calcium channel blockers, and GP IIa/IIIb inhibitors (c statistic = 0.78). The model adjusted for diagnostic procedures included cardiac catheterization (c statistic = 0.79). The model adjusted for therapeutic procedures included nonprimary cardiac catheterization and nonprimary percutaneous coronary intervention (c statistic = 0.80). WBC = white blood cell.

WBC count associated with progressively higher mortality. Although the magnitudes of association were slightly greater among men, gender-specific associations between WBC count and in-hospital mortality remained strong and significant. Our gender-specific analyses agree with results from NRMI 2,37 the Monitoring Trends and Determinants in Cardiovascular Disease investigation,39 the Myocardial Infarction Triage and Intervention study,29 and the trial registry entitled ‘‘Should We Emergently Revascularize Occluded Coronaries for Cardiogenic Shock?’’40 An important strength of this analysis is that, to our knowledge, it is the first to detect a J-curve distribution when in-hospital mortality was evaluated by small WBC count intervals. We also created a subquartile group to eliminate the possibility of including patients with possibly different chronic diseases that affect WBC count. Therefore, we were able to estimate the odds of death by ensuring that the reference group is truly the group with the lowest in-hospital mortality and WBC counts.

The WBC count has long been appreciated as a risk factor for subsequent development of coronary events in patients with or without known ischemic heart disease.12,14–17,41–49 Friedman et al. first observed a relationship between leukocyte count and subsequent development of myocardial infarction in stable patients.12 The correlation between WBC count at admission and in-hospital death in patients with unstable acute coronary physiology was later noted in the Worcester Heart Attack Study, with patients whose WBC counts were .11.4 3 103/mL6 having a significantly increased risk of in-hospital death.19 More recently, this observation was extended to patients with ST-elevated myocardial infarction who were undergoing fibrinolytic treatment in the Thrombosis in Myocardial Infarction (TIMI)-10B study, patients with unstable angina who were receiving oral glycoprotein IIb/IIIa antagonists in TIMI-OPUS 16, and Medicare beneficiaries with AMI in the Cooperative Cardiovascular Project.21,50,51 The WBC count consistently predicted increased adverse in-hospital events and increased risk of death. The present study

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TABLE 6. In-hospital Mortality Rates by Quartile and Odds Ratio for In-hospital Mortality and White Blood Cell Count Quartile with Quartile 1 Serving as the Reference Group in All Patients, in Male Patients, and in Female Patients

Patient Population All patients (n = 115,273) Mortality rate, n (%) Unadjusted OR (95% CI) Adjusted OR (95% CI) Male patients (n = 68,813) Mortality rate, n (%) Unadjusted OR (95% CI) Adjusted OR (95% CI) Female patients (n = 46,460) Mortality rate, n (%) Unadjusted OR (95% CI) Adjusted OR (95% CI)

Quartile 1 5.0–7.7 3 103/mL (n = 27,268)

Quartile 2 7.8–9.7 3 103/mL (n = 28,446)

Quartile 3 9.8–12.4 3 103/mL (n = 28,217)

Quartile 4 $12.5 3 103/mL (n = 27,798)

Subquartile ,5.0 3 103/mL (n = 3,544)

1,208 (4.4) — —

1,756 (6.2) 1.42 (1.32, 1.53) 1.42 (1.31, 1.54)

2,379 (8.4) 1.99 (1.85, 2.13) 1.87 (1.74, 2.02)

4,427 (15.9) 4.09 (3.83, 4.37) 2.71 (2.53, 2.92)

253 (7.1) 1.66 (1.44, 1.91) 1.34 (1.52, 1.56)

615 (3.6) — —

906 (5.2) 1.47 (1.33, 1.63) 1.47 (1.33, 1.66)

1,233 (7.3) 2.10 (1.90, 2.32) 2.00 (1.80, 2.24)

2,109 (13.9) 4.31 (3.93, 4.73) 2.87 (2.59, 3.19)

136 (6.3) 1.45 (1.21, 1.75) 1.32 (1.07, 1.63)

593(5.8) — —

850 (7.7) 1.35 (1.21, 1.50) 1.37 (1.22, 1.54)

1,146 (10.2) 1.83 (1.65, 2.03) 1.72 (1.56, 1.94)

2,318 (18.4) 3.65 (3.32, 4.01) 2.61 (2.36, 2.90)

117 (8.4) 1.49 (1.21, 1.83) 1.36 (1.10, 1.70)

extended this observation to an even broader patient population in a contemporary ongoing registry, including patients of all age groups in a significant number of hospitals across the United States. The role of the WBC count as a significant prognostic factor in AMI patients is now emerging. In the Cooperative Cardiovascular Project, WBC count was one of the seven most important predictors of outcome following AMI among 73 clinical variables. The risk model integrating WBC count on admission demonstrated a predictive value similar to that of an existing, more complex model.20 Higher leukocyte count clearly identifies AMI patients at increased risk for adverse outcomes. Higher WBC count at admission appears to identify AMI patients with more baseline cardiac risk factors such as medical history of myocardial infarction, congestive heart failure, diabetes, and stroke.21,50–52 In addition, the WBC count appears to be associated with a greater ischemic burden, higher angiographic thrombus load, and worse TIMI flow and TIMI myocardial perfusion grades.51 Higher WBC count is also associated with cardiogenic shock, suggesting an association with more significant infarcts.19,21 This was confirmed by observations that the WBC count may identify patients with lower left ventricular function after AMI.53,54 In the present study, increasing WBC count was associated with significant cardiac risk factors such as diabetes and congestive heart failure and was associated with higher Killip class and anterior myocardial infarction, consistent with the prior published observations. However, even after adjusting for these factors, higher WBC count remained a strong independent predictor of in-hospital death. The elevated WBC counts may be indicative of demargination related to sympathetic nervous system activation and catecholamine excess. Through demargination, neutrophils shift from the marginal to circulating pool. The WBC count as

a crude marker of sympathetic activation requires further investigation. As observed in this and prior studies, the WBC count appears to be a simple, inexpensive, and powerful prognostic tool in the assessment of patients presenting with AMI. A number of biochemical markers have now emerged to be potentially useful in early risk assessment, such as C-reactive protein, b-type natriuretic peptide, and troponin.55–60 In particular, in 2002 early risk assessment based on a validated multimarker approach was advocated.61 Other inflammatory markers and cytokines such as interleukin 6 and tumor necrosis factor-a have emerged to have certain prognostic values as well.57,62–65 Despite the potential prognostic importance of these novel biochemical markers, many of them are not routinely available. On the other hand, the WBC count is a simple test that is available universally and is one of the most commonly obtained tests in the emergency department. It can be applied immediately at the bedside, with no investment in new infrastructure or tests. In addition, every provider is familiar with its use and its interpretation in routine clinical practice. Given that the WBC count is a simple yet potent risk factor for adverse events, as evident in this and prior studies, its role in early triage and risk assessment for AMI patients is clear. Further research is needed, however, to assess the comparative roles of the WBC count with other biochemical markers such as C-reactive protein. It remains unclear whether the WBC count is a risk factor or a marker for AMI mortality. Nonetheless, there is increasing evidence to support a pathophysiologic link between WBC count and adverse outcomes following AMI. Among patients with ST-elevation myocardial infarction who were treated with fibrinolytics, increased WBC count was associated with reduced epicardial patency and an increased ischemic burden.51 This resistance to thrombolytic therapy may reflect a hypercoagulable state induced by inflammatory

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mediators. For example, proinflammatory cytokines appear to be up-regulated and may increase monocyte expression of tissue factor and procoagulant activity.66– 68 It has been noted that fibrinolytic therapy is associated with a decrease in surface tissue factor pathway inhibitor 1 on circulating monocytes, providing further insights into the importance of leukocytes in the regulation of coagulation activity. In addition, formation of abnormal platelet–monocyte aggregates may facilitate thrombus formation in AMI patients.69,70 Other potential mechanisms by which leukocytes may play an etiologic role in promoting abnormal coagulation include activation of monocytes, interaction with Mac-1/integrin, interaction with chemoattractants, and promotion of oxidative stress.70–73

LIMITATIONS This study has several limitations. First, patients not included in the analysis were different from those included in terms of age and history of myocardial infarction. Those included were more likely to have been transferred to an NRMI-participating hospital, which may reflect that they were more stable than those excluded, or perhaps they underwent and received different tests and treatments, respectively, depending on whether or not the hospital was considered a tertiary cardiac center. Given that this was an observational study, a precise causal relationship between WBC count and AMI mortality cannot be ascertained. Infection, which is often associated with a leukocytosis and increased mortality, is an unmeasured variable in NRMI and thus cannot be adequately adjusted. However, with the mild relative increases in WBC count seen in the higher quartiles, it seems unlikely that life-threatening infections accounted for a significant portion of the adverse outcomes. In addition, WBC differentials were not available for analysis.

CONCLUSIONS The white blood cell count is an independent predictor of in-hospital mortality and may be useful in assessing the prognosis of AMI. The precise clinicalpathophysiologic link between the WBC count, the underlying inflammatory process, and clinical correlates and events associated with AMI remains unclear. We suggest that the WBC count should be considered in the prognostic risk stratification of AMI patients upon presentation. References 1. Mehta JL, Nichols WW, Mehta P. Neutrophils as potential participants in acute myocardial ischemia: relevance to reperfusion. J Am Coll Cardiol. 1988; 11:1309–16. 2. Ross R. Atherosclerosis—an inflammatory disease. N Engl J Med. 1999; 340:115–26.

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