White cell count in the normal range and short-term and long-term mortality: international comparisons of electronic health record cohorts in England and New Zealand

Abstract

Objectives: Electronic health records offer the opportunity to discover new clinical implications for established blood tests, but international comparisons have been lacking. We tested the association of total white cell count (WBC) with all-cause mortality in England and New Zealand.

Setting: Primary care practices in England (ClinicAl research using LInked Bespoke studies and Electronic health Records (CALIBER)) and New Zealand (PREDICT).

Design: Analysis of linked electronic health record data sets: CALIBER (primary care, hospitalisation, mortality and acute coronary syndrome registry) and PREDICT (cardiovascular risk assessments in primary care, hospitalisations, mortality, dispensed medication and laboratory results).

Participants: People aged 30-75 years with no prior cardiovascular disease (CALIBER: N=686 475, 92.0% white; PREDICT: N=194 513, 53.5% European, 14.7% Pacific, 13.4% Maori), followed until death, transfer out of practice (in CALIBER) or study end.

Primary outcome measure: HRs for mortality were estimated using Cox models adjusted for age, sex, smoking, diabetes, systolic blood pressure, ethnicity and total:high-density lipoprotein (HDL) cholesterol ratio.

Results: We found 'J'-shaped associations between WBC and mortality; the second quintile was associated with lowest risk in both cohorts. High WBC within the reference range (8.65-10.05×109/L) was associated with significantly increased mortality compared to the middle quintile (6.25-7.25×109/L); adjusted HR 1.51 (95% CI 1.43 to 1.59) in CALIBER and 1.33 (95% CI 1.06 to 1.65) in PREDICT. WBC outside the reference range was associated with even greater mortality. The association was stronger over the first 6 months of follow-up, but similar across ethnic groups.

Conclusions: Clinically recorded WBC within the range considered 'normal' is associated with mortality in ethnically different populations from two countries, particularly within the first 6 months. Large-scale international comparisons of electronic health record cohorts might yield new insights from widely performed clinical tests.

Trial registration number: NCT02014610.

Keywords: Cohort studies; Electronic health records; Leukocyte count; Mortality.

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf and declare financial support from the Wellcome Trust, Medical Research Council, National Institute of Health Research and Health Research Council of New Zealand for this work.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Figures

Figure 1

Patient flow diagrams for CALIBER and PREDICT studies. CALIBER, ClinicAl research using LInked Bespoke studies and Electronic health Records.

Figure 2

Unadjusted Kaplan-Meier curves for all-cause mortality by total white cell count, in CALIBER and PREDICT. Graphs are shown for top, middle and bottom quintiles of total white cell count. Patients with extreme high or low values are included. To avoid clutter, the second and fourth quintiles are not shown. CALIBER, ClinicAl research using LInked Bespoke studies and Electronic health Records.

Figure 3

HRs for all-cause mortality by category of total white cell count. Categories are quintiles, with the top and bottom quintiles divided into values within and outside the reference range. ‘Multiple adjustment’ comprised adjustment for age, sex, smoking, diabetes, systolic blood pressure, ethnicity and total:HDL cholesterol ratio. p Values *

Figure 4

Adjusted HRs for all-cause mortality by category of total white cell count in CALIBER, by time period. Categories are quintiles, with the top and bottom quintiles divided into values within and outside the reference range. HRs were adjusted for age, sex, smoking, diabetes, systolic blood pressure, ethnicity and total:HDL cholesterol ratio. p Values *