Blood cell differential count discretisation modelling to predict survival in adults reporting to the emergency room: a retrospective cohort study

Abstract

Objectives: To assess the survival predictivity of baseline blood cell differential count (BCDC), discretised according to two different methods, in adults visiting an emergency room (ER) for illness or trauma over 1 year.

Design: Retrospective cohort study of hospital records.

Setting: Tertiary care public hospital in northern Italy.

Participants: 11 052 patients aged >18 years, consecutively admitted to the ER in 1 year, and for whom BCDC collection was indicated by ER medical staff at first presentation.

Primary outcome: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed haemoglobin, mean cell volume (MCV), red cell distribution width (RDW), platelet distribution width (PDW), platelet haematocrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils and platelets. Discretisation cut-offs were defined by benchmark and tailored methods. Benchmark cut-offs were stated based on laboratory reference values (Clinical and Laboratory Standards Institute). Tailored cut-offs for linear, sigmoid-shaped and U-shaped distributed variables were discretised by maximally selected rank statistics and by optimal-equal HR, respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges and in-hospital admission) were analysed using Cox multivariable regression. Receiver operating curves were drawn by summing the Cox-significant variables for each method.

Results: Of 11 052 patients (median age 67 years, IQR 51-81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted to the hospital. After a 306-day median follow-up (IQR 208-417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73 years (HR=4.6, 95% CI=4.0 to 5.2), in-hospital admission (HR=2.2, 95% CI=1.9 to 2.4), ER admission during SARS-CoV2 surges (Wave I: HR=1.7, 95% CI=1.5 to 1.9; Wave II: HR=1.2, 95% CI=1.0 to 1.3). Gender, haemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophil counts were significant overall. Benchmark-BCDC model included basophils and platelet count (area under the ROC (AUROC) 0.74). Tailored-BCDC model included monocyte counts and PCT (AUROC 0.79).

Conclusions: Baseline discretised BCDC provides meaningful insight regarding ER patients' survival.

Keywords: ACCIDENT & EMERGENCY MEDICINE; Clinical chemistry; STATISTICS & RESEARCH METHODS.

Figure 1

Counts of alive and deceased patients by increasing Tailored unfavourable BCDC score sum

Figure 2

Survival analysis stratified by tailored BCDC score quintile. BCDC, blood cell differential count.