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Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study

Fumagalli, Luca Andrea Mario1; Fumagalli, Riccardo2; Chiarelli, Marco1; Cazzaniga, Massimo1; Bonato, Claudio1; D'Angelo, Luciano1; Cavalieri D'Oro, Luca3; Cerino, Mario1; Terragni, Sabina1; Lainu, Elisa1; Lorini, Cristina1; Scarazzati, Claudio1; Tazzari, Sara1; Porro, Francesca1; Alde', Simone1; Burati, Morena1; Brambilla, William1; Nattino, Stefano4; Locatelli, Matteo1; Valsecchi, Daria1; Spreafico, Paolo1; Tantardini, Valter1; Schiavo, Gianpaolo1; Zago, Mauro1; Fumagalli, Luca1

Research facility: Alessandro Manzoni Hospital
Published Oct 10, 2023 on Dryad. https://doi.org/10.5061/dryad.dncjsxm5g

Data files

Oct 10, 2023 version files 1.78 MB

Abstract

Objectives: to assess survival predictivity of baseline blood cell differential count (BCDC), discretized according to two different methods, in adults visiting the Emergency Room (ER) for illness or trauma over one-year. 

Design: Retrospective cohort study of hospital records. 

Setting: Tertiary care public hospital in northern Italy. 

Participants: 11052 patients aged > 18 years, consecutively admitted to the ER in one 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 hemoglobin, red cell mean volume (MCV) and distribution-width (RDW), platelet distribution-width (PDW), plateletcrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and platelets. Discretization cutoffs were defined by Benchmark and Tailored methods. Benchmark cutoffs were stated on laboratory reference values (CLSI). Tailored cutoffs for linear, sigmoid-shaped and for U-shaped distributed variables were discretized by Maximally Selected Rank Statistics and by Optimal-Equal Hazard Ratio respectively. Explanatory variables (age, gender, ER  admission during SARS-CoV2 surges, in-hospital admission) were analyzed using Cox multivariable regression. ROC curves were drawn by sum of Cox-significant variables for each method.

Results: Of 11052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted in 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 CI=4.0–5.2), in-hospital admission (HR=2.2 CI=1.9–2.4), ER admission during SARS-CoV2 surges (Wave-I HR=1.7 CI=1.5–1.9); Wave-II HR=1.2 CI=1.0–1.3). Gender, hemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophils counts were significant in overall. Benchmark-BCDC model included basophils and platelet count (AUROC 0.74). Tailored-BCDC model included monocyte counts and plateletcrit (AUROC 0.79).

Conclusions: baseline discretized BCDC provides meaningful insight regarding Emergency Room patients survival.