title: Multiplicative interaction between mean corpuscular volume and red cell distribution width in predicting mortality of elderly patients with and without anemia.
abstract: Recent studies have shown that an elevated red cell distribution width (RDW) is an important predictor of adverse outcomes. However, the strength of this biomarker has not been tested in a large outpatient elderly population. Also since increased RDW can be due to a variety of etiologies, additional biomarkers are needed to refine the prognostic value of this variable. We assembled a cohort of 36,226 elderly (≥65yo) patients seen at an outpatient facility within the Einstein/Montefiore system from January 1st 1997 to May 1st 2008 who also had a complete blood count performed within 3 months of the initial visit. With a maximum follow-up of 10 years, we found that an elevated RDW (>16.6) was associated with increased risk of mortality in both non-anemic (HR = 3.66, p < 0.05) and anemic patients (HR = 1.87, p < 0.05). The effect of RDW on mortality is significantly increased in non-anemic patients with macrocytosis (HR = 5.22, p < 0.05) compared to those with normocytosis (HR = 3.86, p < 0.05) and microcytosis (HR = 2.46, p < 0.05). When comparing non-anemic patients with both an elevated RDW and macrocytosis to those with neither, we observed an elevated HR of 7.76 (higher than expected in an additive model). This multiplicative interaction was not observed in anemic patients (HR = 2.23). Lastly, we constructed Kaplan-Meier curves for each RDW/MCV subgroup and found worsened survival for those with macrocytosis and an elevated RDW in both anemia and non-anemic patients. Based on our results, the addition of MCV appears to improve the prognostic value of RDW as a predictor of overall survival in elderly patients.
authors.affiliation: Department of Medicine, Stanford University School of Medicine, Stanford, CA.
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Lam
authors.forename: Krishna
authors.initials: K
authors.lastname: Gundabolu
authors.forename: Ashwin
authors.initials: A
authors.lastname: Sridharan
authors.forename: Rishi
authors.initials: R
authors.lastname: Jain
authors.forename: Pavlos
authors.initials: P
authors.lastname: Msaouel
authors.forename: Grigorios
authors.initials: G
authors.lastname: Chrysofakis
authors.forename: Yiting
authors.initials: Y
authors.lastname: Yu
authors.forename: Ellen
authors.initials: E
authors.lastname: Friedman
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Price
authors.forename: Stanley
authors.initials: S
authors.lastname: Schrier
authors.forename: Amit K
authors.initials: AK
authors.lastname: Verma
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D000740:Anemia / Q000097:blood* / Q000175:diagnosis / Q000401:mortality; D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D004906:Erythrocyte Count; D004909:Erythrocyte Indices; D004913:Erythrocytes, Abnormal; D005260:Female; D005500:Follow-Up Studies; D006784:Hospitals, Teaching; D006801:Humans; D008297:Male; D009026:Mortality; D009519:New York City / Q000453:epidemiology; D010044:Outpatient Clinics, Hospital; D011379:Prognosis; D015203:Reproducibility of Results; D016019:Survival Analysis
chemical_list: D015415:Biomarkers