Identification of Fatigue Subtypes and Their Correlates in Prevalent Heart Failure: A Secondary Analysis of the Atherosclerosis Risk in Communities Study

Author:

Pavlovic Noelle1ORCID,Ndumele Chiadi E.2ORCID,Abshire Saylor Martha1ORCID,Szanton Sarah L.123,Lee Christopher S.4,Shah Amil M.5,Chang Patricia P.6,Florido Roberta7ORCID,Matsushita Kunihiro23ORCID,Himmelfarb Cheryl123,Leoutsakos Jeannie Marie2ORCID

Affiliation:

1. Johns Hopkins School of Nursing, Baltimore, MD (N.P., M.A.S., S.L.S., C.H.).

2. Johns Hopkins School of Medicine, Baltimore, MD (C.E.N., S.L.S., K.M., C.H., J.M.L.).

3. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (S.L.S., K.M., C.H.).

4. Boston College Connell School of Nursing, MA (C.S.L.).

5. University of Texas Southwestern, Dallas, TX (A.M.S.).

6. University of North Carolina School of Medicine, Chapel Hill (P.P.C.).

7. University of Utah, Salt Lake City (R.F.).

Abstract

BACKGROUND: Among patients with heart failure (HF), fatigue is common and linked to quality of life and functional status. Fatigue is hypothesized to manifest as multiple types, with general and exertional components. Unique subtypes of fatigue in HF may require differential assessment and treatment to improve outcomes. We conducted this study to identify fatigue subtypes in persons with prevalent HF in the ARIC study (Atherosclerosis Risk in Communities) and describe the distribution of characteristics across subtypes. METHODS: We performed a cross-sectional analysis of 1065 participants with prevalent HF at ARIC visit 5 (2011–2013). We measured exertional fatigue using the Modified Medical Research Council Breathlessness scale and general fatigue using the Patient Reported Outcomes Measurement Information System fatigue scale. We used latent class analysis to identify subtypes of fatigue. Number of classes was determined using model fit statistics, and classes were interpreted and assigned fatigue severity rating based on the conditional probability of endorsing survey items given class. We compared characteristics across classes using multinomial regression. RESULTS: Overall, participants were 54% female and 38% Black with a mean age of 77. We identified 4 latent classes (fatigue subtypes): (1) high general/high exertional fatigue (18%), (2) high general/low exertional fatigue (27%), (3) moderate general/moderate exertional fatigue (20%), and (4) low/no general and exertional fatigue (35%). Female sex, Black race, lower education level, higher body mass index, increased depressive symptoms, and higher prevalence of diabetes were associated with higher levels of general and exertional fatigue. CONCLUSIONS: We identified unique subtypes of fatigue in patients with HF who have not been previously described. Within subtype, general and exertional fatigue were mostly concordant in severity, and exertional fatigue only occurred in conjunction with general fatigue, not alone. Further understanding these fatigue types and their relationships to outcomes may enhance our understanding of the symptom experience and inform prognostication and secondary prevention efforts for persons with HF.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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