Session Type: ACR Poster Session B
Session Time: 9:00AM-11:00AM
Background/Purpose: Poor sleep quality is an underappreciated complaint commonly observed in patients with SLE. We hypothesize that poor sleep quality contributes to worsening lupus disease activity. The aims of this study are to evaluate the relationship between: 1) subjective sleep measures and active SLE, 2) objective sleep actigraphy and active SLE, and 3) confounding variables that influence sleep quality and active SLE.
Methods: A prospective, longitudinal, observational study was designed to evaluate the relationship between sleep quality and SLE disease activity. Analysis was restricted to the first study visit. 127 subjects from the Lupus Clinic at Washington University who met ACR or SLICC criteria for SLE were enrolled. Patients with hepatitis B/C, HIV, cirrhosis, ESRD, or pregnancy were excluded. Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Patient Reported Outcomes Measurement Instrument System (PROMIS)-Sleep Related Impairment (SRI), and PROMIS-Sleep Disturbance (SD) survey instruments were administered to measure patient reported subjective sleep quality. 39 actigraphy watches recorded objective sleep data (sleep efficiency) over 7 days. SLEDAI 2K Responder Index-50 (S2K RI-50) assessed SLE disease activity (S2K RI-50 >4 = active SLE). Pearson Correlation measured the correlation between individual measures of sleep quality and SLE activity. Univariate and multivariate regression analyses explored predictors of active SLE and poor sleep.
Results: Of the sleep surveys, SRI (Pearson coefficient 0.20, p=0.026) and SD (Pearson 0.20, p=0.031) both correlated with active SLE, whereas ESS (p=0.66) and PSQI (p=0.74) did not. Univariate linear regression of SRI showed that active SLE predicted an increase in SRI that was significant but with modest effect (r2=0.042, p=0.026) whereas PROMIS-Fatigue (r2=0.69, p<0.0001) and PROMIS-Anxiety (r2=0.34, p<0.0001) comorbidities had a stronger effect on predicting SRI. In multivariate linear regression, only the effect of fatigue remained significant (p<0.0001). Univariate and multivariate analyses of predictors of SD showed similar results. Sleep efficiency measured by actigraphy was nearly significant with active SLE (Pearson coefficient -0.30, p=0.064) due to underpowered sample size (n=39). However, sleep efficiency poorly correlated with the subjective sleep surveys: SRI (Pearson -0.18, p=0.28), SD (Pearson -0.19, p=0.26), ESS (Pearson -0.16, p=0.33), PSQI (Pearson -0.33, p=0.09).
Conclusion: Our study reinforces that poor sleep quality remains a problem in SLE patients. We found that the SRI and SD surveys correlated with active SLE, but when considering confounding variables, fatigue best explained the relationship between poor sleep. Interestingly, despite the low sample size, we nearly achieved statistical significance when examining sleep efficiency via actigraphy to active SLE. Furthermore, sleep efficiency poorly correlated with the patient-reported sleep surveys. Thus, these data suggest that sleep quality is an unappreciated need in patients with SLE, and that current, validated sleep surveys may not best represent poor sleep quality for these patients.
To cite this abstract in AMA style:Chu P, Hinze A, Mathis N, Feigl L, Al-Hammadi N, Eisen S, Ju YE, Kim A. Systemic Lupus Erythematosus and the Evaluation of Poor Sleep [abstract]. Arthritis Rheumatol. 2016; 68 (suppl 10). https://acrabstracts.org/abstract/systemic-lupus-erythematosus-and-the-evaluation-of-poor-sleep/. Accessed November 27, 2020.
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