Characterization of Patients Who Present With Insomnia: Is There Room for a Symptom Cluster-Based Approach?

Abstract

Study Objectives: This study examined empirically derived symptom cluster profiles among patients who present with insomnia using clinical data and polysomnography. Methods: Latent profile analysis was used to identify symptom cluster profiles of 175 individuals (63% female) with insomnia disorder based on total scores on validated self-report instruments of daytime and nighttime symptoms (Insomnia Severity Index, Glasgow Sleep Effort Scale, Fatigue Severity Scale, Beliefs and Attitudes about Sleep, Epworth Sleepiness Scale, Pre-Sleep Arousal Scale), mean values from a 7-day sleep diary (sleep onset latency, wake after sleep onset, and sleep efficiency), and total sleep time derived from an in-laboratory PSG. Results:The best-fitting model had three symptom cluster profiles: "High Subjective Wakefulness" (HSW), "Mild Insomnia" (MI) and "Insomnia-Related Distress" (IRD). The HSW symptom cluster profile (26.3% of the sample) reported high wake after sleep onset, high sleep onset latency, and low sleep efficiency. Despite relatively comparable PSG-derived total sleep time, they reported greater levels of daytime sleepiness. The MI symptom cluster profile (45.1%) reported the least disturbance in the sleep diary and questionnaires and had the highest sleep efficiency. The IRD symptom cluster profile (28.6%) reported the highest mean scores on the insomnia-related distress measures (eg, sleep effort and arousal) and waking correlates (fatigue). Covariates associated with symptom cluster membership were older age for the HSW profile, greater obstructive sleep apnea severity for the MI profile, and, when adjusting for obstructive sleep apnea severity, being overweight/obese for the IRD profile. Conclusions:The heterogeneous nature of insomnia disorder is captured by this data-driven approach to identify symptom cluster profiles. The adaptation of a symptom cluster-based approach could guide tailored patient-centered management of patients presenting with insomnia, and enhance patient care. Keywords: insomnia disorder, latent profile analysis, symptom profile, symptom clusters Citation: Crawford MR, Chirinos DA, Iurcotta T, Edinger JD, Wyatt JK, Manber R, Ong JC. Characterization of patients who present with insomnia: is there room for a symptom cluster-based approach? J Clin Sleep Med. 2017;13(7):911-921. I NTRO DUCTI O NInsomnia is the experience of the difficulty falling asleep, difficulty staying asleep, or early morning awakenings. About one-third to one-half of adults complains of these symptoms. Frequently, other complaints, such as sleepiness, fatigue, and hyperarousal, will occur with nocturnal sleep disturbance. An insomnia disorder is defined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 2 as the combination of the nocturnal sleep disturbance with one of these waking complaints at least 3 nights a week for at least 3 months. Approximately 8% to 10% of the adult population meets these criteria. 3,4Insomnia disorder is a heterogeneous condition.5 This can pose a chall...

Fatigue in chronic inflammatory demyelinating polyneuropathy

“…These findings give insight into potential causes of fatigue in CIDP and supportive treatment strategies. The observation that patients in both active and inactive groups did not report excessive daytime sleepiness yet they did report excessive fatigue suggests the presence of autonomic hyperarousal due to increased activation of the hypothalamic‐pituitary‐adrenal (HPA) axis 15,16 . This is akin to the fatigue and lack of daytime sleepiness in patients with chronic insomnia and what has been reported previously in chronic autoimmune diseases such as rheumatoid arthritis, myasthenia gravis, and CIDP 17‐19 .…”

“…The observation that patients in both active and inactive groups did not report excessive daytime sleepiness yet they did report excessive fatigue suggests the presence of autonomic hyperarousal due to increased activation of the hypothalamic-pituitary-adrenal (HPA) axis. 15,16 This is akin to the fatigue and lack of daytime sleepiness in patients with chronic insomnia and what has been reported previously in chronic autoimmune diseases such as rheumatoid arthritis, myasthenia gravis, and CIDP. [17][18][19] From a treatment standpoint, these observations suggest that improved sleep hygiene and regular aerobic physical activity may be effective management strategies.…”

Sleep Apnea and Insomnia

“…One important limitation in this literature is that relatively few studies have included validated insomnia assessments in the analyses, which might account for the variability of the prevalence of COMISA across studies 24 . Crawford et al 25 conducted latent profile analyses on a sample using diagnostic criteria for insomnia and found that the "mild insomnia" subtype had significantly a greater percentage of OSA (70%) compared to the other subtypes (~50%). These studies serve as examples of how data-driven approaches could move the field beyond reliance on the presenting complaint and potentially avoid clinician bias.…”

<p>Impact Of Phenotypic Heterogeneity Of Insomnia On The Patients’ Response To Cognitive-Behavioral Therapy For Insomnia: Current Perspectives</p>

“…In further exploration of this hypothesis, several authors have attempted to subtype the ID population by using sophisticated methodologies such as Latent Class Analysis (LCA), Latent Profile Analysis, and Network Analysis. The patient data used were on one hand sleep variables such as insomnia symptoms, severity, and sleep disturbance,31,32 and on the other hand non-sleep variables, such as functional impairment and comorbid condition,31 socioeconomic state and gender,33 level of distress32–34 and personality traits including neuroticism,35 response to pleasurable emotions and level of reactivity to life events 34…”