Follow-up assessment of sleep-related symptoms in patients after treatment for cancer: responses to continuous positive airway pressure treatment for co-morbid obstructive sleep apnoea.

PURPOSE: To assess changes in sleep-related symptoms in patients with breast cancer, endometrial cancer and melanoma previously examined for sleep-related symptoms and the presence of PSG (polysomnography)-determined OSA, ≥ 3 years post-treatment; and to evaluate how CPAP treatment affects sleep-related symptoms in patients previously diagnosed with OSA. METHODS: Patients initially recruited from breast cancer, endometrial cancer, and melanoma follow-up clinics at Westmead Hospital (Sydney, Australia) participated in this questionnaire-based study. Demographic and change in cancer status data were collected at follow-up. Patients completed the Pittsburgh Sleep Quality Index [poor sleep quality, PSQITOTAL ≥ 5au], Insomnia Severity Index, Epworth Sleepiness Scale and Functional Outcomes of Sleep Questionnaire; with ΔPSQITOTAL ≥ 3au indicating a clinically meaningful change in sleep quality over follow-up. PSG-determined OSA was confirmed using the apnoea-hypopnoea index. CPAP compliance was determined via self-report (CPAP compliant, CPAP; not compliant, non-CPAP). Logistic regression models determined if changes in cancer status, AHI, cancer subgroup or CPAP treatment was predictive of ΔPSQITOTAL ≥ 3 au and p < 0.05 indicated statistical significance. RESULTS: The 60 patients recruited had breast cancer (n = 22), endometrial cancer (n = 15), and melanoma (n = 23). Cancer subgroups were similarly aged, and all had median follow-up PSQITOTAL scores ≥ 5au; breast cancer patients scoring the highest (p < 0.05). The CPAP group had significantly reduced PSQITOTAL scores (p = 0.02) at follow-up, unlike the non-CPAP group. Cancer subgroups had similar median ISITOTAL, ESSTOTAL and FOSQ-10TOTAL scores at follow-up, regardless of CPAP treatment. There were no significant predictors of ΔPSQITOTAL ≥ 3 au at follow-up. CONCLUSION: Sleep-related symptoms persist in patients with cancer ≥ 3 years after treatment, although these symptoms improve with CPAP. Future studies should evaluate how CPAP affects survival outcomes in cancer patients with comorbid OSA.

Screening for obstructive sleep apnoea in post-treatment cancer patients.

BACKGROUND AND AIMS: For cancer patients, comorbid obstructive sleep apnea (OSA) poses additional risk to their surgical/anaesthetic outcomes, quality of life, and survival. However, OSA screening is not well-established in oncology settings. We tested two screening tools (STOP-Bang questionnaire [SBQ] and the at-home monitoring device, ApneaLink™Air), for predicting polysomnography (PSG) confirmed OSA in post-treatment cancer patients. METHODS: Breast (n = 56), endometrial (n = 37) and melanoma patients (n = 50) were recruited from follow-up clinics at Westmead Hospital (Sydney, Australia). All underwent overnight PSG, 137 completed SBQ, and 99 completed ApneaLink™Air. Positive (PPV) and negative (NPV) predictive values for PSG-determined moderate-to-severe OSA and severe OSA, were calculated using an SBQ threshold ≥3 au and ApneaLink™Air apnoea-hypopnea index thresholds of ≥10, ≥15 and ≥30 events/h. RESULTS: Both SBQ and ApneaLink™Air had high NPVs (92.7% and 85.2%-95.6% respectively) for severe OSA, but NPVs were lower for moderate-to-severe OSA (69.1% and 59.1%-75.5%, respectively). PPV for both tools were relatively low (all <73%). Combining both tools did not improve screening performance. CONCLUSIONS: These screening tools may help identify cancer patients without severe OSA, but both are limited in identifying those with moderate-to-severe or severe OSA. PSG remains optimal for adequately identifying and managing comorbid OSA in cancer patients.

Cancer sleep symptom-related phenotypic clustering differs across three cancer specific patient cohorts.

Specific sleep disorders have been linked to disease progression in different cancers. We hypothesised sleep symptom clusters would differ between cancer types. The aim of this study was to compare sleep symptom clusters in post-treatment melanoma, breast and endometrial cancer patients. Data were collected from 124 breast cancer patients (1 male, 60 ± 15 years, 28.1 ± 6.6 kg/m2 ), 82 endometrial cancer patients (64.0 ± 12.5 years, 33.5 ± 10.4 kg/m2 ) and 112 melanoma patients (59 male, 65.0 ± 18.0 years, 29.1 ± 6.6 kg/m2 ). All patients completed validated questionnaires to assess sleep symptoms, including the Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Functional Outcomes of Sleep Questionnaire-10 (FOSQ-10). Snoring, tiredness, observed apneas, age, BMI, and gender data were also collected. Binary values (PSQI, ISI, FOSQ), or continuous variables for sleepiness (ESS) and perceived sleep quality (PSQI), were created and sleep symptom clusters were identified and compared across cancer cohorts. Four distinct sleep symptom clusters were identified: minimally symptomatic (n = 152, 47.7%); insomnia-predominant (n = 87, 24.9%); very sleepy with upper airway symptoms (n = 51, 16.3%), and severely symptomatic with severe dysfunction (n = 34, 11.1%). Breast cancer patients were significantly more likely to be in the insomnia predominant or severely symptomatic with severe dysfunction clusters, whereas melanoma patients were more likely to be minimally symptomatic or sleepy with upper airway symptoms (p <0.0001). Endometrial cancer patients were equally distributed across symptom clusters. Sleep symptom clusters vary across cancer patients. A more personalised approach to the management of sleep-related symptoms in these patients may improve the long term quality of life and survival.