The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective.

Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.

Learning to generalize seizure forecasts.

OBJECTIVE: Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years-long electroencephalographic (EEG) recordings, we previously found that patient-reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal-ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. METHODS: We used retrospective long-term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24-h horizon. RESULTS: With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) = .70 and .69 and median Brier skill score (BSS) = .07 and .08. In direct comparison, individualized models had similar median performance (AUC = .67, BSS = .08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. SIGNIFICANCE: Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient-reported vs. electrographic) or IEA (icEEG vs. sqEEG).

Increasing Seizure Activity During Radiation Treatment for High-grade Gliomas - Final Results of a Prospective Interventional Study.

BACKGROUND/AIM: Little is known regarding seizures during radiotherapy for brain tumors. This prospective study investigated seizure activity in patients irradiated for high-grade gliomas. PATIENTS AND METHODS: Using a seizure diary, progression of seizure activity was evaluated in 22 patients receiving chemoradiation for grade III (n=1) or IV (n=21) gliomas. Progression was defined as increased frequency of any and/or generalized seizures (>50%) or increased anti-epileptic medication (≥25%). Patients' satisfaction with the diary was assessed using a questionnaire (six scales of 1-7 points). Uni- and multivariable analyses were performed including baseline seizure activity, age, sex, resection, tumor site, performance score, and history of epilepsy/seizures. RESULTS: Ten patients (45%) experienced progression of seizure activity during their radiotherapy course, mainly due to increased seizure frequency (nine patients=41%). Mean values of patients' satisfaction scores ranged between 3.92 and 4.92 points. CONCLUSION: Radiotherapy of high-grade gliomas can increase seizure activity. Patients require close monitoring to initiate or adjust anti-epileptic medication.

Evaluation of Sleep Disturbances in Patients With Bladder Cancer Scheduled for Local or Loco-regional Radiochemotherapy.

BACKGROUND/AIM: Organ-sparing treatment is increasingly used for bladder cancer, particularly for patients with significant comorbidities or advanced age. The upcoming treatment can cause distress and sleep disturbances. This study investigated pre-radiotherapy sleep disturbances in these patients. PATIENTS AND METHODS: Twenty-two patients with bladder cancer scheduled for local or loco-regional radiotherapy were retrospectively evaluated. Sixteen characteristics were analyzed for sleep disturbances including age, sex, performance score, comorbidities, previous malignancy, distress score, emotional problems, physical problems, treatment situation, treatment intent, current primary tumor and nodal stage, distant metastasis, treatment volume, concurrent chemotherapy, and Coronavirus Disease 2019 pandemic. RESULTS: Eleven patients (50.0%) reported sleep disturbances that were significantly associated with distress scores ≥5 (p=0.035). Trends were found for age ≤75 years (p=0.183), ≥2 emotional problems (p=0.183), ≥5 physical problems (p=0.064), and distant metastasis (p=0.090). CONCLUSION: Half of the patients reported pre-radiotherapy sleep disturbances. Risk factors facilitate identification of patients requiring psychological support.

Sleep Problems Prior to Radio-chemotherapy in Patients With Locally Advanced Cancer of the Esophagus or the Esophagogastric Junction.

BACKGROUND/AIM: Many patients with locally advanced cancer of the esophagus or esophagogastric junction receive definitive or neoadjuvant radiochemotherapy. Patient anticipation of this treatment can cause or aggravate distress and sleep disorders. This study aimed to identify the prevalence of sleep disorders and risk factors. PATIENTS AND METHODS: Thirty-eight patients assigned to radio-chemotherapy were retrospectively evaluated for pre-treatment sleep disorders. Investigated characteristics included age; sex; performance score; comorbidity index; previous malignancies; family history; distress score; emotional, physical or practical problems; tumor site; histology and grading; tumor stage; planned treatment; and relation to 2019 Coronavirus pandemic. RESULTS: Sleep problems were reported by 15 patients (39.5%). Significant associations were found for higher distress scores (p=0.016) and greater numbers of emotional problems (p<0.0001). A trend was observed for greater numbers of physical problems (p=0.176). CONCLUSION: The prevalence of sleep problems was high. Risk factors were found that can help identify patients requiring psychological support already prior to radio-chemotherapy.

Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction.

OBJECTIVE: Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. METHODS: A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. RESULTS: Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69-100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0-13.0). CONCLUSIONS: Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. SIGNIFICANCE: Ultra long-term sqEEG bears the potential of improving objective seizure quantification.

Reduced mismatch negativity in children and adolescents with autism spectrum disorder is associated with their impaired adaptive functioning.

Children and adolescents on the autism spectrum display sensory disturbances, rigid and repetitive behavior, social communication problems and a high prevalence of impaired adaptive functioning. Autism is associated with slowed behavioral and neural habituation to repeated sensory input and decreased responses to sensory deviations. Mismatch negativity (MMN) reflects a pre-attentive difference in the neural response to sensory deviations relative to regularities and studies overall suggest that children and adolescents with autism tend to have smaller MMN. However, it remains unclear whether reduced MMN in autism is coupled to severity of specific autistic symptoms or more generally to lower level of adaptive functioning. To address these questions, the present study used electroencephalography (EEG) to assess whether auditory MMN in 59 children and adolescents with autism aged 7-14 years compared to 59 typically developing children and adolescents were related to specific autistic symptoms or level in adaptive functioning. As hypothesized, the autism group had a lower MMN amplitude than controls. Smaller MMN amplitudes were specifically associated with lower adaptive functioning in the autistic subjects but not in controls while no apparent relationships were observed with autistic-like social interaction and communication problems, atypical language, rigidity, stereotypy or sensory sensitivity symptoms. Our findings indicate that a blunted response to changes in sensory input may underlie or contribute to the generalized difficulties with adapting to daily life circumstances seen in children and adolescents with autism. LAY SUMMARY: Children and adolescents on the autism spectrum have a high prevalence of impaired adaptive functioning. Neuroimaging studies have reported that children and adolescents with autism display attenuated brain activity when discriminating sensory input. However, it is unknown whether this attenuation is related to autistic symptoms and/or adaptive functioning. The present study used electroencephalogram (EEG) to show that attenuated brain response in discrimination of novel compared to repetitive sounds in children and adolescents with autism is related to their impaired adaptive functioning.

Improvement of Sleep Disorders During a Course of Radiotherapy for Breast Cancer - Final Results of the Prospective Interventional RADIO-SLEEP Trial.

BACKGROUND/AIM: Upcoming radiotherapy may cause distress and sleep disorders (SDO). This prospective interventional trial investigated SDO during a course of radiotherapy for breast cancer. PATIENTS AND METHODS: Fifty patients were eligible. The primary endpoint was improvement of SDO after 15 fractions. Additional endpoints included SDO after 5 fractions and at the end of radiotherapy (EOT). Additional characteristics were analysed including use of smartphones/tablets, age, body mass index, performance score, comorbidity score, surgery, distress score, and emotional/ physical/practical problems. RESULTS: After 15 fractions, 38% of patients reported improvement of SDO (p<0.0001). Improvement rates were 22% after 5 fractions (p=0.003) and 39% at EOT (p<0.0001). Moreover, a significant association was observed for lower distress score after 5 fractions. CONCLUSION: Improvement of SDO occurred more often than expected, most likely due to habituation to radiotherapy. Since SDO did not improve in the majority of patients, timely psychological support should be offered to all patients.

Association Between EEG Patterns and Serum Neurofilament Light After Cardiac Arrest: A Post Hoc Analysis of the TTM Trial.

BACKGROUND AND OBJECTIVES: EEG is widely used for prediction of neurologic outcome after cardiac arrest. To better understand the relationship between EEG and neuronal injury, we explored the association between EEG and neurofilament light (NfL) as a marker of neuroaxonal injury, evaluated whether highly malignant EEG patterns are reflected by high NfL levels, and explored the association of EEG backgrounds and EEG discharges with NfL. METHODS: We performed a post hoc analysis of the Target Temperature Management After Out-of-Hospital Cardiac Arrest trial. Routine EEGs were prospectively performed after the temperature intervention ≥36 hours postarrest. Patients who awoke or died prior to 36 hours postarrest were excluded. EEG experts blinded to clinical information classified EEG background, amount of discharges, and highly malignant EEG patterns according to the standardized American Clinical Neurophysiology Society terminology. Prospectively collected serum samples were analyzed for NfL after trial completion. The highest available concentration at 48 or 72 hours postarrest was used. RESULTS: A total of 262/939 patients with EEG and NfL data were included. Patients with highly malignant EEG patterns had 2.9 times higher NfL levels than patients with malignant patterns and NfL levels were 13 times higher in patients with malignant patterns than those with benign patterns (95% CI 1.4-6.1 and 6.5-26.2, respectively; effect size 0.47; p < 0.001). Both background and the amount of discharges were independently strongly associated with NfL levels (p < 0.001). The EEG background had a stronger association with NfL levels than EEG discharges (R2 = 0.30 and R2 = 0.10, respectively). NfL levels in patients with a continuous background were lower than for any other background (95% CI for discontinuous, burst-suppression, and suppression, respectively: 2.26-18.06, 3.91-41.71, and 5.74-41.74; effect size 0.30; p < 0.001 for all). NfL levels did not differ between suppression and burst suppression. Superimposed discharges were only associated with higher NfL levels if the EEG background was continuous. DISCUSSION: Benign, malignant, and highly malignant EEG patterns reflect the extent of brain injury as measured by NfL in serum. The extent of brain injury is more strongly related to the EEG background than superimposed discharges. Combining EEG and NfL may be useful to better identify patients misclassified by single methods. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT01020916.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models.

This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.

OBJECTIVE: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.

The Effect of Wearable Tracking Devices on Cardiorespiratory Fitness Among Inactive Adults: Crossover Study.

BACKGROUND: Modern lifestyle is associated with a high prevalence of physical inactivity. OBJECTIVE: This study aims to investigate the effect of a wearable tracking device on cardiorespiratory fitness among inactive adults and to explore if personal characteristics and health outcomes can predict adoption of the device. METHODS: In total, 62 inactive adults were recruited for this study. A control period (4 weeks) was followed by an intervention period (8 weeks) where participants were instructed to register and follow their physical activity (PA) behavior on a wrist-worn tracking device. Data collected included estimated cardiorespiratory fitness, body composition, blood pressure, perceived stress levels, and self-reported adoption of using the tracking device. RESULTS: In total, 50 participants completed the study (mean age 48, SD 13 years, 84% women). Relative to the control period, participants increased cardiorespiratory fitness by 1.52 mL/kg/minute (95% CI 0.82-2.22; P<.001), self-reported PA by 140 minutes per week (95% CI 93.3-187.1; P<.001), daily step count by 982 (95% CI 492-1471; P<.001), and participants' fat percentage decreased by 0.48% (95% CI -0.84 to -0.13; P=.009). No difference was observed in blood pressure (systolic: 95% CI -2.16 to 3.57, P=.63; diastolic: 95% CI -0.70 to 2.55; P=.27) or perceived stress (95% CI -0.86 to 1.78; P=.49). No associations were found between adoption of the wearable tracking device and age, gender, personality, or education. However, participants with a low perceived stress at baseline were more likely to rate the use of a wearable tracking device highly motivating. CONCLUSIONS: Tracking health behavior using a wearable tracking device increases PA resulting in an improved cardiorespiratory fitness among inactive adults.

Risk Factors for Sleep Problems Prior to Radiochemotherapy for Malignant Gliomas.

BACKGROUND/AIM: Many patients with malignant gliomas are scheduled for radiochemotherapy, which may cause emotional distress associated with sleep problems. This study aimed to determine the prevalence of such sleep problems in these patients and identify risk factors. PATIENTS AND METHODS: Fifty-seven patients scheduled for radiochemotherapy for grade II-IV gliomas were retrospectively investigated for pre-treatment sleep problems. Fifteen characteristics were evaluated including temporal relation to COVID-19 pandemic, age, gender, performance status, comorbidity, (family) history of malignancies, distress score, emotional problems, physical problems, practical problems, involved sites, glioma grade, upfront surgery, and corticosteroids. RESULTS: Nineteen patients stated pre-treatment sleep problems (prevalence=66.7%). Significant associations with sleep problems were found for female gender (p=0.023), presence of emotional problems (p=0.006), and ≥4 physical problems (p<0.001). A trend was found for distress scores ≥5 (p=0.077). CONCLUSION: The prevalence of sleep problems was high. Risk factors were determined that can be used to identify patients who likely benefit from psychological support.

A prospective interventional study investigating sleep disorders prior to and during adjuvant radiotherapy for breast cancer.

BACKGROUND: Most breast cancer patients with non-metastatic disease receive adjuvant local or loco-regional radiotherapy. To be scheduled for irradiation may cause distress and fears that can lead to sleep disorders. Few reports focused on sleep problems in patients assigned to radiotherapy. This study evaluates the course of sleep disorders during adjuvant radiotherapy for primary breast cancer and potential risk factors including the use of smartphones or tablets at bedtime. METHODS: The main goal is the evaluation of sleep disorders prior to radiotherapy and after 15 fractions of radiotherapy. A potential effect of habituation to the procedure of radiotherapy can be assumed that will likely lead to improvement (decrease) of sleep disorders. Improvement of sleep disorders (compared to baseline before radiotherapy) is defined as decrease of the severity of sleep disorders by ≥2 points on a patient self-rating scale (0 = no problems; 10 = maximum problems) or decrease of distress caused by sleep disorders by ≥2 points on a self-rating scale (0 = no distress; 10 = maximum distress) or reduction of the dose of sleeping drugs by ≥25%. Additional endpoints include sleep disorders after 5 fractions and at the end of radiotherapy. Moreover, potential risk factors including the use of smartphones or tablets at bedtime are evaluated. Fifty-one patients (48 plus potential drop-outs) are required. With this sample size, a one-sample binomial test with a one-sided significance level of 2.5% has a power of 80% to yield statistical significance, if the rate of patients with improvement of sleep disorders is 25% (rate under the alternative hypothesis) and assuming that a decrease of ≤10% has to be judged as a random, non-causal change in this uncontrolled study setting (null hypothesis). DISCUSSION: If a decrease of sleep disorders during the course of radiotherapy is shown, this aspect should be included in the pre-radiotherapy consent discussion with the patients. Moreover, identification of additional risk factors will likely lead to earlier psychological support. If the use of smartphones or tablets at bedtime is a risk factor, patients should be advised to change this behavior. TRIAL REGISTRATION: clinicaltrials.gov (NCT04879264; URL: https://clinicaltrials.gov/show/NCT04879264 ); registered on 7th of May, 2021.

Sleep Disturbances in Lung Cancer Patients Assigned to Definitive or Adjuvant Irradiation.

BACKGROUND/AIM: A considerable number of patients with lung cancer are scheduled for definitive or adjuvant radiotherapy. Prevalence and potential risk factors of pre-radiotherapy sleep disturbances were evaluated. PATIENTS AND METHODS: Nineteen factors were retrospectively investigated for associations with pre-radiotherapy sleep disturbances in 77 lung cancer patients. Factors included COVID-19 pandemic; age; gender; performance score; comorbidity index; history of another malignancy; distress score; number of emotional, physical or practical problems; patient's request for psychological support; histology; tumor stage; upfront surgery; chemotherapy; and type of radiotherapy. RESULTS: Thirty-one patients (40.3%) reported sleep disturbances that were significantly associated with distress score 6-10 (p=0.019), ≥2 emotional problems (p=0.001), ≥5 physical problems (p<0.001), and request for psychological support (p=0.006). Trends were found for female gender (p=0.064) and stereotactic body radiation therapy (p=0.057). CONCLUSION: Many lung cancer patients assigned to radiotherapy reported sleep disturbances. Risk factors can be used to identify patients in need of psychological support already before treatment.

Risk Factors for Sleep Disturbances in Patients Scheduled for Radiotherapy of Head-and-Neck Cancer.

BACKGROUND/AIM: Many patients with head-and-neck cancer are scheduled for irradiation. This study was performed to determine the frequency of and risk factors for pre-radiotherapy sleep disturbances in these patients. PATIENTS AND METHODS: A total of 103 patients with head-and-neck cancer scheduled for radiotherapy were included in this retrospective study. Eighteen characteristics were evaluated including timing of start of radiotherapy relative to COVID-19 pandemic; age; gender; Karnofsky performance score; Charlson comorbidity index; history of another malignancy; family history of malignancy; distress score; number of emotional, physical or practical problems; request for psychological support; tumor site and stage; upfront surgery; planned chemotherapy; and brachytherapy boost. RESULTS: The frequency of pre-radiotherapy sleep disturbances was 42.7%. This was significantly associated with age ≤63 years (p=0.049), Karnofsky performance score ≤80 (p=0.002), Charlson comorbidity index ≥3 (p=0.005), history of another malignancy (p=0.012), emotional (p=0.001) or physical (p<0.001) problems, and request for psychological support (p=0.002). CONCLUSION: Sleep disturbances were frequent in patients assigned to radiotherapy of head-and-neck cancer. Recognizing risk factors for sleep disturbance helps identify patients requiring psychological support.

Frequency and Risk Factors of Sleep Disturbances in Patients With Prostate Cancer Assigned to Local or Loco-regional Radiotherapy.

BACKGROUND/AIM: Many patients with prostate cancer receive definitive or adjuvant radiotherapy. This study aimed to identify the frequency of sleep disturbances and corresponding risk factors prior to radiation treatment. PATIENTS AND METHODS: Data of 48 patients assigned to local or loco-regional irradiation for prostate cancer were retrospectively analyzed for pre-radiotherapy sleep disturbances. Fifteen characteristics were analyzed including age, performance status, comorbidity, history of previous malignancy, distress score, (emotional, physical or practical) problems, prostate-specific antigen, primary tumor stage, Gleason-score, upfront androgen deprivation therapy (ADT), treatment volume, brachytherapy, and COVID-19 pandemic. RESULTS: Pre-radiotherapy sleep disturbances were reported by 20.8% of patients and significantly associated with distress scores ≥4 (p<0.0001) and ≥3 physical problems (p=0.0001). Trends were found for Karnofsky performance score ≤80 (p=0.095), Gleason score 7b-9 (p=0.079), and ADT (p=0.067). CONCLUSION: Pre-radiotherapy sleep disturbances were less common in prostate cancer patients than in other cancer patients. Risk factors were identified that can help identify patients requiring psychological support prior to radiotherapy.

Sleep Disorders Prior to Adjuvant Radiation Therapy for Gynecological Malignancies.

BACKGROUND/AIM: Many patients with gynecological malignancies receive postoperative radiotherapy, which can lead to fear and sleep disorders. We aimed to identify the prevalence of and risk factors for sleep disorders. PATIENTS AND METHODS: Sixty-two patients assigned to radiotherapy for gynecological malignancies were retrospectively evaluated. Seventeen characteristics were analyzed for associations with pre-radiotherapy sleep disorders including age, Karnofsky performance score, Charlson comorbidity index, history of additional malignancy, family history of gynecological cancer, distress score, emotional, physical or practical problems, tumor site/stage; chemotherapy, treatment volume, brachytherapy, and the COVID-19 pandemic. RESULTS: The prevalence of pre-radiotherapy sleep disorders was 46.8%. Sleep disorders were significantly associated with Charlson comorbidity index ≥3 (p=0.012), greater number of physical problems (p<0.0001), and advanced primary tumor stage (p=0.005). A trend was found for greater number of emotional problems (p=0.075). CONCLUSION: Pre-radiotherapy sleep disorders are common in patients with gynecological malignancies, particularly in those with specific risk factors. Patients should be offered early psychological support.

Evaluation of Pre-radiotherapy Sleep Disorders in Patients With Rectal or Anal Cancer.

BACKGROUND/AIM: Radiotherapy and radiochemotherapy are common treatments for rectal and anal cancer. Anticipation of treatment may cause distress and sleep disorders. This study aimed to identify risk factors for sleep disorders. PATIENTS AND METHODS: In 42 patients with rectal or anal cancer scheduled for radiotherapy, 16 characteristics were analyzed for associations with pre-radiotherapy sleep disorders including age, gender, performance score, comorbidity, patient's or family history of additional cancer/melanoma, distress score, emotional/physical/practical problems, tumor site and stage, surgery and relation to COVID-19 pandemic. RESULTS: Overall prevalence of pre-radiotherapy sleep disorders was 42.9%. Sleep disorders were significantly associated with Karnofsky performance score 60-80 (p=0.044), Charlson comorbidity index ≥3 (p=0.0012), distress score 6-10 (p=0.00012), and more emotional (p=0.0012), physical (p=0.0004) or practical (p=0.033) problems. A trend was found for female gender (p=0.061). CONCLUSION: Sleep disorders were common in patients with rectal or anal cancer scheduled for radiotherapy. Risk factors can help identify patients requiring psychooncological support already prior to the start of radiotherapy.