Sleep pattern in a group of patients undergoing hemodialysis compared to control

Background Patients with chronic kidney disease progress regularly every year to end-stage renal disease and have to undergo dialysis. Sleep disturbances have been reported to be frequent among patients receiving dialysis and contributing to the increase of their mortality and morbidity. The present research aimed to study the sleep pattern in hemodialysis patients and the risk factors associated. This cross-sectional case-control study included 40 subjects divided into 2 groups: 20 cases recruited from Ain Shams University Hospital’s dialysis unit and 20 in the control group with normal Pittsburgh Sleep Quality Index score matched for age and sex. Both groups were subjected to overnight polysomnography, and the cases group was assessed by the Pittsburgh Sleep Quality Index to determine their sleep quality. Results Nearly all polysomnographic parameters were significantly abnormal in the cases group except for sleep onset latency (P > 0.05), showing obstructive sleep apnea and periodic limb movement (P value 0.001). Based on their Pittsburgh Sleep Quality Index score, 30% were classified as good sleepers and 70% as bad sleepers. On comparing both groups, a significant difference was found. Poor sleepers had more worse sleep efficiency (62.9%), spent longer time during their sleep in stage 1 (26.6%) with shorter REM onset latency (113.5 ± 99.5), and had a longer duration of illness with lower serum creatinine level compared to good sleepers. Conclusions The prevalence of obstructive sleep apnea and periodic limb movement in hemodialysis patients is high; patients with longer time on dialysis are at more risk of sleep disorders, whereas hemoglobin levels, BUN, and other demographic factors do not seem to play a role in sleep disorder. Hence, patients on hemodialysis need to be screened for sleep disorders so as to improve their mortality and morbidity.

The incidence of sleep disturbances among medical students

Sleep disturbance is a common health problem that can influence the quality of life. There are several types of sleep disorders, such as obstructive sleep apnea, insomnia, narcolepsy, periodic limb movement disorder, and circadian dysregulation. Medical students are probably more prone to sleep disturbances due to their extreme academic stress. In this research, the incidence of sleep disturbance among medical students was examined, and the concomitant risk factors were determined. That was one-time research. A questioning was used to collect social, demographic and sleeping data. 678 1st, 2nd and 3rd year medical students were surveyed. 29% complained of at least one sleep disturbance. The most widely spread sleep disturbance observed among 51.8% medical students included insomnia (initial insomnia and sleep maintenance). 4th year students and those who spend much time on smartphones were more prone to sleep disturbances. Sleep disturbances are common among medical students. They need to be discovered and paid attention to before the situation gets worse.

Automatic Detection of Periodic Limb Movements in Sleep (PLMS), And Analysis of Their Relationship to Heart Rate Signals

Periodic Limb Movement in Sleep (PLMS) are a sleep-related disorder of the limbs that increasingly more research has begun to associate with severe Cardiovascular Diseases (CVD). With that said, Polysomnography (PSG), followed by manual scoring, is the conventional approach being used to monitor the disorder. However, patient inconvenience, and the high costs associated with PSG, has probed the need for alternative screening tools to be developed. Moreover, due to the cumbersome and time-consuming nature of manually scoring for PLMS, more studies have begun to look into automated means of detecting PLMS. Hence, while one of the goals of the current thesis was to use the latest clinical specifications to develop an automated Periodic Limb Movement (PLM) detector, the other goal was to look into alternative signals to monitor PLMS. With that said, in the current thesis, an automated PLM detector was developed and tested on two datasets. In fact, the results were promising in that, correlation coefficients of 0.78 and 0.8, and absolute differences not greater than 9 and 6 (not including the extreme outliers) respectively, were found when comparing the clinical PLM scores with that of the automated algorithm’s PLM scores. Moreover, not only did the automated PLM detector compute PLM scores, it also provided us with PLM segmentation information, i.e., localization of PLM with respect to time. On the other hand, with regards to finding alternative signals to monitor PLMS, the etiology of PLMS was used in order to validate the use of relatively easily acquirable signals, such as Heart Rate (HR) signals, to monitor the condition. Moreover, core features were extracted from the HR signals and the PLM segmentation information from the developed PLM detector was used in order to perform individuaized classification between PLM and non-PLM segments (per subject). Although the results were promising in that, the percent of correctly identifying a given segment as PLM or non-PLM, using the HR features, across most of the subjects, i.e., especially those with PLM Index ≥ 15, were around and well above the 70% range, due to the possibility of other factors interfering with HR during sleep, a more immediate application of the observed PLMS vs HR distinction was, to be able to monitor the autonomic health of an individual, given their PLM information. Specifically, the latter was anticipated to be useful for studies looking into the relationship between PLMS and HR, and thus CVD, or more significantly, those looking into preventing CVD by treating PLM.

Automatic Detection of Periodic Limb Movements in Sleep (PLMS), And Analysis of Their Relationship to Heart Rate Signals

Periodic Limb Movement in Sleep (PLMS) are a sleep-related disorder of the limbs that increasingly more research has begun to associate with severe Cardiovascular Diseases (CVD). With that said, Polysomnography (PSG), followed by manual scoring, is the conventional approach being used to monitor the disorder. However, patient inconvenience, and the high costs associated with PSG, has probed the need for alternative screening tools to be developed. Moreover, due to the cumbersome and time-consuming nature of manually scoring for PLMS, more studies have begun to look into automated means of detecting PLMS. Hence, while one of the goals of the current thesis was to use the latest clinical specifications to develop an automated Periodic Limb Movement (PLM) detector, the other goal was to look into alternative signals to monitor PLMS. With that said, in the current thesis, an automated PLM detector was developed and tested on two datasets. In fact, the results were promising in that, correlation coefficients of 0.78 and 0.8, and absolute differences not greater than 9 and 6 (not including the extreme outliers) respectively, were found when comparing the clinical PLM scores with that of the automated algorithm’s PLM scores. Moreover, not only did the automated PLM detector compute PLM scores, it also provided us with PLM segmentation information, i.e., localization of PLM with respect to time. On the other hand, with regards to finding alternative signals to monitor PLMS, the etiology of PLMS was used in order to validate the use of relatively easily acquirable signals, such as Heart Rate (HR) signals, to monitor the condition. Moreover, core features were extracted from the HR signals and the PLM segmentation information from the developed PLM detector was used in order to perform individuaized classification between PLM and non-PLM segments (per subject). Although the results were promising in that, the percent of correctly identifying a given segment as PLM or non-PLM, using the HR features, across most of the subjects, i.e., especially those with PLM Index ≥ 15, were around and well above the 70% range, due to the possibility of other factors interfering with HR during sleep, a more immediate application of the observed PLMS vs HR distinction was, to be able to monitor the autonomic health of an individual, given their PLM information. Specifically, the latter was anticipated to be useful for studies looking into the relationship between PLMS and HR, and thus CVD, or more significantly, those looking into preventing CVD by treating PLM.

584 Response to Intravenous Ferric Carboxymaltose in the Treatment of Childhood Restless Legs Syndrome/Periodic Limb Movement Disorder

Introduction Pediatric Restless Legs Syndrome (RLS)/Periodic Limb Movement Disorder (PLMD) are treatable disorders affecting quality of life. The first line therapy is oral iron, which may have gastrointestinal side effects or suboptimal absorption. Consequently, parenteral iron preparations are needed, but have been insufficiently studied in children. This study evaluates the response to intravenous ferric carboxymaltose (FCM) in pediatric RLS/PLMD. Methods We performed a retrospective chart review of children who received FCM between May 2018 and January 2019 for treating RLS/PLMD. Serum ferritin before and after the infusion were compared. Where possible, the Clinical Global Impressions of Improvement (CGI-I) was evaluated. Side effects documented in the charts were extracted. The median administered dose of FCM was 10.1 mg/kg (range 9.6–20.8) over 0.6 to2 hours. Results There were 27 patients, with mean age of 10.0 +/-4.2 years. 52% were female. 24 had RLS and 3 had PLMD. 20/27 (69.7%) had prior oral iron therapy; 4/20 (26.0%) experienced side effects. Adverse events from FCM infusion included procedure-related anxiety in 4/27, nausea in 1/27, infusion site pain in 2/27, and tachycardia in 1/27. One patient developed subcutaneous extravasation of iron with brownish skin discoloration and a resulting adjustment disorder. Three patients had phosphorus checked following infusion; all were normal. Serum ferritin was available both before and after the infusion for 17 patients. Mean serum ferritin prior to infusion was 27.2 +/-15.7 µg/L (range 6–58) and after the infusion it was 109.8 +/-49.34 µg/L (range 27–192). Mean ferritin increase was 82.6 +/-41.5 µg/L (range 14–160; p=0.0001). Post-infusion ferritin was over 50 µg/L for all but 2 of the subjects, with follow up ranging from 31–266 days (mean 120 days). A larger increase was seen at higher doses (p=0.01). Ferritin increase was not impacted by age, gender, symptom severity, PLMI or prior ferritin level. CGI-I was applied to 15 patients with sufficient follow-up documentation and showed improvement in 86%, with 79% much or very much improved. Conclusion The administration of FCM in children with RLS/PLMD is associated with a satisfactory rise in serum ferritin and modest symptomatic improvement. Support (if any):