Predictors Of Sleep Disorders Research Articles

Development and validation of a prediction nomogram for sleep disorders in hospitalized patients with acute myocardial infarction

PurposeSleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders.MethodsClinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted.ResultsA total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance.ConclusionsWe identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.

The Prevalence and Predictors of Sleep Disorders and Their Impact on Academic Performance Among Saudi University Students: A Cross-Sectional Study.

University students encounter a variety of sleep problems that have an impact on their health and academic performance. Therefore, the aim of this study was to evaluate the prevalence of sleep disorders and their impact on academic performance among Saudi university students. This is an online cross-sectional study that involved university students and was conducted between November 2022 and February 2023 in Saudi Arabia. Sleep disorders were examined among undergraduate students through online screening surveys including the Epworth Sleepiness Scale (ESS) to assess excessive daytime sleepiness, the Insomnia Severity Index (ISI) to measureinsomnia, and the Berlin questionnaire to examine obstructive sleep apnea (OSA). Restless leg syndrome (RLS) was measured using the RLS rating.SPSSversion 29.0 (IBM Corp., Armonk, NY, USA)was used for all statistical analyses. Binary logistic regression analysis was used to identify predictors of having excessive daytime sleepiness, insomnia, sleep-disordered breathing, and RLS. The sample included 449 participants. According to the ESS, 56.6% (n=254) of students reported excessive daytime sleepiness. Using the ISI, 78.4% (n=352) of students were found to have insomnia. The Berlin questionnaire indicated that 6.7% (n=30) of students had sleep-disordered breathing. Additionally, 13.6% (n=61) of students reported having RLS. Male students had higher odds of sleep-disordered breathing compared to females (p<0.01), while marital status showed that married students had higher odds of sleep-disordered breathing (p<0.05). Students who reported nighttime sleeping had lower odds of insomnia and restless leg syndrome (p<0.05). Having regular sleeping times was associated with higher odds of insomnia (p<0.05). Napping sometimes was associated with higher odds of excessive daytime sleepiness and sleep-disordered breathing (p<0.05). Students working day and night shifts had higher odds of sleep-disordered breathing (p<0.01). Interestingly, students with comorbidities had lower odds of RLS (p<0.01). This study established that sleep disturbances among students significantly reduce grade point averages (GPAs), where the most affected were male students and those who were married. The government should implement sleep education programs, provide resources for the management of sleep, encourage consistent sleep schedules, and come up with targeted interventions for at-risk groups. More research is also recommended on effective sleep interventions.

Sleep problems and their predictors in community-dwelling older adults with diabetes in India: Evidence from the Longitudinal Ageing Study in India

ObjectivesTo ascertain the prevalence and predictors of sleep disorders and poor sleep quality among older adults with Diabetes (DM) in India, and to assess the relationship between sleep quality and DM. MethodsData was utilized from the nationally representative Longitudinal Ageing Study in India (Wave-1, 2017–18), with a total sample of 66606 older adults (≥45 years) selected for the study. Sleep problems and sleep quality score were assessed using an adaptation based on the Jenkins Sleep Scale. Multivariate linear and logistic regressions were conducted to determine the effect of sociodemographic and clinical factors on sleep quality. Mediation analysis (Karlson-Holm-Breen) was done to assess the direct and indirect effects of independent variables on the sleep quality scores. Further, Propensity score matching (PSM) was done to assess the impact of diabetes on sleep problems. ResultsThe prevalence of DM was 12.34% (n = 8564, 95% CI: 11.54, 13.20) among whom 24.38% (95% CI: 21.38, 27.65) reported sleep problems. On adjusted analysis, sleep problems were significantly associated with increasing education, higher wealth quintile, lack of physical activity, and multimorbidity. Mediation analysis showed adherence to anti-diabetes medication improved sleep quality (aB = −0.28 (95% CI: −0.54, −0.02)), while comorbidities worsened sleep quality (aB = 0.79 (95% CI: 0.67, 0.92)). Analysis from PSM indicated that DM was associated with a 6.2% higher chance of sleep problems. ConclusionsPoor sleep quality is present in nearly one in four individuals diagnosed with DM in India and linked with certain adverse social determinants. Focused interventions to improve assessment and treatment of sleep problems in resource-limited primary care settings require prioritization.

Adult ADHD and sleep disorders: Prevalence, severity and predictors of sleep disorders in a sample of Italian psychiatric outpatients

ObjectiveSleep disorders are frequent in adult subjects diagnosed with ADHD. The aims of the present study were to assess the prevalence and the severity of sleep disorders in a sample of Italian psychiatric outpatients, to compare the prevalence and severity of sleep disorders in patients with and without diagnosis of adult ADHD, and to evaluate the role of ADHD as an individual predictor of sleep disturbances severity. Method634 outpatients accessing psychiatric services were assessed with the Mini-International Neuropsychiatric Interview (MINI) Plus V. 5.0.0 interview and the Adult ADHD self-report Scale Symptoms Checklist (ASRS)-V 1.1 Short Form. Patients positive to the ASRS-V 1.1 were assessed with the Diagnostic Interview for ADHD in Adults (DIVA) 2.0. Sleep disorders and sleep disturbances’ severity were assessed with the PROMIS Adult Sleep Disturbance Scale. ResultsSleep disorders were more frequent (p < 0.001) and sleep disturbances were more severe (d = 1.26, p < 0.001) in subjects diagnosed with adult ADHD compared to other outpatients. Among the 44 subjects diagnosed with ADHD, 15 (34.1%) reported no or slight sleep disturbances, 9 (20.5%) a mild sleep disorder, 17 (38.6%) a moderate sleep disorder and 3 (6.8%) a severe sleep disorder. ADHD diagnosis, multiple psychiatric comorbidity and history of suicide attempts emerged as individual predictors of worse sleep disturbances. ConclusionSleep disorders are more frequent and severe in subjects diagnosed with ADHD compared to other adult outpatients. Sleep disturbances might represent an intrinsic feature in adult ADHD subjects, presenting important clinical repercussions, and should be routinely evaluated and monitored in this population.

The Prevalence, Pattern, and Predictors of Sleep Disorders among Pregnant Women Attending Antenatal Clinic in a Southern Nigerian City

Background: Sleep disorders are a common and often undiagnosed chronic health problem associated with an increased incidence of metabolic syndrome and pregnancy complications. Aim: The purpose of this study was to find out how common sleep disturbances are among pregnant women in Calabar, as well as their patterns and risk factors. Materials and Methods: A cross-sectional study was conducted among 360 antenatal attendees in the three major public health facilities in Calabar and 338 completed questionnaires were included in the analysis. Socio-demographic, medical, and obstetrics information were obtained using interviewer-administered questionnaires. Major international sleep classification tools were used to identify the patterns of sleep disturbance. Their weights and heights as well as their blood pressures (BPs) were measured. Data were analyzed using SPSS version 23. The predicting factors were extracted using a logistic regression model. Results: The prevalence of sleep disorder was 44.1%. The most common pattern of sleep disorder was insomnia (34.6%). Nineteen (5.6%) of them reported mixed disorders. On logistic analysis, pregnant women with normal BP (odds ratio [OR]: 0.440, 95% confidence interval [CI]: 0.230–0.843, P = 0.013), normal body mass index (OR: 0.365, 95% CI: 0.133–1.001, P = 0.050) and maternal age 20–30 years, (OR: 0.169, 95% CI: 0.30–0.969, P = 0.046) were significantly less likely to suffer from sleep disorders. Residing in a single a room apartment with family members was an independent social determinant of sleep deprivation (OR: 2.009, 95% CI: 1.003–4.025, P = 0.049). Conclusion: The study suggested that the prevalence of sleep disturbance is high among pregnant women. Counseling on good quality sleep during pregnancy may improve pregnancy outcome by ensuring appropriate weight gain and good BP control.

The Assessment of Risk and Predictors of Sleep Disorders in Patients with Psoriasis-A Questionnaire-Based Cross-Sectional Analysis.

Psoriasis is a chronic, inflammatory skin disease affecting 2–4% of the general population. Accompanying subjective symptoms (pruritus or pain) may cause decreased life quality including sleep disorders (SD). Sixty psoriatic patients fulfilled the following questionnaires: Pittsburgh Sleep Quality Index (PSQI), STOP BANG for the obstructive sleep apnea syndrome (OSAS) assessment, and Restless Legs Syndrome (RLS) Severity Scale. Patients’ laboratory and clinical data were also investigated. All data obtained were compared to 40 participants without dermatoses. Mean PSQI, risk of OSAS, and RLS severity of psoriatics were significantly higher than in controls (p < 0.0001, p < 0.05, p < 0.05 respectively). There was a positive correlation between the time of suffering from psoriasis and the risk of OSAS (R = 0.286, p < 0.05). We did not observe any significant relationship between PSQI, risk of OSAS, or RLS and psoriasis severity assessed with PASI (Psoriasis Area and Severity Index). We identified four possible predictors of RLS: glucose, CRP and total cholesterol concentrations, and PSQI. SD are significantly more frequent in psoriatics than in people without chronic dermatological diseases but surprisingly they are not correlated with psoriasis severity. SD decrease patients’ life quality and may result in serious consequences. Therefore, establishing recommendations concerning screening for SD and their predictors should be considered.

Investigation of sleep disturbance and related factors in patients with primary Sjögren's syndrome

OBJECTIVE To investigate the prevalence of sleep disorders and the relevant determinants in a cohort of primary Sjogren' s syndrome (pSS) patients. METHODS One hundred and eighty-six pSS patients were included in the study, who were admitted to Peking University People' s Hospital and met the criteria of inclusion and exclusion. Sleep quality was assessed using the Pittsburgh sleep quality index(PSQI).Depression, anxiety were evaluated by patient health questionnaire (PHQ)-9, generalized anxiety disorder(GAD)-7, respectively. The demographic and clinical data were also recorded.Disease activity and damage were evaluated with the European League Against Rheumatism Sjogren's syndrome disease activity index (ESSDAI). According to the PSQI score>7, the pSS patients were divided into 152 cases of sleep disorder group and 34 cases of normal sleep group. Mann-Whitney U test, Chi-square test or Fisher' s exact test, independent samples t test, Spearman correlation analysis and Logistic regression were used for statistical analysis. RESULTS The prevalence of sleep disturbance (PSQI > 7) was 81.7% (152 / 186) in the pSS patients, and 52.7% (98/186) had moderate or severe sleep disorders (PSQI≥ 11). The mean PSQI score of sleep disordered group was (12.29±3.30), while the normal sleep group PSQI score was (5.50±1.20). The PSQI score, PHQ-9 score and GAD-7 score in the sleep-disordered group were significantly higher than those in the normal sleep group (P=0.000, 0.035, 0.031). The PSQI score in the sleep disordered group were significantly higher than those in the normal sleep group in seven aspects: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disorders, hypnotic drug use and daytime dysfunction. All of them had statistical significance. According to the results of Spearman correlation analysis, PSQI had significantly positive correlation with course of disease, anxiety, depression score (r=0.151, 0.240, 0.421, P < 0.05), but negatively correlated with C3, C4 (r=-0.021, -0.235, P < 0.05). Logistic analysis identified the course of disease(OR=2.809, 95%CI: 1.21-6.52)and PHQ-9 score(OR=1.422, 95%CI: 1.04-1.94)as predictors of sleep disorders. CONCLUSION The incidence of sleep disorder in the pSS patients was higher, which was closely related to the course of disease, anxiety, depression and other factors. It is critical to assess and manage comprehensively the disease.

Frequency and Predictors of Sleep Disorders in Undergraduate Medical Students

Frequency and Predictors of Sleep Disorders in Undergraduate Medical Students

The Prevalence of Insomnia and the Link between Iron Metabolism Genes Polymorphisms, TF rs1049296 C>T, TF rs3811647 G>A, TFR rs7385804 A>C, HAMP rs10421768 A>G and Sleep Disorders in Polish Individuals with ASD.

Iron deficiency have been found to be linked to sleep disorders. Both genetic and environmental factors are risk factors for skewed iron metabolism, thus sleep disruptions in autism spectrum disorders (ASD). The aim of our study was to assess the prevalence of single nucleotide polymorphisms (SNPs) within transferrin gene (TF) rs1049296 C>T, rs3811647 G>A, transferrin receptor gene (TFR) rs7385804 A>C, and hepcidin antimicrobial peptide gene (HAMP) rs10421768 A>G in Polish individuals with ASD and their impact on sleep pattern. There were 61 Caucasian participants with ASD and 57 non-ASD controls enrolled. Genotypes were determined by real-time PCR using TaqMan SNP assays. The Athens Insomnia Scale (AIS) was used to identify sleep disruptions. There were 32 cases (57.14%) with insomnia identified. In the ASD group, the defined counts of genotypes were as follows: TF rs1049296, C/C n = 41 and C/T n = 20; TF rs3811647, G/G n = 22, G/A n = 34, and A/A n = 5; TFR rs7385804, A/A n = 22, A/C n = 29, and C/C n = 10; and HAMP rs10421768, A/A n = 34, A/G n = 23, and G/G n = 4. There were no homozygous carriers of the TF rs1049296 C>T minor allele in the ASD group. All analyzed SNPs were not found to be linked to insomnia. The investigated polymorphisms are not predictors of sleep disorders in the analyzed cohort of individuals with ASD.

2458-PUB: Prevalence and Correlates of Sleep Disorders in Greek Patients with Type 2 Diabetes: Comparison of an Urban and a Semiurban Population

Background and Aims: Sleep disturbances are common among individuals with type 2 diabetes (T2D). The aim of this study was to estimate the prevalence and correlates of sleep disorders in Greek diabetics and differences between an urban and semi-urban population. Materials and Methods: The study population was comprised of 308 T2D patients [139 patients from Piraeus (urban population, group A) and 169 patients from Sitia (semi-urban population, group B)]. In all patients, quality of sleep was evaluated by administering the Pittsburgh Sleep Quality Index (PSQI) through an interview. The PSQI is a self-report questionnaire that assesses sleep quality and quantity over a month-long period. Results: Mean PSQI score of group A was 11.22 ± 6.44 and 88.5% (n=123) of the participants had a PSQI score ≥5, indicating poor quality of sleep. Mean PSQI score of group B was 12.48 ± 5.85, and 88.8% (n=140) had a PSQI score ≥5. Incidence of a PSQI score ≥5 did not differ between the two groups (P=0.13). Logistic regression analysis, after adjustment for sex, BMI, presence of hypertension and dyslipidemia, smoking, diabetes complications, and oral antidiabetic treatment, showed that PSQI ≥5 in total sample population was associated with insulin treatment [odds ratio (OR): 0.03, 95% confidence interval (95% CI): 0.31-0.99], and HbA1c (OR: 6.56, 95% CI: 1.93-16.14). In group A, poor sleep quality was associated with insulin treatment (OR: 0.02, 95% CI: 0.10-0.84), and HbA1c (OR: 7.55, 95% CI: 1.97-18.81). In group B, poor sleep quality was associated only with age (OR: 1.36, 95% CI: 1.06-1.74). Conclusion: Our study suggested that Greek patients with T2D have a high prevalence of sleep disorders with similar incidence for urban and semi-urban population. Insulin treatment and HbA1c were the greatest predictors of sleep disorders. Identifying sleep disorders in T2D and treating them early might improve patient's quality of life. Disclosure A. Papazafiropoulou: None. M. Stamatelatou: None. E. Xourgia: None. L. Mpoumi: None. E. Georgopoulos: None. G. Tziomalos: None. S. Solidaki: None. M. Dasenaki: None. A. Melidonis: None.

Взаимосвязь эмоционально-личностных особенностей и субъективной оценки нарушений сна

This article discusses the problem of the relationship between the subjective assessment of sleep disorders and emotional and personal characteristics. After all, such features as anxiety, obsessive thoughts and actions, irritability, etc. can act as predictors of sleep disorders, at the same time, long enough sleep disorders can affect the psychological features to changes during life. The study sample included 20 people, of whom 6 were diagnosed with chronic primary insomnia, and 14 had subjective complaints about sleep. It was revealed that disturbance and poor quality of sleeping correlated with emotional-personal characteristics such as emotional instability, neuroticism, irritability, depression.

Predictors of Sleep Disorders among HIV Out-Patients in a Tertiary Hospital

Human immunodeficiency virus (HIV)-infected individuals have been shown to have a high prevalence of sleep disturbances. Both the effects of the virus and the antiretroviral drugs may cause sleep disturbances. We sought to determine the prevalence and predictors of sleep disorders among HIV seropositive adult subjects presenting at an outpatient hospital setting. One hundred and fifty six subjects were recruited for the study by using a sleep disorder screening questionnaire. The mean ages of the participants were 38.7 ± 9.23 and 39.5 ± 9.23 for those with and those without sleep disorder, respectively. The prevalence rate of sleep disorders was 46.2%. Elevated systolic blood pressure, lower CD4 count levels, and being on the highly active antiretroviral therapy combination TDF/3TC/ATZ/lpvr were associated with sleep disorders. The high prevalence rate observed necessitates routine screening for sleep disorders among HIV/AIDs patients.

Predictors of sleep disorders among patients with type 2 diabetes mellitus

AimsThis study examined the prevalence of sleep disorders (SD) and self-reported sleep symptoms and risk factors among adult patients diagnosed with T2DM. MethodsData were obtained from the 2012 US National Health and Wellness Survey, an annual Internet-based survey. A total of 7239 participants reported a diagnosis of T2DM. Patients also provided information on diagnosis of SD (e.g., insomnia, sleep breathing disorder, other sleep conditions, etc.) and regularly experienced sleep symptoms (e.g., difficulty falling asleep, sleep apnea, daytime sleepiness, difficulty staying awake, etc.). Logistic regressions were used to examine factors associated with SD and symptoms. ResultsThe average age was 59.9 (standard deviation=12.2), 59.5% were male, 24.4% were diagnosed with SD, 76.8% reported experiencing any sleep symptom regularly (difficulty falling asleep, 30.5%; sleep apnea, 17.4%; daytime sleepiness, 26.8%; difficulty staying awake, 10.1%). Logistic regression models showed the strongest predictors of diagnosed SD were obesity class (OC) III (OR=2.20), White (OR=1.92), OC II (OR=1.57), smoking (OR=1.57), lower income (OR=1.49–1.19), unemployment (OR=1.38), and comorbidities (OR=1.35), all p<0.05. The strongest predictors of any sleep symptom were OC III (OR=2.22), OC II (OR=1.78), lower income (OR=1.74–1.24), female gender (OR=1.72), OC I (OR=1.60), White (OR=1.54), and smoking (OR=1.47), all p<0.05. ConclusionsAlmost 25% of patients with T2DM were diagnosed with SD and over 75% reported experiencing at least one sleep symptom regularly. SD and symptoms were strongly associated with obesity, White ethnicity, gender, low income, and smoking. Interventions focusing on weight management and smoking cessation have shown to improve T2DM and may improve SD.

Observational study of prevalence of sleep disorder in patients with epilepsy

BackgroundReduced sleep quality with subsequent excessive daytime sleepiness (EDS) is found in patients with epilepsy. AimTo know the frequency of sleep disorders in comparison to control group to know the predictors of sleep disorders that have implications on management of patient with epilepsy. Materials and methods199 patients with epilepsy and 48 controls, who attended Nizam's Institute of Medical Sciences were taken into the study.The Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI) are two questionnaires commonly used in clinical assessment of EDS and sleep quality. Results24.6% of PWE are found to have problems with sleep disorders when compared to controls (10.6%). PWE had significantly higher scores when compared to controls in global PSQI total scores (3.78 vs. 2.43). Sleep latency and medication use (0.83 vs. 0.53 and 0.52 vs. 0.06 respectively) as well as sleep quality and sleep efficiency are poor as indicated by high scores (0.75 vs. 0.40 and 0.22 vs.0.20 respectively). ConclusionPoor sleep quality of PWE in PSQI correlated with EDS of ESS. Poor control of seizures, polytherapy, and partial epilepsy make the PWE statistically more vulnerable to poor sleep quality thereby having therapeutic implications.

Clinical predictors of sleep disorders in sickle-cell disease (SCD) patients in Cameroon

Clinical predictors of sleep disorders in sickle-cell disease (SCD) patients in Cameroon

Sleep disorders in boys with Duchenne muscular dystrophy

Determine the frequency and predictors of sleep disorders in boys with Duchenne Muscular Dystrophy (DMD). Cross-sectional study by postal questionnaire. Sleep disturbances were assessed using the Sleep Disturbance Scale for Children (validated on 1157 healthy children). A total sleep score and six sleep disturbance factors representing the most common sleep disorders were computed. Potential associations between pathological scores and personal, medical and environmental factors were assessed. Sixteen of 63 boys (25.4%) had a pathological total sleep score compared with 3% in the general population. The most prevalent sleep disorders were disorders of initiating and maintaining sleep (DIMS) 29.7%, sleep-related breathing disorders 15.6% and sleep hyperhydrosis 14.3%. On multivariate analysis, pathological total sleep scores were associated with the need to be moved by a carer (OR = 9.4; 95%CI: 2.2-40.7; p = 0.003) and being the child of a single-parent family (OR =7.2; 95%CI: 1.5-35.1; p = 0.015) and DIMS with the need to be moved by a carer (OR = 18.0; 95%CI: 2.9-110.6; p = 0.002), steroid treatment (OR = 7.7; 95%CI: 1.4-44.0; p = 0.021) and being the child of a single-parent family (OR = 7.0; 95%CI: 1.3-38.4; p = 0.025). Sleep disturbances are frequent in boys with DMD and are strongly associated with immobility. Sleep should be systematically assessed in DMD to implement appropriate interventions.

Insomnia among hospitalized elderly patients: Prevalence, clinical characteristics and risk factors

Insomnia can determine an increase in falls and accidents, hospitalization and nursing home placement. The aims of our study are to evaluate prevalence, clinical characteristics and predictors of sleep disorders in elderly inpatients admitted to a Geriatric Acute Care Ward. This longitudinal observational study consecutively recruited patients aged 65 and older admitted to a Geriatric Acute Care Unit between January the 1st 2007 and June 31st 2007. During the 3rd day of length of stay (LOS), patients were asked if they suffered with sleep disorders. Patients reporting sleep disturbances during the first 3 days of LOS were included in the study. Patients were evaluated for insomnia, comorbidity, self-rated health, functional status, cognitive impairment and pain. Of the 280 subjects investigated, 80 referred sleep disorders during LOS with a prevalence of 36.7%. Patients with sleep problems scored significantly worse on the cumulative index rating scale (CIRS) severity index ( p = 0.007), on the numeric rating scale (NRS) ( p = 0.01) and on the activities of daily living (ADL) scale ( p < 0.001). The CIRS severity index resulted the best predictor for insomnia related to hospitalization (OR 7.9, SE 0.85, p = 0.01). The knowledge of insomnia predictors might help in planning preventive strategies to improve patients’ global health status and quality of life.

Sleep disorders in children with cerebral palsy

To determine the frequency and predictors of sleep disorders in children with cerebral palsy (CP) we analyzed the responses of 173 parents who had completed the Sleep Disturbance Scale for Children. The study population included 100 males (57.8%) and 73 females (42.3%; mean age 8y l0mo [SD 1y 11mo]; range 6y–11y 11mo). Eighty-three children (48.0%) had spastic diplegia, 59 (34.1%) congenital hemiplegia, 18 (10.4%) spastic quadriplegia, and 13 (7.5%) dystonic/dyskinetic CP. Seventy-three children (42.2%) were in Gross Motor Function Classification System Level 1,33 (19.1%) in Level II, 30 (17.3%) in Level in, 23 (13.3%) in Level IV, and 14 (8.1%) in Level V. Thirty children (17.3%) had epilepsy. A total sleep problem score and six factors indicative of the most common areas of sleep disorder in childhood were obtained. Of the children in our study, 23% had a pathological total sleep score, in comparison with 5% of children in the general population. Difficulty in initiating and maintaining sleep, sleep-wake transition, and sleep breathing disorders were the most frequently identified problems. Active epilepsy was associated with the presence of a sleep disorder (odds ratio [OR]=17.1,95% confidence interval [CI] 2.5–115.3), as was being the child of a single-parent family (011=3.9,95% CI 1.3–11.6). Disorders of initiation and maintenance of sleep were more frequent in children with spastic quadriplegia (OR=12.9,95% CI 1.9–88.0), those with dyskinetic CP (OR=20.6,95% CI 3.1–135.0), and those with severe visual impairment (OR=l2.5,95% CI 2.5–63.1). Both medical and environmental factors seem to contribute to the increased frequency of chronic sleep disorders in children with CP.

Sleep disorders in children with cerebral palsy

To determine the frequency and predictors of sleep disorders in children with cerebral palsy (CP) we analyzed the responses of 173 parents who had completed the Sleep Disturbance Scale for Children. The study population included 100 males (57.8%) and 73 females (42.2%; mean age 8y 10mo [SD 1y 11mo]; range 6y-11y 11mo). Eighty-three children (48.0%) had spastic diplegia, 59 (34.1%) congenital hemiplegia, 18 (10.4%) spastic quadriplegia, and 13 (7.5%) dystonic/dyskinetic CP. Seventy-three children (42.2%) were in Gross Motor Function Classification System Level I, 33 (19.1%) in Level II, 30 (17.3%) in Level III, 23 (13.3%) in Level IV, and 14 (8.1%) in Level V. Thirty children (17.3%) had epilepsy. A total sleep problem score and six factors indicative of the most common areas of sleep disorder in childhood were obtained. Of the children in our study, 23% had a pathological total sleep score, in comparison with 5% of children in the general population. Difficulty in initiating and maintaining sleep, sleep-wake transition, and sleep breathing disorders were the most frequently identified problems. Active epilepsy was associated with the presence of a sleep disorder (odds ratio [OR]=17.1, 95% confidence interval [CI] 2.5-115.3), as was being the child of a single-parent family (OR=3.9, 95% CI 1.3-11.6). Disorders of initiation and maintenance of sleep were more frequent in children with spastic quadriplegia (OR=12.9, 95% CI 1.9-88.0), those with dyskinetic CP (OR=20.6, 95% CI 3.1-135.0), and those with severe visual impairment (OR=12.5, 95% CI 2.5-63.1). Both medical and environmental factors seem to contribute to the increased frequency of chronic sleep disorders in children with CP.

Age and underdialysis as predictors of sleep disorders in peritoneal dialysis patients

Age and underdialysis as predictors of sleep disorders in peritoneal dialysis patients