Browsing by Subject "Insomnia"
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Item Cognitive Impairment and Insomnia in Celiac Disease: A Systematic Review and Meta-Analysis(Korean Society of Gastroenterology, 2024) Beas, Renato; Godoy, Ambar; Norwood, Dalton A.; Ascencio, Ysaith Orellana; Izquierdo-Veraza, Diego; Montalvan-Sanchez, Eleazar E.; Ramirez, Mirian; Kurada, Satya; Medicine, School of MedicineEmerging evidence suggests a broader spectrum of celiac disease (CeD) system involvement, including neurological manifestations. We aimed to conduct a systematic review and meta-analysis of the available evidence from studies assessing the association of cognitive impairment and insomnia with CeD. A total of 259 participants with CeD were included in the studies investigating insomnia and 179 were included in studies investigating cognitive impairment. The overall pooled odds ratio for insomnia in patients with CeD was 1.83 (95% confidence interval, 1.38 to 2.42; I2=0.00%). The present study provides valuable insights into the available evidence from studies investigating cognitive impairment in patients with CeD and our systematic review and meta-analysis revealed a significant association between CeD and insomnia.Item Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange(MDPI, 2023-05-05) Holler, Emma; Chekani, Farid; Ai, Jizhou; Meng, Weilin; Khandker, Rezaul Karim; Ben Miled, Zina; Owora, Arthur; Dexter, Paul; Campbell, Noll; Solid, Craig; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and TechnologyThis study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011–2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: −1 to −365 days and −180 to −365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011–2013, 2011–2015, and 2011–2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011–2017 data from −1 to −365 and −180 to −365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.Item Factors Associated with the Remission of Insomnia After Traumatic Brain Injury: A Traumatic Brain Injury Model Systems Study(Taylor & Francis, 2020) Lequerica, Anthony H.; Weber, Erica; Dijkers, Marcel P.; Dams-O’Connor, Kristen; Kolakowsky-Hayner, Stephanie A.; Bell, Kathleen R.; Bushnik, Tamara; Goldin, Yelena; Hammond, Flora M.; Physical Medicine and Rehabilitation, School of MedicineObjective: To examine the factors associated with the remission of insomnia by examining a sample of individuals who had insomnia within the first two years after traumatic brain injury (TBI) and assessing their status at a secondary time point. Design and Methods: Secondary data analysis from a multicenter longitudinal cohort study. A sample of 40 individuals meeting inclusion criteria completed a number of self-report scales measuring sleep/wake characteristics (Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Insomnia Severity Index, Sleep Hygiene Index), fatigue and depression (Multidimensional Assessment of Fatigue, Patient Health Questionnaire-9), and community participation (Participation Assessment with Recombined Tools-Objective). One cohort was followed at 1 and 2 years post-injury (n = 19) while a second cohort was followed at 2 and 5 years post-injury (n = 21). Results: Remission of insomnia was noted in 60% of the sample. Those with persistent insomnia had significantly higher levels of fatigue and depression at their final follow-up and poorer sleep hygiene across both follow-up time-points. A trend toward reduced community participation among those with persistent insomnia was also found. Conclusion: Individuals with persistent post-TBI insomnia had poorer psychosocial outcomes. The chronicity of post-TBI insomnia may be associated with sleep-related behaviors that serve as perpetuating factors.Item Is insomnia an independent predictor of incident atherosclerotic cardiovascular disease among HIV-infected veterans?(2017-07) Polanka, Brittanny M.; Stewart, Jesse; Zapolski, Tamika; Hirsh, AdamWhile insomnia/sleep disturbance has been identified as an independent predictor of cardiovascular disease in the general population, no studies have examined whether insomnia contributes to the elevated cardiovascular disease (CVD) risk in people with human immunodeficiency virus (HIV). Thus, the current study examined whether insomnia symptoms predict incident atherosclerotic CVD in the Veterans Aging Cohort Study 9 (VACS9), a prospective cohort of HIV-infected (n = 3,138) and uninfected (n = 3,010) Veterans utilizing self-report measures and administrative data. In partial support of my hypotheses, I found that HIV-infected Veterans bothered a lot by difficulty falling or staying asleep have greater CVD risk than HIV-infected Veterans without these symptoms. This study failed to replicate previous findings that insomnia symptoms are predictive of incident CVD in uninfected adults, which may be due to issues related to the validity of the insomnia symptoms assessment. A number of methodological issues are identified and considered in the interpretation of the current study results. Given the novelty of examining insomnia as a predictor of incident CVD in HIV-infected adults and the limitations of the present study, future research is needed to better elucidate the association between insomnia and future CVD in this population.Item Modeling Acute Care Utilization for Insomnia Patients(2023-08) Zhu, Zitong; Fang, Shiaofen; Ben Miled, Zina; Xia, Yuni; Zheng, JiangyuMachine learning (ML) models can help improve health care services. However, they need to be practical to gain wide adoption. A methodology is proposed in this study to evaluate the utility of different data modalities and cohort segmentation strategies when designing these models. The methodology is used to compare models that predict emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications and cohort segmentation is based on age group and disease severity. The proposed methodology is applied to models developed using a cohort of insomnia patients and a cohort of general non- insomnia patients under different data modalities and segmentation strategies. All models are evaluated using the traditional intra-cohort testing. In addition, to establish the need for disease- specific segmentation, transfer testing is recommended where the same insomnia test patients used for intra-cohort testing are submitted to the general-patient model. The results indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. For insomnia patients, the best ED and IH models using both data modalities or either one of the modalities achieved an area under the receiver operating curve (AUC) of 0.71 and 78, respectively. Our results also show that an insomnia-specific model is not necessary when predicting future ED visits but may have merit when predicting IH visits. As such, we recommend the evaluation of disease-specific models using transfer testing. Based on these initial findings, a language model was pretrained using diagnosis codes. This model can be used for the prediction of future ED and IH visits for insomnia and non-insomnia patients.Item A Practical Application Primer on Cognitive Behavioral Therapy for Insomnia for Medical Residents(Association of American Medical Colleges, 2019-12-13) Chernyak, Yelena; Psychiatry, School of MedicineIntroduction: Cognitive behavioral therapy for insomnia (CBT-I) is a well-established nonpharmacological intervention that is the gold standard treatment for insomnia. CBT-I has been utilized and empirically validated in many modalities, including group treatment, telemedicine, and primary care. Despite the wealth of evidence on its effectiveness, many medical providers, including those in primary care, where most insomnia complaints are raised, have limited exposure, knowledge, and resources to direct or implement this intervention. Methods: Medical educators from an academic medical center developed a module focused on teaching medical residents the techniques of CBT-I. The educational activity was an interactive 90-minute seminar that included a lecture followed by a case presentation illustrating the application of medical knowledge. A postseminar survey was used to evaluate the topic and content of the seminar. Results: In a survey of 32 primary care and psychiatry residents and sleep medicine fellows, 97% of respondents indicated that the topic of CBT-I should be included in the seminar series, and 84% indicated that the topic was of interest to them. Qualitative feedback underscored the relevance of this topic to trainees' clinical practice, as well as its underratedness. Discussion: The seminar on CBT-I was well received and viewed as a valuable tool in practicing medicine. The slides and vignettes provided enable replication of this workshop in other settings with medical learners who have a cursory knowledge of sleep medicine. The workshop is applicable to other health professionals, including medical students, nurses, social workers, and psychology trainees.Item Sleep Disturbance Predicts Less Improvement in Pain Outcomes: Secondary Analysis of the SPACE Randomized Clinical Trial(Oxford, 2019-09-14) Koffel, Erin; Kats, Allyson M.; Kroenke, Kurt; Bair, Matthew J.; Gravely, Amy; DeRonne, Beth; Donaldson, Melvin T.; Goldsmith, Elizabeth S.; Noorbaloochi, Siamak; Krebs, Erin E.; Medicine, School of MedicineObjective Sleep disturbance may limit improvement in pain outcomes if not directly addressed in treatment. Moreover, sleep problems may be exacerbated by opioid therapy. This study examined the effects of baseline sleep disturbance on improvement in pain outcomes using data from the Strategies for Prescribing Analgesics Comparative Effectiveness (SPACE) trial, a pragmatic 12-month randomized trial of opioid vs nonopioid medication therapy. Design Participants with chronic back pain or hip or knee osteoarthritis pain were randomized to either opioid therapy (N = 120) or nonopioid medication therapy (N = 120). Methods We used mixed models for repeated measures to 1) test whether baseline sleep disturbance scores modified the effect of opioid vs nonopioid treatment on pain outcomes and 2) test baseline sleep disturbance scores as a predictor of less improvement in pain outcomes across both treatment groups. Results The tests for interaction of sleep disturbance by treatment group were not significant. Higher sleep disturbance scores at baseline predicted less improvement in Brief Pain Inventory (BPI) interference (β = 0.058, P = 0.0002) and BPI severity (β = 0.026, P = 0.0164). Conclusions Baseline sleep disturbance adversely affects pain response to treatment regardless of analgesic regimen. Recognition and treatment of sleep impairments that frequently co-occur with pain may optimize outcomes.