Assessing Sleep Concerns in Individuals With Acquired Brain Injury: The Feasibility of a Smartpad Sleep Tool

OBJECTIVE

The investigators examined the presence of disrupted sleep in acquired brain injury (ABI) and the utility of a mobile health program, MySleepScript, as an effective clinical tool to detect sleep disturbances.

METHODS

A cross-sectional pilot study of MySleepScript, a customizable electronic battery of validated sleep questionnaires, was conducted. Participants were recruited at the Acquired Brain Injury Clinic at Johns Hopkins Bayview Medical Center.

RESULTS

Sixty-eight adults with ABI (mean age, 46.3 years [SD=14.8]) participated in the study, with a mean completion time of 16.6 minutes (SD=5.4). Time to completion did not differ on individual completion or staff assistance. The mean score on the Pittsburgh Sleep Quality Index was 9.2 (SD=4.7); 83.9% of individuals had poor sleep quality (defined as a score >5). Insomnia Severity Index scores indicated moderate to severe insomnia in 45% of participants; 36.5% of participants screened positive for symptoms concerning sleep apnea, while 39.3% of individuals screened positive for restless legs syndrome.

CONCLUSIONS

Poor sleep quality was highly prevalent in this ABI cohort. MySleepScript may be an effective method of assessing for sleep disturbance in ABI. Further efforts to identify sleep disorders in this patient population should be pursued to optimize ABI management.

Abstract P241: Why Do We Expect Clinicians to Perform the Work of Computers? Developing an Automated Stroke Registry

Background: Stroke registries are established with the primary goal of improving health care quality and performance improvement through increased adherence to evidenced based guideline recommendations. As a Joint Commission certified organization, the Comprehensive Stroke Center uses the American Heart Association’s Get With The Guidelines® (GWTG) stroke patient management tool to collect performance improvement data on patients admitted with a principle diagnosis of stroke. Data abstraction is laborious and time consuming which requires manual entry by multiple clinicians. Therefore, we developed an automated internal stroke database to improve efficiency and augment accuracy.

Methods: We collaborated with software engineers and EPIC report writers to create an innovative stroke database with the capability to extract data from an Electronic Health Record (EHR) for upload to GWTG. The clinicians identified data elements for automation in the EHR. Based on final ICD-10 coding the report writer retrieved end-user data, which were organized into tables and sent to the software engineer. These variables were processed to match GWTG specifications and then presented to clinicians in the user interface for validation. Next, a CSV file was generated and uploaded to GWTG, where the remaining data was entered.

Results: The case completion time was reduced from 45 minutes to one hour to an average of 25 minutes (median 23 minutes), thus achieving our primary goal. Due to the variance in the number of required data elements based on diagnosis and patient specific variables, we aggregated the data elements, automated versus required, and calculated an automation rate of 54%. Approximately half of the 414 fields required in GWTG have the potential for automation, and we continue to strive toward this goal. Due to the complexity of GWTG questions and the way data is captured in the EHR, some data elements require manual entry.

Conclusions: An automated stroke registry has the potential to enhance efficiency and limit the opportunity for human error. By minimizing the clinicians’ data entry tasks to those of higher-level data abstraction, there is more time for performance improvement.

COHORT DIFFERENCES IN SLEEP ENVIRONMENT, BEHAVIORS, AND CONCERNS: DESCRIPTIVE ANALYSES USING MY SLEEP SCRIPT APP

Behavioral and environmental factors influence sleep outcomes. However, we understand little about how enviro-behavioral sleep hygiene practices and related sleep concerns vary across age cohorts. Using data from My Sleep Script, an app-based diagnostic checklist for identifying at risk patients, we described cohort differences in sleep hygiene and new sleep disturbances. 323 adults (46.6% female, 63.9% Caucasian) reported basic demographic, health, sleep, as well as enviro-behavioral data using the Epworth Sleepiness Scale (ESS), Pittsburg Sleep Quality Index (PSQI), and the Johns Hopkins Sleep Environment Instrument. We partitioned participants into four cohorts corresponding to birth year: The Silent Generation (N = 48, 14.9%), Baby Boomers (N = 124, 38.4%), Generation Xers (n = 109, 33.7%), and Millennials (N = 42, 13.0%). Spearman correlations described linkages among environment, behaviors, and sleep outcomes; a chi-square analysis, cohort differences in new sleep concerns. Having weapons, music players, lights, pets, and a disruptive sleep surface in the environment correlated with worse sleep quality. Eating, exercising, working, and sexual activity one hour before bed also correlated with worse sleep quality. Sleeping with pets, electronics, and on a disruptive surface correlated with lower sleep duration. Regarding cohort, we observed significant generational differences in new snoring and sleepiness complaints. Results confirm associations of suboptimal sleep hygiene with poor sleep outcomes and provide insights into their generational differences, warranting additional investigation.

The utility and feasibility of assessing sleep disruption in a men's health clinic using a mobile health platform device: A pilot study

INTRODUCTION

Evidence linking sleep disruption and sexual dysfunction in men is mounting; yet the characterisation of sleep patterns and complaints utilising a clinically feasible method within this patient population remain largely under-reported.

AIM

A pilot study aiming to demonstrate a clinically feasible method to characterise the sleep patterns and complaints in a representative sample of patients treated in a men's health clinic.

METHODS

Male patients (n = 48) completed a battery of validated sleep questionnaires using an mHealth mobile platform, MySleepScript, at the Johns Hopkins Men's Health and Vitality Center. Metrics related to clinical feasibility such as completion time, ease of use, preference of electronic format, and patient satisfaction were also collected.

MAIN OUTCOME MEASURES

Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Berlin Questionnaire, Patient Health Questionnaire (PHQ-9), and Primary Care PTSD Screen (PC-PTSD).

RESULTS

Primary urological chief symptoms for this sample patient population were erectile dysfunction (ED; 80%), hypogonadism (40%), benign prostatic hyperplasia/lower urinary tract symptoms (BPH/LUTS; 40%) and Peyronie's disease (10%). Mean PSQI score was 7.8 [SD 4.2], with 67% of all patients falling within the "poor sleeper" range. At least mild symptoms of depression were noted in 40% and 43% were at risk for obstructive sleep apnea (OSA) on the Berlin Questionnaire.

CONCLUSIONS

This pilot study demonstrated the feasibility and potential utility of an mHealth platform to assist clinicians, within a men's health clinic, in detecting sleep disturbances. Disrupted sleep was revealed in well over half of this sample of patients. As a result of the growing evidence linking poor sleep and sleep disorders (eg, OSA) to the conditions relevant to men's health (eg, erectile dysfunction, hypogonadism and BPH), further efforts beyond this pilot study are necessary to identify the aetiological processes underlying the association between specific disrupted sleep disorders and urological conditions.

Predicting Hemorrhagic Transformation of Acute Ischemic Stroke: Prospective Validation of the HeRS Score

Hemorrhagic transformation (HT) increases the morbidity and mortality of ischemic stroke. Anticoagulation is often indicated in patients with atrial fibrillation, low ejection fraction, or mechanical valves who are hospitalized with acute stroke, but increases the risk of HT. Risk quantification would be useful. Prior studies have investigated risk of systemic hemorrhage in anticoagulated patients, but none looked specifically at HT. In our previously published work, age, infarct volume, and estimated glomerular filtration rate (eGFR) significantly predicted HT. We created the hemorrhage risk stratification (HeRS) score based on regression coefficients in multivariable modeling and now determine its validity in a prospectively followed inpatient cohort.A total of 241 consecutive patients presenting to 2 academic stroke centers with acute ischemic stroke and an indication for anticoagulation over a 2.75-year period were included. Neuroimaging was evaluated for infarct volume and HT. Hemorrhages were classified as symptomatic versus asymptomatic, and by severity. HeRS scores were calculated for each patient and compared to actual hemorrhage status using receiver operating curve analysis.Area under the curve (AUC) comparing predicted odds of hemorrhage (HeRS score) to actual hemorrhage status was 0.701. Serum glucose (P < 0.001), white blood cell count (P < 0.001), and warfarin use prior to admission (P = 0.002) were also associated with HT in the validation cohort. With these variables, AUC improved to 0.854. Anticoagulation did not significantly increase HT; but with higher intensity anticoagulation, hemorrhages were more likely to be symptomatic and more severe.The HeRS score is a valid predictor of HT in patients with ischemic stroke and indication for anticoagulation.