Effects and clinical feasibility of a behavioral treatment for sleep problems in adult attention deficit hyperactivity disorder (ADHD): a pragmatic within-group pilot evaluation.

BACKGROUND: Sleep disturbances, including insomnia, are common in adult Attention Deficit Hyperactivity Disorder (ADHD). Treatment of choice for insomnia is cognitive behavioral therapy (CBT-i), but evidence is lacking for CBT-i in patients with ADHD. The purpose of this study was to investigate if patients with insomnia and other sleep problems, at a specialist clinic for ADHD, benefit from a group delivered behavioral treatment based on CBT-i; whether insomnia severity improves following this treatment. METHODS: This pragmatic within-group pilot study with a pre to post and three-month follow-up design was set at a specialist psychiatric out-patient clinic for adult ADHD. As an adjunct to care-as-usual at the clinic, a CBT-i-based group treatment targeting several sleep problems prevalent in the ADHD-population, was offered as 10 weekly 90-min group sessions and scheduled telephone support. All outcome measures were subjectively reported by participants. Data analyzed with dependent t-tests according to intent-to-treat. RESULTS: Nineteen patients (37 [SD 13.7] years; 68% female) with ADHD and subjectively reported sleep problems provided informed consent and pre-treatment measures. Patients had suffered from sleep problems for 15.3 [SD 13.4] years, 42% used sleep medications, 79% used stimulant medication(s). At post-treatment, insomnia severity (Insomnia Severity Index; score range 0-28) had improved with 4.5 points (95% CI, 2.06-6.99, p = .002), at 3 months with 6.8 points (95% CI, 4.71-8.91, p < .0001) from pre-treatment. CONCLUSIONS: CBT-i adjusted for ADHD is promising for improving insomnia severity in adult patients at specialist psychiatric out-patient clinics, who suffer from ADHD and sleep disturbances. TRIAL REGISTRATION: Study registered with the Regional ethical review board in Stockholm, January 13th 2016, Study id: 2015/2078-31/1. Study registered retrospectively with Clinicaltrials.org, February 21st 2019, ID: NCT03852966.

Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata.

PURPOSE: Automated collection of image data from DICOM headers enables monitoring of patient dose and image quality parameters. Manual monitoring is time consuming, owing to the large number of exposure scenarios, thus automated methods for monitoring needs to be investigated. The aim of the present work was to develop and optimise such a method. MATERIAL AND METHODS: Exposure index values from digital systems in projection radiography were collected over a period of five years, representing data from 1.2 million projection images. The exposure index values were converted to detector dose and an automated method for detection of sustained level shifts in the resulting detector dose time series was applied using the statistical analysis tool R. The method combined handling of outliers, filtering and estimation of variation in combination with two different statistical rank tests for level shift detection. A set of 304 time series representing central body parts was selected and the level shift detection method was optimised using level shifts identified by ocular evaluation as the gold standard. RESULTS: Two hundred and eighty-one level changes were identified that were deemed in need of further investigation. The majority of these changes were abrupt. The sensitivity and specificity of the optimised and automated detection method concerning the ocular evaluation were 0.870 and 0.997, respectively, for detected abrupt changes. CONCLUSIONS: An automated analysis of exposure index values, with the purpose of detecting changes in exposure, can be performed using the R software in combination with a DICOM header metadata repository containing the exposure index values from the images. The routine described has good sensitivity and acceptable specificity for a wide range of central body part projections and can be optimised for more specialised purposes.