A new tool to screen patients with severe obstructive sleep apnea in the primary care setting: a prospective multicenter study

Background The coordination between different levels of care is essential for the management of obstructive sleep apnea (OSA). The objective of this multicenter project was to develop a screening model for OSA in the primary care setting. Methods Anthropometric data, clinical history, and symptoms of OSA were recorded in randomly selected primary care patients, who also underwent a home sleep apnea test (HSAT). Respiratory polygraphy or polysomnography were performed at the sleep unit to establish definite indication for continuous positive airway pressure (CPAP). By means of cross-validation, a logistic regression model (CPAP yes/no) was designed, and with the clinical variables included in the model, a scoring system was established using the β coefficients (PASHOS Test). In a second stage, results of HSAT were added, and the final accuracy of the model was assessed. Results 194 patients completed the study. The clinical test included the body mass index, neck circumference and observed apneas during sleep (AUC 0.824, 95% CI 0.763–0.886, P < 0.001). In a second stage, the oxygen desaturation index (ODI) of 3% (ODI3% ≥ 15%) from the HSAT was added (AUC 0.911, 95% CI 0.863–0.960, P < 0.001), with a sensitivity of 85.5% (95% CI 74.7–92.1) and specificity of 67.8% (95% CI 55.1–78.3). Conclusions The use of this model would prevent referral to the sleep unit for 55.1% of the patients. The two-stage PASHOS model is a useful and practical screening tool for OSA in primary care for detecting candidates for CPAP treatment. Clinical Trial Registration Registry: ClinicalTrials.gov; Name: PASHOS Project: Advanced Platform for Sleep Apnea Syndrome Assessment; URL: https://clinicaltrials.gov/ct2/show/NCT02591979; Identifier: NCT02591979. Date of registration: October 30, 2015.

Ten Good Reasons to Practice Neuroultrasound in Critical Care Setting

In the beginning, cerebral ultrasound (US) was not considered feasible because the intact skull was a seemingly impenetrable obstacle. For this reason, obtaining a clear image resolution had been a challenge since the first use of neuroultrasound (NUS) for the assessment of small deep brain structures. However, the improvements in transducer technologies and advances in signal processing have refined the image resolution, and the role of NUS has evolved as an imaging modality for the brain parenchyma within multiple pathologies. This article summarizes ten crucial applications of cerebral ultrasonography for the evaluation and management of neurocritical patients, whose transfer from and to intensive care units poses a real problem to medical care staff. This also encompasses ease of use, low cost, wide acceptance by patients, no radiation risk, and relative independence from movement artifacts. Bedsides, availability and reliability raised the interest of critical care intensivists in using it with increasing frequency. In this mini-review, the usefulness and the advantages of US in the neurocritical care setting are discussed regarding ten aspects to encourage the intensivist physician to practice this important tool.

Feasibility of the Smart Excretion Care System tethered to Electronic Medical Records in a tertiary hospital and community care setting: A formative research study (Preprint)

BACKGROUND No studies have been conducted on smart devices that provide integrated nursing for patients' urinary & bowel elimination needs. OBJECTIVE To evaluate the feasibility of a smart excretion care system tethered to Electronic Medical Record (EMR) in a tertiary hospital and community care setting and discuss the expectations of the reducing burden of nursing care. METHODS Focus group interviews were conducted based on the Consolidated Criteria for Reporting Qualitative Research Guidelines. The interviews were conducted in March, 2021, involving 67 participants who had worked at Seoul National University Bundang Hospital for more than one year and had experience assisting patients with excretion care. Data were collected using both purposive and snowball sampling methods. RESULTS This research found four themes relevant to the Smart Excretion Care System: expectations of reducing burdens of nursing care, applicable indications (by departments and diseases), preferred features/functions, and expected benefits of using the Smart Excretion Care system at the clinical facilities. Nurses from the Comprehensive Nursing Service wards were found to have the highest burden of excretion care. It was a common opinion that the Smart Excretion Care System would be very useful in the Intensive Care Unit, and it should be applied first to patients with stroke or dementia. CONCLUSIONS Excretion care is one of the most bothersome tasks for nurses that increase their workload. The further development of the Smart Excretion Care System, as a digital health intervention, can help improve nurses’work efficiency and reduce their burden as well as expand to caregivers and guardians.

Diagnosing leukaemia in a primary health care setting

This article synthesises current knowledge about the presentation and diagnosis of leukaemia. It will also suggest strategies for early diagnosis of the disease in primary care. This article is to be used as a guide by GPs in their learning for when to initiate blood tests for possible cases of leukaemia and how to recognise the common and non-specific symptoms of leukaemia.

App-Based Relaxation Exercises for Patients With Chronic Neck Pain: Pragmatic Randomized Trial

Background Chronic neck pain is a highly prevalent condition. Learning a relaxation technique is recommended by numerous guidelines for chronic neck pain. Smartphone apps can provide relaxation exercises; however, their effectiveness, especially in a self-care setting, is unclear. Objective The aim of this pragmatic randomized trial is to evaluate whether app-based relaxation exercises, including audio-based autogenic training, mindfulness meditation, or guided imagery, are more effective in reducing chronic neck pain than usual care alone. Methods Smartphone owners aged 18 to 65 years with chronic (>12 weeks) neck pain and the previous week’s average neck pain intensity ≥4 on the Numeric Rating Scale (0=no pain to 10=worst possible pain) were randomized into either an intervention group to practice app-based relaxation exercises or a control group (usual care and app for data entry only). For both groups, the follow-up data were collected using app-based diaries and questionnaires. The primary outcome was the mean neck pain intensity during the first 3 months based on daily measurements. Secondary outcomes included neck pain based on weekly measurements, pain acceptance, neck pain–related stress, sick-leave days, pain medication intake, and adherence, which were all measured until the 6-month follow-up. For the primary analysis, analysis of covariance adjusted for baseline neck pain intensity was used. Results We screened 748 participants and enrolled 220 participants (mean age 38.9, SD 11.3 years; mean baseline neck pain 5.7, SD 1.3 points). The mean neck pain intensity in both groups decreased over 3 months; however, no statistically significant difference between the groups was found (intervention: 4.1 points, 95% CI 3.8-4.4; control: 3.8 points, 95% CI 3.5-4.1; group difference: 0.3 points, 95% CI −0.2 to 0.7; P=.23). In addition, no statistically significant between-group differences regarding neck pain intensity after 6 months, responder rate, pain acceptance, pain medication intake, or sick-leave days were observed. There were no serious adverse events that were considered related to the trial intervention. In week 12, only 40% (44/110) of the participants in the intervention group continued to practice the exercises with the app. Conclusions The study app did not effectively reduce chronic neck pain or keep the participants engaged in exercising in a self-care setting. Future studies on app-based relaxation interventions should take into account the most recent scientific findings for behavior change techniques. Trial Registration ClinicalTrials.gov NCT02019134; https://clinicaltrials.gov/ct2/show/NCT02019134 International Registered Report Identifier (IRRID) RR2-10.1186/1745-6215-15-490

Predicting Activation Liking of People With Dementia

Physical, social and cognitive activation is an important cornerstone in non-pharmacological therapy for People with Dementia (PwD). To support long-term motivation and well-being, activation contents first need to be perceived positively. Prompting for explicit feedback, however, is intrusive and interrupts the activation flow. Automated analyses of verbal and non-verbal signals could provide an unobtrusive means of recommending suitable contents based on implicit feedback. In this study, we investigate the correlation between engagement responses and self-reported activation ratings. Subsequently, we predict ratings of PwD based on verbal and non-verbal signals in an unconstrained care setting. Applying Long-Short-Term-Memory (LSTM) networks, we can show that our classifier outperforms chance level. We further investigate which features are the most promising indicators for the prediction of activation ratings of PwD.