Croghan, Ivana

Background: Cannabidiol (CBD), one of the major cannabinoids derived from the cannabis plant, is available over the counter. CBD is often used by patients for the management of insomnia, yet research supporting CBDs effectiveness as a treatment for insomnia is inadequate. Objective: The objective of this review was to critically evaluate the literature regarding the therapeutic benefits of CBD in the management of insomnia. Methods: A comprehensive search of the following databases from inception to December 29, 2021, was conducted: Ovid MEDLINE ® and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus. The search included randomized controlled trials, nonrandomized experimental studies, cross-sectional studies, cohort studies, case series, and case reports. Risk of bias was assessed with the Agency for Healthcare Research and Quality design-specific recommended criteria. Results: Thirty-four studies were eligible for inclusion. All studies reported improvement in the insomnia symptoms of at least a portion of their participants. Of the 34 studies, 19 studies used CBD predominant therapy and 21 studies used nearly equal ratios of CBD to Δ 9 -tetrahydrocannabinol (THC). Of the studies that performed hypothesis testing, 4 of 7 studies with a CBD predominant arm and 12 of 16 studies with a nearly equal ratio of CBD to THC arm reported significant improvement in insomnia outcomes. However, only 2 of the 34 studies focused on patients with insomnia, of which 1 study was a case report. Additionally, several studies used nonvalidated subjective measures, and most studies failed to include objective measures for symptom assessment. Conclusions: The results of our systematic review suggest that CBD alone or with equal quantities of THC may be beneficial in alleviating the symptoms of insomnia. Nevertheless, future research assessing CBDs effectiveness in population of patients specifically with insomnia utilizing validated subjective and objective measures is necessary before definitive inferences can be made.

AbstractObjectiveObesity is stigmatized and people with obesity report experiencing stigmatizing situations when seeking health care. The implications of these experiences are not well understood. This study tests an indirect effects model of negative care experiences as an intermediate variable between obesity and care avoidance/utilization and switching primary care doctors.MethodsA survey was completed by 2380 primary care patients in the Learning Health Systems Network (LHSNet) Clinical Data Research Network with a BMI >25 kg/m2. Measures included scales assessing stigmatizing situations, perceived patient‐centered communication, perceived respect, having delayed needed care, and having looked for a new primary doctor in the past 12 months. Sequential and serial indirect effects of care experiences and respect in the association between BMI and care utilization outcomes was modeled.ResultsThe hypothesized model was supported by findings. The associations between BMI and delaying needed care (OR = 1.06, p < 0.001) and attempting to switch primary doctors (OR = 1.02, p = 0.04) was mediated by both stigmatizing situations experienced in a health care context and lower patient‐centered communication. Lower perceived respect mediated the association between care experiences and utilization outcomes.ConclusionsPeople with higher BMIs may avoid care or switch doctors as a result of stigmatizing experiences and poor communication with doctors. These outcomes may contribute to morbidity in people with obesity if they delay or avoid care for health concerns when symptoms first present.

AbstractBackgroundDespite malnutrition being associated with increased mortality and morbidity, there continues to be great difficulty in defining criteria and implementing widespread screening. Tools used to diagnose decreased fat‐free mass (FFM [sarcopenia]) should be easy to use, relatively inexpensive, and safe. Bioelectrical impedance analysis (BIA) has the potential to meet these criteria, but reliability across body mass index (BMI) classes is a concern.MethodsA total of 176 healthy ambulatory participants (aged 18–65 years) were recruited equally (n = 44) in 4 BMI categories: (1) 18.5–24.9, (2) 25.0–29.9, (3) 30–34.9, and (4) ≥35.0. Participants were fasting overnight and had S‐MFBIA (InBody 770) measurements the next morning, with DXA being performed subsequently within 30 minutes.ResultsThe measurement (mean ± SD) for FFM with DXA was 52.8 ± 11.0, and BIA was 53.6 ± 11.0. Delta (S‐MFBIA vs DXA) was 0.8 ± 2.2 (5% limits of agreement −3.5 to +5.2), and concordance correlation coefficient (CCC) was 0.98 (95% CI, 0.97–0.98). The measurements (mean ± SD) for PBF with DXA was 37.5 ± 10.6% and S‐MFBIA was 36.6 ± 11.3%. Delta (S‐MFBIA vs DXA) was −0.9 ± 2.6 (5% limits of agreement 6.0 to +4.2), and CCC was 0.97 (95% CI, 0.96–0.98). The CCC according to the 4 BMI groups for FFM and PBF was between 0.96–0.98 and 0.90–0.94, respectively.ConclusionsFFM and PBF measured by S‐MFBIA had good agreement with DXA across all BMI categories measured in the current study of ambulatory participants.

Introduction The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics. Methods Using the 1997–2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC−/NLP-API−)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs). Results Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC−/NLP-API−. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects. Conclusion Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

Incidental imaging findings require an assessment of risk and clinical relevance, as well as consideration of further evaluation. Incidental findings are common on imaging obtained in the hospital, with pulmonary nodules being among the most frequent findings that may require additional evaluation. We conducted a retrospective study to determine the factors associated with documentation of incidental findings in the hospital discharge summary, using pulmonary nodules reported on abdominal computed tomography (CT) as an example of incidental findings with well‐defined follow‐up guidelines. Between January 1, 2012 and December 31, 2014, 7173 patients underwent in‐patient abdominal CT without concurrent chest CT; of these patients, 62.2% were ≥60 years old, 50.6% were men, and 45.5% were current or former smokers. Incidental pulmonary nodules were reported in 402 patients (5.6%; 95% confidence interval [CI], 5.1%‐6.2%). Based on nodule size, reported size stability, and patients' smoking status, 208 patients (2.9%; 95% CI, 2.5%‐3.3%) required follow‐up surveillance, per the 2005 Fleischner Society guidelines. Of these 208 patients, 48 (23%) received discharge summaries that included documentation of the incidental findings, with 34 summaries including a recommendation for nodule follow‐up and 19 summaries including a time frame for repeat CT. Three factors were positively associated with the inclusion of the pulmonary nodule in the discharge summary: mention of the pulmonary nodule in the summary headings of the radiology report (P ≤ 0.001), radiologist recommendations for further surveillance (P ≤ 0.001), and medical discharging service (P = 0.016). These findings highlight the need for a multidisciplinary systems‐based approach to incidental pulmonary nodule documentation and surveillance.