Development of a prototype clinical decision support tool for osteoporosis disease management: a qualitative study of focus groups

<p>Abstract</p> <p>Background</p> <p>Osteoporosis affects over 200 million people worldwide, and represents a significant cost burden. Although guidelines are available for best practice in osteoporosis, evidence indicates that patients are not receiving appropriate diagnostic testing or treatment according to guidelines. The use of clinical decision support systems (CDSSs) may be one solution because they can facilitate knowledge translation by providing high-quality evidence at the point of care. Findings from a systematic review of osteoporosis interventions and consultation with clinical and human factors engineering experts were used to develop a conceptual model of an osteoporosis tool. We conducted a qualitative study of focus groups to better understand physicians' perceptions of CDSSs and to transform the conceptual osteoporosis tool into a functional prototype that can support clinical decision making in osteoporosis disease management at the point of care.</p> <p>Methods</p> <p>The conceptual design of the osteoporosis tool was tested in 4 progressive focus groups with family physicians and general internists. An iterative strategy was used to qualitatively explore the experiences of physicians with CDSSs; and to find out what features, functions, and evidence should be included in a working prototype. Focus groups were conducted using a semi-structured interview guide using an iterative process where results of the first focus group informed changes to the questions for subsequent focus groups and to the conceptual tool design. Transcripts were transcribed verbatim and analyzed using grounded theory methodology.</p> <p>Results</p> <p>Of the 3 broad categories of themes that were identified, major barriers related to the accuracy and feasibility of extracting bone mineral density test results and medications from the risk assessment questionnaire; using an electronic input device such as a Tablet PC in the waiting room; and the importance of including well-balanced information in the patient education component of the osteoporosis tool. Suggestions for modifying the tool included the addition of a percentile graph showing patients' 10-year risk for osteoporosis or fractures, and ensuring that the tool takes no more than 5 minutes to complete.</p> <p>Conclusions</p> <p>Focus group data revealed the facilitators and barriers to using the osteoporosis tool at the point of care so that it can be optimized to aid physicians in their clinical decision making.</p

Medroxyprogesterone improves nocturnal breathing in postmenopausal women with chronic obstructive pulmonary disease

BACKGROUND: Progestins as respiratory stimulants in chronic obstructive pulmonary disease (COPD) have been investigated in males and during wakefulness. However, sleep and gender may influence therapeutic responses. We investigated the effects of a 2-week medroxyprogesterone acetate (MPA) therapy on sleep and nocturnal breathing in postmenopausal women. METHODS: A single-blind placebo-controlled trial was performed in 15 postmenopausal women with moderate to severe COPD. A 12-week trial included 2-week treatment periods with placebo and MPA (60 mg/d/14 days). All patients underwent a polysomnography with monitoring of SaO(2 )and transcutaneous PCO(2 )(tcCO(2)) at baseline, with placebo, with medroxyprogesterone acetate (MPA 60 mg/d/14 days), and three and six weeks after cessation of MPA. RESULTS: Thirteen patients completed the trial. At baseline, the average ± SD of SaO(2 )mean was 90.6 ± 3.2 % and the median of SaO(2 )nadir 84.8 % (interquartile range, IQR 6.1). MPA improved them by 1.7 ± 1.6 %-units (95 % confidence interval (CI) 0.56, 2.8) and by 3.9 %-units (IQR 4.9; 95% CI 0.24, 10.2), respectively. The average of tcCO(2 )median was 6.0 ± 0.9 kPa and decreased with MPA by 0.9 ± 0.5 kPa (95% CI -1.3, -0.54). MPA improved SaO(2 )nadir and tcCO(2 )median also during REM sleep. Three weeks after cessation of MPA, the SaO(2 )mean remained 1.4 ± 1.8 %-units higher than at baseline, the difference being not significant (95% CI -0.03, 2.8). SaO(2 )nadir was 2.7 %-units (IQR 4.9; 95% CI 0.06, 18.7) higher than at baseline. Increases in SaO(2 )mean and SaO(2 )nadir during sleep with MPA were inversely associated with baseline SaO(2 )mean (r = -0.70, p = 0.032) and baseline SaO(2 )nadir (r = -0.77, p = 0.008), respectively. Treatment response in SaO(2 )mean, SaO(2 )nadir and tcCO(2 )levels did not associate with pack-years smoked, age, BMI, spirometric results or sleep variables. CONCLUSION: MPA-induced respiratory improvement in postmenopausal women seems to be consistent and prolonged. The improvement was greater in patients with lower baseline SaO(2 )values. Long-term studies in females are warranted

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke