Computational protocol: The Effectiveness of Osteoporosis Screening and Treatment in the Midwest

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Protocol publication

[…] After institutional review board approval, patients who presented to our orthopedic trauma service were screened prospectively using our electronic medical record system. Patients who sustained fracture(s) regardless of location and mechanism were consecutively enrolled. Patients 55 years of age and older were chosen for their increased risk of fragility fracture. The study was carried out from May 15, 2016 to July 28, 2016; 100 patients participated. The sample size of 100 was chosen to obtain enough observations to be able to reasonably obtain precise estimates regarding the effectiveness of osteoporosis screening and treatment without exceeding the practical resource budget.After obtaining informed consent, the patients were asked questions from the Fracture Risk Assessment Tool (FRAX; available online at https://www.shef.ac.uk/FRAX/tool.jsp) to determine their FRAX score. The 12-question osteoporosis screening questionnaire included questions about patient demographics (age, sex, height, and weight) as well as questions regarding personal and family history. The UK version, rather than the US version, of the FRAX assessment tool was used to calculate the 10-year risk for hip and other major osteoporotic fractures because it includes management guidelines (US version does not). The UK version of the tool has been accepted by the International Osteoporosis Foundation for the use in all patient populations due to the treatment recommendations it generates. The FRAX assessment tool and the National Osteoporosis Guideline Group recommendations were used to determine the adequacy of osteoporosis management.Patients were also asked questions about osteoporosis through a study-specific questionnaire that aimed to identify their level of understanding, current and past management, and preventative education. Due to lack of availability of medical records in some patients (ie, new patients initially managed elsewhere), it was decided not to use electronic medical records to determine the history of osteoporosis diagnosis. All patients answered every question of the study-specific questionnaire to the best of their ability. Questions included the status of osteoporosis diagnosis; current treatment, if applicable; the level of understanding about treatment and osteoporosis; preventative education (eg, lifestyle changes); use of DXA screening; and history of fractures with the type of mechanism (). Based on their response (yes/no) to the status of osteoporosis diagnosis, patients were separated into 2 groups for evaluation. Group A includes patients who answer “no” to the previous diagnosis of osteoporosis. Group B includes patients who answer “yes” to the previous diagnosis of osteoporosis.A power analysis was performed using G*Power 3.1. We determined that a sample size of 100 would be a sufficient number of patients to provide an 80% power with a significance of .05, given an effect size of 0.3. The effect size was calculated to determine a 10% difference in DXA screening recommendation with a standard deviation of 10%. Statistical analyses were performed using SPSS 24 (IBM Corp, Chicago, Illinois). For categorical variables, Pearson χ2 and Fisher exact tests were performed. Fisher exact tests were performed on 2 × 2 tables, and Pearson χ2 was performed on larger contingency tables. Pearson χ2 tests were performed on the following variables: DXA before fragility fracture, DXA screening or diagnosis specialty, and education provider. Fisher exact tests were performed on the following variables: sex, history of fragility fracture, DXA within the past 2 years, DXA previously recommended, taking calcium and vitamin D, and education received. For continuous variables, Levene test for equality and variances was conducted on all continuous variables to determine the equal or unequal variance. Based on the results of Levene test, t tests assuming equal or unequal variance were performed. T tests were performed on 2 variables: age and number of fragility fractures. A P value <.05 was considered statistically significant. Graphs were created with Origin (OriginLab, Northampton, Massachusetts). […]

Pipeline specifications

Software tools G*Power, SPSS
Application Miscellaneous
Organisms Homo sapiens, Dipturus trachyderma
Diseases Osteoporosis, Wounds and Injuries, Fractures, Bone
Chemicals Calcium, Vitamin D