Prescreening tools to determine who needs DXA

Schwartz, Elliot; Steinberg, Dee

doi:10.1007/bf02686961

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding is consistent with a previous study on the comparison of machine learning methods in various complex discriminating problems for predicting disease 33. Most experts have used conventional methods, including OST, ORAI, SCORE, and OSIRIS, because of their simplicity 34. In this study, we applied complicated mathematical methods for more accurate prediction.…”

Section: Discussionsupporting

confidence: 91%

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

Yoo

Kim

et al. 2013

Yonsei Med J

View full text Add to dashboard Cite

PurposeA number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools.Materials and MethodsWe collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS).ResultsSVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus.ConclusionConsidering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

show abstract

Section: Discussionsupporting

confidence: 91%

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

Yoo

Kim

et al. 2013

Yonsei Med J

View full text Add to dashboard Cite

show abstract

“…A number of CDRs to assist in identifying women with low skeletal BMD have been developed since the mid‐1990s, including SCORE, OST (OSTA), OSIRIS, SOFSURF, NOF, ABONE, pBW, ORAI, and weight‐only‐EPIDOS (Schwartz and Steinberg, 2006). From the viewpoint of triage screening for osteoporosis, high sensitivity (about 90%) and relatively low specificity (about 40–60%) are considered acceptable for these CDRs (Cadarette et al , 2001).…”

Section: Triage Screening Tools For Osteoporosismentioning

confidence: 99%

Triage screening for osteoporosis in dental clinics using panoramic radiographs

Taguchi

2010

Oral Diseases

100

View full text Add to dashboard Cite

Many patients with osteoporosis go undiagnosed because typically no symptoms are present before a fracture. Triage screening to refer patients to appropriate medical professionals for further investigation would be useful to address the increase in the incidence of osteoporotic fractures. Dental clinics may offer a new triage screening pathway because dentists frequently take radiographs of bones in the course of dental treatment. A major premise for such triage screening in dental clinics is that dentists can readily use a screening tool in their dental practice. For example, cortical width and shape of the mandible detected on panoramic radiographs may be appropriate indices for triaging individuals with osteoporosis. To date, several investigators have demonstrated significant associations between cortical indices on panoramic radiographs and bone mineral density of the skeleton generally, such as the spine and femur, biochemical markers of bone turnover and risk of osteoporotic fractures. Further, in two recent Japanese clinical trials, approximately 95% of women who were identified by trained dentists in their clinics using cortical shape findings did have osteopenia or osteoporosis. These findings support the possibility that dental clinics may offer a new triage platform to identify individuals with otherwise undetected osteoporosis.

show abstract

“…Primary prevention consists of interventions such as promotion of calcium and vitamin D supplements and of physical activity 1, 4–6. Screening aims at the identification of women at high risk followed by initiation of pharmacological therapy 7. Recently published 2010 osteoporosis best practice guidelines propose an integrated approach to osteoporosis management guided by an assessment of the patient's 10‐year absolute risk of bone fractures 4.…”

Section: Introductionmentioning

confidence: 99%

An economic evaluation: Simulation of the cost-effectiveness and cost-utility of universal prevention strategies against osteoporosis-related fractures

Nshimyumukiza

Durand

Gagnon

et al. 2012

Journal of Bone and Mineral Research

View full text Add to dashboard Cite

A patient-level Markov decision model was used to simulate a virtual cohort of 500,000 women 40 years old and over, in relation to osteoporosis-related hip, clinical vertebral, and wrist bone fractures events. Sixteen different screening options of three main scenario groups were compared: (1) the status quo (no specific national prevention program); (2) a universal primary prevention program; and (3) a universal screening and treatment program based on the 10-year absolute risk of fracture. The outcomes measured were total directs costs from the perspective of the public health care system, number of fractures, and quality-adjusted life-years (QALYs). Results show that an option consisting of a program promoting physical activity and treatment if a fracture occurs is the most cost-effective (CE) (cost/fracture averted) alternative and also the only cost saving one, especially for women 40 to 64 years old. In women who are 65 years and over, bone mineral density (BMD)-based screening and treatment based on the 10-year absolute fracture risk calculated using a Canadian Association of Radiologists and Osteoporosis Canada (CAROC) tool is the best next alternative. In terms of cost-utility (CU), results were similar. For women less than 65 years old, a program promoting physical activity emerged as cost-saving but BMD-based screening with pharmacological treatment also emerged as an interesting alternative. In conclusion, a program promoting physical activity is the most CE and CU option for women 40 to 64 years old. BMD screening and pharmacological treatment might be considered a reasonable alternative for women 65 years old and over because at a healthcare capacity of $50,000 Canadian dollars ($CAD) for each additional fracture averted or for one QALY gained its probabilities of cost-effectiveness compared to the program promoting physical activity are 63% and 75%, respectively, which could be considered socially acceptable. Consideration of the indirect costs could change these findings.

show abstract

Prescreening tools to determine who needs DXA

Cited by 3 publications

References 20 publications

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

Triage screening for osteoporosis in dental clinics using panoramic radiographs

An economic evaluation: Simulation of the cost-effectiveness and cost-utility of universal prevention strategies against osteoporosis-related fractures

Contact Info

Product

Resources

About