Factors associated with inadequate responses to risedronate in Japanese patients with osteoporosis

Okazaki, Ryo; Muraoka, Ryoichi; Maehara, Masayuki; Inoue, Daisuke

doi:10.1007/s00774-018-0931-2

Cited by 8 publications

(3 citation statements)

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“…There was not enough statistical power to assess antifracture efficacy in this study ( 294 ). Low baseline BTMs were found to be an independent predictor of inadequate response to risedronate ( 295 ).…”

Section: Part 2 the Use Of Btm In Clinical Practice: Potential Proble...mentioning

confidence: 99%

Bone Turnover Markers: Basic Biology to Clinical Applications

et al. 2022

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Bone turnover markers (BTMs) are used widely, in both research and clinical practice. In the last 20 years, much experience has been gained in measurement and interpretation of these markers, which include commonly used bone formation markers bone alkaline phosphatase, osteocalcin, and procollagen I N-propeptide; and commonly used resorption markers serum C-telopeptides of type I collagen, urinary N-telopeptides of type I collagen and tartrate resistant acid phosphatase type 5b. BTMs are usually measured by enzyme-linked immunosorbent assay or automated immunoassay. Sources contributing to BTM variability include uncontrollable components (e.g., age, gender, ethnicity) and controllable components, particularly relating to collection conditions (e.g., fasting/feeding state, and timing relative to circadian rhythms, menstrual cycling, and exercise). Pregnancy, season, drugs, and recent fracture(s) can also affect BTMs. BTMs correlate with other methods of assessing bone turnover, such as bone biopsies and radiotracer kinetics; and can usefully contribute to diagnosis and management of several diseases such as osteoporosis, osteomalacia, Paget’s disease, fibrous dysplasia, hypophosphatasia, primary hyperparathyroidism, and chronic kidney disease-mineral bone disorder.

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Section: Part 2 the Use Of Btm In Clinical Practice: Potential Proble...mentioning

confidence: 99%

Bone Turnover Markers: Basic Biology to Clinical Applications

et al. 2022

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“…Within the treatment profile features, we were able to include relevant variables which may affect BMD response 14 , 16 , 36 including Age, Sex, Weight, Previous vertebral fractures and pelvis (ICD10-S32*), Fracture of shoulder and upper arm (ICD10-S42*), Fracture of forearm (ICD10-S52*), Fracture of femur (ICD10-S72*), Fracture of lower leg including ankle (ICD10-S82*), Medical history of Gout (ICD10-M10*), Rheumatoid arthritis (ICD10-M051), Essential (primary) hypertension (ICD10-I10), Diabetes mellitus (ICD10-E1*), Disorders of lipoprotein metabolism and other lipidemias (ICD10-E78*), Heart failure (ICD10-I50), and Ischemic heart disease (ICD10-I2). Previous steroid usage history was collected including Budesonide inhaler, Hydrocortisone injection, oral or intravenous Dexamethasone, and oral Prednisolone.…”

Section: Methodsmentioning

confidence: 99%

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy

Tanphiriyakun

Rojanasthien

Khumrin

2021

Sci Rep

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Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients. We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. We trained 7 machine learning models with 13,562 osteoporosis treatment instances [comprising 5080 (37.46%) inadequate treatment responses and 8482 (62.54%) adequate responses] and selected the best model (Random Forests with area under the receiver operating curve of 0.70, accuracy of 0.69, precision of 0.70, and recall of 0.89) to individually predict treatment responses of 11 therapeutic regimens, then selected the best predicted regimen to compare with the actual regimen. The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient.

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“…An expert opinion, based on personal experience, found that osteoporotic patients lacking vitamin D have secondary hyperparathyroidism, and that only those with a sufficient level of 25OHD and normal serum PTH levels benefit from good densitometric response to BP [13]. Two recent Japanese studies in more than 1,200 osteoporotic patients treated with risedronate confirm that the densitometric response is better in subjects with vitamin D sufficiency [14,15].…”

Section: Letter To Editormentioning

confidence: 99%