POL-RISK: an algorithm for 10-year fracture risk prediction in the postmenopausal women from the RAC-OST-POL Study

Pluskiewicz, Wojciech; Adamczyk, Piotr; Werner, A.; Bach, Małgorzata; Drozdzowska, Bogna

doi:10.20452/pamw.16395

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The conducted analyses showed that the use of the SMOTE oversampling technique in combination with RU_kNN undersampling allowed us to improve the precision of prediction. The population observed in the current study allowed us to conclude other important considerations on fracture risk assessment [30]. The use of a randomly recruited, epidemiological sample of postmenopausal women enhances the significance of the current observation for practitioners in their daily work with patients.…”

Section: Discussionmentioning

confidence: 83%

Height loss in postmenopausal women predicts multiple fractures in 10-year follow-up: results from the RAC-OST-POL study

Pluskiewicz,

Adamczyk,

Werner

et al. 2024

Endokrynol Pol

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The risk established by medical examination, including, among others, body height measurement, gives a complete picture of the patient's clinical condition. In several studies, height loss, defined as current height compared with the former maximal height, was studied and described as a sign of the risk for future fractures [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. HL is not included in the available calculators as a fracture risk factor, and thus this important clinical information is omitted in widely used fracture risk assessment tools.The aim of the current prospective study was to verify the hypothesis that height loss may predict fracture incidence.

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Section: Discussionmentioning

confidence: 83%

Height loss in postmenopausal women predicts multiple fractures in 10-year follow-up: results from the RAC-OST-POL study

Pluskiewicz,

Adamczyk,

Werner

et al. 2024

Endokrynol Pol

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“…After removing duplicates and screening titles and abstracts, 378 articles remained. According to a full-text review, 53 articles13–67 were included. Fifty-three articles presented the development of one or more prediction models for osteoporotic fracture, while 26 articles described the validation of one or more models.…”

Section: Resultsmentioning

confidence: 99%

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Wu,

Chao,

Bao

et al. 2023

BMJ Open

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ObjectivesEarly identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.MethodsRelevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.ResultsFifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.ConclusionsML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.PROSPERO registration numberCRD42022346896.

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“…The differences between FRAX and Garvan may be partially caused by different designs. More recently another algorithm called POL-RISK was developed and presented [ 25 , 26 ]. This tool is based on a 10-year observation of an epidemiologic female sample aged over 55 years.…”

Section: Discussionmentioning

confidence: 99%

Height Loss Is an Independent Predictor of Fracture Incidence in Postmenopausal Women: The Results from the Gliwice Osteoporosis Study (GO Study)

Pluskiewicz,

Adamczyk,

Werner

et al. 2023

Biomedicines

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Background. The aim of a longitudinal, retrospective study was to establish variables predicting fracture incidence over a decade. Methods. The study sample comprises a group of 457 postmenopausal women aged over 55 years, recruited from the database of an outpatient osteoporotic clinic. Several variables with potential influence on bone status, including the measurement of body height and hip bone densitometry, were collected. BMD at the femoral neck (FN BMD) was established using a Prodigy device (Lunar, GE, USA). Current body height was compared with the maximal historical body height in early adulthood, as reported by the patient. Results. Three hundred and ninety-four women did not have fractures during the follow up, and 63 subjects presented fractures. Subjects with fracture had lower FN BMD with a T-score of −1.86 ± 1.04 compared to −1.44 ± 0.89 in those without fractures (p < 0.001). Mean height loss (HL) was 3.47 ± 2.11 cm in fractured subjects and 2.50 ± 2.47 cm in unfractured ones, and differed significantly, p < 0.01. Fracture incidence was significantly related to age, rheumatoid arthritis, falls, and previous fractures. In the multivariable analysis using logistic regression, FN BMD, baseline fracture, and HL were identified as the significant predictors of fractures of follow up. Conclusions. Osteoporotic fractures in postmenopausal women were predicted by FN BMD, prior fracture(s), and HL easily established during physical examination.

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POL-RISK: an algorithm for 10-year fracture risk prediction in the postmenopausal women from the RAC-OST-POL Study

Cited by 5 publications

References 22 publications

Height loss in postmenopausal women predicts multiple fractures in 10-year follow-up: results from the RAC-OST-POL study

Height loss in postmenopausal women predicts multiple fractures in 10-year follow-up: results from the RAC-OST-POL study

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Height Loss Is an Independent Predictor of Fracture Incidence in Postmenopausal Women: The Results from the Gliwice Osteoporosis Study (GO Study)

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