Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

BACKGROUND: It is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0. METHODOLOGY: We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively. CONCLUSIONS: ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF

25-hydroxy vitamin D levels in healthy premenopausal women: Association with bone turnover markers and bone mineral density

BACKGROUND:Vitamin D deficiency is very common in elderly people while there are very few reports on its incidence, determinants and metabolic consequences in young subjects. RESULTS:In 608 young healthy premenopausal women participating in the BONTURNO study, levels of 25-hydroxyvitamin D [25(OH)D] below 20 ng/ml were found in almost a third of the women. Its levels were inversely (P<0.001) related with age and body mass index (BMI kg/m(2)) and directly with sunlight exposure during the summer time, and latitude: i.e. the higher the latitude over Italy, the higher the 25(OH)D level. In women on contraceptive pill the mean 25(OH)D level was significantly increased even when the data were adjusted for age, BMI and sun exposure. 25(OH)D levels, adjusted for age and BMI, were significantly and positively related with serum C-telopeptide of type 1 collagen, serum phosphate and spine bone mineral density (BMD) and negatively with serum PTH, serum magnesium, serum bone alkaline phosphatase (bone AP). CONCLUSION:Vitamin D deficiency is rather common in young otherwise healthy Italian women and particularly among those living in the Southern part of the country. The most close determinants of vitamin D deficiency were BMI and sunlight exposure. Vitamin D insufficiency is associated with low spine BMD and increased bone AP even in young individuals

The burden of previous fractures in hip fracture patients. The break study (Aging Clinical and Experimental Research (2011) 23, 3 (183-186))

A positive history of fractures in older patients with hip fracture is common. We determined the risk factors associated with a positive history of fractures and the profile of care in hip fracture patients. In the Break Study, we enrolled 1249 women aged ≥60 years, seeking care for a hip fracture. Baseline information included age, body mass index, lifestyle (smoking habit, alcohol consumption), patient's history of fracture after the age of 50 years, family history of fragility fracture and health status (presence of comorbidity, use of specific drugs, pre-fracture walking ability, type of fracture, time to surgery, type of surgery, osteoporosis treatment). In the multivariable model age, smoking, family history, treatment with antiplatelet, anticoagulants and anticonvulsants, were significant predictors of a positive history of fracture. More than 70% of patients underwent surgery more than 48 hours after admission. About 50% were discharged with a treatment for osteoporosis, but more than 30% only with calcium and vitamin D. In conclusion, factors associated with a positive history of fracture are the traditional risk factors, suggesting that they continue to have a negative impact on health even at older ages. Selected drugs, such as antiplatelet and anticoagulants, deserve further consideration as significant factors associated with fractures. Given that delay in surgery is a major cause of mortality and disability, while treatment for osteoporosis decreases significantly the risk of recurrent fractures and disability, interventions to modify these patterns of care are urgently needed

The burden of previous fractures in hip fracture patients. The Break Study

11noGroup Author(s): Break Study GrpreservedA positive history of fractures in older patients with hip fracture is common. We determined the risk factors associated with a positive history of fractures and the profile of care in hip fracture patients. In the Break Study, we enrolled 1249 women aged ≥60 years, seeking care for a hip fracture. Baseline information included age, body mass index, lifestyle (smoking habit, alcohol consumption), patient's history of fracture after the age of 50 years, family history of fragility fracture and health status (presence of comorbidity, use of specific drugs, pre-fracture walking ability, type of fracture, time to surgery, type of surgery, osteoporosis treatment). In the multivariable model age, smoking, family history, treatment with antiplatelet, anticoagulants and anticonvulsants, were significant predictors of a positive history of fracture. More than 70% of patients underwent surgery more than 48 hours after admission. About 50% were discharged with a treatment for osteoporosis, but more than 30% only with calcium and vitamin D. In conclusion, factors associated with a positive history of fracture are the traditional risk factors, suggesting that they continue to have a negative impact on health even at older ages. Selected drugs, such as antiplatelet and anticoagulants, deserve further consideration as significant factors associated with fractures. Given that delay in surgery is a major cause of mortality and disability, while treatment for osteoporosis decreases significantly the risk of recurrent fractures and disability, interventions to modify these patterns of care are urgently needed.mixedMaggi S; Siviero P; Gonnelli S; Caffarelli C; Gandolini G; Cisari C; Rossini M; Iolascon G; Mauro GL; Nuti R; Crepaldi GMaggi, S; Siviero, P; Gonnelli, S; Caffarelli, C; Gandolini, G; Cisari, C; Rossini, M; Iolascon, G; Mauro, Gl; Nuti, R; Crepaldi,

Goodness of fit test for ANNs in identifying patients with a SDI≥1 (A) and SDI≥5 (B).

<p>5×2 cross validation protocol.</p><p>A: Chi square = 0.10; N.S.; B: Chi square = 0.23; N.S.</p

Sensitivity, Specificity and overall accuracy in identifying patients with a SDI≥1 (A) and SDI≥5 (B) by artificial neural networks analysis and traditional statistics.

<p>ANNs: artificial neural networks; LR: logistic regression analysis; SN: sensitivity; SP: specificity; ROC: receiver operating characteristic; AUC: area under the curve.</p><p>**: p<0.01.</p

Variables used in the analysis and variables selected by TWIST system in the subsequent analysis: SDI = 0 vs SDI≥1 (SDI≥1) and SDI = 0 vs SDI≥5 (SDI≥5).

<p>SDI≥1: Variables selected by TWIST system in the analysis aimed to differentiate patients with SDI≥1 from those with SDI = 0 (the number 17, reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027277#pone-0027277-t004" target="_blank">Table 4a</a>, refers to a maximisation of these variables); SDI≥5: Variables selected by TWIST system in the analysis aimed to differentiate patients with SDI≥5 from those with SDI = 0 (the number 25, reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027277#pone-0027277-t004" target="_blank">Table 4b</a>, refers to a maximisation of these variables).</p><p>Twist system can easily select just one of the two binary forms of the variables since that choosing one option implies also the information of its complement.</p

Clinical characteristics of all patients, patients without morphometric vertebral fractures, SDI≥1 and SDI≥5.

<p>Data are expressed as mean±SD, and median (range) for not normally distributed variables, if not differently specified.</p><p>*SDI = 0 vs SDI≥1; #SDI = 0 vs SDI≥5; SDI: Spinal Deformity Index; YSM: Years since menopause; BMI: Body Mass Index: weight (Kg)/height ² (m²); BF: breast feeding expressed in months; COPD: chronic obstructive pulmonary disease; T2D: Type 2 diabetes mellitus; SDI: Spinal Deformity Index calculated according to the method described by Crans (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027277#s2" target="_blank">Methods</a>);</p

ROC curve for artificial neural networks and logistic regression analysis in identifying SDI≥1 and SDI≥5.

<p>The ANN AUC is significantly superior to LR AUC both in identifying SDI≥1 (p<0.01) (A) and SDI≥5 (p<0.001) (B). ROC: Receiver operating characteristic, SN: sensitivity, SP: specificity. ANNs: artificial neural networks; AUC: area under the curve; LR: logistic regression analysis.</p