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

Pain management in cryoglobulinaemic syndrome

Cryoglobulinaemic syndrome (CS) includes clinical signs and symptoms that range from the classic triad of Meltzer and Franklin (purpura, weakness and arthralgias) to multiple organ involvement, and it may be characterised by nociceptive or neuropathic pain. Both types of pain use the same pathways and neurotransmitters, but nociceptive pain has an adaptive system and biological function whereas neuropathic pain does not. Managing CS means dealing with often very different clinical patterns, activity and severity with the aim of preventing irreversible organ damage, reducing pain, improving the patients' quality of life and reducing social costs. However, treatment is still largely empirical, and it is often delayed. The Italian Group for the Study of Cryoglobulinaemia (GISC) strongly recommended a low-antigen-content diet and colchicine for all symptomatic CS patients. Patients with mild-moderate symptoms (such as purpura, weakness, arthralgia and initial neuropathy) have been treated with low or medium doses of steroids, and, in the presence of chronic hepatitis C virus (HCV)-related hepatitis, an attempt has been made to eradicate HCV with pegylated interferon plus ribavirin. In the case of severe or rapidly progressive disease (glomerulonephritis, neuropathy, leg ulcers, widespread vasculitis or hyperviscosity syndrome), more aggressive treatment should be used (e.g., high doses of corticosteroids, plasma exchange plus cyclophosphamide or rituximab). Pain management in CS therefore depends on the type of pain (nociceptive, neuropathic or mixed), the characteristics of the patients and their co-morbidities. Drug therapy should be carefully monitored in order to obtain prompt and beneficial results

Cost of osteoporosis-related fracture in Italy. Results of the BLOCK study

The objectives of the present study were to calculate the cost of illness of osteoporosis and to assess drug utilization patterns in postmenopausal women after a fracture-related hospitalization. The study subjects were enrolled from a large population-based administrative database. Female patients (age ≥ 65 years) who were hospitalized for a typical osteoporotic fracture between 1/1/2000 and 31/12/2005 were included. Patients were classified as exposed/unexposed to treatment according to the presence/absence of at least one prescription for an osteoporosis-related medication in the 6 months following the discharge date. Treatment adherence was calculated for patients who were exposed to bisphosphonate therapy and was defined as at least 80% of treatment coverage during the follow-up period of 18 months after the discharge date. Hospitalizations, medications, diagnostic tests, laboratory tests and specialist visits during the 18-month follow-up period were collected and classified as osteoporosis-related or non-related to osteoporosis. A total of 12,376 patients were included in the study (mean age ± SD, 79.1 ± 7.5 years), out of which 97.9% (n = 12,110) were hospitalized due to an osteoporosis-related fracture and only 2.1% (n = 266) had general osteoporosis diagnosis. Among the 12,110 women with a fracture, 15.2% (n = 1,845) had a subsequent fracture-related hospitalization (63.8% of the patients had hip fracture). Only 32.3% (n = 4,001) of all included patients was exposed to osteoporosis-related medications and out of those patients exposed to bisphosphonates (n = 860) only 34.2% (n = 294) was adherent to therapy. The average cost per patient was € 4,481, of which € 1,089 was for osteoporosis-related and € 3,392 for non-osteoporosis-related items. The average cost of a matching cohort of patients without hospitalizations for fracture was € 2,339. Among osteoporosis-related costs, 87.0% was due to hospitalizations for subsequent fractures, 1.5% was due to subsquent hospitalizations for osteoporosis, 9.0% was due to medications, 2.5% was due to laboratory or diagnostic/ instrumental tests. Osteoporosis costs after a first hospitalization for fracture were relevant (twice the costs for patients without hospitalizations for fracture), evident in the short run (within the first 24 months following the index fracture) and mostly due to re-hospitalizations for a new typical osteoporotic fracture. This is in mainly relatedto a low exposure to pharmacological therapy and to insufficient treatment adherence. This study and publication were supported by Amgen Dompe and GlaxoSmithKline.

Cost of osteoporosis-related fracture in Italy. Results of the BLOCK study

The objectives of the present study were to calculate the cost of illness of osteoporosis and to assess drug utilization patterns in postmenopausal women after a fracture-related hospitalization. The study subjects were enrolled from a large population-based administrative database. Female patients (age ≥ 65 years) who were hospitalized for a typical osteoporotic fracture between 1/1/2000 and 31/12/2005 were included. Patients were classified as exposed/unexposed to treatment according to the presence/absence of at least one prescription for an osteoporosis-related medication in the 6 months following the discharge date. Treatment adherence was calculated for patients who were exposed to bisphosphonate therapy and was defined as at least 80% of treatment coverage during the follow-up period of 18 months after the discharge date. Hospitalizations, medications, diagnostic tests, laboratory tests and specialist visits during the 18-month follow-up period were collected and classified as osteoporosis-related or non-related to osteoporosis. A total of 12,376 patients were included in the study (mean age ± SD, 79.1 ± 7.5 years), out of which 97.9% (n = 12,110) were hospitalized due to an osteoporosis-related fracture and only 2.1% (n = 266) had general osteoporosis diagnosis. Among the 12,110 women with a fracture, 15.2% (n = 1,845) had a subsequent fracture-related hospitalization (63.8% of the patients had hip fracture). Only 32.3% (n = 4,001) of all included patients was exposed to osteoporosis-related medications and out of those patients exposed to bisphosphonates (n = 860) only 34.2% (n = 294) was adherent to therapy. The average cost per patient was € 4,481, of which € 1,089 was for osteoporosis-related and € 3,392 for non-osteoporosis-related items. The average cost of a matching cohort of patients without hospitalizations for fracture was € 2,339. Among osteoporosis-related costs, 87.0% was due to hospitalizations for subsequent fractures, 1.5% was due to subsquent hospitalizations for osteoporosis, 9.0% was due to medications, 2.5% was due to laboratory or diagnostic/ instrumental tests. Osteoporosis costs after a first hospitalization for fracture were relevant (twice the costs for patients without hospitalizations for fracture), evident in the short run (within the first 24 months following the index fracture) and mostly due to re-hospitalizations for a new typical osteoporotic fracture. This is in mainly relatedto a low exposure to pharmacological therapy and to insufficient treatment adherence. This study and publication were supported by Amgen Dompe and GlaxoSmithKline

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

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