A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery

Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].Peer reviewe

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Radiographic Evaluation of Osteoporosis Through Detection of Jaw Bone Changes: a Simplified Early Osteoporosis Detection Effort

Osteoporosis has become a worldwide problem and has been known as a silence disease. Nowadays, there are a lot of diagnostic tools for detecting osteoporosis. Eighty eight postmenopausal were included and underwent digital panoramic, digital periapical, and conventional radiography. Ultrasound bone densitometry of os calcis used as gold standard. Correlation between stiffness index (SI) with a digital dental, digital panoramic and conventional dental radiography are 0.170 (p = 0.11), -0382 (p = 0.001) and 0.246 (p = 0.021) respectively. Significant relationship was found between the SI only with digital panoramic and conventional dental. The highest correlation was found between SI values with mandibular Inferior Cortex on digital panoramic (-0.382, Pearson Correlation Tests). Correlation between digital panoramic radiographs and the SI values was the highest of the three radiographic modalities in this study. This indicates that evaluation of cortical bone is more accurate than cancellous bone. Bone quality evaluation in patients at high risk for osteoporosis using panoramic and dental conventional radiograph by dentist, contributes in preventing further occurrence of osteoporosis which in turn could reduce mortality and morbidity of osteoporosis in&nbsp;Indonesia

Assessment of the predictive capacity of the Garvan calculator of 10 year risk of fracture in a Spanish population

Introducción: En los últimos años se han desarrollado varias herramientas de cálculo o escalas para valorar el riesgo de fractura por fragilidad a largo plazo. La calculadora Garvan no ha sido validada en la población española. El objetivo de este estudio fue observar su capacidad predictiva en una muestra de la población canaria y, por tanto, de la española. Material y métodos: Se incluyó a 121 pacientes a los que se les realizó un seguimiento de 10 años en nuestras consultas. A todos se les valoró el riesgo de fractura usando la calculadora Garvan y basándonos en los datos obtenidos en la primera visita realizada. Resultados: De los 121 pacientes, 30 sufrieron al menos una fractura osteoporótica a lo largo de los 10 años de seguimiento. El grupo de pacientes fracturados tenían en la escala Garvan un valor medio de riesgo de sufrir cualquier fractura por fragilidad de 27%, frente al 13% de aquellos que no sufrieron fractura (p<0,001). El área bajo la correspondiente curva ROC fue de 0,718 (IC-95% = 0,613 ; 0,824). En base a ella, se estimó que el punto de corte óptimo para considerar un alto riesgo de fractura por fragilidad fue 18,5%. A este valor le correspondió una sensibilidad de 0,67 (IC-95% = 0,47 ; 0,83) y una especificidad de 0,67 (IC-95% = 0,56 ; 0,77). Conclusiones: Nuestros resultados muestran que la escala Garvan predice adecuadamente el riesgo de fractura osteoporótica a 10 años en nuestra población. Un valor inferior a 18,5% permitiría establecer un riesgo de fractura bajo, pudiendo ser utilizada como herramienta de cribado.Sociedad Canaria de Osteoporosi

The influence of dairy consumption, sedentary behaviour and physical activity on bone mass in Flemish children : a cross-sectional study

Background: This cross-sectional study aimed to look for an association in young children between whole body bone mineral content (BMC) and areal bone mineral density (aBMD) and dairy consumption as well as sedentary behaviour (SB) and physical activity (PA). Moreover, we investigated whether there was an interaction effect between dairy consumption and SB or PA on BMC and aBMD. Methods: Healthy children (6-12 years) were recruited from primary schools. Body composition and whole body bone mass were measured with dual-energy X-ray absorptiometry (DXA), dairy consumption was assessed with a food frequency questionnaire (FFQ) and PA and SB with an accelerometer. In total, 272 children underwent a DXA scan. Complete FFQ data were available for 264 children and 210 children had matching data from accelerometry recordings. Regression analyses were used to study the associations between (1) BMC and aBMD and (2) dairy consumption, SB and PA, adjusting for age, gender, pubertal stage, height and body composition. Results: Dairy consumption was positively associated with whole body BMC and aBMD (absolute value as well as z-score), after correction for relevant confounders. SB was negatively associated with aBMD z-score and light PA was positively associated with both BMC and aBMD z-score. No gender differences were found. Moreover, an interaction effect between vigorous PA (VPA) and dairy consumption on aBMD (z-score) and BMC z-score was found, indicating that children with both high VPA and high dairy consumption had higher values for BMC and aBMD of the whole body minus the head. Conclusion: Already at young age, PA and dairy consumption positively influence whole body bone mass assessed by DXA. Moreover, this study indicates clearly that SB is negatively associated with whole body bone density. Promoting regular PA and sufficient dairy consumption in young children and limiting SB can be expected to positively influence their bone mass accumulation, which can help in the prevention of osteoporosis later in life