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

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation

Appendicitis risk prediction models in children presenting with right iliac fossa pain (RIFT study): a prospective, multicentre validation study

BACKGROUND: Acute appendicitis is the most common surgical emergency in children. Differentiation of acute appendicitis from conditions that do not require operative management can be challenging in children. This study aimed to identify the optimum risk prediction model to stratify acute appendicitis risk in children. METHODS: We did a rapid review to identify acute appendicitis risk prediction models. A prospective, multicentre cohort study was then done to evaluate performance of these models. Children (aged 5-15 years) presenting with acute right iliac fossa pain in the UK and Ireland were included. For each model, score cutoff thresholds were systematically varied to identify the best achievable specificity while maintaining a failure rate (ie, proportion of patients identified as low risk who had acute appendicitis) less than 5%. The normal appendicectomy rate was the proportion of resected appendixes found to be normal on histopathological examination. FINDINGS: 15 risk prediction models were identified that could be assessed. The cohort study enrolled 1827 children from 139 centres, of whom 630 (34·5%) underwent appendicectomy. The normal appendicectomy rate was 15·9% (100 of 630 patients). The Shera score was the best performing model, with an area under the curve of 0·84 (95% CI 0·82-0·86). Applying score cutoffs of 3 points or lower for children aged 5-10 years and girls aged 11-15 years, and 2 points or lower for boys aged 11-15 years, the failure rate was 3·3% (95% CI 2·0-5·2; 18 of 539 patients), specificity was 44·3% (95% CI 41·4-47·2; 521 of 1176), and positive predictive value was 41·4% (38·5-44·4; 463 of 1118). Positive predictive value for the Shera score with a cutoff of 6 points or lower (72·6%, 67·4-77·4) was similar to that of ultrasound scan (75·0%, 65·3-83·1). INTERPRETATION: The Shera score has the potential to identify a large group of children at low risk of acute appendicitis who could be considered for early discharge. Risk scoring does not identify children who should proceed directly to surgery. Medium-risk and high-risk children should undergo routine preoperative ultrasound imaging by operators trained to assess for acute appendicitis, and MRI or low-dose CT if uncertainty remains. FUNDING: None