Development of nonlaboratory-based risk prediction models for cardiovascular diseases using conventional and machine learning approaches

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities

Three-dimensional modelling and finite element analysis of an ankle external fixator

The use of ankle external fixator to treat pilon fracture Type III is popular amongst surgeons as it can reduce complications such as non-union and mal-union. Even though configurations of the connecting bars are important, the material also plays a major factor for a successful outcome. In this paper, the Delta external fixator with simulated ankle pilon fractures Type III were modelled and analysed under two different materials; titanium alloy and stainless steel. The finite element model includes tibia, fibula, talus, calcaneus, cuboid, navicular, three cuneiforms and five metatarsals bone. To simulate the pilon fractures Type III, a cutting segment was utilised. The ligaments were assigned with linear spring properties and cartilages were modelled using Mooney-Rivlin hyper-elastic behaviour. The Delta external fixator was designed using a three-dimensional software with two different material properties - titanium alloy and stainless steel. High von Mises stress concentrated at the pin-bone interface with the highest value observed for the titanium fixation. The results also showed less deformation for the stainless steel compared to titanium