Development of a multivariable prediction model for early revision of total knee arthroplasty - The effect of including patient-reported outcome measures

BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As revision is becoming increasingly common in patients under 65 years, the need for improved preoperative patient selection is imminently needed. Therefore, this study aimed to identify the most important factors of early revision and to develop a prediction model of early revision including assessment of the effect of incorporating data on patient-reported outcome measures (PROMs). MATERIAL AND METHODS: A cohort of 538 patients undergoing primary TKA was included. Multiple logistic regression using forward selection of variables was applied to identify the best predictors of early revision and to develop a prediction model. The model was internally validated with stratified 5-fold cross-validation. This procedure was repeated without including data on PROMs to develop a model for comparison. The models were evaluated on their discriminative capacity using area under the receiver operating characteristic curve (AUC). RESULTS: The most important factors of early revision were age (OR 0.63 [0.42, 0.95]; P = 0.03), preoperative EQ-5D (OR 0.07 [0.01, 0.51]; P = 0.01), and number of comorbidities (OR 1.01 [0.97, 1.25]; P = 0.15). The AUCs of the models with and without PROMs were 0.65 and 0.61, respectively. The difference between the AUCs was not statistically significant (P = 0.32). CONCLUSIONS: Although more work is needed in order to reach a clinically meaningful quality of the predictions, our results show that the inclusion of PROMs seems to improve the quality of the prediction model

Distribution volume assessment compartment modelling:theoretic phosphate kinetics in steady state hemodialys patients

Purpose Hyperphosphatemia constitutes a major problem in end-stage renal disease patients. At this stage, dialysis efficacy usually plays an important role in obtaining phosphate levels within the normal range. Currently, no practical tool capable of making individualized predictions about phosphate changes during and after hemodialysis (HD) have gained clinical acceptance. As a result, optimal dialysis prescriptions seem to be difficult to achieve. The objective of the present study was to develop and test a quantitative tool to predict intradialytic and postdialytic (2 hours) phosphate kinetics in HD therapy. This included distribution volume assessment. Methods The approach included compartment modeling. Various model attempts were produced and tested using experimental data that included 2 treatment regimens; conventional and nocturnal HD, with 2-hour postdialysis rebound. Graphical comparison and determination of R2 was applied to determine the best model variation. Results 1-, 2- and 3-compartment simulations were produced. Both 2- and 3-compartment model variations showed a close fit with the experimental data. However, a 3-compartment model showed the best graphical fit. This was supported by R2 values in the 0.951–0.979 range. Conclusions The 3-compartment model seems promising for prediction about plasma phosphate and holds the potential to be employed as a decision support tool and to enhance optimal dialysis prescriptions. Furthermore, the results provide specific suggestions about the distribution of phosphate in the body. Despite the promising results, further data and testing are necessary to validate the initial results. </jats:sec

Estimation of Approximating Rate for Neural Network inLwp Spaces

A class of Soblove type multivariate function is approximated by feedforward network with one hidden layer of sigmoidal units and a linear output. By adopting a set of orthogonal polynomial basis and under certain assumptions for the governing activation functions of the neural network, the upper bound on the degree of approximation can be obtained for the class of Soblove functions. The results obtained are helpful in understanding the approximation capability and topology construction of the sigmoidal neural networks