Mathematical modeling and prediction of the effectiveness of surgical treatment in surgery of the spine and pelvic complex

Abstract

Based on the study of the literature on the quality assessment of operative treatment in reconstructive surgery of the spine and pelvic complex, it can be concluded that, as a rule, multiple linear or logistic regression, a decision tree, is used to predict the quality of operative treatment. Neural networks are less commonly used. Forecasting is performed on the basis of a comparison of the pre- and postoperative condition of the patient, assessed according to various ordinal and quantitative scales as a result of interviewing the patient. With a relatively small number of analyzed cases of the disease (several tens or hundreds) and a small number of indicators (no more than two or three dozen), the use of neural networks seems premature for two reasons: a small amount of data allows analyzing them with classical methods of mathematical statistics, and identifying dependencies on a given stage requires constant "manual" intervention, taking into account information from the subject area. The application of statistical analysis methods to data on the treatment of chronic injuries showed the presence of standard problems for medical data. This is the presentation of the initial information in nominal or ordinal scales, the subjective nature of some indicators, as well as the interdependence of the presented characteristics, which reduces the quality of research. The search for the objective function that characterizes the quality of surgical treatment has shown the ambiguity of solving this problem even for a highly specialized situation. The identification of objectively present relationships also revealed a large number of problems, especially related to the choice of the type of surgical treatment, which is largely determined by the experience of the surgeon. Based on the study, it was proposed to build a model for predicting the quality of surgical treatment, based on expert assessments in the form of a forecast tree with recommended surgical treatment options and a statistical forecast based on the available experience. It is assumed that the model will be dynamic with feedback and be able to self-update. To predict the quality of surgical treatment in reconstructive surgery of the spine and pelvic complex, it is advisable to use a forecast tree, which allows us to recommend the type of surgery for a specific case of injury or disease and calculate the predicted values of quality of life indicators.