Development of 30-day mortality forecast model in patients after surgical treatment of proximal hip fracture

Introduction Fractures of the proximal femur often occur in elderly and senile patients. Most of them have an unfavorable comorbid background. In this regard, there is a high risk of complications in the postoperative period, which requires the development and implementation of an effective forecasting model in clinical practice designed to take measures to avoid adverse treatment outcomes. Objective To develop a regression model to predict the probability of lethal outcome within 30 days after surgery in patients with proximal femur fracture. Material and Methods A retrospective analysis of inpatient case histories of all patients (n = 1222) with proximal femur fracture treated in our hospital in 2018-2019 was performed. A total of 388 cases were selected for the study. Results After a detailed statistical analysis of the physiological parameters of the patients, four independent factors were identified that increased the risk of death during 30 days following surgery: albumin less than 30 g / l (regression coefficient – 1.742; OR – 5.708, 95% CI – 1.904–17.114, p = 0.002), the presence of diabetes mellitus (regression coefficient – 1.141; OR – 3.130, 95% CI –1.022–9.588, p = 0.046), the presence of acute renal injury (regression coefficient – 3.141; OR – 23.136, 95% CI – 3.886–137.735, p = 0.001), the presence of pneumonia (regression coefficient – 2.130; OR – 8.411, 95% CI – 2.453–28.838, p = 0.001). A regression model for predicting 30-day mortality was developed: the constant regression coefficient was 4.371, the area under the ROC-curve corresponding to the probability of 30-day mortality was 0.841 with 95% CI: 0.732–0.951, model sensitivity and specificity – 78.9 and 81.2%, respectively. After a detailed statistical analysis of the patients' physiological parameters, four independent factors were identified that increase the risk of fatal outcome during the next 30 days after surgery: albumin less than 30 g / l (regression coefficient, 1.742; OR – 5.708, 95% CI (1.904 – 17.114), p = 0.002), presence of diabetes mellitus (regression coefficient – 1.141; OR – 3.130, 95% CI (1.022 – 9.588), p = 0.046), presence of acute renal injury (regression coefficient – 3.141; OR – 23.136, 95% CI (3.886 – 137.735), p = 0.001), presence of pneumonia (regression coefficient – 2.130; OR – 8.411, 95% CI (2.453 – 28.838), p = 0.001). Thereby we developed a regression model to predict 30-day mortality: regression coefficient of the constant was 4.371; area under the ROC curve, corresponding to the dependence of the probability of 30-day mortality, was 0.841 with 95% CI (0.732 – 0.951); model sensitivity and specificity were 78.9 and 81.2%, respectively. Conclusion Aregression model for predicting mortality in patients with proximal femur fractures based on independent risk factors has a sufficient level of sensitivity and specificity. Its application is possible in practical health care institutions, where patients with trauma are treated.