RESUMEN
PURPOSE: To estimate survival curves in patients with hip fracture according to gender, age, type of fracture, and waiting time for surgery and to compare them with the life expectancy of the general population. The study hypothesis is that survival after hip fractures is significantly lower than in the general population, especially in cases that underwent delayed surgery, regardless of age and gender. METHODS: A survival analysis study was designed and approved by our institutional ethics review board. All patients who were coded with a diagnosis of hip fracture from 2002 to 2018 were included in the study. A total of 1176 patients were included, and the median age was 81 years (18-105 years). Kaplan-Meier curves and log-rank tests were performed to compare survival curves between those who underwent surgery on time and those with surgical delays. An exponential multivariate regression model was estimated, and a hazard ratio (HR) was reported for age, gender, and wait time for surgery. A significance of 5% was used, and a confidence interval level of 95% was reported. RESULTS: The Kaplan-Meier curves for delayed surgery (log-rank, p = 0.00) and the age group (log-rank, p = 0.00) were significantly different. Exponential regression estimated an HR 1.05 (1.05-1.07) for age, HR 1.80 (1.51-2.13) for men, and HR 1.93 (1.61-2.31) for each day of wait for surgery. CONCLUSIONS: The 2 significant findings of this study are that hip fracture patients over 40 years old have a higher risk of dying at any time compared to the general population and that the waiting time for surgery (a modifiable factor) decreases survival rates at any time.
Asunto(s)
Fijación de Fractura/estadística & datos numéricos , Fracturas de Cadera/mortalidad , Vigilancia de la Población/métodos , Medición de Riesgo/métodos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Chile/epidemiología , Femenino , Fracturas de Cadera/cirugía , Humanos , Masculino , Persona de Mediana Edad , Factores de Riesgo , Tasa de Supervivencia/tendencias , Tiempo de Tratamiento , Adulto JovenRESUMEN
The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.