Yonsei medical journal · 2017
Prediction of Anthracofibrosis Based on Clinico-Radiographic Findings.
СтатьяНаблюдательное исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Park Tae Yun, Heo Eun Young, Chung Hee Soon, Jin Kwang Nam, Kim Deog Kyeom
Аннотация
PURPOSE: Because anthracofibrosis (AF) is associated with tuberculosis (TB), detection of AF is clinically relevant in Korea, a TB endemic region. We thus sought to develop and validate a predictive model for AF using clinical radiographic data.
MATERIALS AND METHODS: Between January 1, 2008 and March 31, 2014, 3849 adult patients who underwent bronchoscopies were retrospectively included from an observational registry. We dichotomized patients based on the presence (n=167) or absence (n=242) of AF. After analyzing their clinico-radiographic characteristics, a logistic prediction model was developed. An area under the curve (AUC) was drawn using the weighted score in logistic regression model. To evaluate the degree of overfitting of the predictive model, a 5-fold cross-validation procedure was performed.
RESULTS: In multivariate logistic regression, clinical findings such as age >70 years, female gender, active TB, and computed tomography findings including atelectasis, stenosis, bronchial wall thickening, enlarged and calcified lymph nodes were significant diagnostic predictors for AF. The weighed score had an AUC of 0.939 [95% confidence interval (CI)=0.911-0.960], similar to the AUC obtained from internal validation (AUC=0.926, 95% CI=0.896-0.949).
CONCLUSION: The prediction model may be helpful for predicting AF based only on clinical and radiographic findings. However, further external validation is necessary.
MATERIALS AND METHODS: Between January 1, 2008 and March 31, 2014, 3849 adult patients who underwent bronchoscopies were retrospectively included from an observational registry. We dichotomized patients based on the presence (n=167) or absence (n=242) of AF. After analyzing their clinico-radiographic characteristics, a logistic prediction model was developed. An area under the curve (AUC) was drawn using the weighted score in logistic regression model. To evaluate the degree of overfitting of the predictive model, a 5-fold cross-validation procedure was performed.
RESULTS: In multivariate logistic regression, clinical findings such as age >70 years, female gender, active TB, and computed tomography findings including atelectasis, stenosis, bronchial wall thickening, enlarged and calcified lymph nodes were significant diagnostic predictors for AF. The weighed score had an AUC of 0.939 [95% confidence interval (CI)=0.911-0.960], similar to the AUC obtained from internal validation (AUC=0.926, 95% CI=0.896-0.949).
CONCLUSION: The prediction model may be helpful for predicting AF based only on clinical and radiographic findings. However, further external validation is necessary.