학술논문
A probabilistic model for the prediction of intra-abdominal infection after colorectal surgery
Original Article
Original Article
Document Type
Academic Journal
Source
International Journal of Colorectal Disease. November 2021, Vol. 36 Issue 11, p2481, 8 p.
Subject
Language
English
ISSN
0179-1958
Abstract
Author(s): Carmen Cagigas Fernández [sup.1] [sup.2], Camilo Palazuelos [sup.2] [sup.3], Lidia Cristobal Poch [sup.1] [sup.2], Marcos Gomez Ruiz [sup.1] [sup.2] Author Affiliations: (1) grid.411325.0, 0000 0001 0627 4262, General Surgery [...]
Aim Predicting intra-abdominal infections (IAI) after colorectal surgery by means of clinical signs is challenging. A naïve logistic regression modeling approach has some limitations, for which reason we study two potential alternatives: the use of Bayesian networks, and that of logistic regression model. Methods Data from patients that had undergone colorectal procedures between 2010 and 2017 were used. The dataset was split into two subsets: (i) that for training the models and (ii) that for testing them. The predictive ability of the models proposed was tested (i) by comparing the ROC curves from days 1 and 3 with all the subjects in the test set and (ii) by studying the evolution of the abovementioned predictive ability from day 1 to day 5. Results In day 3, the predictive ability of the logistic regression model achieved an AUC of 0.812, 95% CI = (0.746, 0.877), whereas that of the Bayesian network was 0.768, 95% CI = (0.695, 0.840), with a p-value for their comparison of 0.097. The ability of the Bayesian network model to predict IAI does present significant difference in predictive ability from days 3 to 5: AUC(Day 3) = 0.761, 95% CI = (0.680, 0.841) and AUC(Day 5) = 0.837, 95% CI = (0.769, 0.904), with a p-value for their comparison of 0.006. Conclusions Whereas at postoperative day 3, a logistic regression model with imputed data should be used to predict IAI; at day 5, when the predictive ability is almost identical, the Bayesian network model should be used.
Aim Predicting intra-abdominal infections (IAI) after colorectal surgery by means of clinical signs is challenging. A naïve logistic regression modeling approach has some limitations, for which reason we study two potential alternatives: the use of Bayesian networks, and that of logistic regression model. Methods Data from patients that had undergone colorectal procedures between 2010 and 2017 were used. The dataset was split into two subsets: (i) that for training the models and (ii) that for testing them. The predictive ability of the models proposed was tested (i) by comparing the ROC curves from days 1 and 3 with all the subjects in the test set and (ii) by studying the evolution of the abovementioned predictive ability from day 1 to day 5. Results In day 3, the predictive ability of the logistic regression model achieved an AUC of 0.812, 95% CI = (0.746, 0.877), whereas that of the Bayesian network was 0.768, 95% CI = (0.695, 0.840), with a p-value for their comparison of 0.097. The ability of the Bayesian network model to predict IAI does present significant difference in predictive ability from days 3 to 5: AUC(Day 3) = 0.761, 95% CI = (0.680, 0.841) and AUC(Day 5) = 0.837, 95% CI = (0.769, 0.904), with a p-value for their comparison of 0.006. Conclusions Whereas at postoperative day 3, a logistic regression model with imputed data should be used to predict IAI; at day 5, when the predictive ability is almost identical, the Bayesian network model should be used.