A probabilistic model for the prediction of intra-abdominal infection after colorectal surgery

• Published in: International journal of colorectal disease Vol. 36; no. 11; pp. 2481 - 2488
• Main Authors: Cagigas Fernández, Carmen, Palazuelos, Camilo, Cristobal Poch, Lidia, Gomez Ruiz, Marcos
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-11-2021; Springer; Springer Nature B.V
• Subjects: Abdomen; Abscess; Analysis; Bayesian analysis; Colorectal surgery; Gastroenterology; Health aspects; Hepatology; Infection; Internal Medicine; Intra-abdominal infection; Mathematical models; Medicine; Medicine & Public Health; Original Article; Proctology; Surgery; Intra-abdominal infection; Colorectal surgery; Probabilistic model
• ISSN: 0179-1958; 1432-1262
• DOI: 10.1007/s00384-021-03955-1

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.