A probabilistic model for the prediction of intra-abdominal infection after colorectal 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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/32745 |
| Acceso en línea: | https://hdl.handle.net/10902/32745 |
| Access Level: | acceso abierto |
| Palabra clave: | Colorectal surgery Intra-abdominal infection Probabilistic model |
| Sumario: | 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. |
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