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| Construction and validation of a nomogram model for postoperative intra - abdominal infection in gastrointestinal
cancer patients |
| 1Ning Jing,2Zhou Fangfang,2Lyu Shuhong,3Bao Xiaojian |
| 1Department of Anesthesiology Affiliated Hospital of Jiangsu University Zhenjiang 212000 Jiangsu China
2Department of
Gastrointestinal Surgery Affiliated Hospital of Jiangsu University Zhenjiang 212000 Jiangsu China
3Department of Medical
Management Affiliated Hospital of Jiangsu University Zhenjiang 212000 Jiangsu China |
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Abstract Objective To analyze the risk factors for postoperative intra-abdominal infection IAI in gastrointestinal cancer GIC
patients then construct and validate the nomogram model . Method A total of 230 GIC patients who admitted to our hospital from
January 2018 to December 2023 were selected as the study group. According to a ratio of 7 to 3 patients were randomly divided into a
modeling group of 161 cases and a validation group of 69 cases. Based on modeling group data Lasso-logistic regression was used to
analyze the risk factors of postoperative IAI in GIC then construct and validate the nomogram model. Result Lasso regression screened
five non - zero coefficient indicators which were diabetes ASA grade > grade Ⅱ combined organ resection operation time and
preoperative CONUT score. Multivariate logistic regression analysis showed that ASA grade >Ⅱ combined organ resection surgery
time≥4. 5 hours and preoperative CONUT score≥5 were independent risk factors for postoperative IAI in GIC patients all P<0. 05 .
Based on the above indicators a nomogram model was constructed. The ROC curve showed that the AUC for predicting IAI in the
modeling group and validation group patients were 0. 829 95% CI = 0. 759 - 0. 899 and 0. 862 95% CI = 0. 725 - 0. 999 . The
calibration curve indicated that the model had good consistency between the predicted probability and the actual probability in the
modeling group and validation group. The decision curve indicated that the model had a wide range of clinical net benefits in the
modeling and validation groups. Conclusion The nomogram developed in this study can effectively identify high-risk postoperative IAI
patients in GIC and can be used to guide clinical practice.
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