Prediction of injury localization in preoperative patients with gastrointestinal perforation: a multiomics model analysis

Autor: Pingxia Lu, Yue Luo, Ziling Ying, Junrong Zhang, Xiaoxian Tu, Lihong Chen, Xianqiang Chen, Yingping Cao, Zhengyuan Huang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Gastroenterology, Vol 24, Iss 1, Pp 1-13 (2024)
Druh dokumentu: article
ISSN: 1471-230X
46536612
DOI: 10.1186/s12876-023-03092-9
Popis: Abstract Background The location of gastrointestinal perforation is essential for severity evaluation and optimizing the treatment approach. We aimed to retrospectively analyze the clinical characteristics, laboratory parameters, and imaging features of patients with gastrointestinal perforation and construct a predictive model to distinguish the location of upper and lower gastrointestinal perforation. Methods A total of 367 patients with gastrointestinal perforation admitted to the department of emergency surgery in Fujian Medical University Union Hospital between March 2014 and December 2020 were collected. Patients were randomly divided into training set and test set in a ratio of 7:3 to establish and verify the prediction model by logistic regression. The receiver operating characteristic curve, calibration map, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical applicability of the prediction model, respectively. The multiomics model was validated by stratification analysis in the prediction of severity and prognosis of patients with gastrointestinal perforation. Results The following variables were identified as independent predictors in lower gastrointestinal perforation: monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, free air in peritoneal cavity by univariate logistic regression analysis and stepwise regression analysis. The area under the receiver operating characteristic curve of the prediction model was 0.886 (95% confidence interval, 0.840–0.933). The calibration curve shows that the prediction accuracy and the calibration ability of the prediction model are effective. Meanwhile, the decision curve results show that the net benefits of the training and test sets are greater than those of the two extreme models as the threshold probability is 20–100%. The multiomics model score can be calculated via nomogram. The higher the stratification of risk score array, the higher the number of transferred patients who were admitted to the intensive care unit (P
Databáze: Directory of Open Access Journals
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