Development and validation of a predictive model for acute respiratory distress syndrome in geriatric patients following gastrointestinal perforation surgery.
10.3760/cma.j.cn121430-20250409-00345
- Author:
Ze ZHANG
1
;
You FU
1
;
Jing YUAN
2
;
Quansheng DU
1
Author Information
1. Department of Intensive Care Unit, Hebei General Hospital, Shijiazhuang 050000, China.
2. Hebei Key Laboratory of Clinical Pharmacy, Shijiazhuang 050000, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Respiratory Distress Syndrome/etiology*;
Retrospective Studies;
Aged;
Risk Factors;
Logistic Models;
Postoperative Complications;
Intestinal Perforation/surgery*;
Male;
ROC Curve;
Female;
Middle Aged;
Intensive Care Units;
Nomograms
- From:
Chinese Critical Care Medicine
2025;37(8):749-754
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To identify the risk factors for acute respiratory distress syndrome (ARDS) in geriatric patients following gastrointestinal perforation surgery, and constructed a model to validate its predictive value.
METHODS:A retrospective analysis was conducted. The clinical data of geriatric patients (aged ≥ 60 years) after gastrointestinal perforation surgery admitted to the intensive care unit (ICU) of Hebei General Hospital from October 2017 to October 2024 were enrolled. Two groups were divided according to whether ARDS occurred postoperatively, and the differences in each index between the groups were compared. Lasso regression and multifactorial Logistic regression analyses were used to identify independent risk factors for the development of ARDS, and a prediction model was constructed based on these, which was presented using a nomogram. The receiver operator characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) were plotted to evaluate the discrimination, accuracy, and clinical practicability of the model.
RESULTS:A total of 155 geriatric patients following gastrointestinal perforation surgery were ultimately included in the analysis, among whom 43 developed ARDS, with an incidence rate of 27.7%. There were significantly differences in age, body mass index (BMI), acute kidney injury comorbidity, heart rate, onset time, the duration of surgery, the site of perforation, seroperitoneum, amount of bleeding, shock comorbidity, central venous pressure (CVP), C-reactive protein, and albumin between ARDS and non-ARDS groups. Lasso regression identified nine significant predictors: age, BMI, acute kidney injury comorbidity, onset time, seroperitoneum, shock comorbidity, CVP, hemoglobin, and albumin. Multivariate Logistic regression analysis identified BMI [odds ratio (OR) = 1.310, P < 0.001], hemoglobin (OR = 1.019, P = 0.045), seroperitoneum (OR = 1.001, P = 0.017), and albumin (OR = 0.871, P < 0.001) as independent risk factors for the occurrence of ARDS. A prediction model was constructed based on the above four independent risk factors, and the ROC curve showed that the area under the curve (AUC) of the model for predicting the occurrence of ARDS was 0.885 [95% confidence interval (95%CI) was 0.824-0.946], and internal validation was performed using bootstrap resampling (Bootstrap 500 times), which showed that the AUC value of the model was 0.886 (95%CI was 0.883-0.889). Calibration curves revealed excellent concordance between observed outcomes and model predictions. DCA indicated a high net benefit value for the model, which has good clinical utility.
CONCLUSIONS:BMI, hemoglobin, seroperitoneum, and albumin were identified as independent risk factors for ARDS in geriatric patients following gastrointestinal perforation surgery. The prediction model constructed using these four indicators facilitates early identification of high-risk individuals by clinicians.
