ArticleJournal of inflammation research2023
Diagnosis of Early Bacterial Pneumonia and Sepsis After Cardiovascular Surgery: A Diagnostic Prediction Model Based on LASSO Logistic Regression.
Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Changes in plasma inflammatory injury factors and their clinical significance in pediatric bacterial pneumonia and sepsis.Open life sciences · 2026Article
- Development and validation of a risk prediction model for postoperative pneumonia in elderly non-cardiac surgery patients: a retrospective cohort study.Scientific reports · 2025Article
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Authors and funding
17 authors.
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Abstract
Background: Early postoperative bacterial pneumonia and sepsis (ePOPS), which occurs within the first 48 hours after cardiovascular surgery, is a serious life-threatening complication. Diagnosis of ePOPS is extremely challenging, and the existing diagnostic tools are insufficient. The purpose of this study was to construct a novel diagnostic prediction model for ePOPS. Methods: Least Absolute Shrinkage and Selection Operator (LASSO) with logistic regression was used to construct a model to diagnose ePOPS based on patients' comorbidities, medical history, and laboratory findings. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model discrimination. Results: A total of 1203 patients were recruited and randomly split into a training and validation set in a 7:3 ratio. By early morning on the 3rd postoperative day (POD3), 103 patients had experienced 133 episodes of bacterial pneumonia or sepsis (15 patients had both). LASSO logistic regression model showed that duration of mechanical ventilation (P=0.015), NYHA class ≥ III (P=0.001), diabetes (P<0.001), exudation on chest radiograph (P=0.011) and IL-6 on POD3 (P<0.001) were independent risk factors. Based on these factors, we created a nomogram named DICS-I with an AUC of 0.787 in the training set and 0.739 in the validation set. Conclusion: The DICS-I model may be used to predict the risk of ePOPS after cardiovascular surgery, and is also especially suitable for predicting the risk of IRAO. The DICS-I model could help clinicians to adjust antibiotics on the POD3.
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