Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit
Qiu-Xia Liao1,2, Zhi Feng3, Hui-Chang Zhuo1
1Department of Intensive Care Unit, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Introduction:
Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival.
Methods:
Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival.
Results:
The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different.
Conclusion:
Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.
Insights
Gram-negative bacillary pneumonia in the ICU is serious. High lactic acid, tracheal intubation, and acute kidney injury predict 30-day mortality, enabling risk stratification and early intervention.
Area of Science:
- Critical Care Medicine
- Infectious Diseases
- Pulmonology
Background:
- Gram-negative bacilli are a leading cause of pneumonia in intensive care units (ICUs).
- Predicting mortality in ICU patients with gram-negative bacillary pneumonia is crucial for timely intervention.
Purpose of the Study:
- To identify risk factors for 30-day mortality in ICU patients with gram-negative bacillary pneumonia.
- To develop a predictive model for stratifying patient risk and assessing short-term survival.
Main Methods:
- Retrospective study of 305 ICU patients with gram-negative bacillary pneumonia.
- Logistic regression analysis to identify independent risk factors for 30-day mortality.
- Nomogram construction and validation (Hosmer-Lemeshow, ROC curve, C-index) for risk prediction.
Main Results:
- Lactic acid, tracheal intubation, and acute kidney injury were significant independent risk factors for 30-day mortality.
- The developed nomogram model demonstrated good predictive performance (AUC=0.791).
- Risk stratification into low, medium, and high groups showed significant differences in survival times.
Conclusions:
- Lactic acid, tracheal intubation, and acute kidney injury are key predictors of 30-day mortality in this patient population.
- The nomogram provides a valuable tool for assessing short-term survival and guiding clinical decisions.
- Early identification and intervention based on risk stratification can improve patient outcomes.
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