Construction of a predictive model for hospital-acquired pneumonia risk in patients with mild traumatic brain injury based on LASSO-Logistic regression analysis.
10.3760/cma.j.cn121430-20240823-00715
- Author:
Xin ZHANG
1
;
Wenming LIU
2
;
Minghai WANG
3
;
Liulan QIAN
4
;
Jipeng MO
5
;
Hui QIN
1
Author Information
1. Graduate School of Nanjing Medical University, Nanjing 211166, Jiangsu, China.
2. Department of Critical Care Medicine, the Third Affiliated Hospital of Nanjing Medical University (the Second People's Hospital of Changzhou), Changzhou 213003, Jiangsu, China.
3. Department of Neurosurgery, the Third Affiliated Hospital of Nanjing Medical University (the Second People's Hospital of Changzhou), Changzhou 213003, Jiangsu, China.
4. Department of Education and Teaching, the Third Affiliated Hospital of Nanjing Medical University (the Second People's Hospital of Changzhou), Changzhou 213003, Jiangsu, China. Corresponding author: Qin Hui, Email: qinhui@njmu.edu.cn.
5. Department of Emergency Medicine, the Third Affiliated Hospital of Nanjing Medical University (the Second People's Hospital of Changzhou), Changzhou 213003, Jiangsu, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Risk Factors;
Case-Control Studies;
Logistic Models;
Healthcare-Associated Pneumonia/epidemiology*;
Brain Injuries, Traumatic/complications*;
Male;
Female;
ROC Curve;
Pneumonia/etiology*;
Middle Aged;
Adult
- From:
Chinese Critical Care Medicine
2025;37(4):374-380
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To identify early potential risk factors for hospital-acquired pneumonia (HAP) in patients with mild traumatic brain injury (mTBI), construct a risk prediction model, and evaluate its predictive efficacy.
METHODS:A case-control study was conducted using clinical data from mTBI patients admitted to the neurosurgery department of Changzhou Second People's Hospital from September 2021 to September 2023. The patients were divided into two groups based on whether they developed HAP. Clinical data within 48 hours of admission were statistically analyzed to identify factors influencing HAP occurrence through univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection to identify the most influential variables. The dataset was divided into training and validation sets in a 7:3 ratio. A multivariate Logistic regression analysis was then performed using the training set to construct the prediction model, exploring the risk factors for HAP in mTBI patients and conducting internal validation in the validation set. Receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and calibration curve were utilized to assess the sensitivity, specificity, decision value, and predictive accuracy of the prediction model.
RESULTS:A total of 677 mTBI patients were included, with 257 in the HAP group and 420 in the non-HAP group. The significant differences were found between the two groups in terms of age, maximum body temperature (MaxT), maximum heart rate (MaxHR), maximum systolic blood pressure (MaxSBP), minimum systolic blood pressure (MinSBP), maximum respiratory rate (MaxRR), cause of injury, and laboratory indicators [C-reactive protein (CRP), procalcitonin (PCT), neutrophil count (NEUT), erythrocyte sedimentation rate (ESR), fibrinogen (FBG), fibrinogen equivalent units (FEU), prothrombin time (PT), activated partial thromboplastin time (APTT), total cholesterol (TC), lactate dehydrogenase (LDH), prealbumin (PAB), albumin (Alb), blood urea nitrogen (BUN), serum creatinine (SCr), hematocrit (HCT), hemoglobin (Hb), platelet count (PLT), glucose (Glu), K+, Na+], suggesting they could be potential risk factors for HAP in mTBI patients. After LASSO regression analysis, the key risk factors were enrolled in the multivariate Logistic regression analysis. The results revealed that the cause of injury being a traffic accident [odds ratio (OR) = 2.199, 95% confidence interval (95%CI) was 1.124-4.398, P = 0.023], NEUT (OR = 1.330, 95%CI was 1.214-1.469, P < 0.001), ESR (OR = 1.053, 95%CI was 1.019-1.090, P = 0.003), FBG (OR = 0.272, 95%CI was 0.158-0.445, P < 0.001), PT (OR = 0.253, 95%CI was 0.144-0.422, P < 0.001), APTT (OR = 0.689, 95%CI was 0.578-0.811, P < 0.001), Alb (OR = 0.734, 95%CI was 0.654-0.815, P < 0.001), BUN (OR = 0.720, 95%CI was 0.547-0.934, P = 0.016), and Na+ (OR = 0.756, 95%CI was 0.670-0.843, P < 0.001) could serve as main risk factors for constructing the prediction model. Calibration curves demonstrated good calibration of the prediction model in both training and validation sets with no evident over fitting. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model in the training set was 0.943 (95%CI was 0.921-0.965, P < 0.001), with a sensitivity of 83.6% and a specificity of 91.5%. In the validation set, the AUC was 0.917 (95%CI was 0.878-0.957, P < 0.001), with a sensitivity of 90.1% and a specificity of 85.0%. DCA indicated that the prediction model had a high net benefit, suggesting practical clinical applicability.
CONCLUSIONS:The cause of injury being a traffic accident, NEUT, ESR, FBG, PT, APTT, Alb, BUN, and Na+ are identified as major risk factors influencing the occurrence of HAP in mTBI patients. The prediction model constructed using these parameters effectively assesses the likelihood of HAP in mTBI patients.
