The predictive value of heparin-binding protein for bacterial infections in patients with severe multiple trauma
Li, L.; Tian, X.-x.; Feng, G.-l.; Chen, B.
Show abstract
IntroductionHeparin-binding protein is an inflammatory factor with predictive value and participates in the inflammatory response through antibacterial effects, chemotaxis, and increased vascular permeability. The role of heparin-binding protein in sepsis has been progressively demonstrated, but few studies have been conducted in the context of multiple trauma combined with bacterial infections. This study aims to investigate the predictive value of heparin-binding protein for bacterial infections in patients with severe multiple trauma. Materials and methodsPatients with multiple trauma in the emergency intensive care unit were selected for the study, and plasma heparin-binding protein concentrations and other laboratory parameters were measured within 48 hours of admission to the hospital. A two-sample comparison and univariate logistic regression analysis were used to investigate the relationship between heparin-binding protein and bacterial infection in multiple trauma patients. A multifactor logistic regression model was constructed, and the ROC curve was plotted. ResultsNinety-seven patients with multiple-trauma were included in the study, 43 with bacterial infection and 54 without infection. According to data analysis, heparin-binding protein was higher in the infected group than in the control group [(32.00{+/-}3.20) ng/mL vs. (18.52{+/-}1.33) ng/mL]. Univariate logistic regression analysis shows that heparin-binding protein is related to bacterial infection (OR=1.10, Z=3.91, 95%CI:1.05[~]1.15, P=0.001). Multivariate logistic regression equations showed that patients were 1.12 times more likely to have bacterial infections for each value of heparin-binding protein increase, holding neutrophils and PCT constant. ROC analysis shows that heparin-binding protein combined with neutrophils and PCT has better predictive value for bacterial infection [AUC=0.935, 95%CI:0.870[~]0.977]. ConclusionsHeparin-binding protein may predict bacterial infection in patients with severe multiple trauma. Combining heparin-binding protein, PCT, and neutrophils may improve bacterial infection prediction.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Efficacy and safety of decompressive craniectomy with non-suture duraplasty in patients with traumatic brain injury 96%
- Upregulation of interleukin-1β and interleukin-18 in traumatic brain injury patients and their potential as biomarkers 96%
- Effects of joint mobilization combined with acupuncture on pain, physical function, and depression in stroke patients with chronic neuropathic pain: a randomized controlled trial 95%
Similar papers in this journal
- Magnesium Sulfate Attenuates Lethality and Oxidative Damage Induced by Different Models of Hypoxia in Mice 94%
- Expression of nitric oxide synthase and nitric oxide levels in peripheral blood cells and oxidized low-density lipoprotein levels in saliva as early markers of severe dengue 93%
- Design of multi epitope-based peptide vaccine against E protein of human COVID-19: An immunoinformatics approach 91%
Similar papers in this journal
- Comparison of Efficacy of Dexamethasone and Methylprednisolone in Improving the Partial Pressure of Arterial Oxygen and Fraction of Inspired Oxygen Ratio among COVID-19 Patients 96%
- Post mortem pathological findings in COVID-19 cases: A Systematic Review 94%
- Comparative study between first and second wave of COVID-19 deaths in India - a single center study 94%
Similar papers in this journal
Similar papers in this journal
- The PBL teaching method in Neurology Education in the Traditional Chinese Medicine undergraduate students: An Observational Study 93%
- Combination therapy of Tocilizumab and steroid for management of COVID-19 associated cytokine release syndrome: A single center experience from Pune, Western India 93%
- Upregulation of ARHGAP9 is correlated with poor prognosis and immune infiltration in clear cell renal cell carcinoma 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.