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PLR/SII/D-dimer iron triangle: a novel precision prediction strategy for Mycoplasma pneumoniae pneumonia-associated
Minxuan Feng1, Wenyan Li2, Tao Ai1
1Department of Respiratory, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Insights
Plastic bronchitis (PB) is a serious lung infection. A new model combining platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and D-dimer can predict PB occurrence in children more accurately.
Area of Science:
- Pulmonary Medicine
- Infectious Diseases
- Hematology
Background:
- Plastic bronchitis (PB) is a severe pulmonary disease with poorly established early diagnostic methods.
- Accurate and early prediction of PB is crucial for timely clinical intervention.
Purpose of the Study:
- To identify independent risk factors for PB in patients with Mycoplasma pneumoniae pneumonia.
- To develop a clinically applicable predictive model for early PB diagnosis.
Main Methods:
- Retrospective analysis of 132 hospitalized patients with Mycoplasma pneumoniae lobar pneumonia.
- Univariate and multivariate logistic regression analyses to identify risk factors.
- Development and evaluation of a predictive model using platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and D-dimer via ROC curves.
Main Results:
- Several factors, including PLR, SII, and D-dimer, were identified as independent risk factors for PB.
- A combined model of PLR, SII, and D-dimer demonstrated higher predictive value than individual factors.
- Continued macrolide therapy beyond two courses did not significantly reduce PB occurrence.
Conclusions:
- PLR, SII, and D-dimer are key indicators for predicting PB in children.
- The combined model offers improved sensitivity and specificity for PB prediction.
- This model aids in guiding the duration of macrolide antibiotic therapy.
Objective:
Plastic bronchitis (PB) is a life-threatening pulmonary infection disease, and the early recognition and diagnostic prediction of PB are currently not well established. This study aims to identify independent risk factors for PB and develop a clinically applicable predictive model to help clinicians make earlier and more accurate judgments about the potential occurrence of PB.
Methods:
This study included 132 hospitalized patients with lobar pneumonia caused by Mycoplasma pneumoniae infection who underwent fiberoptic bronchoscopy. The study group consisted of 44 PB patients and 88 non-PB patients. Clinical data were collected and analyzed using chi-square tests, t-tests, non-parametric tests, Pearson χ 2 tests, continuity-corrected χ 2 tests, and Fisher's exact probability tests. Univariate analysis was performed to identify potential risk factors, and logistic regression analysis was used to determine the main independent risk factors for PB. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive potential of single-factor models and a combined model of platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and D-dimer for PB occurrence.
Results:
The results of univariate analysis showed that N%, L%, NLR, PLR, CRP, PCT, SII, LDH, D-dimer, and the duration of macrolide antibiotic therapy were all independent risk factors for PB. It was also suggested that continued use of macrolides after two courses did not significantly reduce the occurrence of PB. The results of multivariate regression analysis indicated that a combined analysis of PLR, SII, and D-dimer had higher predictive value for PB occurrence. This was further supported by plotting ROC curves and establishing a triad model of these indicators to achieve simple data calculation for predicting PB in clinical practice.
Conclusion:
This study demonstrates that PLR, SII, and D-dimer are important indicators for predicting the occurrence of PB in children. The combined model of these three indicators is more sensitive and specific than the individual risk factors in predicting PB occurrence. Additionally, this model provides synergistic guidance for the duration of macrolide antibiotic therapy.
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