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Published on: August 7, 2017
Interpretable machine learning identifies a clinically inferred inflammasome-associated inflammatory injury phenotype
Jie Li1, Yabin Wu1, Yang Huang1
1Department of Pediatric Respiratory, Maternal and Child Health Hospital of Hubei Province, Wuhan, China.
Insights
This study developed a machine-learning model using routine clinical data to accurately predict severe pediatric adenovirus pneumonia and identify inflammatory phenotypes. The model highlights fever duration and a novel Inflammasome-associated Inflammatory Injury Index (IAI) for severity assessment.
Area of Science:
- Pediatric Infectious Diseases
- Computational Biology
- Biomarker Discovery
Background:
- Severe adenovirus pneumonia in children presents with persistent fever, systemic inflammation, and organ injury.
- Direct inflammasome biomarkers are not readily available in pediatric clinical practice.
- Adenovirus infection can activate inflammasome pathways, contributing to disease severity.
Purpose of the Study:
- To develop an interpretable machine-learning framework for classifying adenovirus pneumonia severity in children.
- To stratify patients based on inflammatory phenotypes.
- To identify key clinical predictors of severe disease.
Main Methods:
- Retrospective analysis of 82 hospitalized children with adenovirus pneumonia.
- Calculation of a surrogate Inflammasome-associated Inflammatory Injury Index (IAI) using clinical and laboratory data.
- Evaluation of logistic regression, support vector machine, and random forest classifiers using cross-validation, with SHAP values for interpretation.
Main Results:
- The random forest model achieved high performance (AUC 0.965) in discriminating severe from mild cases.
- Fever duration and IAI were identified as the most significant predictors of severity.
- Unsupervised clustering revealed an 'inflammatory injury-high' phenotype exclusively in severe pneumonia cases.
Conclusions:
- Interpretable machine learning using routine clinical variables can effectively discriminate severity and identify phenotypes in pediatric adenovirus pneumonia.
- The identified inflammatory injury phenotype may reflect inflammasome-related inflammation.
- Further prospective studies with direct inflammasome biomarkers are needed for biological validation.
Background:
Severe adenovirus pneumonia in children is characterized by persistent fever, systemic inflammation, and extrapulmonary tissue injury. Although adenovirus can activate inflammasome pathways, direct inflammasome biomarkers are not routinely available in pediatric practice. This study developed an interpretable machine-learning framework for retrospective severity discrimination and inflammatory phenotype stratification.
Methods:
We retrospectively analyzed 82 hospitalized children with adenovirus pneumonia, including 32 mild and 50 severe cases. Demographic, clinical, and laboratory variables were extracted from medical records. Laboratory variables were obtained from the first available blood tests within 24 hours after admission, whereas fever duration reflected the total febrile course. A surrogate Inflammasome-associated Inflammatory Injury Index (IAI) was calculated as the mean z-score of lactate dehydrogenase, C-reactive protein, aspartate aminotransferase, neutrophil-to-lymphocyte ratio, and maximal temperature. Logistic regression, support vector machine with radial basis function kernel, and random forest classifiers were evaluated using repeated stratified 5-fold cross-validation. SHAP values and unsupervised clustering were used for model interpretation and phenotype discovery. A small post-2019 cohort was used for exploratory single-center temporal validation.
Results:
Severe cases were younger and had longer fever duration, higher maximal temperature, lower hemoglobin, and higher alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, and creatine kinase-MB levels than mild cases. IAI was significantly higher in severe disease. Random forest showed the best internal performance, with an AUC of 0.965 ± 0.031, PR-AUC of 0.980 ± 0.018, accuracy of 0.890, and F1-score of 0.913. Five-fold out-of-fold analysis yielded an AUC of 0.966 and a Brier score of 0.088. SHAP identified fever duration and IAI as the two leading contributors. Clustering identified an inflammatory injury-high phenotype comprising 11 patients, all with severe pneumonia. In exploratory temporal validation, the frozen random forest model achieved an AUC of 0.840 in 39 post-2019 cases.
Conclusion:
Routine clinical variables can support interpretable severity discrimination and phenotype discovery in pediatric adenovirus pneumonia. The inflammatory injury-high phenotype suggests a clinically detectable systemic inflammatory injury pattern biologically compatible with inflammasome-related inflammation. Because IAI is a surrogate clinical index rather than a direct molecular measure of inflammasome activation, prospective multicenter studies incorporating direct inflammasome biomarkers are required for biological validation.
