Shock (Augusta, Ga.) · 2026
Development and Validation of A Lasso-Logistic Regression Model for Predicting Disseminated Intravascular Coagulation in Pediatric Hemophagocytic Lymphohistiocytosis.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Gan Jinpeng, Li Xun, Luo Ting, Yang Haixia, Zhang Benshan, Luo Haiyan, Xie Longlong, Yan Haipeng, Huang Jiaotian, Zhang Xinping, Wang Xiangyu, Lu Xiulan
Аннотация
BACKGROUND: Pediatric hemophagocytic lymphohistiocytosis (HLH) patients who develop disseminated intravascular coagulation (DIC) experience rapid disease progression and substantially elevated mortality. Currently, no validated early identification strategies exist for this life-threatening complication. We aimed to develop a prediction model for early DIC detection in pediatric HLH patients.
METHODS: This retrospective cohort study included patients from the Hunan Children's Hospital HLH database (January 2018-August 2024). Data imbalance was addressed through combined oversampling and undersampling techniques, including Synthetic Minority Oversampling Technique. The cohort was divided into training and validation sets (7:3 ratio). A Least Absolute Shrinkage and Selection Operator (LASSO)-logistic regression model was developed and validated internally, with external validation using prospectively collected cases (January-August 2025). Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.
RESULTS: Of 265 included patients, 217 cases were analyzed after the Synthetic Minority Oversampling Technique application. DIC incidence was 42.1% (64/152) and 44.6% (29/65) in training and validation cohorts, respectively. LASSO regression (lambda.1se = 0.08) identified six potential predictors: C-reactive protein, globulin, cholesterol, high-density lipoprotein cholesterol, prothrombin time, and interferon-γ. Multivariable logistic regression confirmed three independent predictors: C-reactive protein prothrombin time, and interferon-γ (all P < 0.05). The model demonstrated robust discriminative performance with an area under the receiver operating characteristic curve (AUROC) of 0.865 (95% confidence interval [CI] 0.809-0.922) in the training cohort and a bootstrap-corrected C-index of 0.857 (95% CI 0.828-0.886). Internal validation yielded an AUROC of 0.797 (95% CI 0.686-0.908), whereas external validation achieved an AUROC of 0.950. Decision curve analysis showed positive net benefit across threshold probabilities of 0% - 99% in training and 0% - 88% in validation sets.
CONCLUSIONS: The LASSO-logistic prediction model, incorporating three readily available biomarkers, demonstrated promising discriminative ability for predicting DIC risk in pediatric HLH. This tool may facilitate early risk stratification and timely therapeutic interventions to improve clinical outcomes.
METHODS: This retrospective cohort study included patients from the Hunan Children's Hospital HLH database (January 2018-August 2024). Data imbalance was addressed through combined oversampling and undersampling techniques, including Synthetic Minority Oversampling Technique. The cohort was divided into training and validation sets (7:3 ratio). A Least Absolute Shrinkage and Selection Operator (LASSO)-logistic regression model was developed and validated internally, with external validation using prospectively collected cases (January-August 2025). Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.
RESULTS: Of 265 included patients, 217 cases were analyzed after the Synthetic Minority Oversampling Technique application. DIC incidence was 42.1% (64/152) and 44.6% (29/65) in training and validation cohorts, respectively. LASSO regression (lambda.1se = 0.08) identified six potential predictors: C-reactive protein, globulin, cholesterol, high-density lipoprotein cholesterol, prothrombin time, and interferon-γ. Multivariable logistic regression confirmed three independent predictors: C-reactive protein prothrombin time, and interferon-γ (all P < 0.05). The model demonstrated robust discriminative performance with an area under the receiver operating characteristic curve (AUROC) of 0.865 (95% confidence interval [CI] 0.809-0.922) in the training cohort and a bootstrap-corrected C-index of 0.857 (95% CI 0.828-0.886). Internal validation yielded an AUROC of 0.797 (95% CI 0.686-0.908), whereas external validation achieved an AUROC of 0.950. Decision curve analysis showed positive net benefit across threshold probabilities of 0% - 99% in training and 0% - 88% in validation sets.
CONCLUSIONS: The LASSO-logistic prediction model, incorporating three readily available biomarkers, demonstrated promising discriminative ability for predicting DIC risk in pediatric HLH. This tool may facilitate early risk stratification and timely therapeutic interventions to improve clinical outcomes.