DEVELOPMENT OF CLINICAL AND MACHINE LEARNING MODELS FOR PREDICTING CATHETER-ASSOCIATED VENOUS THROMBOSIS IN HOSPITALIZED CHILDREN

@article{huber2026development,
  title = {{DEVELOPMENT OF CLINICAL AND MACHINE LEARNING MODELS FOR PREDICTING CATHETER-ASSOCIATED VENOUS THROMBOSIS IN HOSPITALIZED CHILDREN}},
  author = {Stephany Cares HUBER and Mohamad Al BANNOUD and Silmara Aparecida de Lima MONTALVÃO and Sandra Martins SILVA and Sumara Zuanazi Pinto RIGATTO and Ricardo Onofre Benez VILELA and Arlindo Nascimento Lemos JUNIOR and Fábio Husemann MENEZES and Adriana Rodrigues da Luz FERREIRA and Marcone Lima SOBREIRA and Mônica Pinheiro de Almeida VERÍSSIMO and Nelson WOLOSKER and Marcelo Calil BURIHAN and Márcia Fayad Marcondes ABREU and Guilherme Camargo Gonçalves ABREU and Tiago Dias MARTINS and Gabriel Yoshiaki OTTAIANO and Rubens Maciel FILHO and Walter AGENO and Leonardo Rodrigues BRANDÃO and Joyce Maria ANNICHINO-BIZZACCHI},
  journal = {Hematology, Transfusion and Cell Therapy},
  year = {2026},
  month = oct,
  doi = {10.1016/j.htct.2026.108081},
  url = {https://doi.org/10.1016/j.htct.2026.108081},
  note = {CC BY},
}

TY  - JOUR
TI  - DEVELOPMENT OF CLINICAL AND MACHINE LEARNING MODELS FOR PREDICTING CATHETER-ASSOCIATED VENOUS THROMBOSIS IN HOSPITALIZED CHILDREN
AU  - Stephany Cares HUBER
AU  - Mohamad Al BANNOUD
AU  - Silmara Aparecida de Lima MONTALVÃO
AU  - Sandra Martins SILVA
AU  - Sumara Zuanazi Pinto RIGATTO
AU  - Ricardo Onofre Benez VILELA
AU  - Arlindo Nascimento Lemos JUNIOR
AU  - Fábio Husemann MENEZES
AU  - Adriana Rodrigues da Luz FERREIRA
AU  - Marcone Lima SOBREIRA
AU  - Mônica Pinheiro de Almeida VERÍSSIMO
AU  - Nelson WOLOSKER
AU  - Marcelo Calil BURIHAN
AU  - Márcia Fayad Marcondes ABREU
AU  - Guilherme Camargo Gonçalves ABREU
AU  - Tiago Dias MARTINS
AU  - Gabriel Yoshiaki OTTAIANO
AU  - Rubens Maciel FILHO
AU  - Walter AGENO
AU  - Leonardo Rodrigues BRANDÃO
AU  - Joyce Maria ANNICHINO-BIZZACCHI
PY  - 2026
DA  - 2026/10/23
JO  - Hematology, Transfusion and Cell Therapy
DO  - 10.1016/j.htct.2026.108081
UR  - https://doi.org/10.1016/j.htct.2026.108081
AB  - Background: Venous catheters (VC) are a leading cause of venous thromboembolism in hospitalized children. Existing risk scores rely on baseline characteristics and inadequately capture acute clinical and procedural factors influencing Venous Catheters Thrombosis (VCT). Aims: To develop and internally validate a clinical risk score and, in parallel, a machine learning (ML)-based model for VCT in a multicenter hospitalized pediatric cohort, comparing their performance and interpretability. Methods: A prospective multicenter cohort registry (7 centers) which included 854 children with VC. All ultrasound screening for VCT during hospitalization were performed in defined times. Clinical and baseline characteristics were assessed. Data were split into derivation/training (70%) and validation/test (30%) sets. For the clinical score, predictors were selected by multivariable logistic regression, with variable weights and cutoffs optimized using the Grey Wolf Optimizer metaheuristic; performance was assessed by bootstrap resampling. In parallel, ten clinically relevant variables were evaluated using Light Gradient Boosting Machine (LightGBM), Random Forest, and Decision Tree algorithms, with metrics estimated via bootstrap 95% confidence intervals (CI) and interpretability assessed using SHapley Additive exPlanations (SHAP). Results: Patients developing VCT (23.8%, n = 203) were significantly younger (median 18 vs. 53.5 months) and exhibited higher baseline comorbidities, including respiratory (35.0% vs. 20.7%), congenital (12.8% vs. 6.0%), and cardiovascular diseases (10.3% vs. 4.6%), with 56.6% requiring ICU admission. VCT cases presented higher rates of internal jugular access (73.4% vs. 41.9%), right upper limb placement (70.4% vs. 58.7%), and central vascular catheters (87.2% vs. 57.3%). Anticoagulation therapy was administered to 20.7% of VTE patients versus 5.4% of non-VCT controls (n = 651). The clinical score incorporates systemic infection (+3), major surgery (+2), right-sided placement (+1), catheter diameter >4 Fr (+1), >3 attempts (+1), and platelets >350 × 10⁹/L (+1), HCM ≤26.5 pg (+1), showing strong discrimination (C-index derivation 0.76; validation 0.79). A ≥4-point threshold balanced performance (NPV 0.87–0.91; VTE rate 48–52% vs. 13–15% in low-risk), whereas a ≥5-point threshold prioritized specificity (>0.99) and PPV (0.90–0.95). Among machine learning models, LightGBM performed best (C-index 0.74, sensitivity 0.60, specificity 0.83), with SHAP confirming systemic infection and major surgery as top predictors. Discussion: ML and clinical scoring models converged on identical biological predictors for VCT, reinforcing their clinical validity. The clinical score enables rapid bedside risk stratification, whereas the ML model offers higher sensitivity by accounting for complex nonlinear variable interactions. Conclusion: This novel pediatric VCT risk score demonstrates robust performance using readily available variables. A ≥4-point cutoff offers optimal utility for risk stratification, while a ≥5-point cutoff identifies a subgroup at extremely high thrombotic risk. The high incidence of asymptomatic VCT reinforce further research into monitoring and intervention strategies for high-risk pediatric patients. Approved by Ethics Committee of UNICAMP (CAAE:63131922.8.0000.5404). Supported by FAPESP (#2021/11963-0).
SN  - 2531-1379
SN  - 2531-1387
ER  -