VTE Risk Prediction Model for Lung Transplant Recipients on ECMO

Yan Zhu's study developed a machine learning model to predict venous thromboembolism (VTE) in lung transplant recipients supported by ECMO. The work is based on a retrospective review of 189 patients who underwent elective lung transplantation at the Second Affiliated Hospital of Zhejiang University from May 2023 to November 2024. The Random Forest model demonstrated an AUC of 0.895 on the validation set.

Use Cases

• Developing risk stratification tools for VTE based on clinical factors identified in the study
• Benchmarking machine learning models for clinical prediction tasks using the described performance metrics
• Informing clinical protocols for VTE prophylaxis in lung transplant patients supported by ECMO

Strengths

• Model performance is quantitatively reported with an AUC of 0.895 and accuracy of 89.7%
• The study includes a specific cohort of 189 patients from a single institution
• Model development used a defined methodology including Recursive Feature Elimination and 5-fold cross-validation

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download
• The dataset scope is limited to 21.2 KB, suggesting it contains model outputs or summary statistics rather than the full patient-level data
• Data may reflect geographic and temporal bias inherent to a single hospital study

Provenance

Source

Second Affiliated Hospital of Zhejiang University

Collection Method

Retrospective review of medical records

Time Range

May 2023 to November 2024

Freshness

Last updated 2026-06-04 05:47:25

Geography

China