What is the clinical evidence from this study?

Study design: Cohort. Population: Postpartum venous thromboembolism (n=279). Intervention: Low-molecular-weight heparin (nadroparin). Primary outcome: Imaging-based thrombus regression (AUC-ROC 0.831, 95% CI 0.696-0.967).

What question did this study set out to answer?

The aim is to develop a predictive model for thrombus regression in postpartum patients with venous thromboembolism (VTE).

April 10, 2026Open Access

A novel predictive model for evaluating thrombus regression in postpartum venous thromboembolism patients

Resultado clave

The random forest model optimally predicted thrombus regression in postpartum VTE patients receiving LMWH, achieving an AUC-ROC of 0.831.

Puntos clave

The aim is to develop a predictive model for thrombus regression in postpartum patients with venous thromboembolism (VTE).
Data analysis from three cohorts of postpartum VTE patients
Development of a random forest (RF) model
Identification of key predictors including anti-Xa activity and D-dimer levels
The RF model was identified as the optimal predictive model for thrombus regression events
Key predictors include anti-Xa activity, antithrombin III levels, D-dimer levels, and BMI
The model may assist in assessing changes in the thrombotic state of postpartum VTE patients

Diseño del estudio

Tipo

Cohort (n=279)

Multicéntrico

Sí

PICO estructurado

Does a Random Forest model predict thrombus regression events in patients with postpartum venous thromboembolism?

Población

Patients with postpartum venous thromboembolism (VTE) from three cohorts

Intervención

Random Forest (RF) predictive model

Resultado

Thrombus regression eventssurrogate

A novel Random Forest model utilizing anti-Xa activity, antithrombin III, D-dimer, and BMI can predict thrombus regression in postpartum VTE patients to potentially guide precision medication.

Resultado numérico

Estimación del efecto: AUC-ROC 0.831 (95% CI 0.696-0.967)

Limitaciones

Small sample size, particularly for the external validation cohort (n = 29)
Training data came from a single-center retrospective cohort, which may introduce selection bias
Cannot eliminate the possibility of other unmeasured factors, such as haemoglobin levels
Model was developed in a Chinese population, and its applicability to other ethnic groups requires further investigation

Resumen

Based on data from the three cohorts of patients with postpartum VTE, the RF model was identified as the optimal model for predicting thrombus regression events, with anti-Xa activity, antithrombin III levels, D-dimer levels, and BMI serving as key predictors. This study may help assess changes in the thrombotic state of postpartum VTE patients and guide clinical precision medication.

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Discussion

Authors

Meixiang Yu

Yanbian University Hospital

Congcong Jia

Zhenyu Zhou

Journals

International Journal of Clinical Pharmacy

Actions

Institutions

Shanghai First Maternity and Infant Hospital

International Peace Maternity & Child Health Hospital

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

A novel predictive model for evaluating thrombus regression in postpartum venous thromboembolism patients

Resultado clave

Puntos clave

Diseño del estudio

PICO estructurado

Resultado numérico

Limitaciones

Resumen

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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