Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care01:29

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care

633
Diagnosing Pulmonary EmbolismDiagnosing pulmonary embolism (PE) involves clinical assessment and advanced imaging tests. The preferred diagnostic tool is the spiral (helical) CT scan or CT angiography (CTA), which uses intravenous contrast media to visualize the pulmonary vasculature and identify emboli.A ventilation-perfusion (V/Q) scan is an alternative for patients unable to receive contrast media. This scan includes both perfusion and ventilation scanning. Perfusion scanning involves...
633
Endocarditis IV: Nursing Management01:29

Endocarditis IV: Nursing Management

560
Infective endocarditis (IE) is a chronic infection of the heart's endocardium, primarily affecting the heart valves. A detailed nursing assessment for a patient with IE involves collecting subjective and objective data to ensure an accurate diagnosis and timely intervention.Subjective DataThe nurse gathers information about the patient's symptoms and complaints during the subjective assessment. Patients with infective endocarditis often report non-specific symptoms that can mimic other...
560
Venous Thrombosis III: Interprofessional Care01:29

Venous Thrombosis III: Interprofessional Care

459
Venous thrombosis requires effective prevention and treatment strategies to improve patient outcomes and reduce potential complications.Prevention StrategiesHealthcare providers must prioritize preventing venous thromboembolism (VTE) for all adult patients upon admission. Interventions depend on bleeding and thrombosis risk, medical history, current medications, diagnoses, planned procedures, and patient preferences. Patients on bed rest should change positions every two hours and, if not...
459

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Research Progress on Histone Modification Regulation Mechanisms and Breeding Applications in Plant Abiotic Stress Responses.

Plants (Basel, Switzerland)·2026
Same author

How Ligand Protonation and Hydrogen Bonding Govern Oxygen Reduction Reaction Selectivity by a Dinuclear Copper Catalyst: Insights from DFT Calculations.

Inorganic chemistry·2026
Same author

Clinical efficacy and safety of porcine fibrin sealant in video-assisted thoracoscopic surgery lobectomy for lung cancer.

Frontiers in surgery·2026
Same author

Metal element fingerprints combined with chemometrics deciphering the discrimination of different Calculus bovis and a novel risk-benefit assessment.

Frontiers in chemistry·2026
Same author

Peptide OM-LV20 demonstrates neuroprotective effects by attenuating mitochondria-mediated neuronal apoptosis and dysfunction in mice with traumatic brain injury.

Zoological research·2025
Same author

Contralateral metastatic papillary thyroid carcinoma and complicated by primary hyperaldosteronism: A case report.

World journal of clinical cases·2025

Related Experiment Video

Updated: Mar 23, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K

A predictive model for PICC-related thrombosis in sepsis patients using XGBoost algorithm.

Wei Hao1, Tian-Yu She2, Zhen-Nan Yuan1

  • 1Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China.

Scientific Reports
|March 22, 2026
PubMed
Summary

This study developed an effective XGBoost model to predict the risk of central venous catheter (PICC) related thrombosis in sepsis patients. Identifying high-risk individuals can improve clinical management and patient outcomes for prolonged intravenous therapy.

Keywords:
PICCPredictive modelingSepsisThrombosisXGBoost

Related Experiment Videos

Last Updated: Mar 23, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.2K

Area of Science:

  • Critical Care Medicine
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Percutaneous insertion of central venous catheters (PICCs) are essential for sepsis patients needing prolonged intravenous therapy.
  • PICC use is associated with significant complications, notably thrombosis, impacting patient outcomes.
  • Accurate risk identification for PICC-related thrombosis is crucial for effective clinical management.

Purpose of the Study:

  • To develop and validate a predictive model for PICC-related thrombosis in sepsis patients.
  • To utilize the XGBoost algorithm for enhanced predictive accuracy.
  • To identify key risk factors contributing to PICC-related thrombosis.

Main Methods:

  • Analysis of a large dataset (n=8,128) of sepsis patients with PICCs from the MIMIC-IV 3.1 database.
  • Development of an XGBoost predictive model using demographic, laboratory, and clinical variables.
  • Model validation using area under the receiver operating characteristic curve (AUC), SHAP analysis, and decision curve analysis.

Main Results:

  • The XGBoost model demonstrated strong predictive performance with AUCs of 0.761 (training) and 0.766 (validation).
  • SHAP analysis identified key predictors including white blood cell count, platelet count, hemoglobin, creatinine, PICC indwelling time, and age.
  • Decision curve analysis confirmed the model's clinical utility, outperforming standard strategies.

Conclusions:

  • The developed XGBoost model is a reliable predictor of PICC-related thrombosis in sepsis patients.
  • The model's identified risk factors provide insights for targeted clinical interventions.
  • This predictive tool has the potential to guide clinical decision-making and improve outcomes for high-risk patients.