Clinical trial NCT06957587: A Prediction Model of Basic Blood Volume Based on Ultrasound
Official title: Development and Validation of a Quantitative Prediction Model of Blood Volume Based on Ultrasound
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Blood Volume Analysis
Ultrasound
Machine Learning
Lead sponsor: Shanghai 6th People's Hospital
Sponsor class: OTHER
First posted: 2025-05-04
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients prepare to receive surgery.

Brief summary: Accurate assessment of blood volume (BV) is crucial for optimizing fluid management in critical care, perioperative settings, and hemodynamic monitoring. Traditional methods, such as invasive central venous pressure (CVP) monitoring or indicator dilution techniques, are limited by their invasiveness, cost, and logistical challenges. In contrast, point-of-care ultrasound (POCUS) offers a noninvasive, real-time alternative for evaluating intravascular volume status through dynamic venous parameters.

Recent studies have demonstrated the clinical utility of inferior vena cava (IVC) ultrasound in assessing fluid responsiveness, with IVC collapsibility index (IVC-CI) \>50% strongly correlating with hypovolemia. Similarly, subclavian vein (SCV) ultrasound provides reliable measurements due to its superficial location and reduced respiratory variability, showing a strong inverse correlation with CVP. Additionally, internal jugular vein (IJV) ultrasound has been explored for volume assessment, with changes in IJV cross-sectional area (CSA) reflecting intravascular volume shifts.

Despite these advances, current approaches often rely on single-vein assessments (e.g., IVC alone) or qualitative measures (e.g., fluid responsiveness rather than absolute BV estimation). Moreover, conventional statistical models struggle to capture the complex nonlinear relationships between multiple venous parameters and BV. Machine learning (ML) techniques, such as support vector machines (SVM) and neural networks, have shown promise in integrating high-dimensional ultrasound data for predictive modeling, as evidenced by recent studies achieving AUCs \>0.90 in hemodynamic prediction tasks.

This study aims to develop and validate a quantitative BV prediction model by integrating multiparametric venous ultrasound (IVC, SCV, and IJV) with ML algorithms. By leveraging dynamic measurements (e.g., respiratory variation, CSA, and collapsibility indices) and advanced computational methods, this research seeks to provide a noninvasive, real-time BV estimation tool for clinical decision-making in fluid management. The findings could significantly enhance perioperative and critical care practices by reducing reliance on invasive monitoring and improving hemodynamic stability prediction.


Eligibility criteria: Inclusion Criteria:

* Agree to join this study and sign the informed consent form;
* Age between 18 and 75 years old (inclusive);
* BMI (body mass index) is between 18 and 30 kg/m2;
* American Society of Anesthesiologists (ASA) grades I-II

Exclusion Criteria:

* Preoperative hemoglobin (Hb) \<10g/dl
* Cardiac dysfunction (NYHA class III-IV), respiratory dysfunction (ATS class 2-4), history of liver and kidney dysfunction (such as transaminase / albumin / bilirubin abnormalities, hepatitis history, serum creatinine / urea nitrogen rise, etc.), nervous system abnormalities (those who cannot cooperate due to stroke or its sequelae, Alzheimer, etc.);
* The ultrasonic display of inferior vena cava, internal jugular vein or subclavian vein is extremely poor, venous thrombosis or anatomical abnormalities;
* Multiple injury with chest, abdomen or brain;
* Pregnant woman

Primary outcomes:
measure: Quantitative estimation of blood volume
timeFrame: within 30mins before the surgery
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Shanghai Jiao Tong University Affiliated Sixth People's Hospital
state: Shanghai
Study officials:
References: