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Background <p>This&#xa0;study aimed to derive and validate a prediction rule for estimating the central venous pressure (CVP) from the inferior vena cava collapsibility index (IVCCI) in adults.</p> Methods <p>Five hundred forty paired CVP-IVCCI measures were obtained from 180 adults. The CVP was measured via a central venous catheter connected to a pressure transducer, and the IVCCI was estimated by abdominal ultrasound. The dataset was randomly split at the patient level into training (121 patients, 363 observations) and validation (59 patients, 177 observations) samples. A generalized estimating equation for repeated measures was used to fit a prediction rule from the training sample, which was validated on the validation subset.</p> Results <p>The model showed promising performance with a mean absolute error of 2.13 mmHg and 75.7% of predictions falling within ± 3 mmHg of the true values. The limits of agreement ranged from –6.0 to + 4.3 mmHg, 85.9% of predictions fell within a prespecified maximum accepted difference of ± 4 mmHg, and the direction of change was correctly predicted in 65.3% of instances. Calibration analysis showed the model tended to systematically underestimate high values and vice versa, but recalibration by applying a shrinkage factor worsened the model’s performance.</p> Conclusions <p>The suggested rule exhibits promising performance for most instances, rendering it useful for non-invasive trend monitoring and triaging purposes. However, single-point estimates carry considerable uncertainty that precludes their use for definitive, high-stakes clinical decisions. Validation on larger cohorts and recalibration utilizing more elaborate modeling methods are recommended before it is adopted for regular use.</p> Trial registration <p>The trial was prospectively registered at the clinicaltrials.gov registry (<a href="https://register.clinicaltrials.gov/">https://register.clinicaltrials.gov/</a>, registration number: NCT06166875, registration date: 4 December 2023).</p>

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Development and validation of a proposed rule for estimating central venous pressure from inferior vena cava dynamics: a clinical prediction model study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Sameh M. Hakim,
  • Nermeen S. Ahmed,
  • Sahar M. Talaat,
  • Sana F. Wasfy,
  • Adham M. Haggag

摘要

Background

This study aimed to derive and validate a prediction rule for estimating the central venous pressure (CVP) from the inferior vena cava collapsibility index (IVCCI) in adults.

Methods

Five hundred forty paired CVP-IVCCI measures were obtained from 180 adults. The CVP was measured via a central venous catheter connected to a pressure transducer, and the IVCCI was estimated by abdominal ultrasound. The dataset was randomly split at the patient level into training (121 patients, 363 observations) and validation (59 patients, 177 observations) samples. A generalized estimating equation for repeated measures was used to fit a prediction rule from the training sample, which was validated on the validation subset.

Results

The model showed promising performance with a mean absolute error of 2.13 mmHg and 75.7% of predictions falling within ± 3 mmHg of the true values. The limits of agreement ranged from –6.0 to + 4.3 mmHg, 85.9% of predictions fell within a prespecified maximum accepted difference of ± 4 mmHg, and the direction of change was correctly predicted in 65.3% of instances. Calibration analysis showed the model tended to systematically underestimate high values and vice versa, but recalibration by applying a shrinkage factor worsened the model’s performance.

Conclusions

The suggested rule exhibits promising performance for most instances, rendering it useful for non-invasive trend monitoring and triaging purposes. However, single-point estimates carry considerable uncertainty that precludes their use for definitive, high-stakes clinical decisions. Validation on larger cohorts and recalibration utilizing more elaborate modeling methods are recommended before it is adopted for regular use.

Trial registration

The trial was prospectively registered at the clinicaltrials.gov registry (https://register.clinicaltrials.gov/, registration number: NCT06166875, registration date: 4 December 2023).