Diagnostic accuracy of subclavian vein versus inferior vena cava collapsibility index for predicting postinduction hypotension: An observational study Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.4103/sja.sja_222_24
Background: Hypotension following induction of general anesthesia (GA) is commonly observed. Ultrasound (US) measurement of collapsibility index (CI) of the inferior vena cava (IVC) for predicting postinduction hypotension has been studied. As there is limited data available comparing the diagnostic accuracy of subclavian vein (SCV) versus IVC-CI, we performed this observational study. Methods: A total of 132 adult patients scheduled for elective surgery under GA were enrolled. US measurements of three readings of maximum and minimum diameters of SCV and IVC were recorded during both quiet and deep breathing, and the mean of three values was calculated. CI was derived using the formula: (dmax – dmin) × 100/dmax. Subsequently, GA was administered using standard technique, irrespective of the findings of SCV and IVC measurements. The administered drugs and dosage were recorded. Hemodynamic parameters were collected at baseline and then at every minute for the first 20 min. The primary objective was to compare the diagnostic accuracies of SCV-CI and IVC-CI for prediction of postinduction hypotension during quiet breathing. The secondary objectives were to compare the diagnostic accuracies during deep breathing and find the correlation between IVC-CI and SC-CI during quiet and deep breathing, incidence of hypotension, and time required to acquire US images. Results: Fifty-seven patients developed postinduction hypotension. During quiet breathing, SCV-CI ≥10% had a sensitivity of 68% and specificity of 56% (area under curve [AUC] [95% confidence interval {CI}] of 0.659 [0.56–0.75]; P = 0.002), while IVC-CI ≥34% had a sensitivity of 70% and specificity of 59% (AUC [95% CI] of 0.672 [0.58–0.76]; P = 0.001) for prediction of postinduction hypotension. During deep breathing, both SCV-CI and IVC-CI had moderate accuracy ( P = 0.001 for both). Pearson’s correlation showed a significant positive correlation between SCV-CI and IVC-CI with a correlation coefficient (r) of 0.313 during quiet breathing and 0.379 during deep breathing ( P < 0.001). The time required for acquiring US images was significantly less for SCV compared to IVC during both quiet and deep breathing ( P < 0.001 for both). Conclusion: Both SCV-CI and IVC-CI were found to have good and comparable diagnostic accuracy for the prediction of postinduction hypotension. We also found a significant positive correlation between SCV-CI and IVC-CI. In comparison to IVC, US scanning of SCV took lesser time to acquire the images.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4103/sja.sja_222_24
- OA Status
- diamond
- Cited By
- 3
- References
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4403063053Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4103/sja.sja_222_24Digital Object Identifier
- Title
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Diagnostic accuracy of subclavian vein versus inferior vena cava collapsibility index for predicting postinduction hypotension: An observational studyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-01Full publication date if available
- Authors
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Gaurav Chaudhary, Sadik Mohammed, Ghansham Biyani, Swati Chhabra, Pradeep K. Bhatia, Manoj Kamal, Rakesh Kumar, Kamlesh KumariList of authors in order
- Landing page
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https://doi.org/10.4103/sja.sja_222_24Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.4103/sja.sja_222_24Direct OA link when available
- Concepts
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Medicine, Observational study, Inferior vena cava, Radiology, Cardiology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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