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Carotid peak flow velocity variation as a surrogate of aortic peak flow velocity variation in a pediatric population
Federico Cristiani1, Juan Pablo Bouchacourt2, Juan Riva2
1Facultad de Medicina, Centro Hospitalario Pereira Rossell, Universidad de la República, Montevideo, Uruguay. federicocristiani9@gmail.com.
Insights
Carotid peak velocity variation (ΔVpeakCar) is a reliable surrogate for aortic peak velocity variation (ΔVpeakAo) in schoolchildren. However, its accuracy decreases in younger children, indicating age-dependent reliability.
Area of Science:
- Pediatric Anesthesiology
- Cardiovascular Physiology
- Hemodynamic Monitoring
Background:
- Carotid peak velocity variation (ΔVpeakCar) is explored as an alternative to aortic peak velocity variation (ΔVpeakAo) in pediatric patients.
- Children's physiological and anatomical variability necessitates evaluating the age-dependent predictive value of ΔVpeakCar.
- The study hypothesizes that the surrogate capability of ΔVpeakCar for ΔVpeakAo changes across childhood development stages.
Purpose of the Study:
- To assess the concordance between ΔVpeakCar and ΔVpeakAo.
- To evaluate the tracking ability of ΔVpeakCar as a surrogate for ΔVpeakAo across different pediatric age groups.
- To determine the reliability of ΔVpeakCar in pediatric patients aged 0-12 years.
Main Methods:
- Inclusion of pediatric patients aged 0-12 years, stratified into three groups: under 12 months (G1), 12-60 months (G2), and over 60 months (G3).
- Measurement of maximal and minimal aortic and carotid peak flow post-anesthesia induction and mechanical ventilation.
- Statistical analysis including Pearson correlation, linear regression, Bland-Altman analysis, and 4-quadrant/polar vector analysis to assess concordance and tracking.
Main Results:
- Correlation coefficients (r) between ΔVpeakAo and ΔVpeakCar were 0.44 (G1), 0.56 (G2), and 0.85 (G3), with significant p-values (<0.001) for all groups.
- Bland-Altman analysis revealed mean biases of -0.37 (G1), -0.07 (G2), and 0.55 (G3), with decreasing limits of agreement from G1 to G3.
- Concordance rates were high, particularly in older children: 93% (G1), 95% (G2), and 100% (G3).
Conclusions:
- ΔVpeakCar demonstrates good correlation and tracking ability with ΔVpeakAo in schoolchildren (G3).
- The reliability of ΔVpeakCar as a surrogate for ΔVpeakAo is significantly reduced in younger children (G1 and G2).
- Age-specific considerations are crucial when utilizing ΔVpeakCar for hemodynamic monitoring in pediatric populations.
Background:
Carotid peak velocity variation (ΔVpeakCar) is an alternative to aortic peak velocity variation (ΔVpeakAo) and has been used in the pediatric population. Children's physiology and anatomy are heterogeneous throughout their growth. For this reason, the predictive value of ΔVpeakCar as a surrogate of ΔVpeakAo can vary at different ages. We hypothesize that the ability of ΔVpeakCar as a surrogate of ΔVpeakAo changes throughout childhood.
Aim:
Analyze the concordance and the tracking ability of ΔVpeakCar and the ΔVpeakAo at different stages of development.
Methods:
Patients from 0 to 12 years were included. Three groups were defined: under 12 months (G1), between 12 and 60 months (G2), and over 60 months (G3). After anesthesia induction and mechanical ventilation, maximal and minimal aortic and carotid peak flow were measured. ΔVpeakAo and ΔVpeakCar were calculated. Pearson test and simple linear regression were performed. Bland-Altman analysis was performed to determine concordance. 4-quadrant analysis was used, followed by polar analysis of the vectors, to complement the concordance analysis and determine the tracking ability of ΔVpeakCar to surrogate ΔVpeakAo.
Results:
Sixty-seven patients were enrolled. 22 (32.4%) patients in G1, 21 (31.3%) in G2 and 24 (35.8%) in G3. The determination coefficient (r) between ΔVpeakAo and ΔVpeakCar in G1 was 0.44 (p < 0.001) with a slope value of 0.61 (SE = 0.11; 95% CI:0.3-0.91). In G2, r2 = 0.56 (p < 0.001) with a slope value of 0.59 (SE = 0.14; 95% CI:0.35-0.82); and in G3, r2 = 0.85 (p < 0.001) with a slope value of 1.11 (SE = 0.10; 95% CI:0.91-1.31). Bland-Altman analysis showed to G1 a mean bias of -0.37 (LOA - 7.87 to 7.53), to G2 -0.07 (LOA - 7.37 to 7.23) and G3 0.55 (-3.81 to 4.91). Concordance rates were 100% in G3, 95% in G2, and 93% in G1.
Conclusions:
ΔVpeakCar showed good correlation and tracking ability with ΔVpeakAo in schoolchildren. In younger children, it was not reliable enough.
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