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The impact of haemodialysis arteriovenous fistula on haemodynamic parameters of the cardiovascular system

View Article: PubMed Central - PubMed

ABSTRACT

Background: Satisfactory vascular access flow (Qa) of an arteriovenous fistula (AVF) is necessary for haemodialysis (HD) adequacy. The aim of the present study was to further our understanding of haemodynamic modifications of the cardiovascular system of HD patients associated with an AVF. The main objective was to calculate using real data in what way an AVF influences the load of the left ventricle (LLV).

Methods: All HD patients treated in our dialysis unit and bearing an AVF were enrolled into the present observational cross-sectional study. Fifty-six patients bore a lower arm AVF and 30 an upper arm AVF. Qa and cardiac output (CO) were measured by means of the ultrasound dilution Transonic Hemodialysis Monitor HD02. Mean arterial pressure (MAP) was calculated; total peripheral vascular resistance (TPVR) was calculated as MAP/CO; resistance of AVF (AR) and systemic vascular resistance (SVR) are connected in parallel and were respectively calculated as AR = MAP/Qa and SVR = MAP/(CO − Qa). LLV was calculated on the principle of a simple physical model: LLV (watt) = TPVR·CO2. The latter was computationally divided into the part spent to run Qa through the AVF (LLVAVF) and that part ensuring the flow (CO − Qa) through the vascular system. The data from the 86 AVFs were analysed by categorizing them into lower and upper arm AVFs.

Results: Mean Qa, CO, MAP, TPVR, LLV and LLVAVF of the 86 AVFs were, respectively, 1.3 (0.6 SD) L/min, 6.3 (1.3) L/min, 92.7 (13.9) mmHg, 14.9 (3.9) mmHg·min/L, 1.3 (0.6) watt and 19.7 (3.1)% of LLV. A statistically significant increase of Qa, CO, LLV and LLVAVF and a statistically significant decrease of TPVR, AR and SVR of upper arm AVFs compared with lower arm AVFs was shown. A third-order polynomial regression model best fitted the relationship between Qa and LLV for the entire cohort (R2 = 0.546; P < 0.0001) and for both lower (R2 = 0.181; P < 0.01) and upper arm AVFs (R2 = 0.663; P < 0.0001). LLVAVF calculated as % of LLV rose with increasing Qa according to a quadratic polynomial regression model, but only in lower arm AVFs. On the contrary, no statistically significant relationship was found between the two parameters in upper arm AVFs, even if mean LLVAVF was statistically significantly higher in upper arm AVFs (P < 0.0001).

Conclusions: Our observational cross-sectional study describes statistically significant haemodynamic modifications of the CV system associated to an AVF. Moreover, a quadratic polynomial regression model best fits the relationship between LLVAVF and Qa, but only in lower arm AVFs.

No MeSH data available.


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A third-order polynomial regression model best fitted the relationships between Qa and LLV, respectively, in lower arm AVFs (filled diamond, continuous line = best fitted regression line) and upper arm AVFs (filled square, dotted line = best fitted regression line).
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SFW063F3: A third-order polynomial regression model best fitted the relationships between Qa and LLV, respectively, in lower arm AVFs (filled diamond, continuous line = best fitted regression line) and upper arm AVFs (filled square, dotted line = best fitted regression line).

Mentions: Demographic, clinical and haemodynamic data for the 86 patients enrolled into the study are reported in Table 1. Data are given for both the entire cohort and for their categorization into lower and upper arm AVFs. Fifty-six patients bore a lower arm AVF and 30 an upper arm AVF (20 brachio-basilic and 10 brachio-cephalic). The Student's t-test for unpaired data showed a statistically significant difference among the three groups as far as CO, LLV, LLVAVF and TPVR are concerned (Table 1). The Mann–Whitney U test showed a statistically significant difference among the three groups as far as dialysis duration, AVF vintage, Qa, CPR, AR and SVR are concerned (Table 1). Figure 1 illustrates the haemodynamic modifications of the CV system of HD patients associated with an AVF (Figure 1 is reproduced as a colour image in the Supplementary data). A third-order polynomial regression model best fitted the relationship between Qa and CO for the entire cohort of 86 patients (y = 0.527x3 − 2.224x2 + 4.031x + 4.209; R2 = 0.386; P < 0.0001) and for both lower (y = 0.447x3 − 1.912x2 + 3.286x + 3.913; R2 = 0.392; P < 0.0001) and upper arm AVFs (y = 0.476x3 − 2.002x2 + 3.913x + 3.964; R2 = 0.426; P < 0.0001) (results not shown in any table or figure). Similarly, a third-order polynomial regression model best fitted the relationship between Qa and LLV for the entire cohort of 86 patients (Figure 2) and for both lower and upper arm AVFs (Figure 3).Table 1.


The impact of haemodialysis arteriovenous fistula on haemodynamic parameters of the cardiovascular system
A third-order polynomial regression model best fitted the relationships between Qa and LLV, respectively, in lower arm AVFs (filled diamond, continuous line = best fitted regression line) and upper arm AVFs (filled square, dotted line = best fitted regression line).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036899&req=5

SFW063F3: A third-order polynomial regression model best fitted the relationships between Qa and LLV, respectively, in lower arm AVFs (filled diamond, continuous line = best fitted regression line) and upper arm AVFs (filled square, dotted line = best fitted regression line).
Mentions: Demographic, clinical and haemodynamic data for the 86 patients enrolled into the study are reported in Table 1. Data are given for both the entire cohort and for their categorization into lower and upper arm AVFs. Fifty-six patients bore a lower arm AVF and 30 an upper arm AVF (20 brachio-basilic and 10 brachio-cephalic). The Student's t-test for unpaired data showed a statistically significant difference among the three groups as far as CO, LLV, LLVAVF and TPVR are concerned (Table 1). The Mann–Whitney U test showed a statistically significant difference among the three groups as far as dialysis duration, AVF vintage, Qa, CPR, AR and SVR are concerned (Table 1). Figure 1 illustrates the haemodynamic modifications of the CV system of HD patients associated with an AVF (Figure 1 is reproduced as a colour image in the Supplementary data). A third-order polynomial regression model best fitted the relationship between Qa and CO for the entire cohort of 86 patients (y = 0.527x3 − 2.224x2 + 4.031x + 4.209; R2 = 0.386; P < 0.0001) and for both lower (y = 0.447x3 − 1.912x2 + 3.286x + 3.913; R2 = 0.392; P < 0.0001) and upper arm AVFs (y = 0.476x3 − 2.002x2 + 3.913x + 3.964; R2 = 0.426; P < 0.0001) (results not shown in any table or figure). Similarly, a third-order polynomial regression model best fitted the relationship between Qa and LLV for the entire cohort of 86 patients (Figure 2) and for both lower and upper arm AVFs (Figure 3).Table 1.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Satisfactory vascular access flow (Qa) of an arteriovenous fistula (AVF) is necessary for haemodialysis (HD) adequacy. The aim of the present study was to further our understanding of haemodynamic modifications of the cardiovascular system of HD patients associated with an AVF. The main objective was to calculate using real data in what way an AVF influences the load of the left ventricle (LLV).

Methods: All HD patients treated in our dialysis unit and bearing an AVF were enrolled into the present observational cross-sectional study. Fifty-six patients bore a lower arm AVF and 30 an upper arm AVF. Qa and cardiac output (CO) were measured by means of the ultrasound dilution Transonic Hemodialysis Monitor HD02. Mean arterial pressure (MAP) was calculated; total peripheral vascular resistance (TPVR) was calculated as MAP/CO; resistance of AVF (AR) and systemic vascular resistance (SVR) are connected in parallel and were respectively calculated as AR = MAP/Qa and SVR = MAP/(CO &minus; Qa). LLV was calculated on the principle of a simple physical model: LLV (watt) = TPVR&middot;CO2. The latter was computationally divided into the part spent to run Qa through the AVF (LLVAVF) and that part ensuring the flow (CO &minus; Qa) through the vascular system. The data from the 86 AVFs were analysed by categorizing them into lower and upper arm AVFs.

Results: Mean Qa, CO, MAP, TPVR, LLV and LLVAVF of the 86 AVFs were, respectively, 1.3 (0.6 SD) L/min, 6.3 (1.3) L/min, 92.7 (13.9) mmHg, 14.9 (3.9) mmHg&middot;min/L, 1.3 (0.6) watt and 19.7 (3.1)% of LLV. A statistically significant increase of Qa, CO, LLV and LLVAVF and a statistically significant decrease of TPVR, AR and SVR of upper arm AVFs compared with lower arm AVFs was shown. A third-order polynomial regression model best fitted the relationship between Qa and LLV for the entire cohort (R2 = 0.546; P &lt; 0.0001) and for both lower (R2 = 0.181; P &lt; 0.01) and upper arm AVFs (R2 = 0.663; P &lt; 0.0001). LLVAVF calculated as % of LLV rose with increasing Qa according to a quadratic polynomial regression model, but only in lower arm AVFs. On the contrary, no statistically significant relationship was found between the two parameters in upper arm AVFs, even if mean LLVAVF was statistically significantly higher in upper arm AVFs (P &lt; 0.0001).

Conclusions: Our observational cross-sectional study describes statistically significant haemodynamic modifications of the CV system associated to an AVF. Moreover, a quadratic polynomial regression model best fits the relationship between LLVAVF and Qa, but only in lower arm AVFs.

No MeSH data available.


Related in: MedlinePlus