12+

PHARMACOLOGICAL CORRECTION OF INTERCEPT HEMODYNAMICS IN ACUTE KIDNEY DAMAGE (PART 1)

Introduction: Development of vasoconstriction of kidney arterioles and reduction of renal blood flow is one of the main mechanism of acute kidney injury (AKI) formation. Methods for evaluation of intrarenal hemodynamics status are rather limited. Evident interest for the clinician is the possibility of rapid and non-invasive assessment of renal hemodynamics using the dopplerography method. The method makes it possible to visualize the kidney vessels and conduct a qualitative and quantitative evaluation of renal blood flow. Peculiarities of disturbed blood flow in the kidneys can determine the individuality of pharmacological correction and intensive care in patients with AKI.

Objectives: The aim of our study was to reveal the peculiarities of renal blood flow disorders, depending on the variant, stage and severity of AKI; to evaluate the opportunities of individual pharmacological correction and intensive care in studied AKI patients.

Methods: A prospective nonrandomized study. Inclusion criteria: patients with prerenal, renal and subrenal AKI in the stage of oligoanuria and restoration of diuresis; exclusion criteria: AKI patients after cardiac surgery and operations on the large vessels. 250 ICU patients with prerenal (130), renal (81) and subrenal (39) AKI were examinedby ultrasound dopplerography.

Results and discussion: Сomparative data of intrarenal blood flow dopplerographic examination in patients with various variants of AKI are presented. All patients initially, at admission in ICU revealed disorders of renal hemodynamics, the severity of which was different depending on the AKI module. During intensive care, as diuresis  was restored, the parameters of renal blood flow improved. The speed and completeness of hemodynamics recovery was determined by both the modulus and severity of AKI. The heterogeneity of the prerenal module of AKI was determined due  the data of renal blood flow and the rate of restoration of diuresis. So, we divided prerenal module in 2 groups: 1) real (genuine) prerenal AKI and 2) AKI prerenal for reason. The expediency of such selection is substantiated. It was established that resistive index (RI) in the main trunk of the renal artery is an early criterion of severity of AKI (F), and its dynamics during intensive care makes it possible to diagnose the transformation of AKI in chronic renal failure (CRF). Strong direct correlation RI with the duration of oligoanuria (r = 0.72), which is the main retrospective marker of the severity of AKI, was revealed yet upon admission to the ICU. It was found that the peculiarities of renal hemodynamics disturbance are an important criterion for differential diagnosis of the AKI module: renal dopplerometry data significantly (p <0.05) differ in prerenal, renal and subrenal AKI. It was found that the risk of death is statistically significant decreased (p = 0.001), OR = 0.009 (95% CI 0.001 - 0.050) when performing individual pharmacological correction (nephroprotection) in the ICU, based on the advanced diagnosis of the AKI module.

Conclusions: The revealed peculiarities of renal blood flow disorders in patients with different AKI modules point to the need for an individual pharmacological correction and intensive care in AKI patients.

Иллюстрации

Fig. 1. RI and oliguria (in days) correlation link

Fig. 2. Comparative characteristics of Vps in the main trunk of the renal artery with prerenal (1, 2), renal (3, 4) and subrenal (5, 6) AKI in oliguria.

Note: Var 1, 3, 5 - right kidney; Var 2, 4, 6 - left kidney.Vps right versus left kidneys P1-2, 3-4, 5-6 ≥0.05

 

Fig. 3. Comparative characteristic of RI in the main trunk of the renal artery in prerenal (1,2), renal (3,4) and subrenal (5,6) AKI in oliguria.

Note: Var 1,3,5-right kidney; Var 2,4,6-left kidney.Vps right versus left kidneys P1-2, 3-4, 5-6 ≥0.05

Fig. 4. Variation of Vps in the prerenal AKI group

 Fig. 5. Variation of RI in the prerenal AKI group

Fig. 6. Variation of Vps in the renal AKI group

Fig. 7. Variation of RI indices in the renal AKI group

Fig. 8-11. Restoration of blood flow in the main trunk of the renal artery

Fig. 12. ROC-curves of models for predicting the risk of death (0 - the optimal values of sensitivity and specificity of models are indicated, the curves are constructed on all 106 cases): 1 - linear neural network model built on all 16 factor attributes, 2 - linear neural network model built on 2marked factor characteristics.

Table 1. Coefficients of the 3-factor model for predicting the risk of a lethal outcome (logistic regression model)

Notes: * – the difference from 0 is statistically significant, p<0,05.

DOI: 10.18413/2313-8971-2017-3-3-110-120
Количество просмотров: 1167 (смотреть статистику)
Количество скачиваний : 445
Полный текст (HTML)Полный текст (PDF)К списку статейВ раздел
  • Комментарии
  • Список литературы

Пока никто не оставил комментариев к этой публикации.
Вы можете быть первым.

Оставить комментарий: