6 курс / Нефрология / Острое_повреждение_почек_после_паратиреоидэктомии_по_поводу_первичного

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necessary, with a diuresis diary. Patients who were diagnosed with AKI, received conservative treatment according to the recommendations [201].

2.6. Statistical analysis.

To assess whether the quantitative variables were distributed normally the Shapiro-

Wilk test was used. Normally distributed variables were described as mean ± standard deviation (M±SD). Variables with non-normal distribution were described using median (Me) and the first and the third quartiles (Q1-Q3). Median difference was estimated using the Hodges-Lehmann method with a 95% confidence interval (CI) calculation. Nominal variables were described with absolute values and percentages. Means of normally distributed samples of the quantitative data were compared using the Student's t-test or the Welch’s t-test (in case of samples with unequal variances). For independent samples comparisons in case of non-normal distribution the Mann-Whitney U-test was used. Nominal data was compared using the Pearson’s chi-squared (χ2) test. To assess the significance of the differences the Fisher’s exact test was used for less than 5 observations in any of the 2 x 2 contingency table cell. A relative risk (RR) and an odds ratio (OR) with 95% CI calculated were used as quantitative measure of effect for relative parameters comparisons.

The Spearman's rank correlation coefficient – a nonparametric method, was used to assess a correlation of quantitative non-normally distributes variables. The correlation coefficient ρ, its 95%CI and p-value were calculated. The correlation coefficient ρ, its

95% CI and p-value were calculated. The correlation coefficient ρ was interpreted according to the Chaddock scale.

Quantitative variables diagnostic ability was assessed using ROC analysis. The area under the ROC curve (AUC ROC) with 95% CI was used for the classifier quality quantitative assessment. A value with the maximum Youden’s index (Youden's J statistic) was chosen as the optimal cut-off point of a quantitative indicator providing the best discriminatory ability. For this point a sensitivity and a specificity were calculated; OR was calculated as a measure of contingency. For all the estimates 95% CI were calculated.

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Screening balance accuracy (SBA) was used to assess the informativeness of the selected cut-off point.

Multivariate analysis of risk factors for AKI was performed using binary logistic regression. The particular method was chosen since the dependent variable was binary in nature, with this, the independent variables were both nominal and quantitative ones. The model is expressed mathematically as follows:

1= 1 + –

= 0 + 1 1 + 2 2 + … + ,

where P is the probability of the studied outcome, x1... xn –risk factors values measured in nominal, ordinal or quantitative scale, b0... bn – regression coefficients.

The goal of the multivariate analysis first stage was not to predict the risks, but to assess the certain factors contribution taking to account other covariates, thus, all predictors were forced to stay in a model. The OR estimate (regression coefficients exponent), its 95%CI and p-value were provided.

For the model validation standardized residues were evaluated and a test for significant multicollinearity was performed using the variance inflation factor. The obtained model statistical significance was determined using the chi-squared test. The model goodness of fit (compliance with the actual data) was assessed using the HosmerLemeshow test. TThe dependent variable explained variation was determined based on the Nagelkerke pseudo-R2 value (RN).

At the second stage of multifactorial analysis, we were faced with the task of predicting the risk of developing AKI based on the value of predictors. To do this, the most significant factors identified in the previous study stages were included in the model as predictors with their subsequent sequential removing from the complete model to simplify it taking into account the Wald's statistics. Then the model was again validated.

The ROC analysis of the model predicted values was performed for the most effective reverse patients classification (i.e. to assign them either in AKI high or low risk group). The predicted value of AKI probability providing the best discriminatory ability based on the Youden’s index was chosen as the cut-off point.

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In order to establish approaches for AKI risk reduction in patients classified to the high-risk group, it was important to compare the predictors contribution to AKI probability. However, different ranges of the quantitative variables values did not allow to directly compare a contribution of predictor variability per unit to the AKI risk by analyzing obtained OR when using the predictors natively. To overcome this limitation, a procedure of predictor standardization was applied:

Standardized predictor value= − ,

where is the observed predictor value, is the mean predictor value, SD is the standard deviation.

Thus, the effect on AKI probability was estimated with different predictors expressed in the same units (i.e. the change in AKI probability per unit of predictor variability).

To assess the before and after surgery PTH level difference (∆PTH) correlation with other quantitative variables, linear regression was used.

The statistical analysis and the results visualization were conducted in GraphPad Prism v.8.0.1 (GraphPad Software) and IBM SPSS Statistics v.23 (IBM Corporation). The two-sided significance level was applied. The p-value of <0.05 was considered statistically significant.

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CHAPTER 3. POST PARATHYROIDECTOMY FOR PRIMARY

HYPERPARATHYROIDISM ACUTE KIDNEY INJURY PREVALENCE AND

RISK FACTORS

Selective parathyroidectomy is a relatively minimally invasive short-term surgical intervention devoid of such traditional AKI risk factors for as controlled hypotension, use of cardiopulmonary bypass system, large doses of X-ray contrast agents. This allows reducing inpatient period after PTx in a specialized endocrine surgery hospital to 1-2 days, moreover outpatient surgery is practiced in some countries. At the same time, this also leads to low concern of postoperative AKI despite traditional risk factors for the complication exist initially. Thus, the first study objective was to analyze the post PTx AKI incidence, as well as to identify its risk factors.

3.1. Acute kidney injury prevalence

The prevalence of post PTx AKI is difficult to assess due to the lack of routine renal function monitoring in the postoperative period. High postoperative AKI incidence was found in this study in patients operated for PHPT comprising 36.6% of cases (106/290). In most AKI patients 93.39% (99/106) disease stage 1 was observed, AKI stage 2 was observed in 5.66% of patients (6/106), AKI stage 3 - in 1 of 106 patients (0.9%).

Women predominated among the operated patients comprising 93.8% (272/290), the proportion of men was 6.2% (18/290). Such gender distribution is natural and can be explained by the specific PHPT epidemiology as the disease affects mainly postmenopausal women. The median age in all patients was 59 years [Q1-Q3: 50; 67], in women - 59.5 [Q1-Q3: 51; 67], in men - 56.5 [Q1-Q3: 37,25; 60,75]. Thus, the age differences in men and women were close to the statistical significance borderline, nevertheless, were statistically significant, p = 0.0381.

The prevalence of AKI was 36.03% (98/272) in women and 44.44% (8/18) in men. There was no difference in AKI incidence by gender (RR=0.81 [95%CI 0.52; 1.49], OR=0.7 [95%CI 0.67; 1.82], p=0.64).

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It is well known the AKI risk increases with age. This is due to the natural agerelated GFR decrease, as well as a number of other interrelated reasons: unfavorable comorbidities, use of many concomitant medicines, reduced functional status in elderly patients, etc. In the study sample AKI was more common in patient group of older age (60 years or more consider as old age, according to WHO classification [203]): RR=1.4 [95%CI 1.04; 1.93], OR=1.72 [95%CI 1.06; 2.83], p=0.0265. Median age in patients with AKI was 62.5 years [Q1-Q3: 55; 69], without AKI - 58 years [Q1-Q3: 48; 66], p = 0.0053. (Figure 3.1). The ROC curve was obtained to assess correlation of age and AKI probability (Figure 3.19). The area below the age resulting ROC curve was 0.598 [95%CI 0.53; 0.67], p=0.0053. The age cut-off point (i.e. the point providing the highest sensitivity and specificity) was 56.5 years. With this age cut-off point, the sensitivity (Se) was 0.698 [95%CI 0.61; 0.78], the specificity (Sp) was 0.462 [95%CI 0.39; 0.53], the screening balanced accuracy (SBA) was 0.58, p=0,0075. Thus, the patients of 57 full years of age and older have increased AKI risk: RR=1.56 [95%CI 1.1; 2.2], OR=1.99 [95%CI 1.2; 3.2].

Age, y			observations
			n of
	yes	no		under 59 60 and older

AKI

AKI

No AKI

Figure 3.1. Left: age comparison in the patients groups (AKI or no AKI) with the Mann-Whitney test. The medians, the first and third quartiles are given, the shape of the figures reflects the variable’s distribution. Right: AKI prevalence in different age groups.

No statistically significant association between AKI severity and age group was observed: AKI stage 1 was observed in 88.89% of cases (40/45) in patients group of under 60 years old, AKI stage 2 and 3 - in 11.11% (5/45); AKI stage 1 was observed in 96.72%

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(59/61) in patients group of 60 or more years old, and AKI stage 2 and 3 AKI in 3.28% (2/61) of these patients (RR = 0.9 [95%CI 0.79; 1.02], OR=0.27 [95%CI 0.053; 1.39], p=0.132).

In most cases, AKI proceeded without oliguria development - 90.6% of cases (96/106), the oliguric form occurred in 9.4% of cases (10/106).

In women, the non-oliguric AKI form was observed in 91.8% of cases (90/98), the oliguric form - in 8.2% of cases (8/98); in men, the non-oliguric form incidence was 75% (6/8), the oliguric form incidence - 25% (2/8). There was no statistical significance of AKI (oliguric/non-oliguric form) and gender correlation - RR=0.33 [95%CI 0.1; 1.3], OR=0.27 [95%CI 0.05; 1.5], p=0.165.

No statistically significant correlation was determined for the AKI form and age: the non-oliguric AKI from incidence was 88.89% (40/45) in patients group of under 60 years old, the oliguric AKI form - 11.11% (5/45); the non-oliguric and oliguric AKI from incidences in patients group of 60 or more years old were 91.8% (56/61) and 8.2% (5/61), respectively (RR = 1.36 [95%CI 0.44; 4.1], OR=1.4 [95%CI 0.42; 4.7], p=0.7).

In most cases AKI stage 1 was not accompanied by diuresis decrease: oliguria was observed in 5% (5/99) of patients in this group, diuresis remained intact in 95% of patients (94/99). Naturally, oliguria was more common in patients with more severe AKI (stage 2 and 3) and observed in 5 of 7 (71.4%) cases, while intact diuresis occurred in 2 of 7 (28.6%) patients in this group. The risk of oliguria in case of AKI progression to stage 2- 3 was significantly higher: RR = 14.1 [95%CI 5.07; 35.5], OR=47 [95%CI 6.4; 252.3], p<0.0001. (Figure 3.2)

n of observations

		oliguria
Fischer’s exact
test		no oliguria

stage 1	stage 2+3
	AKI

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Figure 3.2. Frequency of oliguria in different AKI stages.

Only the patients undergone the surgery at the beginning of the week (Monday to Wednesday) were included in the inpatient period duration analysis - the hospitalization duration was mainly caused by paramedical reasons in other cases. The median duration of postoperative period in patients with AKI and without AKI was 1 day [Q1-Q3: 1; 2, 1 to 8] and 1 day [Q1-Q3: 1; 1, 1 to 7], respectively, p<0.0001. Thus, despite the majority of patients in both groups were discharged a day after the surgery, the number of the inpatient days was statistically significantly higher in AKI patients.

3.2. Acute kidney injury risk factors

The risk factors assessed can be divided into: 1) specific factors directly related to PHPT and surgery (according to this study); 2) factors related to the initial kidney status; 3) premorbid factors.

3.2.1. Premorbid factors

The median body mass index (BMI) of patients with AKI was slightly higher (28.25 kg/m2 [Q1-Q3: 25; 33], 17.7 to 53) compared to patients without AKI (27.3 kg/m2

[Q1-Q3: 24.2; 30.1], 16.3 to 43.8), p = 0.0336 (Figure 3.3). Although the differences were formally statistically significant, the clinical significance of this correlation is not obvious: the median difference was only 1.4 kg/m2 [95%CI 0.1; 2.7]. The ROC curve was obtained to assess correlation of BMI and AKI probability (Figure 3.19). The area under the obtained BMI ROC curve was 0.575 [95%CI 0.51; 0.64], p=0.0336. The BMI cut-off point was 27.8 kg/m2.With the selected cut-off point, BMI Se was 0.546 [95%CI 0.47; 0.62], Sp was 0.604 [95%CI 0.51; 0.69], SBA was 0.575, p = 0.0138. The patients with BMI greater than 27.8 kg/m2 have the higher AKI risk: RR=1.28 [95%CI 1.05; 1.5], OR=1.84 [95%CI 1.1; 2.9].

No statistically significant association of AKI incidence and BMI groups (according to the WHO classification [203]: BMI up to 24.9 kg/m2 – normal weight, from 25 to 29.9 kg/m2 - overweight, above 30 kg/m2 - obesity) was observed (p = 0.2558). The

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proportion of obese patients among the patients with AKI was slightly higher compared to those without AKI, 38.7% (41/106) vs. 30.1% (55/183) of cases, respectively, however the obesity itself had no statistically significant impact to the AKI risk (RR=1.16 [95%CI 0.96; 1.43], OR=1.47 [95%CI 0.89; 2.4], p=0.1336).

BMI, kg/m2

yes no AKI

Figure 3.3. BMI comparison in the patients’ groups with and without AKI (MannWhitney test). The medians, the first and third quartiles are given, the shape of the figures reflects the variable’s distribution.

Comorbidities severity expressed in CIRS scores statistically significantly impact the AKI risk: the median score in groups with and without AKI were 8 points [Q1-Q3: 6; 9, from 2 to 15] and 7 points [Q1-Q3: 5; 8, from 2 to 14], respectively, p=0.0028 (Figure 3.4). The observed differences prompted a more detail examination of individual comorbidity contribution to the AKI risk.

Comorbidity, CIRS score

yes no AKI

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Figure 3.4. Comorbidity comparison, assessed in CIRS score, in patients groups with or without AKI (Mann-Whitney test). The medians, the first and third quartiles are given, the shape of the figures reflects the variable’s distribution.

Arterial hypertension was observed in 77.4% (82 of 106) of patients with AKI and in 64.1% (118 of 184) of patients without AKI - Figure 3.5. Hypertension anamnesis significantly increased the AKI risk: RR = 1.54 [95%CI 1.07; 2.28], OR = 1.91 [95%CI 1.1; 3.3], p = 0.019. More than half of all patients with AH (66%, 132 of 200) was using ACEi and ARB for blood pressure control. The use of ACEi/ARB increased the AKI risk: RR=1.54 [95%CI 1.14; 2.1], OR=1.98 [95%CI 1.2; 3.2], p=0.0053. However, a more detailed analysis of AKI association with ACEi/ARB intake in a subgroup of patients with hypertension showed no statistical significance for the therapy taken impact on the AKI risk: RR =1.2 [95%CI 0.94; 1.5], OR=1.58 [95%CI 0.86; 2.9], p=0.14.

n of observations					n of observations
							yes	no
		yes	no
		Hypertension					ACEi/ARB intake

AKI

No AKI

Figure 3.5. The incidence of AKI depending on concomitant hypertension in patients (left), on concomitant ACEi/ARB intake (right).

No statistically significant association was observed for the AKI risk and concomitant calcium channel blockers (p = 0.1616), beta-blockers (p = 0.1229), statins (p = 0.2915), antiplatelet medications (p = 0.2614) and metformin (p = 0.3317) intake. The proportion of patients taking diuretics was slightly higher among patients with AKI compared to those without AKI: 26.4% (28/106) vs. 17.4% (32/184), however, no

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statistical significance was shown (RR=1.38 [95%CI 0.98; 1.87], OR=1.7 [95%CI 0.96; 2.99], p=0.0677).

Diabetes mellitus (DM) and coronary artery disease (CAD) in patients increased the AKI risk with no statistical significance: RR = 1.17 [95%CI 0.72; 1.73], OR = 1.29 [95%CI 0.62; 2.7], p = 0.51 for each of the diseases. In addition, no association of preexisting CKD and the AKI risk was identified, p = 0.7318.

Anemia, a known risk factor, slightly increased the AKI risk in the study population: RR=1.6 [95%CI 1.05; 2.2], OR=2.36 [95%CI 1.1; 5.3], p=0.0313 – Figure 3.6. Despite this, hemoglobin the patient groups with and without AKI differed slightly: 135 g/L [Q1-Q3: 127; 146] vs 138 g/L [Q1-Q3: 131; 146] respectively, p = 0.065. The median difference was 3 g/L [95%CI 0; 6].

			of observations
Hb level, g/l
			n
	yes	no		yes	no
		AKI		Anemia

AKI

No AKI

Figure 3.6. Left: hemoglobin levels in patient groups with and without AKI (the Mann-Whitney test). The medians, the first and third quartiles are given, the shape of the figures reflects the variable distribution. Right: AKI incidence in patient groups depending on concomitant anemia.

Another well-known risk factor for renal function impairment is use of X-ray contrast agents. For the purpose of topical diagnosis contrasted neck CT was performed on the day of or the day before surgery in some patients (48 of 290, 16.6%) enrolled in the study. Contrast agent volume used was the same 100 mL in all cases. The use of contrast agents did not statistically significantly affect the AKI risk: RR=1.3 [95%CI 0.85; 2.14], OR=1.49 [95%CI 0.77; 2.9], p=0.245.