Prognostic value of nutrition for contrast-induced nephropathy in patients undergoing peripheral vascular intervention

Abstract

Background: The objective was to examine the predictive value of malnutrition, assessed via the Controlling Nutritional status (CONUT) and Prognostic Nutrition Index (PNI) scores, in the development of contrast-associated acute kidney injury (CA-AKI) following peripheral vascular intervention (PVI).

Methods: This retrospective cross-sectional observational study included the enrollment of 243 consecutive patients who underwent PVI. Patients were categorized into two groups based on the occurrence of CA-AKI.

Results: Patients with CA-AKI had lower PNI scores and the PNI score was an independent predictor of CA-AKI development (Odds Ratio: 0.518, 95% CI: 2.295–0.908, p = 0.021). Nomogram had higher discriminative ability than both PNI and CONUT scores and discriminative abilities were similar for PNI and CONUT scores.

Conclusion: Malnutrition, as identified by the CONUT and PNI, was found to be associated with a high risk of CA-AKI development following PVI.

Plain language summary

Article highlights.

• Peripheral artery disease has a substantial global impact, with an estimated prevalence exceeding 200 million worldwide.

• Endovascular treatment is commonly advocated as the primary therapeutic approach for select individuals, but one of its significant limitations is its potential to induce acute kidney injury.

• The different pathophysiological mechanisms underlying contrast-associated acute kidney injury remain incompletely understood, with numerous factors postulated, including vasoconstriction induced by contrast media, hypoxia, ischemia and direct tubular toxicity.

• Malnutrition increases susceptibility to acute kidney injury among hospitalized patients and recent studies have increasingly utilized Prognostic Nutrition Index (PNI) and Controlling Nutritional Status (CONUT) scores to detect malnutrition.

• This retrospective study was designed to investigate the predictive value of malnutrition, assessed via the CONUT and PNI, in the development of contrast-associated acute kidney injury following peripheral vascular intervention.

• Malnutrition, identified using the CONUT and PNI, was found to correlate with a greater risk of developing contrast-associated acute kidney injury following peripheral vascular intervention.

• Assessing malnutrition in patients before peripheral vascular intervention could aid clinicians in identifying those at elevated risk for developing contrast-associated acute kidney injury.

• In addition to nutritional indexes, we identified estimated glomerular filtration rate, left ventricular ejection fraction and age as independent predictors for the development of contrast-associated acute kidney injury after peripheral vascular intervention.

1. Introduction

Peripheral artery disease (PAD) is characterized by arterial constriction, primarily affecting the lower extremities, excluding the cerebral and coronary systems [1]. Although often underdiagnosed, PAD has a substantial global impact, with an estimated prevalence exceeding 200 million worldwide [2]. Approximately 20% of individuals with PAD exhibit symptoms, typically either intermittent claudication or chronic limb-threatening ischemia (CLTI) [3]. CLTI poses a considerable risk of limb amputation and is associated with a one-year mortality risk approaching 30% [4,5]. Endovascular treatment (EVT) is often recommended as the primary therapy for certain patients, successfully providing long-term revascularization for those with CLTI [6]. One of the significant limitations associated with EVT is its potential to induce Contrast-associated acute kidney injury (CA-AKI).

CA-AKI is defined as the occurrence of acute kidney injury (AKI) within a 48–72-h period after the administration of iodinated contrast media (CM), irrespective of CM being identified as the causative factor [7]. CA-AKI, a potentially severe complication subsequent to EVT, has the potential to result in elevated mortality rates, the necessity for dialysis and prolonged hospitalization [8–10]. The different pathophysiological mechanisms that underly CA-AKI remain incompletely understood. There are numerous factors that have been postulated, such as vasoconstriction induced by CM, hypoxia, ischemia and direct tubular toxicity [11].

The number of effective interventions to prevent CA-AKI is limited. It is crucial to consider well-defined risk factors such as diabetes mellitus (DM), age, dehydration, anemia, CM volume and type and chronic kidney disease [12,13]. Malnutrition increases susceptibility to AKI among hospitalized patients and is associated with an unfavorable prognosis in those with PAD [14,15]. Malnutrition can increase the risk of CA-AKI due to several factors such as an increased inflammatory response, weakened immune functions leading to higher susceptibility to infections and reduced renal perfusion. While many scoring systems detect malnutrition, recent studies use Prognostic Nutrition Index (PNI) and Controlling Nutritional Status (CONUT) scores [16,17]. This study aims to investigate the predictive capability of malnutrition assessed through CONUT and PNI in the occurrence of CA-AKI after EVT in individuals with PAD.

2. Methods

2.1. Study population

This retrospective cross-sectional study enrolled 277 consecutive patients referred for peripheral angiography due to suspected PAD, conducted at a tertiary care hospital from January 2021 to September 2022. During our study period, although our hospital remaining a referral center, peripheral vascular interventions (PVI) decreased compared with previous years due to the COVID-19 pandemic. AKI can occur without CM use in conditions like acute coronary syndrome (ACS) and vasculitis, where PNI and CONUT scores may also be influenced independently of nutritional status. Diagnosing CA-AKI in patients with severe kidney disease is challenging, so they were excluded from the study. Exclusion criteria comprised individuals with

• infection (n = 13)

• malignancy (n = 8)

• vasculitis (n = 7)

• severe kidney disease (glomerular filtration rate [GFR] <15 ml/min/1.73 m²) (n = 4)

• ACS (n = 2)

A total of 34 patients were excluded based on these criteria. Informed consent was obtained from all participants before the procedure. The study protocol was approved by the Ethics Committee of Kartal Kosuyolu Research and Education Hospital (approval number: 2024/12/856) following the Declaration of Helsinki guidelines.

Clinical, epidemiological, laboratory and procedural data were obtained from our institution’s electronic health records. After a 12-h fast, blood samples were collected before peripheral angiography and analyzed for hemoglobin levels, blood glucose, creatinine, lipid profile, liver enzymes, sodium, potassium, serum albumin (SA) and C-reactive protein (CRP). Patients did not receive any CA-AKI preventive treatment before the procedure. Following angiography, intravenous hydration was administered continuously for at least 12 h. All patients received a nonionic iso-osmolar contrast agent.

2.2. Peripheral angiographic evaluation

Two interventional cardiologists analyzed peripheral angiography images, classifying occlusions of 70% or greater as severe PAD. The aortoiliac, femoropopliteal and infrapopliteal arteries were categorized according to the Trans-Atlantic Inter-Society Consensus-II classification. Lesion characteristics were determined based on angiographic findings. EVT included balloon angioplasty and stent placement. Operational failure was defined as the inability to navigate the guidewire through the occlusion or achieve distal perfusion after EVT.

2.3. Diagnosis of CA-AKI

The diagnosis of CA-AKI was ascertained through criteria defining at least a 25% elevation in creatinine levels or absolute creatine increase of 0.5 mg/dl during a 72-h period subsequent to the administration of CM. The patient cohort was categorized into two different groups on the basis of the absence or presence of confirmed CA-AKI, such as CA-AKI- and CA-AKI+.

2.4. PNI & CONUT scores for malnutrition risk assessment

The PNI and CONUT scores serve as tools for assessing the risk of malnutrition. The PNI is one among several scoring systems designed for this purpose, involving the computation of a score that is based on the SA level and lymphocyte (LYM) count, expressed by the formula: PNI = 10 × SA (g/dl) + 5 × total LYM-count (/nl). The CONUT score was calculated based on SA concentration, total lymphocyte count and total cholesterol levels. SA concentrations of ≥3.50 g/dl, 3.00–3.49 g/dl, 2.50–2.99 g/dl and <2.50 g/dl were scored as 0, 2, 4 and 6 points, respectively. Total lymphocyte counts of ≥1600 mm³, 1200–1599 mm³, 800–1199 mm³ and <800 mm³ were scored as 0, 1, 2 and 3 points, respectively. Total cholesterol levels of ≥180 mg/dl, 140–179 mg/dl, 100–139 mg/dl and <100 mg/dl were scored as 0, 1, 2 and 3 points.

2.5. Statistical analysis

All statistical analyses were conducted with R statistical software v.4.1.2 (Institute for Statistics and Mathematics in Vienna, Austria). The Kolmogorov–Smirnov test was performed to see if the data were regularly distributed. A p-value higher than 0.05 indicated a normally distributed data in the Kolmogorov–Smirnov test. Categorical data were expressed as percentages and numbers. The Fisher’s exact test or X2 test was used when comparing the categorical variables between the two groups. Continuous variables that had normal distribution were expressed as the mean ± standard deviation, while those with distribution that was not normal were expressed as the median (interquartile range). The independent Student’s t and Mann–Whitney U tests were used when comparing the continuous variables between the two groups. The associations between the variables and the presence of CA-AKI were evaluated through the application of univariable logistic regression (LR) analysis. Then, a penalized least absolute shrinkage and selection operator (LASSO) model was applied to the variables that were detected at a significant level in the univariable LR analysis or clinically relevant for selection to input into the multivariable model to avoid overfitting. LASSO method performs shrinkage with a penalty factor to the coefficients and forced the coefficients of noprominent variables to be zero. By this way, we can easily select the nonzero coefficient variables in the multivariable model to better fit the multivariable model, which is essential for generability of the model. We used lambda.min which was chosen based on cross-validation, for aiming to select the simplest model (because of the small sample size) within one standard error of the minimum cross-validation error. We used LASSO for variable selection. Finally, seven variables including age, left ventricular ejection fraction (LVEF), GFR, CRP, white blood cell, PNI score and CONUT score were selected from the penalized LASSO model. Multicollinearity was assessed using a tolerance value of 0.1 and variance inflation factor value of >3. Because there was high collinearity between the CONUT and PNI, the CONUT score was not input into the multivariable model. After variable selected with nonzero coefficients, we include those variables in the multivariable LR model with lasso penalization with optimal lambda value as: 0.0008690911 and the final model was as follows; Logit (p) = 1.6839 – (0.0931 × PNI) + (0.0651 × age) – (0.0481 × LVEF) – (0.0275 × GFR) + (0.0179 × CRP) + (0.1290 × white blood cell). Multivariable LR was used to create prediction model by accounting contributions of all selected variables together at the same time for getting a better estimation for the outcome. Then, based on the X2 values, the variables in the multivariable model were prioritized according to their importance. A nomogram was created for the prediction of risks of patients for CA-AKI using nomogram function in rms package of R. The nomogram was calculated based on the coefficients of variables in the multivariable model in which coefficients are converted to a score or point that reflects their contributions to the predicted outcome. The nomogram can be used for risk prediction for all patients separately as a risk calculator. You could calculate points of each variables for a patient by drawing a vertical line from variable value to the points line at the top of the nomogram and note that point. After noting all points, you can calculate the total points by summing all that points. This total score is then marked on the total point scale and the risk of developing CA-AKI is calculated with a vertical line drawn to the probability scale. The generalizability of the nomogram model in new data was assessed with a calibration plot. To make a comparison of the discriminative qualities of the nomogram model, the CONUT and PNI scores of patients with CA-AKI+ from CA-AKI-, the receiver operating characteristic (ROC) curve comparison was displayed. The data were analyzed using a two-sided p-value that was <0.05 and a 95% CI.

3. Results

There were 243 patients who underwent EVT due to PAD included in the study. CA-AKI developed in 51 (20.1%) patients after EVT (Figure 1). The demographic as well as the clinical characteristics of the patients who had CA-AKI+ and CA-AKI- are presented in Table 1. Age was statistically higher in the CA-AKI+ group (59.5 [53.0; 68.0] vs. 65.0 [62.5; 74.0], p < 0.001). HT and DM were more prevalent in the CA-AKI+ group (77 (40.1%) vs. 33 (64.7%), p = 0.003; 85 (44.3%) vs. 32 (62.7%), p = 0.029). CRP and creatine were determined to be at significantly higher levels in the CA-AKI+ group in comparison with the CA-AKI- group (Table 2). Additionally, the LVEF was significantly lower in the CA-AKI+ group (65.0 [60.0; 65.0] vs. 55.0 [35.0; 65.0], p < 0.001).

Figure 1.

Consort flow diagram for inclusion in the study.

Table 1.

Demographic and procedure characteristics of the study groups.

	CA-AKI(-) (n = 192)	CA-AKI(+) (n = 51)	p-value
Age (years)	59.5 [53.0–68.0]	65.0 [62.5–74.0]	<0.001
Sex (male)	167 (87.0%)	46 (90.2%)	0.703
DM	85 (44.3%)	32 (62.7%)	0.029
Hypertension	77 (40.1%)	33 (64.7%)	0.003
Smoker	118 (61.5%)	37 (72.5%)	0.193
BMI	25.5 [24.2–28.8]	24.4 [22.0–28.7]	0.065
Coronary artery disease	107 (55.7%)	34 (66.7%)	0.212
Cerebro vascular disease	75 (39.1%)	18 (35.3%)	0.741
CKD	11 (5.73%)	26 (51.0%)	<0.001
LVEF	65.0 [60.0–65.0]	55.0 [35.0–65.0]	<0.001
Intervention localization			0.239
Aortoiliac	60 (31.2%)	14 (27.5%)
Femoral	117 (60.9%)	29 (56.9%)
BTK	15 (7.81%)	8 (15.7%)
TASC classification			0.001
A	34 (17.7%)	1 (1.96%)
B	37 (19.3%)	6 (11.8%)
C	71 (37.0%)	18 (35.3%)
D	50 (26.0%)	26 (51.0%)
Balloon size	5.00 [5.00; 6.00]	5.00 [5.00; 6.00]	0.026
Intervention success	169 (88.0%)	42 (82.4%)	0.406
Length of hospital stay	3.00 [2.00; 6.00]	8.00 [7.00; 10.0]	<0.001
Renal replacement therapy	0 (0.00%)	5 (9.80%)	<0.001
Sepsis	3 (1.56%)	9 (17.6%)	<0.001
Cardiovascular death	0 (0.00%)	3 (5.88%)	0.009
Amputation	6 (3.12%)	8 (15.7%)	0.002
PNI	41.3 [38.3–44.6]	37.0 [34.1–39.9]	<0.001
CONUT	0.00 [0.00–1.00]	2.00 [1.00–3.50]	<0.001

BTK: Below-the-knee; CA-AKI: Contrast-associated acute kidney injury; CKD: Chronic kidney disease; CONUT: Control of Nutritional Status; DM: Diabetes mellitus; LVEF: Left ventricular ejection fraction; PNI: Prognostic Nutrition Index.

Table 2.

Laboratory of the study groups.

	CA-AKI(-) (n = 192)	CA-AKI(+) (n = 51)	p-value
Glucose	107 [90.8–170]	128 [99.5–162]	0.104
GFR	94.8 [76.0–113]	55.6 [44.2–77.7]	<0.001
Creatinine	0.81 [0.70–0.97]	1.28 [1.00–1.58]	<0.001
LDL cholesterol	128 [100–156]	129 [102–170]	0.758
HDL cholesterol	43.0 [34.0–50.0]	36.0 [31.5–44.5]	0.004
Total cholesterol	202 [169–236]	190 [163–236]	0.750
Triglyceride	141 [96.5–207]	142 [105–220]	0.431
Albumine	3.94 [3.70–4.30]	3.70 [3.40–4.00]	0.006
CRP	3.62 [3.10–11.9]	10.4 [3.71–25.4]	<0.001
Baseline Hb	14.1 [12.4–15.0]	12.2 [11.3–14.1]	<0.001
WBC	8.70 [7.40–10.4]	9.20 [7.30–11.2]	0.307
AST	19.2 [17.4–23.7]	20.0 [16.5–28.1]	0.536
ALT	17.1 [12.6–22.7]	17.0 [12.1–27.5]	0.560
Sodium (mEq/l)	138 [135–140]	138 [136–140]	0.451
Potassium (mEq/l)	4.35 [4.08–4.50]	4.50 [4.18–4.85]	0.018

ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; CA-AKI: Contrast-associated acute kidney injury; CRP: C-reactive protein; GFR: Glomerular filtration rate; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; WBC: White blood cell.

A significant association was found between the severity of the PAD and the probability of developing CA-AKI. Moreover, in the patients with TASC-D lesions, there was a higher incidence of developing CA-AKI (50 [26.0%] vs. 26 [51.0%], p < 0.001). No significant relationship was observed between procedural success and CA-AKI. However, in the CA-AKI+ group, the hospitalization duration was longer and the rates of sepsis, amputation and cardiovascular mortality were higher.

The penalized LASSO model was used for variable selection for the multivariable LR model to avoid overfitting and seven variables were selected (Supplementary Material). Because of the high correlation that was found between the CONUT and PNI scores, the CONUT score was not input into the multivariable model. The mutivariable LR analysis revealed that age, the LVEF, GFR and PNI score served as independent predictors of CA-AKI development (Table 3). The most prominent variable was the GFR, followed by LVEF in second, age in third and the PNI was the fourth, as sorted by chi-squared values in the multivariable model (Figure 2). A nomogram was created using the multivariable model to estimate the risk of patients who are undergoing EVT for developing CA-AKI (Figure 3). The calibration plot showed good calibration, which indicated the generalizability of the risk prediction nomogram model in unseen patients (Figure 4). The ROC comparisons revealed that the nomogram model had the highest discrimination ability when compared with both the CONUT and PNI scores and the PNI score was not inferior compared with the CONUT score. (area under the ROC curve values:0.896, 0.790 and 706, respectively and De-Long test p-values for comparisons: <0.001 for the nomogram vs. the PNI score, 0.013 for the nomogram vs. the CONUT score and 0.112 for CONUT vs. PNI) (Figure 5).

Table 3.

Multivariable LR analysis results for detecting independent associations of variables with CA-AKI.

Variables	OR	95% CI	p-value
Age	2.656	1.256–5.618	0.011
LVEF	0.786	0.667–0.927	0.004
GFR	0.366	0.202–0.663	0.001
CRP	1.175	0.933–1.479	0.171
WBC	1.491	0.989–2.249	0.056
PNI	0.518	0.295–0.908	0.021

CA-AKI: Contrast-associated acute kidney injury; CRP: C-reactive protein; GFR: Glomerular filtration rate; LR: Logistic regression; LVEF: Left ventricular ejection fraction; PNI: Prognostic Nutrition Index; WBC: White blood cell.

Figure 2.

Prominence of variables in the multivariable model based on chi-square values for comparing the contributions of variables to the full model.

Figure 3.

A nomogram was developed using a multivariable model to predict the risk of contrast-associated acute kidney injury.

Figure 4.

Calibration plot of nomogram model for evaluating how well the predicted probabilities of patients align with the actual outcomes observed in the data.

Figure 5.

ROC curve comparisons of CONUT score, PNI and nomogram model for comparing the discriminative abilities between patients with CA-AKI (+) and CA-AKI (-).

CA-AKI: Contrast-associated acute kidney injury; CONUT: ; PNI: Prognostic Nutrition Index; ROC: Receiver operating characteristic.

4. Discussion

As best as is known, this is the first investigation examining the association between malnutrition and CA-AKI following PVI using two different nutrition indices:the PNI and the CONUT. As a result, malnutrition, as identified by both the CONUT and PNI, was determined to have an association with an increase in the risk of developing CA-AKI among patients receiving PVIs. In addition to nutritional indexes, we identified GFR, LVEF and age as independent predictors for the development of CA-AKI after PVI.

CA-AKI following angiography is extensively documented in coronary literature;however, its occurrence and implications after PVIs remain relatively underexplored [18–21]. Significantly, several risk factors for CA-AKI identified in this context closely resemble those established in prior research on PVIs and coronary interventions. Chronic renal insufficiency (Cr >1.5 mg/dl or GFR <60 ml/min) has been consistently shown to increase the risk of CA-AKI [22]. In our study, the most significant factor was naturally GFR. Inadequate renal perfusion in the setting of chronic heart failure or hemodynamic instability is also associated with an increased risk of CA-AKI [23]. In our study, we also identified LVEF as an independent predictor for the development of CA-AKI. Age is a well-recognized independent predictor of CA-AKI in observational studies [24]. In our study, patients who developed CA-AKI were older compared with those who did not develop CA-AKI. This indicates that elderly individuals with poor renal function are more likely to develop CA-AKI after PVIs, particularly if they also have poor cardiac function.

DM is one of the strongest predictors of AKI following percutaneous coronary interventions (PCI) [25]. Similarly, in our study, the incidence of CA-AKI was higher in the group with DM. Anemia has also been identified as an independent risk factor, possibly due to decreased oxygen delivery to tubular cells [26]. In our study, patients with low hemoglobin levels before PVI had a higher incidence of CA-AKI. Therefore, PVIs should be performed with extra caution in patients with anemia and DM and contrast dosing should be minimized. In a comprehensive statewide collaborative study conducted by Grossman et al., their findings indicated that DM, anemia, GFR, heart failure, critical limb ischemia and the dosage of contrast agent emerged as independent variables that were found to have a significant association with the occurrence of CA-AKI after PVI [23]. Furthermore, the present study revealed that the nutritional status of patients before PVI has a significant association with the frequency of CA-AKI occurrence after PVIs.

Malnutrition, defined as inadequate or unbalanced nutrient intake, is a significant health concern influenced by economic disparities, geographical characteristics, regional conflicts and irregular dietary habits [27]. It includes both undernutrition and overnutrition, affecting individuals regardless of body weight. Malnutrition assessments consider the patient’s health status, including physical condition, protein metabolism and immune function [28].

Previous investigations have demonstrated that malnutrition has an association with an increased likelihood of AKI, both among hospitalized patients and those presenting with conditions such as sepsis, malignancy, or ACS [29–31]. Certain studies have indicated that individual nutritional markers, such as the levels of SA or pre-SA, have an association with a higher risk of CA-AKI following PCI [32,33].

One possible underlying mechanism linking malnutrition to CA-AKI is oxidative stress, which predisposes patients to CA-AKI. Oxidative stress has been reported to play a key role in the etiology and pathogenesis of CA-AKI [34]. SA, a widely used biomarker of nutritional status in clinically stable conditions, is the most abundant circulating protein and is essential for its antioxidant and anti-inflammatory properties [35,36]. Low SA levels in malnourished patients may contribute to the development of AKI by impairing endothelial function and promoting oxidative inflammatory pathways [37].

Another potential mechanism is the high inflammatory burden that activates CA-AKI. Nutritional status is closely linked to the body’s inflammatory burden. Atherosclerosis, a chronic inflammatory disease, results primarily from the abnormal accumulation of macrophages, white blood cells and lipids in the arterial wall, leading to the formation of mature plaques [38]. Malnutrition is associated with elevated levels of inflammation, which increases the atherosclerotic burden. This vicious cycle, recently described as malnutrition-inflammation-atherosclerosis syndrome, appears to be a significant risk factor for the development of CA-AKI [39].

While the CONUT has primarily been investigated in the context of malignancies and is predominantly recognized as a reliable predictor of outcomes in cancer patients, there exist studies examining its applicability in various cardiovascular diseases as well [40–42]. For instance, Wei et al. used the CONUT score to classify nutritional status and found that malnutrition was associated with a higher risk of CA-AKI in elderly patients undergoing PCIs [43]. Additionally, Chen et al. explored the incidence of CA-AKI in CAD patients receiving coronary angiography and noted a prevalence of malnutrition, as categorized by the CONUT score, which was linked to an increase in the risk of developing CA-AKI [44]. In the present study, the CONUT score in the CA-AKI+ group exhibited a significant elevation compared with the CA-AKI- group, with the CONUT score identified as being able to independently predict the probability of developing CA-AKI.

Previous research has illustrated the PNI’s efficacy as a reliable instrument for prognostic assessment in patients who have stable CAD, chronic total occlusion and ACS [45–47]. Han et al. revealed a significant association between the PNI scores and CA-AKI occurrence follwing PCI [48]. Similarly, a study recently conducted by Chen et al. reported that malnutrition, particularly as defined by the Geriatric Nutritional Risk Index and PNI, was linked to an increased susceptibility to CA-AKI in PCI patients [49]. In this study, the PNI scores were significantly lower in the CA-AKI+ group compared with the CA-AKI- group, with the PNI identified as being able to independently predict the probability of developing CA-AKI.

Nutritional deficiencies are prevalent among patients diagnosed with PAD and are closely associated with an unfavorable prognosis [50]. Objective indices for measuring nutrition, like the PNI and CONUT scores, play a crucial role in assessing the nutritional status of individuals with PAD and serve as significant prognostic indicators for adverse outcomes in this patient population [10,51]. Based on the findings herein, we recommend using the CONUT and PNI as effective tools to identify patients at a heightened risk of developing CA-AKI following PVI. The simplicity of these formulas enables healthcare providers to quickly assess malnutrition. Significantly, the current findings offer new insight into preventive strategies for CA-AKI post-PVI. Improving nutritional status with personalized diets, high-quality protein, vitamin-rich foods and necessary supplements may effectively prevent CA-AKI in high-risk patients. Additionally, the predictive ability of the PNI and CONUT scores assists clinicians in assessing the risk of CI-AKI before contrast exposure. This enables them to implement preemptive interventions for patients at a heightened risk of CI-AKI, such as appropriate hydration therapy or the use of combined drug prophylaxis.

4.1. Limitations

This study had several limitations. First, being a cross-sectional, single-center and observational study, there was a possibility of selection bias. The prediction models developed in the single center therefore, these results should be validated in larger, more diverse populations. Mainly, because of demographic, local healthcare practice, recruitment practice differences, single-center studies may not represent the population. Single-center studies lead to small sample size and also limited outcome event numbers, which cause the lack of generalizability of the data in multicenter unseen data. Therefore, models derived from single-center studies may not accurately predict risk in new multicenter data. Another limitation of single-center design is the lack of unmeasured variables which could be clinically prominent and could improve the prediction capability of model. Second, SA level and LYM were assessed only at admission without subsequent measurements, limiting our understanding of changes over time. Third, the delay in serum creatinine changes beyond the 72-h postcontrast administration window might have underestimated CA-AKI incidence, especially with potential renal function deterioration postdischarge. Additionally, PNI and CONUT were assessed only once at admission, ignoring possible fluctuations during long-term follow-up. It is evident that the volume of contrast medium used during angiography is crucial for the development of CA-AKI. Unfortunately, our patient data does not include information on the volume of CM. Finally, future prospective studies are necessary to elucidate the pathophysiological roles of the CONUT and PNI.

5. Conclusion

In conclusion, malnutrition, identified using the CONUT and PNI, was found to have a correlation with a greater risk of developing CA-AKI following PVI. Assessing malnutrition in patients before PVI could aid clinicians in identifying those at elevated risk for developing CA-AKI. Predicting the risk of CA-AKI allows for the optimization of procedures by minimizing their duration, reducing the volume of contrast medium used and implementing prophylactic measures to prevent CA-AKI.

Supplemental material

Supplemental data for this article can be accessed at https://doi.org/10.1080/17520363.2024.2395248

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval (Kartal Kosuyolu Research and Education Hospital (approval number: 2024/12/856)) and/or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations.

In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.