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Research Article
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Platelet-to-lymphocyte ratio predicts
contrast-induced acute kidney injury in
diabetic patients with ST-elevation
myocardial infarction
Bartosz Hudzik*,1,2 , Janusz Szkodziński1 , Ilona Korzonek-Szlacheta2 , Krzysztof Wilczek1 ,
Marek Gierlotka1 , Andrzej Lekston1 , Barbara Zubelewicz-Szkodzińska2 & Mariusz Gasior1
1
Third Department of Cardiology, SMDZ in Zabrze, Medical University of Silesia in Katowice, Silesian Center for Heart Disease,
Zabrze, Poland
2
Department of Nutrition-Related Disease Prevention, School of Public Health in Bytom, Medical University of Silesia in Katowice,
Bytom, Poland
* Author for correspondence: Tel.: +48 323 733 788; Fax: +48 322 732 679; [email protected]
Aim: There has been a rise in contrast-induced acute kidney injury (CI-AKI). We examined the role of
platelet-to-lymphocyte ratio (PLR) in predicting CI-AKI episodes in patients with myocardial infarction (MI)
and diabetes. Methods: A total of 719 patients with diabetes and MI were enrolled. Study population was
divided into: group 1 (n = 615) without CI-AKI and group 2 (n = 104) with CI-AKI. Results: Patients with CIAKI had higher in-hospital mortality and a longer in-hospital stay. Median PLR was higher in patients with
CI-AKI. Receiver operating characteristic analysis indicated PLR to be a good predictive tool in assessing
the risk of CI-AKI. PLR was an independent predictor of CI-AKI (OR: 1.22; p < 0.0001). Conclusion: These
results suggest potential role for PLR as a biomarker of CI-AKI among diabetic patients with MI who
undergo percutaneous coronary intervention.
First draft submitted: 9 April 2017; Accepted for publication: 5 June 2017; Published online: 29
September 2017
Keywords:contrast-induced acute kidney injury • diabetes mellitus • myocardial infarction • platelet-to-lymphocyte
ratio
Percutaneous coronary interventions (PCIs) have become the mainstay in the management of ST-elevation myocardial infarction (STEMI). However, with the increasing trends in the interventional procedures, there has been a
concomitant rise in the contrast-induced acute kidney injury (CI-AKI) [1]. It is common and the reported incidence
after PCI varies between 3 and 19% [1,2]. Yet, pathophysiology of this condition is not fully understood. Various
data indicate that CI-AKI is associated with adverse clinical outcomes, including but not limited to cardiovascular
complications, provision of dialysis and death [3–5]. Transient and persistent renal dysfunction after CI-AKI was
associated with increased short- and long-term mortality and morbidity in STEMI patients treated by PCI [6].
Various clinical prediction models have been developed and numerous laboratory biomarkers have been tested in
predicting CI-AKI [1,7].
The quest for assessing risk factors and identifying new, simple and inexpensive biomarkers of CI-AKI is an area of
extensive research nowadays. Inflammation seems to play an important role in the development of CI-AKI. Studies
demonstrate an increased concentration of IL-1β, IL-6, IL-8, IL-10 and TNF-α in CI-AKI [8,9]. Lymphocytes may
play an important role in the modulation of innate and adaptive inflammatory responses in AKI models [10]. In
addition, contribution of platelets in the development of CI-AKI and the predictive role of platelet indices has also
been reported [11].
Recent data indicate that platelet-to-lymphocyte ratio (PLR) is a novel marker of poor outcomes. It was first
introduced as a potential marker to determine inflammation in oncologic disorders [12,13]. Interestingly, studies
demonstrated PLR to be a significant marker for prediction of death in cancer population [14–16]. Moreover,
it exhibits strong prognostic value in predicting adverse outcomes in patients with acute coronary syndromes
C 2017 Future Medicine Ltd
10.2217/bmm-2017-0120 Biomark. Med. (Epub ahead of print)
ISSN 1752-0363
Research Article
Hudzik, Szkodziński, Korzonek-Szlacheta et al.
(ACS) [17–19]. There is a biological rationale for using PLR in patients with cancer or cardiovascular disease to
predict outcome. Previous data indicate that platelets play various roles in inflammatory processes, for example,
they facilitate neutrophils adhesion to endothelium by releasing chemokines and cytokines [20]. Moreover, platelets
are reported to promote tumor progression [21,22]. In contrast, lymphocytes are known to hinder tumor cell
proliferation and metastasis [23]. In terms of cardiovascular disease, inflammation and increased platelet activation
play a central role in the initiation and the progression of the complex atherosclerotic process. Studies demonstrate
an important relation between high-circulating platelet count and major adverse cardiovascular disease [24]. In
addition, lymphocyte count decreases in cases of chronic inflammation. More importantly, a decreased lymphocyte
count has been reported to be related to major adverse cardiovascular outcomes [25].
The risk of CI-AKI is elevated in patients with diabetes mellitus (DM), which may be due to the presence of
endothelial dysfunction and an exaggerated vasoconstrictive response to contrast medium [26,27].
Given the potential role of inflammation and platelets in CI-AKI, we proposed to examine the potential role
of PLR as a biomarker of CI-AKI in patients with STEMI and DM, both of which are independent predictors of
CI-AKI.
Materials & methods
The study conforms to the Declaration of Helsinki. All patients signed informed consent for data analysis according
to the Polish law on patients’ rights regarding data registration. The local bioethics committee on human research
waived the approval for analyzing recorded data, given the retrospective nature of the study.
This is a single-center study. Patients admitted with a diagnosis of STEMI, within 12 h from symptom onset
were primarily enrolled in the study. Exclusion criteria have been described previously [19].
A total of 719 patients with DM and STEMI, undergoing primary PCI were enrolled. Based on the development
of CI-AKI, the study population was divided into two groups: group 1 (n = 615) without CI-AKI and group 2
(n = 104) with CI-AKI.
Coronary angiography was performed with iohexol (OmnipaqueTM [GE Healthcare, Norway]; 240 mgI/ml;
391 mOsm/l) contrast agent. CI-AKI was defined as: increase in serum crestinine (SCr) ≥0.3 mg/dl (≥26.5 μmol/l)
within 48 h or increase in SCr ≥1.5-times the baseline, which is known or presumed to have occurred within prior
7 days [28].
On admission and within the following: 24, 48, 72, 96 and 120 h, venous blood samples were routinely
obtained from all patients before and after PCI. The estimated glomerular filtration rate (eGFR) was calculated
using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation because it is more precise than
the Modification of Diet in Renal Disease formula according to the recommendations of clinical practice guideline
for the evaluation and management of chronic kidney disease [28,29].
PLR was evaluated based on the complete blood counts (CBCs) obtained on admission. Platelets, lymphocytes,
neutrophils, hemoglobin and white blood cells were measured as part of the CBC. Detailed information on venous
blood collection and handling has been described in detail previously [19]. PLR has been calculated as the ratio
platelet count (103 /mm3 ) to lymphocyte count (103 /mm3 ).
The following definition for DM was used which include: pre-existing disease diagnosed before STEMI (patients
on insulin, oral glucose-lowering drugs or on a diet) and new condition diagnosed, based on fasting plasma
glucose ≥7.0 mmol/l or 2 h plasma glucose ≥11.1 mmol/l in an oral glucose tolerance test [30]. Given the acute
hyperglycemia phenomenon, elevated fasting plasma glucose indicative of DM was considered only from the fourth
day of hospital stay onward. Similarly, oral glucose tolerance test was performed on day 4 of hospital stay or later.
The following definition of STEMI was used in the study: first, ST-segment elevation consistent with MI of at least
2 mm in contiguous precordial leads and/or ST-segment elevation of at least 1 mm in two or more limb leads or
new left bundle branch block, and second, elevated cardiac necrosis markers (CK-MB and/or troponin).
All patients received loading doses of acetylsalicylic acid (300 mg) and clopidogrel (600 mg) before admission
to our hospital (either in the referring hospital or ambulance) according to the guidelines, followed by 75 mg of
acetylsalicylic acid maintenance dose and 75 mg of clopidogrel maintenance dose [31]. Coronary angiography and
PCI were performed using standard protocols and guidelines. A culprit lesion was defined as the presence of an
acute occlusion, intraluminal filling defects (or the presence of thrombus), ulcerated atherosclerotic plaques and
the presence of dissection or intraluminal flaps. The definition of a successful PCI included: a postprocedural
residual-diameter stenosis <30%, with Thrombolysis in Myocardial Infarction (TIMI) 3 flow in the infarct-related
artery and no procedural complications.
10.2217/bmm-2017-0120
Biomark. Med. (Epub ahead of print)
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Research Article
Platelet-to-lymphocyte ratio & acute kidney injury
Table 1. Patients’ baseline and clinical characteristics.
Baseline clinical features
Group 1 (n = 615)
Group 2 (n = 104)
Age, years (mean ± SD)
60 ± 11
63 ± 9
0.03
Males, n (%)
349 (56.7%)
65 (62.5%)
0.3
p-value
Systemic hypertension, n (%)
413 (67.1%)
75 (72.1%)
0.3
Prior myocardial infarction, n (%)
190 (30.9%)
38 (36.5%)
0.2
Time from symptom onset, hours
(median [interquartile range])
5.0 (3.0–7.0)
5.0 (3.0–6.0)
0.4
Cardiogenic shock, n (%)
122 (19.8%)
18 (17.3%)
0.3
CKD (stage ≥3), n (%)
145 (23.6%)
25 (24.0%)
0.9
BMI (mean ± SD)
26.9 ± 2.1
27.1 ± 2.3
0.9
Antidiabetic medications prior the
admission† :
Insulin, n (%)
Metformin, n (%)
Sulfonylurea, n (%)
182 (29.6%)
215 (35.0%)
175 (28.4%)
33 (31.4%)
39 (37.5%)
26 (25.0%)
0.8
0.7
0.5
LVEF, % (median [interquartile range])
42 (37–47)
40 (33–50)
0.4
Hospital stay, days (median
[interquartile range])
9 (5–12)
10 (7–12)
0.04
In-hospital death, n (%)
82 (13.3%)
30 (28.8%)
0.0005
†
Some patients received more than one antidiabetic medication.
CKD: Chronic kidney disease; LVEF: Left ventricular ejection fraction; SD: Standard deviation.
An elective 12-month clinical follow-up was scheduled for all study participants. We monitored the patients for
major adverse cardiac and cerebrovascular events defined as the composite of all-cause death, rehospitalization for
ACS (nonfatal MI or unstable angina) and stroke.
Statistical analysis
Quantitative data are given as means and standard deviations or medians and interquartile ranges. Qualitative
data are displayed as frequencies. The Shapiro–Wilk test was employed to determine whether random samples
came from a normal distribution. The χ2 test with Yates’ correction was used to compare categorical variables.
The unpaired t-test was used to compare normally-distributed continuous variables while the Mann–Whitney
U-test was used for group analyses of continuous non-normally distributed variables. Associations between PLR
and clinical/laboratory features was estimated by Spearman’s rank correlation coefficient. A receiver operating
characteristic (ROC) analysis was performed to assess the ability of PLR to predict CI-AKI and to identify the
optimal cutoff value. All clinical, angiographic and laboratory data were included in the regression models. The
criteria for inclusion were set at p-value <0.05 in the univariate analysis. Those variables which met the criteria
entered the multivariate logistic regression models using a Wald statistic backward stepwise selection. Multivariate
analysis was performed to estimate odds ratios and 95% confidence intervals to identify independent predictors of
CI-AKI while adjusting for potential confounders. A value of p < 0.05 was considered significant.
Results
A total of 719 patients were enrolled in this study. Of these, 104 (14.5%) developed CI-AKI. Baseline clinical
characteristics are presented in Table 1. Patients who subsequently developed CI-AKI were older in comparison
to patients who did not develop CI-AKI. The prevalence of hypertension, hyperlipidemia and cardiogenic shock
at admission was similar in both groups. Patients in group 2 had a higher in-hospital mortality rate and a longer
in-hospital stay. Anterior STEMI with left anterior descending artery being the infarct-related artery was more
prevalent in patients who developed CI-AKI (54.8 vs 32.0%; p < 0.0001) (Table 2). Successful PCI (defined as final
TIMI 3 flow in the infarct-related artery) was more frequently achieved in group 1. Patients with subsequent CIAKI received higher volumes of contrast medium during PCI. Laboratory findings are presented in Table 3. There
were significant differences in CBCs between the study groups. PLR was higher in patients who developed CI-AKI
following PCI (median 120 vs 108; p < 0.001) driven by both higher platelet count and lower lymphocyte count.
PLR had a significant but weak-to-moderate linear correlation with SCr and eGFR (Figure 1A–1D). Moreover, a
significant correlation was noted between PLR and δ-SCr and δ-eGFR (Figure 1E & 1F). ROC analysis indicated
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10.2217/bmm-2017-0120
Research Article
Hudzik, Szkodziński, Korzonek-Szlacheta et al.
160
180
Spearman R = 0.30 p < 0.0001
Spearman R = -0.16 p < 0.0001
160
140
Admission eGFR
Admission SCr
140
120
100
80
120
100
80
60
60
40
40
0
50
100
150
200
250
300
20
350
0
50
100
150
280
160
Spearman R = 0.30 p < 0.0001
260
160
140
120
100
80
350
Spearman R = -0.24 p < 0.0001
100
80
60
40
60
20
0
50
100
150
200
250
300
0
350
PLR
160
0
50
100
150
200
250
300
350
PLR
80
Spearman R = 0.20 p < 0.0001
140
Spearman R = -0.12 p < 0.0001
60
120
40
Delta eGFR
100
Delta SCr
300
120
Lowest eGFR
Peak SCr
220
200
180
80
60
40
20
20
0
-20
-40
0
-60
-20
-40
250
140
240
40
20
200
PLR
PLR
0
50
100
150
200
250
300
350
-80
0
50
100
150
200
250
300
350
PLR
PLR
Figure 1. Correlation. Correlations between platelet-to-lymphocyte and (A) admission SCr; (B) peak SCr; (C) admission eGFR; (D) the
lowest eGFR; (E) δ-SCr (the difference between peak SCr and admission SCr); and (F) δ-eGFR (the difference between the lowest eGFR and
the admission eGFR).
eGFR: Estimated glomerular filtration rate; PLR: Platelet-to-lymphocyte ratio; SCr: Serum creatinine.
10.2217/bmm-2017-0120
Biomark. Med. (Epub ahead of print)
future science group
Research Article
Platelet-to-lymphocyte ratio & acute kidney injury
Table 2. Angiographic findings.
Angiographic features
Group 1 (n = 615)
Group 2 (n = 104)
p-value
Infarct-related artery:
– LAD, n (%)
– Cx, n (%)
– RCA, n (%)
– Other, n (%)
197 (32.0%)
94 (15.3%)
308 (50.1%)
16 (2.6%)
57 (54.8%)
5 (4.8%)
39 (37.5%)
3 (2.9%)
0.0003
Multivessel CAD, n (%)
320 (52.1%)
52 (50.0%)
0.5
Final TIMI 3 flow, n (%)
550 (89.4%)
83 (79.8%)
0.02
Total volume of contrast media, (ml)
237 ± 53
249 ± 55
0.02
Contrast media volume/eGFR ratio
3.3 ± 0.8
3.7 ± 0.9
0.045
Cx: Circumflecx artery; eGFR: Estimated glomerular filtration rate; LAD: Left anterior descending artery; RCA: Right coronary artery; TIMI: Throbolysis in Myocardial Infarction.
Table 3. Laboratory findings.
Laboratory features
Group 1 (n = 615)
Group 2 (n = 104)
p-value
Leukocytes (103 /mm3 )
14.4 ± 5.6
14.1 ± 5.0
0.1
Lymphocytes (103 /mm3 )
2.3 ± 0.4
2.0 ± 0.4
0.001
Erythrocytes (106 /mm3 )
4.6 ± 0.5
4.5 ± 0.6
0.4
Hemoglobin (mmol/l)
8.8 ± 1.4
8.9 ± 1.8
0.4
Hematocrit (%)
43 ± 4
40 ± 4
0.02
Platelet count (103 /mm3 )
216 ± 61
258 ± 48
0.0001
Mean platelet volume (fl)
9.8 (8.4–10.7)
10.3 (8.4–12.1)
0.048
Platelet-to-lymphocyte ratio
108 (70–122)
120 (100–174)
0.001
Platelet distribution width (fl)
9.6 (8.5–10.7)
9.7 (9.2–10.7)
0.1
Admission glucose (mmol/l)
9.4 ± 3.6
9.5 ± 4.0
0.8
HbA1c (%)
7.5 (6.8–8.9)
7.6 (6.7–9.0)
0.7
Total cholesterol (mmol/l)
6.3 (5.0–7.3)
6.4 (5.2–7.5)
0.2
HDL cholesterol (mmol/l)
1.3 (1.1–1.8)
1.2 (1.1–1.7)
0.7
LDL cholesterol (mmol/l)
4.1 (3.3–4.9)
4.2 (3.2–4.9)
0.7
Triglycerides (mmol/l)
1.6 (1.0–2.4)
1.7 (1.0–2.6)
0.6
Admission SCr (μ mol/l)
89 (79–101)
88 (78–102)
0.8
Peak SCr (μ mol/l)
93 (81–114)
124 (112–144)
⬍0.001
␦-SCr (μ mol/l)
13 (1–23)
36 (28–48)
⬍0.001
Admission eGFR (ml/min/1.73 m2 )
71 (61–80)
73 (62–75)
0.2
Lowest eGFR (ml/min/1.73 m2 )
62 (56–76)
52 (39–59)
⬍0.001
␦-eGFR (ml/min/1.73 m2 )
-5 (-1 to -9)
-23 (-20 to -29)
⬍0.001
eGFR: Estimated glomerular filtration rate; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; SCr: Serum creatinine.
PLR to be a good predictive tool in assessing the risk of developing CI-AKI following PCI (Figure 2). PLR had
an area under the curve value of 0.72 (CI: 0.68–0.75; p < 0.0001) on the ROC curve. For a cutoff value of 110,
the PLR had 71% sensitivity and 63% specificity for predicting CI-AKI. PLR, among others, was an independent
predictor of CI-AKI (Table 4).
Discussion
We have set out to determine the diagnostic value of PLR in predicting CI-AKI following PCI in diabetic patients
with STEMI. There are several key findings of our study. First and foremost, the rate of CI-AKI was 14.5% which is
relatively high. Second, patients who subsequently developed CI-AKI had higher PLR values. Third, PLR showed
correlation with both SCr and eGFR. More importantly, PLR correlated with the increase in SCr and the decline
in eGFR following PCI. Additionally, PLR showed good predictive value in ROC analysis. And finally, PLR was
one of the independent risk factors of developing CI-AKI.
CI-AKI constitutes an increasing problem due to the growing number of contrast-enhanced imaging studies,
including coronary angiography and PCI. It is associated with increased morbidity and mortality [32]. The reported
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10.2217/bmm-2017-0120
Research Article
Hudzik, Szkodziński, Korzonek-Szlacheta et al.
100
Sensitivity
80
60
40
Sensitivity: 71%
Specificity: 63%
Cut-off: >110
PPV: 25%
NPV: 93%
20
0
0
20
40
60
80
100
100-specificity
Figure 2. Area under the curve. Diagnostic value of
platelet-to-lymphocyte ratio in predicting
contrast-induced acute kidney injury.
NPV: Negative predicting value; PPV: Positive predicting
value.
Table 4. Independent predictors of contrast-induced acute kidney injury.
Predictors of contrat-induced acute kidney injury
Univariate
Multivariate
OR (95%CI)
p-value
OR (95%CI)
1.3 (1.01–1.08)
0.03
p-value
PLR (per 10-unit increment)
1.22 (1.10–1.34)
Anterior MI
2.47 (1.58–3.67)
⬍0.0001
1.22 (1.10–1.34)
⬍0.0001
0.0004
2.00 (1.27–3.16)
Admission eGFR (per 10 ml/min/1.73 m2 decrement)
0.002
1.10 (1.00–1.22)
0.05
1.22 (1.10–1.40)
0.002
Contrast medium volume (per 50 ml increment)
1.22 (1.02–1.49)
0.03
1.33 (1.08–1.64)
0.007
SCr (per 10 μ mol/l increment)
3.74 (2.95–4.86)
⬍0.0001
2.87 (2.49–3.42)
0.003
Time from symptom onset (per 1 h increment)
1.03 (1.01–1.05)
0.004
Age (per 10-year increment)
eGFR: Estimated glomerular filtration rate; MI: Myocardial infarction; OR: Odds ratio; PLR: Platelet-to-lymphocyte ratio; SCr: Serum creatinine.
incidence after PCI varies between 3 and 19% [1,2]. In our study, the prevalence of CI-AKI in diabetic patients
with STEMI was 14.5%. Such a high rate of CI-AKI is not surprising, given that DM is one of the major factors
contributing to the risk of CI-AKI [33,34].
The contribution of platelet indices and inflammatory markers in the development of CI-AKI has been reported [8–9,11]. Neutrophil-to-lymphocyte ratio has been recently described to be an independent predictor of
CI-AKI in patients with STEMI along with advanced age, low baseline GFR, high amount of contrast media use
and DM [35]. PLR reflects the interplay between two major components of atherothrombosis: platelets (thrombosis)
and white blood cells, lymphocytes in particular (inflammation). It has been shown to predict poor outcomes across
the whole spectrum of ACS [17–19]. There are only two studies analyzing the association of PLR and CI-AKI,
neither of which examines solely diabetic patients [36,37]. Velibey et al. investigated the relationship between PLR
and CI-AKI in STEMI patients undergoing PCI. The prevalence of CI-AKI was 6.4%, which was lower in comparison to our study but diabetic patients constituted only 20.9% of the study population [36]. They have reported
that patients with CI-AKI group had significantly higher PLR than those without CI-AKI (169.18±81.01 vs
149.49±74.54; p < 0.001). The similar trend was noted in our study, but the median PLR values were lower (120
vs 108; p < 0.001, respectively) [36]. Moreover, the authors have found that PLR was an independent predictor
of CI-AKI (OR: 1.77; 95% CI: 1.24–2.53; p = 0.002 for PLR >177.5). This is in agreement with our findings.
However, Velibey et al. have suggested a cutoff value for PLR >177.5 which yielded 60% sensitivity and 72%
specificity, but showed just mild-to-moderate predictive value (C-statistic 0.61) [36]. The cutoff value for predicting
CI-AKI is much higher in comparison to the one obtained in our study (PLR >115). Moreover, PLR predictive
value in our study was better (C-statistic 0.72). Since we have enrolled more high-risk patients (all patients had
DM, patients with cardiogenic shock were not excluded, lower left ventricular ejection fraction), this discordance
10.2217/bmm-2017-0120
Biomark. Med. (Epub ahead of print)
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Research Article
Platelet-to-lymphocyte ratio & acute kidney injury
is not surprising. Reasonably, high-risk patients (or patients with many comorbid conditions if you will) are more
prone to developing CI-AKI following PCI thus allowing for a lower cutoff value for predicting CI-AKI.
Our results also match those reported by Kocas et al [37]. They have studied 488 patients with non-ST-segment
elevation ACS and, similarly, reported that patients with CI-AKI group had significantly higher PLR than those
without CI-AKI (152.9 ± 99.6 vs 120.4 ± 66.1; p < 0.001). Likewise, PLR was an independent predictor of renal
dysfunction following PCI. Interestingly, they have found a similar to our PLF cutoff (PLR >112) for predicting
CI-AKI. It is a well-known fact that multimorbidity is more prevalent among non-ST-segment elevation ACS
patients [38]. Hence, our population (all diabetics, 19.5% with cardiogenic shock, more impaired left ventricular
systolic function) resembles the population of Kocas et al. [37] to a greater extent than this of Velibey et al. [36]. A
possible explanation for this might be that the degree of interaction between thrombotic and inflammatory milieu
(expressed as lower PLR cutoff values) necessary to cause or predict renal impairment may be less pronounced in
high-risk patients.
In terms of time from symptom onset, some studies have found it to be an independent risk factor of CI-AKI [39].
Shacham et al. reported that patients having longer time for reperfusion had significantly more AKI, complicating
the course of STEMI (3 vs 11 vs 13%; p = 0.007 for cutoff points of the time to reperfusion tertiles <120,
120–300 and >300 min, respectively) [39]. However, the main difference was derived from a significant difference
between tertile 1 (<120 min) and tertile 3 (>300 min). Given that the median time from symptom onset was
5 h and the lower (25th) quartile was 3 h in our cohort, we did not find any relationship between time from
symptom onset and the rate of CI-AKI. Other factors that could have influenced our results include different
baseline characteristics (all diabetic patients, inclusion of patients with cardiogenic shock and higher rate of prior
MI). Other studies also suggest that ejection fraction is an important factor in CI-AKI development [40]. In contrast
to this study, we did not confirm that relationship in our cohort. Perhaps, the same or similar factors as stated for
the time from symptom onset could have played a role in this discrepancy.
So far, this discussion has focused on the possible associations between PLR and CI-AKI. Given the study
was performed among diabetic population, the issue of antidiabetic medications, metformin in particular, also
needs to be addressed. Early studies have demonstrated an association between metformin use, CI-AKI and lactic
acidosis [41,42]. However, we found similar rates of metformin use prior to admission in patients who did and did
not subsequently develop CI-AKI. Our results agree with recent studies [43,44]. Zeller et al. studied the effect of prior
metformin use in 372 diabetic patients with STEMI treated with PCI. They demonstrated that chronic metformin
treatment prior to primary PCI had no significant impact on the risk of subsequent CI-AKI development [43].
Conclusion
Taken together, these results suggest a potential role for PLR being a biomarker of increased risk of CI-AKI among
diabetic patients with STEMI who undergo PCI. The findings of this research provide insights for the association
between PLR and contrast-induced renal impairment.
Strength & limitations
We investigated a real-life population of diabetic patients with STEMI in which we did not exclude patients with
severe comorbidities, including cardiogenic shock. These comorbidities may have predisposed some of the patients
to a greater risk of CI-AKI. This was a single center study with a cross-sectional design. The retrospective nature
of the study also has its shortcomings. We did not measure any other biomarkers of CI-AKI including CRP,
IL-6, blood urea nitrogen, albuminuria and neutrophil gelatinase-associated lipocalin among others. Despite these
limitations, we present the first study that focused on the predictive value of PLR in the settings of STEMI among
patients with DM. It would seem that PLR could be an inexpensive and simple tool readily available in everyday
clinical practice that would aid us in identifying patients at risk for CI-AKI development.
Financial & competing interests disclosure
The authors have no relevant affiliations or 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.
No writing assistance was utilized in the production of this manuscript.
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Research Article
Hudzik, Szkodziński, Korzonek-Szlacheta et al.
Ethical conduct of research
The study conforms to the Declaration of Helsinki. All patients signed informed consent for data analysis according to the Polish
law on patients’ rights regarding data registration. The local bioethics committee on human research waived the approval for
analyzing recorded data given the retrospective nature of the study.
Summary points
r Percutaneous coronary interventions (PCIs) have become the mainstay in the management of ST-elevation
myocardial infarction (STEMI).
r However, with the increasing trends in the interventional procedures, there has been a concomitant rise in the
contrast-induced acute kidney injury (CI-AKI).
r Various data indicate that CI-AKI is associated with adverse clinical outcomes.
r The quest for identifying new, simple and inexpensive risk factors and possible biomarkers of CI-AKI is an area of
extensive research nowadays.
r Recently, platelet-to-lymphocyte ratio (PLR) has emerged as a new and strong marker of worse outcomes.
r Given the potential role of inflammation in platelets in CI-AKI, we proposed to examine the role of PLR in
predicting CI-AKI episodes in patients with STEMI and diabetes mellitus.
r The rate of CI-AKI was 14.5% which is relatively high. Patients who subsequently developed CI-AKI had higher
PLR values. PLR showed correlation with both serum crestinine and estimated glomerular filtration rate. More
importantly, PLR correlated with the increase in serum crestinine and the decline in estimated glomerular
filtration rate following PCI. Additionally, PLR showed good predictive value in receiver operating characteristic
analysis. And finally, PLR was one of the independent risk factors of developing CI-AKI.
r Taken together, these results suggest a role for PLR in predicting CI-AKI among diabetic patients with STEMI who
undergo PCI.
r The findings of this research provide insights for the association between PLR and contrast-induced renal
impairment.
References
Papers of special note have been highlighted as: • of interest; •• of considerable interest
1.
Silver SA, Shah PM, Chertow GM, Harel S, Wald R, Harel Z. Risk prediction models for contrast-induced nephropathy: systematic
review. BMJ 351, h4395 (2015).
•
Describes potential risk factors of contrast-induced acute kidney injury (CI-AKI).
2.
Marenzi G, Assanelli E, Campodonico J et al. Contrast volume during primary percutaneous coronary intervention and subsequent
contrast-induced nephropathy and mortality. Ann. Intern. Med. 150(3), 170–177 (2009).
3.
James MT, Samuel SM, Manning MA et al. Contrast-induced acute kidney injury and risk of adverse clinical outcomes after coronary
angiography: a systematic review and meta-analysis. Circ. Cardiovasc. Interv. 6(1), 37–43 (2013).
••
Describes potential risk factors of CI-AKI associated with coronary angiogrpahy.
4.
Gruberg L, Mintz GS, Mehran R et al. The prognostic implications of further renal function deterioration within 48 h of interventional
coronary procedures in patients with pre-existent chronic renal insufficiency. J. Am. Coll. Cardiol. 36(5), 1542–1548 (2000).
5.
Bartholomew BA, Harjai KJ, Dukkipati S et al. Impact of nephropathy after percutaneous coronary intervention and a method for risk
stratification. Am. J. Cardiol. 93(12), 1515–1519 (2004).
6.
Wi J, Ko YG, Kim JS et al. Impact of contrast-induced acute kidney injury with transient or persistent renal dysfunction on long-term
outcomes of patients with acute myocardial infarction undergoing percutaneous coronary intervention. Heart 97(21), 1753–1757
(2011).
7.
Andreucci M, Faga T, Riccio E, Sabbatini M, Pisani A, Michael A. The potential use of biomarkers in predicting contrast-induced acute
kidney injury. Int. J. Nephrol. Renovasc. Dis. 9, 205–221 (2016).
••
Studies potential biomarkers in prediction of CI-AKI.
8.
Akcay A, Nguyen Q, Edelstein CL. Mediators of inflammation in acute kidney injury. Mediators Inflamm. 2009, 137072 (2009).
••
Studies potential inflammatory biomarkers in prediction of CI-AKI.
9.
Hudzik B, Szkodzinski J, Danikiewicz A et al. Serum interleukin-6 concentration predicts contrast-induced nephropathy in patients
undergoing percutaneous coronary intervention. Eur. Cytokine Netw. 21(2), 129–135 (2010).
10. Rabb H. The T-cell as a bridge between innate and adaptive immune systems: implications for the kidney. Kidney Int. 61(6), 1935–1946
(2002).
11. Sahin I, Karabulut A, Avci Ii et al. Contribution of platelet indices in the development of contrast-induced nephropathy. Blood Coagul.
Fibrinolysis 26(3), 246–249 (2015).
10.2217/bmm-2017-0120
Biomark. Med. (Epub ahead of print)
future science group
Research Article
Platelet-to-lymphocyte ratio & acute kidney injury
12. Kwon HC, Kim SH, Oh SY et al. Clinical significance of preoperative neutrophil-lymphocyte versus platelet-lymphocyte ratio in
patients with operable colorectal cancer. Biomarkers 17(3), 216–222 (2012).
13. Feng JF, Huang Y, Zhao Q, Chen QX. Clinical significance of preoperative neutrophil lymphocyte ratio versus platelet lymphocyte ratio
in patients with small cell carcinoma of the esophagus. ScientificWorldJournal 2013, 504365 (2013).
14. Proctor MJ, Morrison DS, Talwar D et al. A comparison of inflammation-based prognostic scores in patients with cancer. A Glasgow
Inflammation Outcome Study. Eur. J. Cancer 47(17), 2633–2641 (2011).
15. Smith RA, Ghaneh P, Sutton R, Raraty M, Campbell F, Neoptolemos JP. Prognosis of resected ampullary adenocarcinoma by
preoperative serum CA19–9 levels and platelet-lymphocyte ratio. J. Gastrointest. Surg. 12(8), 1422–1428 (2008).
16. Smith RA, Bosonnet L, Raraty M et al. Preoperative platelet-lymphocyte ratio is an independent significant prognostic marker in resected
pancreatic ductal adenocarcinoma. Am. J. Surg. 197(4), 466–472 (2009).
17. Azab B, Shah N, Akerman M, Mcginn JT, Jr. Value of platelet/lymphocyte ratio as a predictor of all-cause mortality after
non-ST-elevation myocardial infarction. J. Thromb. Thrombolysis 34(3), 326–334 (2012).
18. Kurtul A, Yarlioglues M, Murat SN et al. Usefulness of the Platelet-to-lymphocyte ratio in predicting angiographic reflow after primary
percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction. Am. J. Cardiol. 114(3), 342–347
(2014).
19. Hudzik B, Szkodzinski J, Gorol J et al. Platelet-to-lymphocyte ratio is a marker of poor prognosis in patients with diabetes mellitus and
ST-elevation myocardial infarction. Biomark. Med. 9(3), 199–207 (2015).
20. Jenne CN, Urrutia R, Kubes P. Platelets: bridging hemostasis, inflammation and immunity. Int. J. Lab. Hematol. 35(3), 254–261 (2013).
21. Egan K, Crowley D, Smyth P et al. Platelet adhesion and degranulation induce prosurvival and proangiogenic signaling in ovarian cancer
cells. PLoS ONE 6(10), e26125 (2011).
22. Labelle M, Begum S, Hynes RO. Platelets guide the formation of early metastatic niches. Proc. Natl Acad. Sci. USA 111(30),
E3053–E3061 (2014).
23. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature 454(7203), 436–444 (2008).
24. Nikolsky E, Grines CL, Cox DA et al. Impact of baseline platelet count in patients undergoing primary percutaneous coronary
intervention in acute myocardial infarction (from the CADILLAC trial). Am. J. Cardiol. 99(8), 1055–1061 (2007).
25. Ommen SR, Gibbons RJ, Hodge DO, Thomson SP. Usefulness of the lymphocyte concentration as a prognostic marker in coronary
artery disease. Am. J. Cardiol. 79(6), 812–814 (1997).
26. Weisberg LS, Kurnik PB, Kurnik BR. Risk of radiocontrast nephropathy in patients with and without diabetes mellitus. Kidney
Int. 45(1), 259–265 (1994).
27. Heyman SN, Rosenberger C, Rosen S, Khamaisi M. Why is diabetes mellitus a risk factor for contrast-induced nephropathy? Biomed.
Res. Int. 2013, 123589 (2013).
28. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron. Clin. Pract. 120(4), c179–c184 (2012).
29. Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150(9), 604–612
(2009).
30. Ryden L, Standl E, Bartnik M et al. Guidelines on diabetes, prediabetes and cardiovascular diseases: executive summary. The Task Force
on Diabetes and Cardiovascular Diseases of the European Society of Cardiology (ESC) and of the European Association for the Study of
Diabetes (EASD). Eur. Heart J. 28(1), 88–136 (2007).
31. Silber S, Albertsson P, Aviles FF et al. Guidelines for percutaneous coronary interventions. The Task Force for Percutaneous Coronary
Interventions of the European Society of Cardiology. Eur. Heart J. 26(8), 804–847 (2005).
32. Chen SL, Zhang J, Yei F et al. Clinical outcomes of contrast-induced nephropathy in patients undergoing percutaneous coronary
intervention: a prospective, multicenter, randomized study to analyze the effect of hydration and acetylcysteine. Int. J. Cardiol. 126(3),
407–413 (2008).
33. Mehran R, Aymong ED, Nikolsky E et al. A simple risk score for prediction of contrast-induced nephropathy after percutaneous
coronary intervention: development and initial validation. J. Am. Coll. Cardiol. 44(7), 1393–1399 (2004).
34. Toprak O, Cirit M, Yesil M et al. Impact of diabetic and prediabetic state on development of contrast-induced nephropathy in patients
with chronic kidney disease. Nephrol. Dial. Transplant. 22(3), 819–826 (2007).
35. Kaya A, Kaya Y, Topcu S et al. Neutrophil-to-lymphocyte ratio predicts contrast-induced nephropathy in patients undergoing primary
percutaneous coronary intervention. Angiology 65(1), 51–56 (2014).
36. Velibey Y, Oz A, Tanik O et al. Platelet-to-lymphocyte ratio predicts contrast-induced acute kidney injury in patients with ST-segment
elevation myocardial infarction undergoing primary percutaneous coronary intervention. Angiology 68(5), 419–427 (2016).
••
Examines the value of platelet-to-lymphocyte ratio in predicting CI-AKI in patients with acute myocardial infarction.
37. Kocas C, Yildiz A, Abaci O et al. Platelet-to-lymphocyte ratio predicts contrast-induced nephropathy in patients with non-ST-segment
elevation acute coronary syndrome. Angiology 66(10), 964–968 (2015).
future science group
10.2217/bmm-2017-0120
Research Article
Hudzik, Szkodziński, Korzonek-Szlacheta et al.
38. Roffi M, Patrono C, Collet JP et al. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting
without persistent ST-segment elevation: Task Force for the Management of Acute Coronary Syndromes in Patients Presenting without
Persistent ST-Segment Elevation of the European Society of Cardiology (ESC). Eur. Heart J. 37(3), 267–315 (2016).
39. Shacham Y, Leshem-Rubinow E, Gal-Oz A et al. Relation of time to coronary reperfusion and the development of acute kidney injury
after ST-segment elevation myocardial infarction. Am. J. Cardiol. 114(8), 1131–1135 (2014).
40. Shacham Y, Leshem-Rubinow E, Gal-Oz A et al. Association of left ventricular function and acute kidney injury among ST-elevation
myocardial infarction patients treated by primary percutaneous intervention. Am. J. Cardiol. 115(3), 293–297 (2015).
41. Sirtori CR, Pasik C. Re-evaluation of a biguanide, metformin: mechanism of action and tolerability. Pharmacol. Res. 30(3), 187–228
(1994).
42. Thomsen HS, Morcos SK. Contrast media and the kidney: European Society of Urogenital Radiology (ESUR) guidelines. Br. J.
Radiol. 76(908), 513–518 (2003).
43. Zeller M, Labalette-Bart M, Juliard JM et al. Metformin and contrast-induced acute kidney injury in diabetic patients treated with
primary percutaneous coronary intervention for ST-segment elevation myocardial infarction: a multicenter study. Int. J. Cardiol. 220,
137–142 (2016).
44. Bell S, Farran B, Mcgurnaghan S et al. Risk of acute kidney injury and survival in patients treated with metformin: an observational
cohort study. BMC Nephrol. 18(1), 163 (2017).
10.2217/bmm-2017-0120
Biomark. Med. (Epub ahead of print)
future science group
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