Prognostic value of platelet-to-lymphocyte ratios among critically ill patients with acute kidney injury
Zheng et al. Critical Care
Prognostic value of platelet-to-lymphocyte ratios among critically ill patients with acute kidney injury
Chen-Fei Zheng 3
Wen-Yue Liu 2
Fang-Fang Zeng 1 7
Ming-Hua Zheng 6
Hong-Ying Shi 5
Ying Zhou 4
Jing-Ye Pan 0
0 Department of Intensive Care, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou 325000 , China
1 Department of Epidemiology, School of Basic Medical Sciences, Jinan University , Guangzhou 510632 , China
2 School of the First Clinical Medical Sciences, Wenzhou Medical University , Wenzhou 325000 , China
3 Department of Nephrology, the First Affiliated Hospital of Wenzhou Medical University , Wenzhou 325000 , China
4 Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Key Laboratory of Nephrology, Ministry of Health and Guangdong Province , Guangzhou 510000 , China
5 Department of Preventive Medicine, Wenzhou Medical University , Wenzhou 325000 , China
6 Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou 325000 , China
7 Guangdong Provincial Key Laboratory of Food , Nutrition, and Health , School of Public Health, Sun Yat-sen University , Guangzhou 510000 , China
Background: Inflammation plays an important role in the initiation and progression of acute kidney injury (AKI). However, evidence regarding the prognostic effect of the platelet-to-lymphocyte ratio (PLR), a novel systemic inflammation marker, among patients with AKI is scarce. In this study, we investigated the value of the PLR in predicting the outcomes of critically ill patients with AKI. Methods: Patient data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care Database III version 1.3. PLR cutoff values were determined using smooth curve fitting or quintiles and were used to categorize the subjects into groups. The clinical outcomes were 30-day and 90-day mortality in the intensive care unit (ICU). Cox proportional hazards models were used to evaluate the association between the PLR and survival. Results: A total of 10,859 ICU patients with AKI were enrolled. A total of 2277 thirty-day and 3112 ninety-day deaths occurred. A U-shaped relationship was observed between the PLR and both 90-day and 30-day mortality, with the lowest risk being at values ranging from 90 to 311. The adjusted HR (95% CI) values for 90-day mortality given risk values < 90 and > 311 were 1.25 (1.12-1.39) and 1.19 (1.08-1.31), respectively. Similar trends were observed for 30-day mortality or when quintiles were used to group patients according to the PLR. Statistically significant interactions were found between the PLR and both age and heart rate. Younger patients (aged < 65 years) and those with more rapid heart rates (≥89.4 beats per minute) tended to have poorer prognoses only when the PLR was < 90, whereas older patients (aged ≥ 65 years) and those with slower heart rates (<89.4 beats per minute) had higher risk only when the PLR was > 311 (P < 0.001 for age and P < 0.001 for heart rate). Conclusions: The preoperative PLR was associated in a U-shaped pattern with survival among patients with AKI. The PLR appears to be a novel, independent prognostic marker of outcomes in critically ill patients with AKI.
Platelet-to-lymphocyte ratio; Acute kidney injury; Prognosis; Intensive care unit
More than 5 million patients are admitted to intensive care
units (ICUs) each year in America [
], and 6–24% of
these patients have acute kidney injury (AKI) . In the
presence of AKI, patient mortality increases to as high as
60–70%, especially within 1 year after ICU admission [
Considering the high incidence of AKI in the ICU and its
poor prognosis, an increasing number of observational
studies over the past 2 decades have been devoted to
identifying the clinical predictors of mortality in AKI.
Systemic inflammation is an integral part of disease
progression in critical illness and is commonly
associated with sepsis, leading to an increased risk of mortality
]. Inflammation plays an important role in the
initiation and progression of AKI [
], and morphological
and/or functional changes in vascular endothelial cells
and/or in the tubular epithelium are observed in patients
with AKI. Leukocytes, including lymphocytes, infiltrate
the injured kidneys and the entire body via the
circulatory system and induce the generation of inflammatory
mediators such as cytokines and chemokines, which
damage the kidney and other organs . The
antithrombotic effects of platelets can evolve into
atherogenesis via the secretion of proinflammatory cytokines [
whereas the binding of platelets to endothelial cells can
trigger leukocyte transmigration and adhesion, especially
in the presence of shear stress [
platelet-tolymphocyte ratio (PLR) has been introduced as a
potential marker of inflammation in cardiovascular disease
(CVD) and tumors, which are also inflammation-related
]. A positive monotonic association
between a high PLR and a poor prognosis for these
diseases has been reported [
]. On the basis of the
results of these studies, it is reasonable to speculate that
the PLR might affect the prognosis of AKI. However, to
the best of our knowledge, no epidemiological study to
date has explored the prognostic effect of the PLR in
patients with AKI.
This study was based on the publicly and freely available
database known as the Multiparameter Intelligent
Monitoring in Intensive Care Database III version 1.3
(MIMIC-III v1.3). This database comprises de-identified
health-related data associated with over 40,000 patients
treated in a variety of critical care units at Beth Israel
Deaconess Medical Center (Boston, MA, USA) between
2001 and 2012 [
]. To apply for permission to access
the database, researchers must complete the National
Institutes of Health’s web-based course known as
Protecting Human Research Participants (certification
The establishment of this database was approved by
the institutional review boards of Massachusetts
Institute of Technology (MIT, Cambridge, MA, USA)
and Beth Israel Deaconess Medical Center. All included
patients were de-identified to protect their privacy.
Population selection criteria
A total of 58,976 ICU admissions were recorded in the
MIMIC-III database. Eligible patients were those who
were older than 18 years of age at first admission and who
stayed in the hospital > 2 days. Patients were excluded
from our study if (1) > 5% of their individual data were
missing and (2) outliers were present. Outliers were
defined as values exceeding the mean ± 3 times the SD.
The occurrence of AKI was determined on the basis of
Kidney Disease: Improving Global Outcomes (KDIGO)
], which specifies that serum creatinine
(SCr) changes ≥ 1.5 times baseline must have occurred
within the prior 7 days; a 0.3 mg/dl increase in SCr must
have occurred within a 48-h period; and urine output
must be < 0.5 ml/kg/h per 6 h. Stage 1 is defined as an
increase in SCr to a level ≥ 1.5 times baseline or 0.3 mg/dl
or urine output < 0.5 ml/kg/h per 6 h. Stage 2 is defined as
an increase in SCr to a level ≥ 2.0 times baseline or urine
output < 0.5 ml/kg/h per 12 h. Stage 3 is defined as an
increase in SCr to a level ≥ 3.0 times baseline, an increase
in SCr to a level ≥ 4.0 mg/dl, the initiation of renal
replacement therapy (RRT), or urine output < 0.5 ml/kg/h
per 12 h. Urine output was observed for the first 24 h after
ICU admission. For patients who did not have an available
SCr value prior to hospitalization, we followed the
recommendation of the International Club of Ascites and used
the first measured value during hospitalization as the
baseline SCr [
Patient data were exacted from MIMIC-III using
Structured Query Language (SQL) with MySQL tools (version
5.6.24). The extracted data, including patient identifiers,
demographic parameters, clinical parameters, laboratory
parameters, and scoring systems, were collected from
2001 to 2012 at Beth Israel Deaconess Medical Center.
Records containing baseline characteristics were
extracted within the first 24 h after patient admission.
Laboratory measurements included platelets, white
blood cells, lymphocytes, neutrophils, SCr levels, blood
urea nitrogen (BUN) levels, serum potassium levels,
serum sodium levels, serum pH, partial pressure of
carbon dioxide, partial pressure of oxygen (PO2), serum
glucose levels, and urine output. The PLR was defined
as the ratio of the absolute platelet count to the absolute
Severity-of-illness scores, including the Simplified Acute
Physiology Score II (SAPS II), Sequential Organ Failure
Assessment (SOFA) score, and Glasgow Coma Scale
(GCS) score, were recorded and calculated for each
patient. In addition, the Elixhauser comorbidity score was
used as a comorbidity estimate. Three other standard
scoring systems were evaluated, enabling a comparison
with our SAPS II score with glucose variability parameters
(SOFA score, and Elixhauser comorbidity score). All
scores were calculated using physiological measurements
and clinical information according to published
recommendations and accepted formulae.
The start date for follow-up was the date of the
patient’s admission. The date of death was obtained from
Social Security Death Index records from the U.S.
government. All patients were followed for at least
3 months. The outcomes of our study were defined as
30-day and 90-day mortality.
Statistical analysis and modeling strategy
Baseline characteristics were grouped by PLR cutoffs
and are presented as frequency (percent) for categorical
data and as mean (SD) or IQR for continuous data.
Comparisons between groups were made using the
chisquare test for categorical variables and analysis of
variance or the Kruskal-Wallis test for continuous variables.
Survival curves were generated using the Kaplan-Meier
method and compared using the log-rank test. Cox
proportional hazards models were used to test the
associations between 90-day mortality (primary outcome) and
baseline covariates, with results presented as HRs with
95% CIs. We also analyzed associations between the PLR
and 30-day mortality. To determine whether the PLR
was independently associated with endpoints, we
performed multivariable analysis using a forward selection
For each endpoint, two multivariate models were
constructed on the basis of PLR group inclusion according to
quintiles or cutoffs derived with curve-fitting methods
based on 90-day mortality. The second quartile or the
lower-limit group was treated as the reference group. In
model 1, covariates were adjusted only for age and sex; in
model 2, we further adjusted for PO2, ethnicity, GCS
score, vasopressin use, ventilator use, systolic blood
pressure (SBP), cardiac arrhythmias, valvular disease,
pulmonary circulation, chronic pulmonary disease, liver disease,
lymphoma, solid tumors, deficiency anemia, heart rate,
SBP, potassium, SCr, urine output, BUN, and ph. We
conducted stratification analyses to examine whether the
effect of the PLR differed across various subgroups
classified by AKI stage, RRT use, age, sex, ethnicity, PO2, GCS,
heart rate, SBP, potassium, SCr, urine output, BUN,
vasopressin use, ventilator use, comorbidities (i.e., cardiac
arrhythmias, valvular disease, pulmonary circulation,
chronic pulmonary disease, liver disease, lymphoma,
metastatic cancer, solid tumors, and deficiency anemias),
and cardiac surgery. Multiplicative interactions were
estimated by adding interaction terms according to the
likelihood ratio test. All statistical analyses were
performed using the IBM SPSS Statistics version 19.0
(IBM, Armonk, NY, USA), EmpowerStats (http://
www.empowerstats.cn/), and MedCalc (MedCalc
Software, Ostend, Belgium) software programs. A
twotailed P value < 0.05 was considered statistically
Patient records from 14,354 subjects who underwent
ICU treatment at Beth Israel Deaconess Medical Center
were initially extracted from the MIMIC-III database.
After patients who did not meet the inclusion criteria
were excluded, 10,859 eligible subjects were enrolled.
The subjects included 5931 men and 4928 women with
a mean age of 65.4 (15.8) years. Of these subjects, 6881
(63.4%) patients were recruited from the medical ICU,
and 3978 (36.6%) patients were recruited from the
The overall mean (SD) PLR was 285.7 (256.9). When
the patients were divided on the basis of 90-day
mortality according to the curve-fitting method (Fig. 1), 1708
(15.7%) were in the low-PLR group (PLR < 90), 6699
(22.6%) were in the mid-PLR group (90–311), and 2454
(22.6%) were in the high-PLR group (PLR > 311).
Selected characteristic and hematologic laboratory data
across PLR groupings are provided in Table 1.
Participants with higher calibrated PLRs (PLR > 311) were
more likely to be elderly, female, and white and to report
a history of chronic pulmonary disease, metastatic
cancer, solid tumors, and iron-deficiency anemia; they
also had higher levels of serum potassium, BUN, white
blood cells, neutrophils, platelets, urine output, and
eGFR and were more likely to use RRT than those with
lower PLRs (PLR < 90).
Association between platelet-to-lymphocyte ratio and 30day and 90-day outcomes
A total of 2277 thirty-day and 3112 ninety-day deaths
occurred during the follow-up period. A U-shaped
relationship was observed between the PLR and 90-day
mortality, and the patients in the mid-PLR group (90–
Abbreviations: BUN Blood urea nitrogen, DBP Diastolic blood pressure, eGFR, Estimated glomerular filtration rate; GCS Glasgow Coma Scale, KDIGO Kidney Disease:
Improving Global Outcomes, PCO2 Partial pressure of carbon dioxide, PO2 Partial pressure of oxygen, SBP Systolic blood pressure, SOFA Sequential Organ Failure Assessment
Normally distributed data are presented as the mean (SD) (analysis of variance); non-normally distributed data are presented as median (IQR) (nonparametric Wilcoxon
test); and categorical variables are presented as n (%) (chi-square test)
311) had the lowest 30-day mortality rate when
compared with rates in the PLR < 90 and PLR > 311 groups
(both P < 0.001). The adjusted HRs (95% CIs) for PLRs <
90 and > 311 were 1.25 (1.12–1.39) and 1.19 (1.08–1.31),
respectively. A similar trend was observed for 30-day
mortality, and the risk was less evident with higher PLRs
(P = 0.047 for PLR > 311). Following the stratification of
PLRs into quintiles and using the second quintile (PLR
101.2–155.6) as a reference, both extremely low (<101.2)
and extremely high (>330.2) PLRs were associated with
an increased risk of 90-day mortality. For 30-day
mortality, the lowest PLRs (<101.2, P = 0.001) were
associated with an increased risk, whereas marginally
increased risk was associated with extremely high
PLRs (>330.2, P = 0.088) after adjustment for potential
confounders (Figs. 1 and 2 and Table 2).
In the subgroup analyses, the association between the
PLR and the risk of 90-day mortality was similar for
< 101.1 2172/532 1.39 (1.22–1.58) <0.001 1.30 (1.12–1.50) 0.001
101.2–155.6 2172/406 1.00
155.7–220.2 2173/423 1.05 (0.91–1.20) 0.516 1.12 (0.96–1.31) 0.141
220.3–330.1 2171/432 1.06 (0.93–1.22) 0.388 1.04 (0.89–1.21) 0.649
≥ 330.2 2171/484 1.22 (1.07–1.39) 0.003 1.14 (0.98–1.32) 0.088
Models 1 and 2 were derived from Cox proportional hazards regression models: model 1 covariates were adjusted for age and sex; model 2 covariates were
adjusted for age, sex, partial pressure of oxygen, ethnicity, Glasgow Coma Scale score, vasopressin use, ventilator use, systolic blood pressure, cardiac arrhythmias,
valvular disease, pulmonary circulation, chronic pulmonary disease, liver disease, lymphoma, solid tumor, deficiency anemias, heart rate, potassium creatinine,
urine output, blood urea nitrogen, and pH
most strata (P = 0.083–0.983) (Table 3). Significant
interactions were observed only for age (P < 0.001). Patients
younger than 65 years of age had a significantly higher
risk of 90-day mortality for a PLR < 90 (HR 1.37, 95% CI
1.16–1.62, P < 0.001), whereas only older patients
(≥65 years) showed an increased risk for a PLR > 311
(HR 1.25 95% CI 1.22–1.40, P < 0.001). Similarly,
patients with a heart rate < 89.4 beats per minute had a
significantly higher risk of 90-day mortality with a PLR >
311 (HR 1.29, 95% CI 1.22–1.48, P < 0.001), whereas
patients with a heart rate ≥ 89.4 beats per minute (aged ≥
65 years) showed an increased risk only with a PLR < 90
(HR 1.39, 95% CI 1.20–1.61, P < 0.001).
In this study, we observed a U-shaped relationship
between the PLR and 30-day and 90-day mortality, and
both low and high PLRs were associated with increased
all-cause mortality. Proctor et al. [
] investigated the
correlation between the PLR and overall survival in a
large-scale cohort of 8759 patients with cancer. In
contrast to our results, their study showed a positive
correlation between the PLR and mortality when using a
similar PLR cutoff (PLR < 150, HR 1; PLR 150–300, HR
1.19; P < 0.001; PLR > 300, HR 1.71, P < 0.001). Yaprak
et al. [
] recently evaluated the correlation between the
PLR and mortality in a small cohort of patients with
end-stage renal disease (ESRD) and demonstrated that
the PLR could independently predict all-cause mortality
in this population. A main reason for this difference is
the insufficient number of patients with low PLRs.
However, our study addresses this limitation. In the
Framingham Heart Study, blood cell composition was treated as
a prognostic factor for CVD, and the association
between hematocrit and CVD mortality showed a
Ushaped association in both men and women [
Both AKI and chronic kidney disease (CKD) are
associated with local and systemic inflammation [
Researchers in many observational studies have
described high circulating levels of inflammatory mediators
and adverse outcomes for these conditions. These
inflammatory mediators include blood cells, components
of endothelial cells, platelets, lymphocytes, macrophages,
mast cells, and fibroblasts. The PLR has been
investigated as a new inflammatory marker for predicting
major adverse events associated with CVD [
]. In a
study of 2563 patients, Velibey et al. [
that increased PLRs are independently associated with a
greater risk of contrast-induced AKI in patients
undergoing primary percutaneous coronary intervention. A
recent study showed that a high PLR is related to the
presence of coronary artery disease and is correlated
with C-reactive protein and fibrinogen levels [
PLRs in patients with ESRD were also associated with
high levels of inflammation. Balta et al. [
] showed that
inflammation is better predicted by the PLR than by the
neutrophil-to-lymphocyte ratio in ESRD. On the basis of
the association between PLR-related inflammation and
disease severity, we speculated that excessively high
PLRs could predict the same poor outcomes as other
inflammation biomarkers in AKI populations.
In a population-based cohort study comparing the
mortality rates of 605 ICU patients with those of
patients with acute renal failure (ARF), Mehta et al. [
found that a platelet count < 20,000/mm3 is a criterion
for hematologic failure and that the risk of mortality was
more than threefold higher among patients who had
hematologic failure than among control patients with
normal platelet levels (OR 3.39, 95% CI 2.08–5.52).
Using data from the Program to Improve Care in Acute
Renal Disease, a multicenter observational study of ARF,
Chertow et al. [
] examined the correlates of mortality
in 618 ICU patients with ARF. Thrombocytopenia was
associated with mortality at the time of consultation. In
addition, among 512 ICU patients requiring acute
dialysis, a platelet count < 50,000/mm3 was a potential risk
factor for mortality in multivariate analysis [
Thrombocytopenia is common among critically ill
patients and is often associated with poor outcomes
]. The mechanisms underlying thrombocytopenia
include either reduced platelet production or excessive
platelet destruction related to an underlying illness and
therapeutic interventions . Taken together, these
findings show that low platelet counts could result from
a low PLR in patients with AKI and could lead to high
mortality, thus helping to explain our observation of a
U-shaped relationship between the PLR and mortality.
Although AKI in the ICU is associated with high
mortality, other factors appear to contribute to poor
outcomes. Potential factors that may affect the outcome of
AKI include blood pressure [
], renal function [
urine output [
], and other clinical parameters (i.e.,
SCr, BUN, and pH [
]), as well as comorbidities (e.g.,
cardiac disease [
]). In the present study, when patients
were stratified according to potential confounders, no
significant interactions were observed for sex, ethnicity,
PO2, GCS, SBP, potassium, SCr, urine output, BUN,
vasopressin use, or ventilator use. Although possible
interactions between all-cause mortality and both age
and heart rate were observed, there was no
heterogeneity of clinical factors among those effects and the PLR.
However, little is known about the mechanism
underlying the interaction between age and the PLR. Recently,
in a multicenter cross-sectional study of a healthy Indian
population, Sairam et al. [
] found lower levels of
platelets in elderly people. Kweon et al. [
median PLRs in a healthy Korean population and
suggested that the PLR cutoff values for disease evaluation
P for interaction
should be established separately according to age.
Therefore, we should consider age when evaluating the
relationship between the PLR and mortality among critically
ill patients with AKI.
The limitations of this study should be acknowledged.
First, it was a single-center retrospective analysis, and
different conclusions could be reached when using
patient records from other centers. Therefore, subject
selection bias cannot be ignored, suggesting that a
prospective multicenter study is needed. However, the
strength of the present study is its representative and
ethnically diverse population. Second, because of a lack
of data on kidney function prior to 3 months before
patient admission, we could not assess CKD status
among patients with AKI or determine the role of CKD
in the association between the PLR and mortality. Third,
the PLR can be measured in patients only upon
admission to the ICU. A single measure of the PLR does not
fully reflect inflammation, which would be better
assessed by simultaneously measuring other
inflammatory mediators. Fourth, these preliminary data suggest
that the PLR could be a risk adjustment tool with
prognostic implications for AKI. Fifth, to establish the PLR
as a prognostic marker, researchers must further validate
its clinical significance. The cutoff value must be
established in one cohort of patients and tested in another,
and the number of patients in each group needs to be
considered in statistical analyses. Finally, we did not
assess the modification of sepsis and shock, which both
might increase patient morbidity and predict higher
mortality among patients with AKI [
], on association
between PLR and outcomes, owing to lack of related
We found a U-shaped relationship between the PLR and
mortality in which both low and high PLRs were
associated with increased overall mortality in critically ill
patients with AKI. The PLR is therefore potentially
useful in the clinical setting as a cost-effective and readily
available biomarker. Our findings need to be confirmed
by other studies, especially large prospective studies with
AKI: Acute kidney injury; ARF: Acute renal failure; BUN: Blood urea nitrogen;
CKD: Chronic kidney disease; CVD: Cardiovascular disease; DBP: Diastolic blood
pressure; eGFR: Estimated glomerular filtration rate; ESRD: End-stage renal
disease; GCS: Glasgow Coma Scale; ICU: Intensive care unit; KDIGO: Kidney
Disease: Improving Global Outcomes; MIMIC-III: Multiparameter Intelligent
Monitoring in Intensive Care Database III; PCO2: Partial pressure of carbon
dioxide; PLR: Platelet-to-lymphocyte ratio; PO2: Partial pressure of oxygen;
RRT: Renal replacement therapy; SAPS II: Simplified Acute Physiology Score II;
SBP: Systolic blood pressure; SCr: Serum creatinine; SQL: Structured Query
Language; SOFA: Sequential Organ Failure Assessment
The authors thank Fang-Fang Zeng for her excellent technical assistance with
data management and the manuscript.
The medical innovation discipline of Zhejiang Province (critical care medicine,
Y2015) and Wenzhou Committee of Science and Technology of China (ZS2017008)
supported this work.
Availability of data and materials
The datasets used and analysed during the current study are available from
the corresponding author on reasonable request.
This study was based on the publicly and freely available database known as
the Multi Parameter Intelligent Monitoring in Intensive Care III, version 1.3
(MIMIC-III v1.3). This database comprises de-identified health-related data
associated with over 40,000 patients treated in a variety of critical care units in
the Beth Israel Deaconess Medical Center between 2001 and 2012. To apply for
permission to access the database, researchers must complete the National
Institutes of Health’s web-based course known as Protecting Human Research
Participants (certification number 1605699). The establishment of this database
was approved by the institutional review boards of Massachusetts Institute of
Technology (Cambridge, MA, USA) and Beth Israel Deaconess Medical Center.
All included patients were de-identified to protect their privacy.
CFZ designed the study; collected, analyzed, and interpreted data; and
drafted the manuscript. WYL collected, analyzed, and interpreted data. FFZ
and HYS collected and analyzed data and drafted the manuscript. YZ drafted
the manuscript and interpreted data. MHZ designed the study, drafted the
manuscript, and interpreted data. JYP designed and supervised the study,
obtained funding, and drafted the manuscript. All authors read and
approved the final manuscript.
Ethics approval and consent to participate
The MIMIC-III database has received ethical approval from the institutional review
boards (IRBs) at Beth Israel Deaconess Medical Center and Massachusetts Institute
of Technology. Because the database does not contain protected health information,
a waiver of the requirement for informed consent was included in the IRB approval.
Consent for publication
The authors declare that they have no competing interests.
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