1 INTRODUCTION

The complete blood count (CBC) is used comprehensively in clinical healthcare to review individuals’ overall health and monitor or diagnose medical conditions. In addition to individual cell types relative and absolute concentrations, ratios of different cell types have been suggested to improve medical decision-making. Specifically, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), which can all be calculated using the CBC, have proven to be clinically useful biomarkers for various diseases.

Neutrophil-to-lymphocyte ratio has been extensively studied as a prognostic biomarker in infectious diseases,1 postoperative complications,2, 3 and in oncologic diseases.4, 5 Further, it has been proposed as a predictor for mortality and outcome in cardiovascular diseases (eg, myocardial infarction and cardiac failure).6 Similarly, PLR, a marker for inflammation and thrombosis has been associated with increased mortality and complications for cardiovascular events, including ST Elevation Myocardial Infarction (STEMI) and pulmonary embolism.7, 8 In addition, LMR has evolved as a prognostic marker for solid tumors.9, 10

All three parameters are simple, inexpensive, objective, and rapidly available biomarkers for inflammation, derived from a routine CBC. Most data on NLR, PLR, and LMR are based on adult studies including recently published reference intervals (RIs) for the adult population.11 However, all current pediatric studies are based on case-control comparisons only. To consider these parameters as meaningful biomarkers in the pediatric population, validated RIs are needed. As pediatric laboratory analytes underlie substantial changes from the neonatal period to adolescence, it is essential to establish separate validated RIs for NLR, PLR, and LMR for children to allow classification of samples in the context of intra- and interindividual variation. The aim of this report is to establish RIs for children of all ages for NLR, PLR, and LMR.

2 MATERIAL AND METHODS 2.1 Study population

All measurements for lymphocytes, neutrophils (segmented and banded), platelets, and monocytes performed between 01.01.2010 and 31.12.2019 in patients from 0 to 18 years at the University Hospital Erlangen, Department of Pediatrics and Adolescent Medicine were retrieved from the laboratory's database. Both in- and outpatient samples were selected and no stratification according to ethnicity was performed. The dataset was divided according to sex and age (18 age groups). We excluded test results from oncologic patients and patients from pediatric and neonatal intensive care units. Additionally, we removed results from patients with an increased C-reactive protein (CRP ≥ 5 mg/L) or procalcitonin (PCT ≥ 0.5 ng/mL) within ±3 days (relative to the CBC). If multiple measurements from a single individual were available, one measurement was randomly selected per age group (Figure 1).

Flowchart presenting the steps of inclusion and exclusion of subjects

For the analysis of specific diseases (Appendicitis, asthma, cystic fibrosis [CF], Bell's palsy, Henoch–Schonlein purpura [HSP]), laboratory test results were selected from patients who received the respective diagnosis within one week (±7 days). We included n = 321 patients with appendicitis, n = 1,124 patients with asthma, n = 112 patients with CF, n = 204 patients with Bell's palsy and n = 452 patients with HSP.

2.2 Analytical procedures

We analyzed the three ratios NLR, PLR, and LMR calculated from the absolute number of neutrophils, platelets, lymphocytes, and monocytes. Leucocyte subsets and total blood counts were measured on a Sysmex XE 2100 hematological analyzer (Sysmex Europe, Norderstedt, Germany), in accordance with standard operating procedures. All measurements have been performed in the pediatric laboratory of the Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Germany, and quality control according to German regulations was performed and passed. Both machine-counted and microscopic leukocyte subtypes were used for further analyses. If both data sets were available, the microscopic findings were used. Measurements from both in- and outpatients have been included, and nearly all samples were analyzed within 2 hours of specimen collection, without differences between in- and outpatient samples.

2.3 Calculation of reference intervals

Reference intervals and corresponding confidence intervals (CIs) were calculated with an indirect data mining approach described and validated previously. The applied refineR algorithm assumes that the input dataset is composed of a major fraction of physiological test results and a minor fraction of pathological test results. It is presumed that the physiological results can be described with a parametric distribution (Box-Cox-transformed normal distribution), whereas for the pathological results no specific distribution is assumed. refineR uses an inverse modeling approach, thereby trying to fit a model that best explains the observed data in the original domain. In a data pre-processing step, the algorithm first determines a region around the main peak and computes a histogram representation of the data. Afterward, the algorithm estimates a Box-Cox-transformed normal distribution and identifies the model that yields a maximum log-likelihood describing the histogram of the routine data. The optimal model is then utilized to estimate RIs, and confidence intervals are calculated via bootstrapping. The refineR algorithm has been shown to outperform other indirect algorithms for datasets with a high fraction and overlap of pathological test results, for example, the kosmic algorithm.12

2.4 Ethics

This study was approved by the institutional review board of the University Hospital Erlangen (reference number 97_17 Bc).

3 RESULTS

For the analysis, 232 746 full blood counts from 60 685 patients from birth to 18 years of age were available. The study population was divided into age groups of one year and by sex. For NLR, we had mean of 1,448 patients (range: 729-2969), for PLR mean of 2291 patients (range: 1340-9103) and LMR mean of 2284 patients (range: 1332-9063) per sex-specific age group. The corresponding number of patients per age group is shown in Figure 1.

We calculated age- and sex- specific RIs and CIs for NLR, PLR, and LMR. Age-dependent RIs and CIs for children from the first year of life to 18 years are shown in Figure 2A-C and in the Table S1-S3. All three ratios showed substantial age-specific dynamics, especially in the first years of life. NLR (Figure 2A) and LMR (Figure 2C) revealed a similar pattern of age-dependent changes during the first two years of life including wider RIs. NLR showed higher values directly after birth, and LMR a sharp increase between year one and two, with a narrowing of the RIs in the following years. NLR showed wider RIs for the first year of life and after puberty in the age groups from 15 to 18 years. From the age of 3-18 years, the 50th percentile of NLR is constantly increasing from 0.99 to 1.76 for both male and female individuals. LMR is slightly decreasing from a maximum of 5.78 for girls and 5.58 for boys at 1-2 years to a minimum of 3.73 for females and 3.55 for males during adolescence (age groups of 16-17 and 17-18 years).

Reference intervals (RIs) and confidence intervals (CIs) for (A) Neutrophil-to-lymphocyte ratio (NLR), (B) Platelet-to-lymphocyte ratio (PLR) and (C) Lymphocyte-to-monocyte ratio (LMR) from birth to 18 years.).▲, ● and ▼ represent 2.5th, 50th, and 97.5th percentiles, and vertical bars show the respective 90% CIs)

For PLR (Figure 2B), we identified a constant increase from a minimum of 61.36 (male) and 63.36 (female) at the first year of life to 112.6 (male) and 118.78 (female) at 17-18 years.

No significant sex differences between male and female children were found for all three parameters (NLR: P = 1.0; PLR: P =.752; LMR: P = .827).

We analyzed the proportion of NLR, PLR, and LMR ratios outside of the newly established RIs for patients with a concurrent diagnosis of an inflammatory disease. Of particular interest were diseases for which significantly altered ratios have been published: asthma, Bell's palsy, appendicitis, HSP, and CF. We found that 65.7% of patients with appendicitis show a higher NLR than the calculated upper reference limit, while LMR test results were below calculated RIs in 33.6%, 31.4% of PLR values were above age- and sex-specific RIs (Figure S1).

For asthma, we identified 20.6% of patients with higher NLR values than calculated RIs and 11.7% with higher PLR values (Figure S2). Similar results have been found in patients with CF. Here, 22.7% of the patients had pathological NLR values and in 12.5% had PLR test results above the calculated RI (Figure S3).

In patients with Bell's palsy and HSP, we identified 9.9% and 13.9% of NLR values higher than the RIs, for LMR: 8.8% and 6.9% and for PLR 7.8% and 8.2%, respectively (Figures S4 and S5, see Table S4 for details).

4 DISCUSSION

Neutrophil-to-lymphocyte ratio, PLR, and LMR can be calculated using the differential blood count. In recent years, these biomarkers have seen a substantial clinical and scientific appraisal. However, their application in the pediatric setting has been limited because validated pediatric RIs were missing. RIs are an essential tool for the interpretation of individual patients’ laboratory test results and facilitate decision-making in the clinical setting. In this study, we provide the first age- and sex-specific RIs for NLR, PLR, and LMR for pediatric patients from birth to adulthood.

Across the different age groups, NLR increases with age due to an increase in neutrophils from neonates to adolescents.13 Further, there is a decrease in lymphocytes from birth until the age of 18, influencing the NLR.13 Similarly, PLR showed a continuous increase over the investigated age range, mainly due to a higher platelet count in the first months of life, which decreases afterward during childhood.14 A rising PLR despite falling platelets is related to an even greater decrease in lymphocytes.13, 14 Both monocytes and lymphocytes decrease until adulthood. As the decrease of the lymphocytes is proportionally higher than that of monocytes, this results in a decrease of LMR over time.

Recently, Fei et al published adult RIs for NLR and PLR from 18 to 70 and older. NLR increased constantly from 18 to >70 years of age. PLR decreases from younger to the older male population. For female, PLR increases to a maximum between 41 and 50 years and then slowly decreases to a minimum between 61 and 70 years.11 In comparison, the RIs established by us exceed those from Fei et al, highlighting different dynamics in children, and hence the need for pediatric RIs.

The study did not observe a difference in sex for NLR, but an increase of PLR with age for the female and a decrease for the male population, which results in a significant sex difference for PLR.11 Despite sex-specific differences for platelets during childhood14 we did not observe any significant differences between male and female for PLR in children.

To explore the diagnostic benefit of NLR, PLR, and LMR in the clinical context we analyzed the proportion of ratios outside the newly created RIs in diagnoses where previous studies reported significant differences. It was described that NLR is a more sensitive method to diagnose appendicitis than white blood cell count alone suggesting a cut off value of NLR above 3.5,15 and PLR has been reported as a good predictive parameter for acute appendicitis.16 We identified 65.7% of patients with acute appendicitis presenting with increased NLR above the RI, therefore NLR might be a reliable additional parameter in the diagnosis of acute abdominal symptoms. Additionally, for LMR and PLR, more than 1/3 of the patients with appendicitis showed increased ratios.

In the study of Kim et al, higher NLR was associated with increased risk of renal impairment or gastrointestinal involvement due to HSP.17, 18 In our dataset, NLR was a weak parameter to predict HSP in general. However, we were only able to evaluate whether patients with HSP showed increased values, we did not investigate whether there was a higher incidence of renal involvement. Another pediatric study investigating patients in the age of 9-18 years with CF showed that a higher NLR (NLR ≥3) correlates with clinical status including lower body mass index and reduced forced expiratory volume per second (FEV1). However, in patients older than 16 years the 97.5th percentile of NLR is >3.1.19 We have found that 22.7% of CF patients in our dataset had pathological NLR values above the age- and sex-specific RIs.

In pediatric patients with asthma, NLR was reported to be higher in comparison to healthy children with a mean NLR of 2.07 compared to 1.77 in healthy controls.20 According to our analysis, both values would still be within the age-appropriate normal range. In our cohort, 20.6% of asthma patients presented with NLR >97.5th percentile, whereas PLR and LMR were weaker predictive parameters.

Most of the studies investigating one of the three biomarkers in children are based on case-control comparisons. When comparing these data with the RIs for children that we have generated accordingly, we identified that although significant differences were found in several studies, the values are often within the age-appropriate RIs, as seen in the asthma study.20 Therefore, we assume that this might reduce the ratios’ value for individual decision-making. The study of Jaszczura et al reported significantly elevated NLR and PLR in patients with immunoglobulin A vasculitis and systemic involvement compared to children without systemic involvement. The reported mean PLR of 139 is still within the established age- and sex-specific RI for 6-year-olds.21 The NLR value was above the 97.5th percentile with 2.77 and a cut off value to predict systemic involvement of 2.73.

The case-control comparisons without consideration of RIs make it difficult to assess their clinical impact. Therefore, we suggest that NLR, PLR, and also LMR in children have to be discussed critically as biomarkers and the RIs have to be considered in order to obtain a clinical relevance.

5 LIMITATIONS

We used an indirect approach (refineR) to establish pediatric RIs, although so-called direct approaches are considered the gold standard.22 This approach was selected, as ethical and practical objections limit the establishment of pediatric RIs using direct approaches, especially, when fine-grained age-resolution and inclusion of infants is required. Importantly, we filtered the dataset (exclusion of intensive care patients and oncological patients) and removed samples from children with elevated inflammatory markers (CRP and PCT) to reduce the proportion of abnormal test results in the input datasets. Additionally, the used refineR algorithm has been shown to generate valid RIs even in the setting of a high proportion of pathological values in the input dataset. We analyzed the proportion of NLR, PLR, and LMR ratios outside of the newly established RIs for patients with a concurrent diagnosis of an inflammatory disease. However, we did not perform a sensitivity analysis of how well the values predict the disease, as this was outside the scope of our study.

6 CONCLUSION

Comprehensive RIs that cover all age ranges from birth to adulthood are essential for the evidence-based interpretation of pediatric test results. We established RIs for NLR, PLR, and LMR to enable an improved assessment of these biomarkers in the clinical context during childhood.

ACKNOWLEDGMENTS

The presented work was performed in fulfillment of the requirements for obtaining the degree “Dr med” at “Friedrich-Alexander University of Erlangen-Nürnberg (FAU)” of Anja Krusemark. Open access funding enabled and organized by ProjektDEAL.

CONFLICT OF INTEREST

The authors have no competing interests.

AUTHOR CONTRIBUTIONS

JM designed the study, analyzed, and interpreted the data, and wrote the manuscript. TA developed the refineR algorithm, analyzed, and interpreted the data. AK, SD, MM, MR, and JW acquired, analyzed, and interpreted the data. JZ designed the study, developed the refineR algorithm, acquired, analyzed, and interpreted the data, and wrote the manuscript. All authors reviewed and approved the final manuscript.

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