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Neutrophil Lymphocyte Ratio and Platelet Lymphocyte Ratio as predictors of
systemic inflammation and chronic illness in a pre-surgery setting
Principle Investigator: Dr. Lashmi Venkatraghavan
Department of Anesthesia
Toronto Western Hospital
339, Bathurst Street
Toronto M5T 2S8
Co- Investigators: Dr. Tze Ping Tan
Department of Anesthesia
Toronto Western Hospital
Dr Arun Prasad
Department of Anesthesia
Toronto Western Hospital

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Background
Systemic inflammation has been associated with poorer surgical outcome and can
serve as a prognostic factor in both cardiovascular diseases and various types of
solid organ cancers 1-2.
This is due to the fact that inflammation can enhance tumor growth, invasion,
angiogenesis and metastasis3. In addition, chronic inflammation can initiate and
contribute to the progression of atherosclerosis which can lead to acute myocardial
infarction4.
Other chronic illness such as diabetes mellitus, obesity, hypertension and smoking
had also been associated with low-grade systemic inflammation5-6.
Systemic inflammation (as characterized by raised C-reactive protein, serum
amyloid A and serum interleukin-6) had been associated with reduced survival in
cancer and in myocardial infarction7-8. In addition, high CRP had been associated
with increased length of stay and delayed surgical complications after orthopedic
surgeries9. However, these specific inflammation biomarkers are not commonly
used in a preoperative setting, is expensive and can be time-consuming.
Complete Blood Count (CBC) is a routinely performed test in anesthetic
preadmission clinic. Information from CBC such as neutrophil, platelet and
lymphocyte count can serve as important biomarkers of systemic inflammation.
Elevated peripheral blood neutrophil and platelet counts were associated with poor
survival in cancer patients10-11. Low lymphocyte is negatively associated with
survival in cancer patients12.
Neutrophil-lymphocyte ratio (NLR) is a simple biomarker which had been shown to
be predictive of outcome and as a prognostic factor in various types of cancer such
as colorectal, renal cell carcinoma, breast cancer, stomach cancer and oral cavity
squamous cell carcinoma13-17. High NLR had also been associated with increased
cardiovascular morbidity and mortality after acute coronary syndrome,
percutaneous coronary intervention and coronary artery bypass surgery18-20.
Platelet-lymphocyte ratio (PLR) is another novel inflammatory marker which had
been demonstrated to predict prognosis for pancreatic or ovarian cancer 21-22. It
had also been linked to end-stage renal disease23.

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A simple biomarker such as NLR or PLR can be important in assessing the burden of
systemic illness in patients and can be potentially used as a tool to risk stratify
patients in terms of pre-operative optimization and post-operative care. However,
both NLR and PLR had not been specifically studied in an anesthetic preadmission
setting, especially in a non-cancer population. Their relationship with chronic illness
such as diabetes, hypertension or obesity in this patient population is unknown.
Aim of our study is to determine the prevalence of these novel inflammatory
markers in the surgical population and to study the differences in the markers
between the cancer and non-cancer surgical patients.
References
1. Folsom Ar, Aleksiv N, Catellier D, Juneja HS, Wu KK. C-reacive protein and
incidence of coronary heart disease in the Atherosclerosis Risk in
Communities (ARIC) study. Am Heart J 2002, 144: 233-238.
2. Roxburg CS, McMillan DC. Role of systemic inflammatory response in
predicting survival in patients with primary operable cancer. Future
Oncology 2010; 6(1): 149-63.
3. Balkwill F, Manovani A. Inflammation and cancer: back to Virchow? Lancet
2001; 37:539-45.
4. Libby P, Ridker PM, Hansson GK. Leducq Transalantic Network on
atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to
practice. J am coll Cardiol 2009; 54: 2129-38.
5. Pitsavos C, Tampourio M, Panagiotakos DB, Skoumas Y, Chrysohoou C,
Nomikos T, et al. Association between low-grade systemic inflammation and
type 2 diabetes mellitus among men and women from the ATTICA study. Rev
Diabet Study 2007, 4: 98-104.
6. Marsland AL, McCaffey JM, Muldoom MF, Manuck SB. Systemic inflammation
and the metabolic syndrome among middle-aged community volunteers.
Metabolism 2010, 59: 1801-8.
7. Pierce BL, Ballard-Barash R, Berstein L, et al. Elevated biomarkers of
inflammation are associated with reduced survival among breast cancer
patients. J Clin Oncol 2009. 21: 3437-44.
8. Buckley DI, Fu R, Freeman M et al, C-reactive protein as a risk factor for
coronary artery disease: a systemic review and meta-analysis for the U.S.
Preventive Services Task Force. Ann Intern Med 2009; 151: 483-95.
9. Ackland GL, Scollay JM, Parks RW, De Beaux I and Mythen MG. Pre-operative
high sensitivity C-reactive protein and postoperative outcome in patients
undergoing elective orthopaedic surgery. Anaesthesia 2007; 67: 888-94.

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10. Teramukai S, Kitano T, Kishida Y, Kawahara M et al. Pretreatment neutrophil
count as an independent prognostic factor in advanced non-small-cell lung
cancer: an analysis of Japan Multinational Trial Organisation LC00-03. Eur J
Cancer 2009; 49(11): 190-8.
11. Lin MS, Huang JX, Zhu JY et al. Elevation of platelet count in patients with
colorectal cancer predicts tendency to metastases and poor prognosis.
Hepaogastroenterology. 2012; 59(118): 1687-90.
12. Forgar P, Specti C, Basso D et al. Decreased total lymphocyte counts in
pancreatic cancer: an index of adverse outcome. Pancreas. 2006; 32: 22-8.
13. Cook EJ, Walsh SR, Farroq N, Alberts JC et al. Post-operative neutrophil-lymphocyte
ratio predicts copmlications following colorectal surgery. Int J
Surg 2007; 5: 27-30.
14. Pichler M, Hutterer GC, Stoeckigt C, Chromecki TF et al. Validation of the pre-treatment
neutrophil-lymphocyte ratio as a prognostic facor in a large
European cohort of renal cell carcinoma patients. Br J Cancer 2013; 108: 901-
7.
15. Azab B, Bhatt V, Phookhan J et al. Usefulness of the neutrophil-to lymphocyte
ratio in predicting short- and long-term mortality in breast cancer patients.
Ann Surg Oncol 2012; 19: 217-24.
16. Kim YH and Choi WJ. The effectiveness of postoperative neutrophils to
lymphocyte ratio in predicting long-term recurrence after stomach cancer
surgery. J Koran Surg Soc 2012: 83: 352-9.
17. Fang HY, Huang XY, Chien HT, Chang JT et al. Refining the role of
peroperative c-reactive protein by neutrophil/lymphocyte ratio in oral cavity
squamous cell cancer. Laryngoscope 2013 Apr 25. doi: 10.1002/lary.24105.
[Epub ahead of print]
18. Bhat T, Teli S, Rijal J et al. Neutrophil to lymphocyte ratio and cardiovascular
diseases: a review. Expert Rev Cardiovasc Ther 2013; 11(1): 55-9.
19. Duffy BK, Gurm HS, Rajagopal V, et al. Usefulness of an elevated neutrophil to
lymphocyte ratio in predicting long-term mortality after percutaneous
coronary intervention. Am J Cardiol 2006; 97(7): 1405-10.
20. Gibson PH, Croal BL, Cuthbertson BH et al. Preoperative neutrophil-lymphocyte
ratio and outcome from coronary artery bypass grafting. Am
Heart J 2007; 154(5): 995-1002.
21. Smith R, Bosonnet L, Raraty M et al. Preoperative platelet-lymphocyte ratio is
an independent significant prognostic marker in resected pancreatic ductal
adenocarcinoma. Am J Surg 2009; 197: 466-72.
22. Asher V, Lee J, Innammaa A et al. Preoperative platelet lymphocyte ratio as
an independent prognostic marker in ovarian cancer. Clin Transl Oncol 2011;
13: 499-503.

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23. Turkmen K, Erdur FM, Ozcicek F et al, Platelet-to-lymphocyte ratio better
predicts inflammation than neutrophil-to-lymphocyte ratio in end-stage
renal disease patients. Hemodial Int. 2013 Mar 24. doi: 10.1111/hdi.12040.
[Epub ahead of print]
Hypothesis
Neutrophil-lymphocyte ratio (NLR) and Platelet-lymphocyte ratio (PLR) are
independently correlated with the presence of systemic inflammation and chronic
disease in an anesthetic pre-admission setting. NLR is not superior to PLR in
predicting presence of systemic disease.
Objective of study
The main objective of this study is to determine the prevalence of these novel
inflammatory markers in the surgical population and to determine the relationship
between NLR or PLR and presence of systemic inflammation and/or chronic
diseases.
Secondary objectives include:
a) Examining the relationship of these markers and anesthetic risk indices such
as ASA physical status and Revised Cardiac Risk Index
b) Examining the difference of these markers between cancer and non-cancer
patients.
Proposed Methodology
Research ethics
The study will be conducted in accordance with guidelines set forth by the
University of Toronto, University Health Network Ethics Committee for use of
human subjects in research. All information gathered during the course of the study
will be stored in a secure, locked file cabinet. Only research personnel will have
access to this cabinet key. Patient numbers will be used instead of names to ensure
confidentiality upon entry and analysis of data. When results are prepared for

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presentation or publication, they will be presented in a way that makes it impossible
to identify individual participants. Ethics board approval will be obtained.
Study Design
This will be a retrospective cohort study.
Study population
All elective patients who attended anesthetic preadmission clinic at Toronto
Western Hospital from 1st January 2013 to 30st April 2013
Inclusion criteria
Elective surgical patients
Attending preadmission clinic
Complete blood count test done
Exclusion criteria
Patients who had no Complete blood count performed
Emergency surgery
Study Centre
This study will be performed at the Toronto Western Hospital, University Heath
network.
Study protocol
We plan to do a retrospective chart review of all consecutive patients who attended
preadmission clinic between January 1st and April 30th. The list of patients will be
obtained from preadmission.
All data collected will be from the clinical anesthesia information system (CAIS) and
Electronic Patient Record (EPR).
Data collection
Data collected will include:
o Age
o Gender
o ASA status
o Type of surgery
 Medical history
o Cardiovascular
 History of current or previous ischemic heart disease

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 History of congestive cardiac failure
 History of Arrhythmia
 Hypertension
o Respiratory
 Smoking history (active or previous)
 Asthma
 Chronic Obstructive Pulmonary Disease
 Obstructive Sleep Apnea
 Respiratory malignancy
o GI/Hepatic
 Presence of gastrointestinal tract malignancy
 Inflammatory bowel disease (ulcerative colitis or crohn’s
disease)
o Renal
 Renal impairment (esp if serum creatinine > 176 umol/L)
 Renal malignancy
o Hematology
 Hematology malignancy (leukemia or
 History of venous thromboembolism (Deep vein thrombosis or
pulmonary embolism)
o Endocrine
 Insulin dependent Diabetes Mellitus
 Non-insulin dependent Diabetes Mellitus
 Thyroid Diseases
 Other endocrine pathology
o Musculoskeletal
 History of Rheumatoid Arthritis
 History of other inflammatory joint disease (such as
Scleroderma, Psoriasis Arthritis,
o Neurological
 History of cerebrovascular diseases (stroke or transient
ischemic attack)
 History of intracranial tumor or malignancy
o Breast/Gynaecological
 Breast or gynaecological malignacy
o Medications
 Insulin
 Oral hypoglycemic
 Anti-hypertensive medications
 Steroid

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 Beta-Blocker
 Diuretic
 Immunosupressive drugs (such as azathioprine, sulfasalazine)
 Chemotherapy agents
 Physical examination
o Weight
o BMI
 Investigations
o Neutrophil count
o Platelet count
o Lymphocyte count
Data Analysis
NLR is the ratio of absolute count of neutrophil to lymphocyte count.
PLR is the ratio of absolute count of platelet to lymphocyte count.
Patient data will be categorized to quartiles according to the baseline, 25th, 50th, 75th
NLR and PLR percentiles.
Mean and standard deviation for continuous variables, and frequency and
percentages for categorical variables are calculated.
For comparison between group, chi-square and Fisher’s exact tests are used for
categorical variables and analysis of variance (ANOVA) or Kuskal Wallis Test were
performed for continuous variables.
Spearmen correlation coefficients were computed to examine the association
between 2 continuous variables.
A P value < 0.05 is considered statistically significant.
Sample size
There are no previous studies to determine the sample size. We estimated that
approximately 1000 patient would have attended the preadmission clinic between
Jan 1st and April 30th. The data from 4 month (1000 patients) would be a good
sample size to determine the prevalence.