Meta-analysis of the Prognostic Value of Lymphocyte to Monocyte Ratio (LMR) in Non-metastatic Renal Cell Carcinoma. (LMR)

A systematic review of the literature will be carried out using Patient, Intervention, Comparison and Outcome method (PICO), with the aim of answering the following clinical question: "Is pretreatment under lymphocyte to monocyte ratio (LMR) a prognostic factor in non-metastatic renal cell carcinoma ? "

All analyzes will be based on previously published studies. For this reason, patient consent and ethical approval will not be required.

An exhaustive search will be conducted in PubMed, Science Direct and Cochrane Database of Systematic Reviews for eligible studies that explored the prognostic role of LMR in patients with localized renal tumors, who underwent to partial or radical nephrectomy from January 1965 to July 2019. The terms search will include: "monocyte lymphocyte ratio", "renal cancer", "prognosis".

Cohort studies or observational studies will be included, patients with localized renal tumors who underwent partial or radical nephrectomy, with histopathologically confirmed neoplasms, who had access to the full text and without language limitation.

Inclusion:

Any observational study (cross-sectional, case-control, longitudinal with cross-sectional data) will be included.

Patients with localized renal tumors who underwent partial or radical nephrectomy, with histopathologically confirmed neoplasms, who had access to the full text and without language limitation will be included.

Exclusion: Reviews, case reports, conference abstracts, letters, animal or cell studies.

Relevant articles will be identified in duplicate by two independent reviewers by first screening the titles and abstracts followed by the full text against inclusion and exclusion criteria. Any disagreement will be resolved by consensus with a third reviewer experienced in the renal cancer management.

The studies reflected the hazard ratio (HR) and corresponding 95% confidence intervals (CI), in which the overall survival (OS), specific cancer specific survival (CSS), recurrence-free survival (RFS), disease-free survival (DFS) and progression-free survival (PFP).

Information will be extracted for the first author, publication year, geographic location, study design, patient information (sample size, mean/median age, sex distribution, performance status), LMR, endpoint (OS, CSS, RFS, DFS, PFS), therapy, follow up duration, multivariate factors, hazard ratio (HR) and corresponding 95% confidence intervals (CIs), or exact P values. When univariate HR and multivariate HR were both reported, only the multivariate HR will be used.

OS is defined as the interval from the date of surgery in the primary tumor until death. CSS is defined as the interval from the date of surgery in the primary tumor to death for urological tumors. RFS is defined as the interval from the date of surgery in the primary tumor to local, regional or distant recurrence or death from any cause. DFS is defined as the interval from the date of surgery in the primary tumor to local, regional or distant recurrence. PFS is defined as the interval from the date of surgery in the primary tumor to the progression of the disease (including local recurrence or distant metastasis) or death.

The Newcastle-Ottawa Scale (NOS) will be used to assess the quality of studies. A maximum of 9 points can be given for each study in the categories of: selection of patients, comparability of the study groups, and assessment of outcomes. We will define high-quality studies with scores >7.

If the necessary data are available, subgroup analyses will be performed to explore the potential sources of heterogeneity according to country, analysis type, tumor type, sample size and cut-off value.

Statistical study:

Data will be combined using random effect models. The Cochrane χ² (Cochrane Q) statistic and the I² test will be used to analyze heterogeneity.

Before calculating the combined results for all trials, statistical heterogeneity will be evaluated by using the I² statistic and p-value, which assessed the appropriateness of pooling the individual study results. The I² value provided an estimate of the amount of variance across studies because of heterogeneity rather than chance. I² values of 25%, 50%, and 75% corresponded to low, moderate, and high levels of heterogeneity, respectively. If p> 0.05, the heterogeneity will be not substantial. Thus, a fixed-effect model will be used to calculate forest plots. If p < 0.05, however, the heterogeneity will be considered substantial. Then a random effects models were used.

The publication bias will be graphically explored through funnel plot, and Duval and Tweedie's "trim-and-fill" test will be used to correct possible publication bias.

Statistical significance was defined as p less than 0.05.

Statistics will done using R 3.5.0 (R Core Team, 2018) and the meta (v4.1-5; Schwarzer, Guido, 2019) package.