Association between platelet-to-lymphocyte ratio and prognosis in patients receiving coronary artery bypass grafting: a meta-analysis

Introduction

Coronary artery disease (CAD) represents one of the leading causes of morbidity and mortality worldwide, accounting for nearly seven million deaths and 129 million cases of disability annually. Although mortality rates have declined in high-income countries due to advances in medical care and preventive strategies, they remain disproportionately high in low- and middle-income regions. Substantial geographic and demographic heterogeneity has been observed in both the incidence and mortality of CAD (1). Therapeutic strategies for coronary atherosclerotic heart disease have evolved in parallel with medical progress. Pharmacological interventions, such as statins, antiplatelet agents, and PCSK9 inhibitors, remain fundamental in reducing low-density lipoprotein cholesterol levels and inhibiting platelet aggregation (2,3). Increasing recognition of the role of inflammation in atherogenesis has stimulated the development of novel anti-inflammatory therapies, including monoclonal antibodies, which have demonstrated promising efficacy (4,5). In addition to medical therapy, interventional approaches such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) continue to play pivotal roles (6,7). Artificial intelligence (AI) is improving diagnosis and treatment with advanced imaging analysis (8), while alternative therapies, such as traditional Chinese medicine and natural products, are being investigated (9). CABG offers unique advantages by utilizing autologous vessels to bypass obstructed coronary arteries, thereby restoring myocardial perfusion. Compared with PCI, CABG demonstrates superior outcomes in complex disease presentations, such as left main or triple-vessel disease (10,11). In patients with diabetes and multivessel CAD, CABG is associated with reduced rates of major adverse cardiovascular events (MACEs) and improved long-term survival compared with PCI (12), highlighting its effectiveness for high-risk groups.

The immune-inflammatory response has emerged as a crucial determinant of long-term prognosis in patients with coronary heart disease (CHD). Recent evidence suggests that inflammatory biomarkers, including the systemic immune-inflammation index (SII) and the neutrophil-to-lymphocyte ratio (NLR), provide valuable prognostic information. Elevated SII is strongly correlated with the severity of coronary artery stenosis (13), and higher levels predict an increased risk of adverse cardiovascular outcomes and long-term mortality, particularly in elderly patients with acute coronary syndrome (ACS) (14). Similarly, elevated NLR is associated with plaque vulnerability, more severe stenosis, larger infarct size, and unfavorable outcomes in both CHD and ACS (15). Combining SII with aortic valve calcification (AVC) significantly increases the risk of MACE in CHD patients, particularly those with high SII and AVC levels (16). Platelet-to-lymphocyte ratio (PLR), an inflammatory marker derived from platelets and lymphocytes, can predict CAD patient outcomes post-CABG. Gungor et al. (17) proved high preoperative PLR as a strong atrial fibrillation predictor after surgery, while Parlar and Şaşkın (18) demonstrated that high preoperative and early postoperative PLR levels correlate with a greater likelihood of acute kidney injury (AKI) following CABG.

Qiu et al. (19) examined studies from 2013 to 2019 and demonstrated the link of PLR to CAD severity and slow coronary flow; however, the review did not explore the relation of PLR to specific treatment outcomes. Wang et al. (20) reviewed studies up to February 7, 2025, and demonstrated that elevated PLR levels are predictive of major cardiovascular events in ACS patients undergoing PCI. Nevertheless, that meta-analysis did not investigate the association between PLR and clinical outcomes following CABG in patients with chronic CHD. Moreover, emerging studies have yielded inconsistent findings. Our study seeks to reevaluate the prognostic utility of PLR in patients undergoing CABG, drawing upon updated evidence and meta-analytical techniques to support the development of more robust predictive models clinically. We present this article in accordance with the PRISMA reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1539/rc).

Methods

Literature search

The protocol of our study was registered on the International Prospective Systematic Evaluation Registry. Two investigators devised the search strategy and independently used subject terms and keywords to retrieve articles across PubMed, Embase, Web of Science, and Cochrane Library before April 29, 2025. The equation for PubMed search is as follows: (((((((((“Coronary Artery Bypass”[Mesh]) OR (Coronary Artery Bypasses)) OR (CABG)) OR (Coronary Artery Bypass Surgery)) OR (Aortocoronary Bypass)) OR (Aortocoronary Bypasses)) OR (CABG)) AND ((((“Lymphocytes”[Mesh]) OR (Lymphocyte)) OR (Lymphoid Cells)) OR (Lymphoid Cell))) AND ((((((“Blood Platelets”[Mesh]) OR (Blood Platelet)) OR (Platelets)) OR (Platelet)) OR (Thrombocytes)) OR (Thrombocyte))) AND (Ratio). Table S1 presents the detailed strategy.

Study selection

Inclusion criteria were: (I) CAD diagnosis via coronary angiography; (II) undergoing CABG or off-pump coronary artery bypass grafting (OPCAB) surgery; (III) investigating the prognostic significance of PLR on CABG or OPCAB; (IV) containing sufficient data to extract or calculate odds ratio (OR), 95% confidence interval (CI), and (or) standardized mean difference (SMD); (V) grouping of high-PLR and low-PLR cohorts according to cut-off values; (VI) full texts. Exclusion criteria were: (I) reviews, comments, conference abstracts, case reports, as well as letters; (II) lacking information for OR and 95% CI and (or) SMD computation; (III) containing no PLR data; (IV) duplicate or overlapping data.

Two independent reviewers (Z.L. and H.S.) checked titles and abstracts and reviewed relevant full texts for eligible studies. Dissents were addressed via discussion and consensus.

Data extraction

Data were extracted by two investigators independently. Dissents were settled via consensus. Extracted information encompassed the first author, publication year, country (location), design, sample size, population, age, length, treatment, timing of detection, cut-off value, ORs (95% CIs), and/or SMD for classification variables describing the prognosis of CABG or OPCAB.

Quality assessment

The study quality was rated via the Newcastle-Ottawa Scale (NOS) in three domains of selection, comparability, and outcomes. The maximum achievable score was 9 (21); 7–9 denoted high quality (22).

Statistical analysis

The prognostic value of PLR for the CAD population treated with CABGs was examined using the pooled ORs with 95% CIs. Heterogeneity was examined via Cochran’s Q test and Higgins’ I² statistic (23). I²>50% or P<0.1 suggests significant heterogeneity. Data were analyzed utilizing a random-effects model. The result stability and possible heterogeneity source were examined via sensitivity and subgroup analyses. Publication bias was assessed through funnel plots and Egger’s tests. P<0.05 denoted statistical significance. Each statistical analysis was enabled by STATA 15.0 and Review Manager 5.4.

Results

Study characteristics

Three hundred and one records were retrieved from databases initially. Following the removal of 84 duplicates, 1 non-English article, 12 derivative publications, and 14 review papers, 190 studies underwent preliminary assessment. After excluding 163 irrelevant studies due to ineligible titles/abstracts, 27 full-text papers were thoroughly checked. The final analysis incorporated all 27 studies, covering a pooled sample of 17,120 patients (18,24-46) (Tables S1,S2) into the study (Figure 1).

Figure 1 Flow chart of literature screening.

The analysis includes nine cohort and 18 case-control studies, with 37 control groups extracted from 27 studies. Among these 27 studies, one was conducted in Iran, Brazil, Australia, and Korea; two were from India; three were from Poland; and the remaining 18 were carried out in Turkey. Notably, of the eligible articles, five (18,24,28,35,36) encompassed two case control studies respectively, one (34) contained four case control studies, resulting in 28 case control studies, and two included two cohort studies (44) (Table S2), resulting in 11 cohort studies. The analyzed case-control and cohort studies, published in English between 2015 and 2025, exclusively involved CAD patients treated with CABG or OPCAB, stratified into high- and low-PLR groups. PLR was measured preoperatively in 19 studies, postoperatively in one, at timepoints in six, while one study omitted this detail.

With respect to PLR evaluation, the included studies addressed a range of clinical outcomes. Specifically, seven studies investigated the link between PLR and all-cause mortality (ACM), while one study focused on cardiac-specific mortality. Twelve studies assessed PLR as a predictor of postoperative atrial fibrillation (POAF), and eight studies evaluated its prognostic value for AKI. Four studies explored the relation of PLR to saphenous vein graft disease (SVGD), and two studies examined its relation to adverse cardiovascular events. Additionally, one study investigated the prognostic relevance of PLR in ischemic stroke (IS), four studies evaluated its impact on postoperative delirium (POD), and three studies assessed its relation to postoperative bleeding, sternum revision, and total hospital stay duration, respectively. Furthermore, one study examined the prognostic significance of PLR for neurologic events.

The characteristics of eligible studies are presented in Table S2.

Study quality

Thirty-seven studies were encompassed in all 27 articles. Of these, six studies scored 6 on the NOS, while the remaining studies scored between 7 and 9, indicating generally high methodological quality (Tables S3,S4).

Meta-analysis results

Primary endpoints

PLR and POAF

In our categorical variable analysis, the link of PLR to POAF was investigated using data from nine control groups comprising 7,403 participants. All included studies reported preoperative PLR values exclusively. The pooled analysis demonstrated the significant relation of an elevated PLR to a higher likelihood of POAF (OR =1.02; 95% CI: 1.01–1.02; P<0.001; Figure 2A). Subgroup analyses regarding study design, age, geographic location, and PLR threshold were carried out. Notably, PLR did not exhibit predictive value for POAF in populations aged 65 years and above (OR =0.99; 95% CI: 0.89–1.11; P=0.90; Table 1), in studies conducted in Australia (OR =0.90; 95% CI: 0.74–1.08; P=0.25; Table 1), or in those having a PLR threshold ≥150 (OR =0.93; 95% CI: 0.69–1.24; P=0.61; Table 1). Cohort (OR =1.01; 95% CI: 1.01–1.01; P<0.001; Table 1) and case-control studies (OR =1.03; 95% CI: 1.02–1.04; P<0.001; Table 1) all demonstrated the marked predictive value of PLR for POAF. Among the age-based subgroups, a notable predictive relation was observed only in patients younger than 65 years (OR =1.01; 95% CI: 1.01–1.02; P=0.001; Table 1). Geographically, studies from Turkey (OR =1.01; 95% CI: 1.01–1.02; P=0.001; Table 1) and India (OR =1.04; 95% CI: 1.02–1.05; P<0.001; Table 1) indicated strong predictive value. Furthermore, in the subgroup with a PLR threshold <150, PLR was still a strong POAF predictor (OR =1.02; 95% CI: 1.01–1.03; P<0.001; Table 1).

Figure 2 Forest plot. (A) Forest plot POAF (categorical variable); (B) Forest plot POAF (continuous variable); (C) Forest plot AKI (categorical variable); (D) Forest plot AKI (continuous variable); (E) Forest plot ACM (categorical variable). AKI, acute kidney injury; CI, confidence interval; IV, inverse variance; POAF, postoperative atrial fibrillation; SD, standard deviation; SE, standard error.

Similarly, for the continuous variable analysis of POAF, substantial heterogeneity was noted (I²=99%, P<0.001; Figure 2B). Therefore, a random-effects model was adopted. The results aligned with those observed in the categorical analysis, indicating that patients who developed POAF exhibited a markedly higher mean PLR in comparison to those without POAF (SMD =1.61, 95% CI: 0.81–2.41, P<0.001; Figure 2B).

PLR and AKI

In the categorical analysis of AKI, 1,096 patients from four studies were encompassed. A higher PLR was not a significant predictor of postoperative AKI (OR =1.04, 95% CI: 0.97–1.11, P=0.30; Figure 2C).

In the continuous variable analysis of AKI, eight studies encompassing 2,792 patients were included. The mean PLR of patients who developed postoperative AKI was markedly higher than that of those who did not (SMD =1.03, 95% CI: 0.60–1.45, P<0.001; Figure 2D). Subgroup analyses based on country (study location) and timing of PLR measurement were performed. Subgroup analysis by country proved the notable predictive value of increased PLR for AKI in the Iranian subgroup (P<0.001; Table 1) and the Turkish subgroup (I²=95%, P<0.001; Table 1). Regarding the timing of PLR assessment, significant predictive value was observed only in the preoperative subgroup (SMD =1.15, 95% CI: 0.78–1.53, P<0.001; Table 1), but the postoperative subgroup demonstrated no statistical significance (SMD =0.82, 95% CI: −0.24 to 1.88, P=0.13; Table 1).

PLR and ACM

For the categorical analysis of ACM, four studies comprising 2,638 patients were included. An elevated PLR was significantly related to postoperative ACM (OR =1.69, 95% CI: 1.04–2.74, P=0.03; Figure 2E).

In our continuous variable analysis, four studies on 3,889 patients were incorporated. The mean PLR was markedly elevated among patients who died postoperatively in contrast to survivors (SMD =0.43, 95% CI: 0.13–0.72, P=0.005; Figure 3A).

Figure 3 Forest plot. (A) Forest plot ACM (continuous variable); (B) Forest plot delirium (categorical variable); (C) Forest plot delirium (continuous variable); (D) Forest plot SVGD (categorical variable); (E) Forest plot SVGD (continuous variable); (F) Forest plot adverse events (continuous variable). CI, confidence interval; IV, inverse variance; SD, standard deviation; SE, standard error; SVGD, saphenous vein graft disease.

Secondary endpoints

PLR and delirium

For the categorical analysis of POD, two studies, including 1,554 patients, were assessed. A high PLR was a significant POD predictor (OR =1.05, 95% CI: 1.03–1.07, P<0.001; Figure 3B).

In contrast, in the continuous variable analysis, four studies, including 3,490 patients, were analyzed. No significant difference in mean PLR was noted across people with and without POD (SMD =0.75, 95% CI: −0.18 to 1.68, P=0.11; Figure 3C).

PLR and SVGD

For the categorical analysis of postoperative SVGD, two studies involving 936 patients were included. The analysis indicated an insignificant relation of elevated PLR to SVGD (OR =1.34, 95% CI: 0.81–2.20, P=0.25; Figure 3D).

In the continuous variable analysis, four studies with 1,480 patients were evaluated. The findings demonstrated that patients who developed postoperative SVGD had a markedly higher mean PLR than individuals who did not (SMD =1.78, 95% CI: 0.61–2.96, P=0.003; Figure 3E).

PLR and adverse events

For the continuous variable analysis of postoperative adverse events, two studies encompassing 1,525 patients were included. Significant disparity was not found in mean PLR across people with and without postoperative adverse events (SMD =1.47, 95% CI: −1.34 to 4.27, P=0.31; Figure 3F).

Sensitivity analysis

The robustness of the findings regarding the clinical significance of PLR was rated via sensitivity analyses (Figure 4A-4K). The results demonstrated that the outcomes for POAF (categorical and continuous; Figure 4A,4B), AKI (continuous; Figure 4D), ACM (continuous; Figure 4F), delirium (categorical; Figure 4G), and SVGD (categorical and continuous; Figures 4I,4J) remained stable after sequential exclusion of individual studies. Our results were not disproportionately influenced by any study and were therefore robust. However, the findings for AKI (categorical; Figure 4C), ACM (categorical; Figure 4E), delirium (continuous; Figure 4H), and adverse events (continuous; Figure 4K) demonstrated instability, indicating susceptibility to the influence of individual studies.

Figure 4 Sensitivity analysis. (A) Sensitivity analysis POAF (categorical variable); (B) Sensitivity analysis POAF (continuous variable); (C) Sensitivity analysis AKI (categorical variable); (D) Sensitivity analysis AKI (continuous variable); (E) Sensitivity analysis ACM (categorical variable); (F) Sensitivity analysis ACM (continuous variable); (G) Sensitivity analysis delirium (categorical variable); (H) Sensitivity analysis delirium (continuous variable); (I) Sensitivity analysis SVGD (categorical variable); (J) Sensitivity analysis SVGD (continuous variable); (K) Sensitivity analysis adverse events (continuous variable). CI, confidence interval.

Publication bias

Publication bias was detected via funnel plots and Egger’s test (Figure 5A-5K), with no significant bias found for the meta-analyses concerning POAF (categorical; P=0.58), AKI (categorical, P=0.45; continuous, P=0.39), ACM (continuous; P=0.86), delirium (continuous; P=0.74), and SVGD (continuous; P=0.96). These results were further supported by symmetrical funnel plots (Figure 5A,5C,5D,5F,5H,5J). Conversely, significant publication bias was observed for POAF (continuous; P=0.02) and ACM (categorical; P=0.008), as evidenced by asymmetric funnel plots (Figure 5B,5E). Since eligible studies were limited (<3), publication bias assessment could not be performed for the remaining outcomes (Figure 5G,5I,5K).

Figure 5 (A) Funnel plot POAF (categorical variable); (B) Funnel plot POAF (continuous variable); (C) Funnel plot AKI (categorical variable); (D) Funnel plot AKI (continuous variable); (E) Funnel plot ACM (categorical variable); (F) Funnel plot ACM (continuous variable); (G) Funnel plot delirium (categorical variable); (H) Funnel plot delirium (continuous variable); (I) Funnel plot SVGD (categorical variable); (J) Funnel plot SVGD (continuous variable); (K) Funnel plot Adverse events (continuous variable). OR, odds ratio; SE, standard error; SMD, standardized mean difference.

Discussion

Research on immune-related genes has identified possible biomarkers and signaling pathways implicated in the pathogenesis of CAD (47). In the early stages of CAD, immune-inflammatory markers have been closely related to endothelial dysfunction, with biomarkers like C-reactive protein and matrix metalloproteinase-9 serving as indicators of the severity of this dysfunction (48,49).

There is a strong association between inflammatory mediators, oxidative stress markers, and coronary artery calcification scores among the CHD population, underscoring the significance of inflammation in CHD onset and progression (50).

The present study established that PLR, as a surrogate marker of systemic inflammatory cytokine activity, possesses significant prognostic value for various clinical outcomes in the CHD population undergoing CABG. Specifically, elevated PLR levels were significantly related to a higher incidence of POAF. This relation was noted in categorical (OR =1.02, 95% CI: 1.01–1.02, P<0.001) and continuous analyses (SMD =1.61, 95% CI: 0.81–2.41, P<0.001), indicating a consistent predictive relationship. Concerning postoperative AKI, elevated PLR levels demonstrated significant predictive value in studies using continuous variables (SMD =1.03, 95% CI: 0.60–1.45, P<0.001), but the link was insignificant in studies employing categorical variables (OR =1.04, 95% CI: 0.97–1.11, P=0.30). Elevated PLR was significantly predictive of postoperative ACM in categorical (OR =1.69, 95% CI: 1.04–2.74, P=0.03) and continuous (SMD =0.43, 95% CI: 0.13–0.72, P=0.005) analyses. Regarding POD, elevated PLR exhibited predictive significance only in categorical analyses (OR =1.05, 95% CI: 1.03–1.07, P<0.001), with continuous analyses yielding insignificant results (SMD =0.75, 95% CI: −0.18 to 1.68, P=0.11). Similarly, for SVGD, elevated PLR demonstrated significant predictive value in continuous analyses (SMD =1.78, 95% CI: 0.61–2.96, P=0.003), and not in categorical analyses (OR =1.34, 95% CI: 0.81–2.20, P=0.25). Notably, in assessing the predictive utility of PLR for postoperative adverse events, the limited availability of categorical data and the absence of significant associations in continuous analyses (SMD =1.47, 95% CI: −1.34 to 4.27, P=0.31) warrant attention.

Sensitivity analyses revealed instability in the predictive value of PLR for AKI (categorical), ACM (categorical), and delirium (continuous). Given the low level of evidence and limited reliability, these relations should be interpreted cautiously and confirmed by future studies. Moreover, analyses of POAF (continuous) and ACM (categorical) revealed evidence of publication bias, possibly due to variations in study design or regional differences. Therefore, future prospective or multicenter studies are necessary to confirm the foregoing outcomes.

In contrast to prior meta-analyses (51), this study focuses specifically on the prognostic value of PLR in people receiving a defined treatment modality, namely CABG. While this approach inherently narrows the study population and scope, it enhances the ability to discern outcome variations across treatment contexts. Furthermore, unlike earlier studies, this analysis provides a comprehensive examination of CABG-associated outcomes, including POAF, AKI, ACM, SVGD, delirium, and other adverse events. Additionally, this study includes a larger number of trials and greater sample sizes. Compared to another meta-analysis (19), this study also employs sensitivity and subgroup analyses, which enhance the robustness of results and help detect sources of heterogeneity.

The prognostic value of PLR was not observed in patients aged over 65 years. This lack of association may arise from age-related declines in immune and inflammatory function. Accordingly, future study designs should strive to include participants across a broader age spectrum to mitigate the potential confounding effects introduced by age-related physiological changes. Similarly, regional subgroup analysis demonstrated that the predictive value of PLR was significantly higher in Asian populations compared to those in Oceania. This variation may reflect the differing epidemiological characteristics of CHD across geographical regions. Hence, future investigations are encouraged to adopt multicenter and multinational study designs to reduce the bias introduced by regional heterogeneity. Moreover, patients with PLR cutoff values set at ≥150 exhibited diminished predictive efficacy. This finding suggests that, where feasible, lower PLR thresholds should be employed in order to enhance predictive accuracy. Additionally, PLR measured postoperatively demonstrated inferior prognostic performance compared to preoperative measurements. This may be due to the immune-modulatory effects of surgical interventions, which can alter systemic inflammatory responses. Therefore, future studies should prioritize the use of preoperative PLR values as predictive indicators whenever possible.

The immune-inflammatory response is integral to the development and advancement of atherosclerosis, a chronic inflammatory condition featuring lipid accumulation within the arterial wall and immune cell aggregation. The innate and adaptive immune systems promote atherosclerosis progression (52,53). In its early stages, atherosclerosis is primarily initiated via endothelial cell activation and recruitment of monocytes, which differentiate into macrophages and foam cells within the arterial wall (54,55). As the disease progresses, immune cell heterogeneity and adaptive immune responses become increasingly prominent. Advances in high-dimensional mass cytometry and single-cell RNA sequencing demonstrated substantial diversity among immune cell subtypes and their adaptive behavior within atherosclerotic lesions (56). The migration, compositional, and phenotypic alterations, as well as adaptive responses of immune cells, are vital in the progression of atherosclerosis (57). Additionally, cytokines are integral to the inflammatory response linked to atherosclerosis. Research demonstrates that certain cytokines possess pro-atherosclerotic and anti-atherosclerotic properties, rendering them a central topic of investigation and debate (58). Throughout the various stages of atherosclerosis, cytokines exert a significant influence on disease progression by activating cells involved in the pathophysiological processes (59).

Platelets, important in hemostasis, are also crucial in the occurrence and progression of coronary atherosclerosis (60). Following endothelial damage, platelets adhere to the endothelium, triggering activation and the release of pro-inflammatory cytokines (61). This attracts leukocytes, like monocytes and neutrophils, which are essential in forming atherosclerotic lesions (62). Lymphocytes, crucial for adaptive immunity (63), regulate inflammation throughout atherosclerosis. Their apoptosis in lesions can lead to plaque and lipid core formation, and a reduced lymphocyte count often indicates a poor prognosis in CAD patients (64).

POAF is a common complication following CABG, in which inflammation plays a central role. Elevated inflammatory mediators, including interleukin-6 (IL-6), mitochondrial DNA, myeloperoxidase (MPO), and IL-12p70 in pericardial fluid, have been associated with an increased risk of POAF (65,66). SII is a reliable predictor of POAF (67), and increased levels of NLR and PLR are also associated with new-onset atrial fibrillation post-CABG (33), underscoring the pivotal role of inflammation. Activation of NLRP3 inflammasomes has similarly been linked to POAF, reinforcing the importance of inflammatory mechanisms (68). Statins have been shown to reduce the incidence of POAF in CABG patients, primarily through their anti-inflammatory effects (69).

Similarly, the development of SVGD has been significantly associated with inflammatory markers such as NLR and PLR (38,70). Following CABG, surgical stress and extracorporeal circulation trigger a systemic inflammatory response, leading to platelet and leukocyte activation and promoting cellular interactions. This response not only jeopardizes graft patency but may also contribute to early graft failure. Evidence suggests that postoperative interactions between platelets and T cells play a key role in immune regulation, further highlighting the centrality of inflammation in SVGD pathogenesis (71). Post-CABG inflammation affects both short-term graft patency and long-term clinical outcomes (72).

Notably, a well-known expert review (73) on postcardiac surgery myocardial ischemia comprehensively addressed the etiology, diagnosis, and management of perioperative myocardial ischemia and early graft failure. These observations parallel the limitations identified in the present meta-analysis, wherein elevated PLR was associated with increased risks of POAF, AKI, ACM, delirium, adverse events, and SVGD, but demonstrated no significant association with perioperative myocardial infarction (PMI) or early graft failure.

Limitations

This study has several limitations that merit careful consideration. First, most included studies were retrospective (27 studies: 18 case-control and 9 cohort studies), without prospective, randomized controlled trials to confirm the findings. Retrospective designs are inherently prone to selection bias, information bias, and residual confounding, for instance, differences in baseline comorbidities (e.g., hypertension, severity of diabetes), perioperative pharmacotherapy (e.g., anti-inflammatory agents, anticoagulants), and heterogeneous PLR measurement protocols across centers. Such factors may have influenced the observed associations between PLR and postoperative outcomes, thereby limiting internal validity and causal inference. Second, there was a marked geographic imbalance in the included literature. Of the 27 studies, 18 originated from Turkey, 2 from India, 3 from Poland, and 1 each from Iran, Brazil, Australia, and Korea. This disproportionate representation of Turkish and Asian cohorts restricts the generalizability of findings to other populations, including those in Europe, Africa, and the Americas. Variability in CAD epidemiology, encompassing risk factor distribution (e.g., dietary patterns, smoking prevalence), healthcare systems, and genetic backgrounds, may influence the applicability of the results across regions. Third, the absence of standardized PLR cutoff values contributed to significant heterogeneity. Reported thresholds varied widely (e.g., 6.15, 86, 106.3, 142, 150.9, 197), with 11 studies failing to provide a specific cutoff (denoted as “NA” in Table S2). This variability increased statistical heterogeneity in the meta-analysis (e.g., I²=99% for continuous POAF analysis) and hindered cross-study comparability. Subgroup analyses indicated that a PLR cutoff ≥150 demonstrated weaker predictive value for POAF, whereas lower cutoffs (<150) yielded stronger associations. Without a unified threshold, clinical application of PLR as a prognostic biomarker remains limited. Fourth, discrepancies in the timing of PLR measurement further complicated interpretation. Nineteen studies measured PLR preoperatively, one postoperatively, six both pre- and postoperatively, and one failed to specify timing. Subgroup analysis revealed superior prognostic performance of preoperative PLR for AKI (SMD =1.15, 95% CI: 0.78–1.53, P<0.001) compared with postoperative PLR (SMD =0.82, 95% CI: −0.24 to 1.88, P=0.13), likely reflecting immunomodulatory effects of surgical trauma. Fifth, evidence of publication bias was observed in analyses of continuous POAF (P=0.02) and categorical ACM (P=0.008), as indicated by asymmetric funnel plots (Figure 5B,5E). This bias may result from preferential publication of positive findings, methodological variations (e.g., small sample sizes inflating effect estimates), or regional disparities in publication practices. For outcomes with fewer than three studies (e.g., categorical delirium, continuous adverse events), publication bias could not be assessed, raising the possibility of underreporting of null results. Sixth, sensitivity analyses demonstrated instability in the predictive value of PLR for categorical AKI (Figure 4C), categorical ACM (Figure 4E), continuous delirium (Figure 4H), and continuous adverse events (Figure 4K). Sequential exclusion of individual studies yielded substantial fluctuations in pooled estimates, suggesting that associations may be disproportionately influenced by a small subset of studies rather than representing robust biological relationships. Finally, this study focused solely on PLR as an inflammatory biomarker, without examining interactions with other immune-inflammatory indices (e.g., NLR, SII) or relevant clinical variables (e.g., left ventricular ejection fraction, graft patency). Emerging evidence indicates that composite biomarker models may enhance prognostic accuracy in CABG populations. However, due to the absence of individual patient data, stratified and interaction analyses could not be performed, limiting the identification of high-risk subgroups and refinement of risk stratification models. In summary, this meta-analysis provides important insights into the prognostic role of PLR in CABG patients, but the aforementioned limitations necessitate caution in translating these findings into clinical practice. Future investigations should prioritize prospective, multicenter trials with standardized PLR assessment, include diverse geographic populations, and evaluate the incremental prognostic value of PLR when combined with other biomarkers.

Conclusions

PLR demonstrates a potential correlation with POAF, AKI, ACM, delirium, SVGD, and adverse events in patients undergoing CABG. Elevated PLR levels are indicative of poorer prognoses. Subgroup analyses suggest that factors like age, geographic region, PLR cut-off values, and detection modalities may influence its predictive performance. Given the predominance of retrospective studies and the geographic concentration of current research, along with the observed heterogeneity and potential publication bias, international, multicenter, prospective clinical trials are necessary. Such studies are essential to further corroborate the prognostic value of PLR in the CABG population and facilitate the development of more accurate clinical models for identifying high-risk individuals and guiding timely, individualized interventions.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1539/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1539/prf

Funding: None.

Conflicts of Interest: The authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1539/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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