Next Article in Journal
Cardiac Rehabilitation After Open Heart Surgery: A Narrative Systematic Review
Previous Article in Journal
Different Proteins as Biomarkers for Sac Shrinkage After Endovascular Aortic Repair of Abdominal Aortic Aneurysms
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCDD
Volume 11
Issue 11
10.3390/jcdd11110375
jcdd-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery

by
Krzysztof Greberski
1,2,
Jakub Batko
3,*,
Paweł Bugajski
1,2,
Maciej Łuczak
2,
Maciej Brzeziński
4 and
Krzysztof Bartuś
3
1
Faculty of Health Sciences, University of Medical Sciences, 60-572 Poznan, Poland
2
Department of Cardiac Surgery, J. Strus Municipal Hospital, 61-285 Poznan, Poland
3
Department of Cardiovascular Surgery and Transplantology, Institute of Cardiology, Jagiellonian University Medical College, 30-688 Krakow, Poland
4
Department of Cardiovascular Surgery, Medical University of Gdansk, 80-210 Gdansk, Poland
*
Author to whom correspondence should be addressed.
J. Cardiovasc. Dev. Dis. 2024, 11(11), 375; https://doi.org/10.3390/jcdd11110375
Submission received: 22 September 2024 / Revised: 15 November 2024 / Accepted: 19 November 2024 / Published: 20 November 2024
(This article belongs to the Topic Biomarkers in Cardiovascular Disease—Chances and Risks, 2nd Volume)
Browse Figures
Versions Notes

Abstract

Background: Coronary heart disease is the most common cause of death worldwide. It is responsible for almost a third of deaths in patients over the age of 35. Various biomarkers are currently being studied in detail for their value in predicting postoperative mortality in patients undergoing CABG. Aim: The aim of this study is to analyze the predictive value of certain blood morphological parameters in CABG and off-pump coronary artery bypass grafting (OPCAB). Methods: A total of 520 patients who underwent surgery in two consecutive years and underwent CABG (404) or OPCAB (116) were included in this retrospective study. Gender, age, comorbidities, five-year survival rate, detailed information on hospitalization, surgery, intensive care unit parameters and preoperative blood samples from the cubital vein were recorded. Inverse propensity treatment weighting was applied to adjust for confounding factors at baseline. Results: No differences were found between OPCAB and CABG as an isolated comparison. In the standardized population, patients with abnormal lymphocyte counts had an increased risk of death at one-year and five-year follow-up. In the standardized population, abnormal red blood cell distribution width (RDW-SD), neutrocyte-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were associated with increased mortality in each period analyzed. Conclusions: Abnormal PLR, RDW-SD and NLR are associated with increased early and late mortality in patients undergoing CABG and OPCAB. Abnormal lymphocytes are only associated with increased late mortality.

1. Introduction

Coronary heart disease is the most common cause of death worldwide. It is responsible for almost a third of deaths in patients over the age of 35 [1]. The risk factors for coronary heart disease include not only the aforementioned age, but also high blood pressure, dyslipidemia, diabetes mellitus, tobacco consumption, obesity and lack of exercise [2,3,4,5,6,7,8]. Coronary artery disease can lead to acute coronary syndrome, which is a life-threatening diagnosis [9]. It can be confirmed by characteristic symptoms, changes in the electrocardiogram and elevated high-sensitivity cardiac troponins [8]. In some cases, invasive and non-invasive imaging techniques can be helpful to confirm this diagnosis [8]. In case of a confirmed diagnosis, rapid percutaneous coronary intervention is crucial for a higher survival rate [8,10]. In patients with coronary artery anatomy that is unfavorable for percutaneous coronary intervention, emergency coronary artery bypass grafting (CABG) should be performed [7,8]. If the acute coronary syndrome leads to mechanical complications, CABG should be performed during such repair in patients requiring revascularization [7,8]. In chronic coronary artery disease, CABG is usually recommended to treat clinically significant narrowing of the main coronary arteries that cannot be treated with percutaneous coronary intervention [7]. Coronary artery bypass off-pump grafting (OPCAB), which does not require cardiopulmonary bypass, was developed as an evolution of the CABG technique [11]. It was thought to improve long-term outcomes, but several clinical trials have shown it to be superior only in terms of reducing short-term postoperative complications [12]. In addition, some reports suggest that it leads to less efficient myocardial revascularization [13].
Predicting postoperative mortality after CABG may be helpful in determining patients who are prone to this complication [14]. The most important predictors are New York Heart Association class above III, reoperation and preoperative dialysis [7,15,16]. Nowadays, many biomarkers that are readily available in daily practice are used to predict increased mortality risk in patients and in the general population [17,18,19,20,21,22,23]. The most common but underestimated biomarkers are neutrocyte-to-lymphocyte ratio (NLR), red blood cell distribution width standard deviation (RDW-SD), platelet-to-lymphocyte ratio (PLR), mean platelet volume (MPV) and platelet distribution width (PDW). All of these markers can be assessed by simple morphologic analysis of peripheral blood. However, few biomarkers have been extensively studied for their value in predicting postoperative mortality in patients undergoing CABG [24].
Therefore, the aim of this study is to analyze the predictive value of certain blood morphological parameters in coronary artery bypass grafting and to compare their value between CABG and OPCAB.

2. Materials and Methods

2.1. Study Population

A total of 520 patients who underwent CABG (404) or OPCAB (116) in two consecutive years were included in this retrospective study. Gender, age, comorbidities, five-year survival rate, detailed information on hospitalization, surgery, intensive care unit (ICU) parameters and preoperative blood samples from the cubital vein were collected. Inclusion and exclusion criteria were as follows: patients who had undergone isolated CABG or OPCAB, had a blood sample taken preoperatively and had a complete medical history. All patients who did not have the above data or who underwent a concomitant procedure were excluded. A detailed flowchart of patient inclusion in the study can be found in Figure 1.

2.2. Blood Parameters

The following blood parameters were analyzed with the normal ranges and units given in Table 1: white blood cells (WBC), neutrocytes, lymphocytes, NLR, RDW-SD, platelets, PLR, MPV and PDW. For parameters for which there is no laboratory standard range, the standards proposed in experimental studies were used [25].

2.3. Statistical Analysis

The data were analyzed with IBM SPSS Statistics 29.0 (Predictive Solutions, Pittsburgh, PA, USA). The statistical size of the study population was calculated using the following parameters: Type I error rate equal to 0.05, Type 2 error rate equal to 0.2, proportion of subjects in each group equal to 0.2, proportion of individuals in the population exposed equal to 0.25, prevalence of the outcome in the population equal to 0.15, and estimated risk ratio equal to 2. The minimum sample size was calculated as 515. Categorical variables are presented as numbers (n) or percentages. Quantitative variables are presented as mean with standard deviation. The normal distribution was analyzed using the Shapiro–Wilk test. Differences between normally distributed quantitative parameters were assessed using the Student t-test. Differences between categorical variables were tested for independence using the chi-square test. Correlations were tested using the Pearson correlation (two-tailed test, α = 0.05, β = 0.2). Kaplan–Meier curves were generated to compare 5-year survival between patients operated on with different techniques and patients with blood parameters within and outside the range using the Mantel–Cox log-rank test. Odds ratios (OR) with 95% confidence intervals were calculated for 30-day mortality, one-year and five-year mortality and complications when comparing patients with blood parameters within and outside the standard range. Inverse propensity treatment weighting (IPTW) was used to standardize the baseline parameters in the population. A detailed description of the method can be found in our previous publication [26]. The population was standardized for: age, BMI, sex, CCS class, history of myocardial infarction, percutaneous coronary intervention, history of smoking, diabetes and its treatment method, hypertension, hyperlipidemia, chronic kidney damage, vascular disease, and left main coronary trunk obstruction. ORs with 95% confidence intervals (CI) for 30-day mortality, one-year and five-year mortality and complications were calculated for the standardized population, comparing patients with blood parameters within and outside the standard range. A p-value < 0.05 was considered statistically significant.

2.4. Bioethical Approval

The Bioethical Committee of University of Medical Sciences, Poznan, Poland approval was waived due to retrospective character of this study (Decision nr 748/24). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki.

3. Results

3.1. Non-Standardized Population

3.1.1. Baseline and Procedural Characteristics

The detailed characteristics of the study population are listed in Table 2. Statistically significant differences were found in the age of the patients, the duration of hospitalization and the duration of surgery.

3.1.2. Preoperative Blood Parameters

The detailed results of the preoperative blood parameters are shown in Table 3. Significant differences were found both in the mean PDW and in the number of patients with a PDW outside the range between the OPCAB and CABG groups.

3.1.3. Mortality

The overall mortality at hospitalization, after 30 days, after one year and after five years was 1.5%, 3.3%, 7.5% and 16.2%, respectively. Detailed survival curves comparing CABG and OP-CAB with Kaplan–Meier estimation can be found in Figure 2A. Detailed survival curves comparing patients with blood values within and outside the normal range can be found in Figure 2B–D. The OR of 30-day mortality in the general population was statistically significantly increased in the group of patients with PLR outside the normal range (3.195, 1.207–8.452 95% CI). The OR of 1-year mortality was statistically significantly increased in patients with RDW-SD outside the normal range (2.265, 1.157–4.436 95% CI). Both RDW-SD and PLR out of range increased the OR of 5-year mortality (1.658, 1.001–2.749 95% CI and 1.755, 1.071–2.874 95% CI, respectively). In the isolated CABG group, only the out-of-range platelet count increased the OR of one-year mortality (2.535, 1.026–6.262 95% CI).

3.1.4. Correlations

In the general population, weak correlations were observed between Euroscore and lymphocytes (r = −0.097; p = 0.026), RDW-SD (r = 0.104; p = 0.018) and PLR (r = 0.121; p = 0.006). In the CABG group, a weak correlation was observed between operation time and RDW-SD (r = 0.136; p = 0.006); Euroscore and PLT (r = 0.105; p = 0.036) and PLR (r = 0.119; p = 0.017). In the OPCAB group, weak correlations were observed between 30-day and one-year survival and NLR (r = −0.187; p = 0.048 and r = −0.185; p = 0.049, respectively) and five-year survival and PLR (r = −0.218; p = 0.019).

3.2. IPTW Standardized Population

3.2.1. Baseline and Procedural Characteristics

The detailed characteristics of the study population are listed in Table 4. Statistically significant differences were found in the duration of hospitalization and the duration of surgery.

3.2.2. Preoperative Blood Parameters

The detailed results of the preoperative blood parameters are shown in Table 5. Significant differences were found both in the mean PDW, MPV, PLR and in the number of patients with PDW, MPV, WBC, Neutrocytes, Lymphocytes outside the range between the OPCAB and CABG standardized groups.

3.2.3. Mortality

The all-cause hospitalization, 30-day, one-year, and five-year mortality rates were 0.9%, 2.8%, 6.3%, and 13.9%, respectively, in the standardized general cohort. The detailed OR of 30-day, 1-year and 5-year mortality in OPCAB, CABG and the general population in patients with each analyzed blood parameter within and outside of the standard range can be found in Table 6.

4. Discussion

4.1. Results Discussion

The aim of our study was to analyze the predictive value of daily blood morphology parameters for the survival rate of patients undergoing CABG, comparing two groups—classical CABG and OPCAB. When comparing the survival rate, we found no significant differences between these groups. Moreover, there was only one parameter—platelet count—whose abnormal values were exclusively associated with an increased risk of one-year mortality. Based on these results, it can be assumed that most blood parameters have a similar predictive value for CABG and OPCAB patients. Based on our results, a good predictor of 30-day mortality could be an abnormal PLR value, which is associated with an increased risk of death in CABG patients. On the other hand, a normal RDW-SD value proved to be a good predictor of long-term survival, both at one and five years. NLR out of range was associated with statistically significant differences in the overall survival curves of patients, but only in the 1-year period. Therefore, this result should be interpreted with caution. The correlations found were weak and should be analyzed with caution. Further statistically significant results were found when analyzing the comparison of the standardized populations. In general, patients with abnormal lymphocyte levels were found to have a more than 2-fold increased mortality rate [OR: 2.119 (1.227–3.661) 95%CI] at one-year follow-up compared to patients with lymphocytes within the standard range. This effect diminished at 5-year follow-up but remained statistically significant [OR: 1.580 (1.045–2.387) 95% CI]. In the standardized population, abnormal RDW-SD, NLR and PLR were associated with increased mortality in each time frame analyzed. PLR was associated with the greatest increase in mortality rate at 30-day and 5-year follow-up [OR: 6.149 (2.727–13.868) 95%CI and OR: 2.071 (1.427–3.008) 95%CI, respectively]. NLR was associated with the greatest increase in the probability of mortality at 1-year follow-up [OR: 2.836 (1.664–4.834) 95%CI].
Despite the different invasiveness of CABG and OPCAB, no significant differences were found between these groups in terms of short- and long-term mortality in the groups of patients with abnormal blood parameters in our study. This may indicate that even if the surgical technique affects the short-term homeostasis of patients, these differences are not as pronounced in terms of long-term mortality. We decided to additionally analyze the results of blood parameters based on their values within or outside the range to facilitate clinical implementation, as cut-off points are difficult to assess, remember and interpret in daily practice.

4.2. Historical Results Comparison

In a meta-analysis conducted by Pruc et al., abnormal preoperative PLR was associated with an increased risk of death in patients with acute coronary syndrome [26]. In the study by Tzikos et al., postoperative abnormal PLR values were associated with an increased risk of early mortality in cardiac surgery patients [27]. There were no previous publications on the long-term effects of preoperative PLR values on long-term mortality in CABG and OPCAB patients. Our results are consistent with previously published studies and demonstrate that PLR can be used as a predictive factor for premature mortality in patients with cardiovascular disease and patients undergoing cardiac surgery, particularly CABG.
Lee et al. analyzed the impact of RDW-SD changes on early adverse events after isolated CABG [28]. They demonstrated that RDW-SD changes are independent predictors of early adverse events after this procedure. Joshi et al. provided further insight into the importance of RDW-SD by analyzing its impact on predicting in-hospital mortality in patients without OPCAB and demonstrating that its abnormality is an isolated predictor of this complication [29]. Bujak et al. analyzed the crucial pathophysiology associated with the adverse effect of abnormal RDW-SD in patients with coronary artery disease and provided a molecular explanation for previous observations [30]. Gurbuz et al. investigated the impact of elevated RDW-SD on the long-term prevalence of cardiovascular events in CABG patients and found a strong association between elevated RDW-SD and a higher incidence of such events [31]. Our study proves that abnormal RDW-SD in patients undergoing CABG and OPCAB leads to an increased risk of both early and long-term mortality, further supporting previous findings.
Lim et al. demonstrated in his study on the impact of NLR on postoperative CABG outcomes that high preoperative NLR is associated with early mortality [32]. However, Sahin and Sisli, who analyzed OPCAB and CABG patients, found that only postoperative NLR abnormalities were associated with increased mortality [33]. Silberman et al. found NLR to be a predictor of operative and late mortality in a large cohort of patients [34]. Our study supports these findings.

4.3. What Are PLR, NLR and RDW-SD?

PLR is an inexpensive inflammatory biomarker that can be easily calculated in daily practice. It is calculated on the basis of the number of platelets and lymphocytes. It has long been known as a negative prognostic marker in oncology and hematology but is now also used in other areas of medicine. In the literature, it has been associated with complex endpoints, adverse cardiovascular events or atrial fibrillation in patients undergoing cardiac surgery [35]. Our study particularly highlights its greatest impact on the increased probability of death within the parameters analyzed.
The RDW-SD is an inexpensive, routinely measured parameter that is measured at every blood morphology examination. It measures the differences in erythrocyte size and volume. Its changes have been observed in various diseases, including peripheral arterial disease, heart failure and atrial fibrillation [36]. It has been suggested to be a predictor of complications and mortality in cardiac surgery [37,38,39]. Our study proves that RDW-SD is an additional predictor of postoperative mortality, with its abnormal values being slightly less associated with increased mortality compared to PLR and NLR
NLR is an inexpensive inflammatory biomarker that can be easily calculated in daily practice. It is calculated as a simple ratio of the number of neutrophils and lymphocytes. Recently, it has been analyzed in terms of evaluating the homeostasis of the immune system, as neutrophils are mainly responsible for the native immune response and lymphocytes for the adaptive immune response associated with antibody synthesis [40,41]. The NLR has previously been shown to be an independent prognostic factor for mortality in the general population and in various diseases [41,42,43,44,45]. Our study sheds light on its role as a predictive factor for mortality in patients undergoing CABG treatment.

4.4. Clinical Implications

Based on our results, patients with abnormal preoperative PLR should be monitored with greater caution during hospitalization and rehabilitation, as they are particularly prone to short-term mortality. Patients with abnormal NLR should be invited for frequent follow-up visits to perform further investigations and prevent premature death. Patients with abnormal preoperative NLR, PLR and RDW-SD are prone to increased postoperative short-term and long-term mortality.

4.5. Limitations and Further Studies

In our study, a retrospective analysis of 520 patients was performed. Only the preoperative blood parameters were analyzed. Our population was largely homogeneous except for age. To compensate for this confounder, we performed an IPTW analysis. Future studies should include the results of postoperative and long-term follow-up of blood parameters to determine the impact of their changes on patient mortality. In the future, other isolated interventions should also be included in the analysis to identify high-risk patients before the start of cardiac surgery. In addition, concomitant interventions that have been shown to reduce or increase the risk of bypass surgery should be included in the analysis [46,47]. Additional parameters, including lipoprotein levels, should be investigated as their levels have been shown in the past to be associated with a higher risk of death in patients with coronary syndromes [48]. The specific anatomy of the patient should be considered as another important factor in CABG [49,50,51]. In addition, cardiovascular imaging, especially advanced echocardiographic parameters, should be considered as an additional predictive factor [52,53,54].

5. Conclusions

Abnormal PLR, RDW-SD and NLR are associated with increased early and late mortality in patients undergoing CABG and OPCAB. Abnormal lymphocytes are only associated with increased late mortality.

Author Contributions

Conceptualization, K.G., J.B., P.B., M.Ł., M.B. and K.B.; methodology K.G.; software, J.B.; validation, K.G. and K.B.; formal analysis K.G.; investigation, K.G.; resources, K.G.; data curation, K.G.; writing—original draft preparation, K.G.; writing—review and editing, J.B., P.B., M.Ł., M.B. and K.B.; visualization, K.G.; supervision, J.B.; project administration, K.G.; funding acquisition, K.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The Bioethical Committee of University of Medical Sciences, Poznan, Poland approval was waived due to retrospective character of this study (Decision nr 748/24). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki.

Informed Consent Statement

Patient consent was waived due to retrospective character of this study.

Data Availability Statement

Data are available from corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ferreira-González, I. The Epidemiology of Coronary Heart Disease. Rev. Española Cardiol. 2014, 67, 139–144. [Google Scholar] [CrossRef]
  2. Goff, D.C.; Lloyd-Jones, D.M.; Bennett, G.; Coady, S.; D’Agostino, R.B.; Gibbons, R.; Greenland, P.; Lackland, D.T.; Levy, D.; O’Donnell, C.J.; et al. 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk. J. Am. Coll. Cardiol. 2014, 63, 2935–2959. [Google Scholar] [CrossRef] [PubMed]
  3. Nicholls, S.J.; Tuzcu, E.M.; Kalidindi, S.; Wolski, K.; Moon, K.-W.; Sipahi, I.; Schoenhagen, P.; Nissen, S.E. Effect of Diabetes on Progression of Coronary Atherosclerosis and Arterial Remodeling. J. Am. Coll. Cardiol. 2008, 52, 255–262. [Google Scholar] [CrossRef]
  4. Teo, K.K.; Ounpuu, S.; Hawken, S.; Pandey, M.; Valentin, V.; Hunt, D.; Diaz, R.; Rashed, W.; Freeman, R.; Jiang, L.; et al. Tobacco Use and Risk of Myocardial Infarction in 52 Countries in the INTERHEART Study: A Case-Control Study. Lancet 2006, 368, 647–658. [Google Scholar] [CrossRef] [PubMed]
  5. Matsuzawa, Y. The Metabolic Syndrome and Adipocytokines. FEBS Lett. 2006, 580, 2917–2921. [Google Scholar] [CrossRef] [PubMed]
  6. Virani, S.S.; Newby, L.K.; Arnold, S.V.; Bittner, V.; Brewer, L.C.; Demeter, S.H.; Dixon, D.L.; Fearon, W.F.; Hess, B.; Johnson, H.M.; et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation 2023, 148, E9–E119. [Google Scholar] [CrossRef] [PubMed]
  7. Vrints, C.; Andreotti, F.; Koskinas, K.C.; Rossello, X.; Adamo, M.; Ainslie, J.; Banning, A.P.; Budaj, A.; Buechel, R.R.; Chiariello, G.A.; et al. 2024 ESC Guidelines for the Management of Chronic Coronary Syndromes. Eur. Heart J. 2024, 45, 3415–3537. [Google Scholar] [CrossRef] [PubMed]
  8. Byrne, R.A.; Rossello, X.; Coughlan, J.J.; Barbato, E.; Berry, C.; Chieffo, A.; Claeys, M.J.; Dan, G.-A.; Dweck, M.R.; Galbraith, M.; et al. 2023 ESC Guidelines for the Management of Acute Coronary Syndromes. Eur. Heart J. 2023, 44, 3720–3826. [Google Scholar] [CrossRef]
  9. Mohammad, M.A.; Stone, G.W.; Koul, S.; Olivecrona, G.K.; Bergman, S.; Persson, J.; Engstrøm, T.; Fröbert, O.; Jernberg, T.; Omerovic, E.; et al. On the Natural History of Coronary Artery Disease: A Longitudinal Nationwide Serial Angiography Study. J. Am. Heart Assoc. 2022, 11, e026396. [Google Scholar] [CrossRef]
  10. Armstrong, P.W.; Gershlick, A.H.; Goldstein, P.; Wilcox, R.; Danays, T.; Lambert, Y.; Sulimov, V.; Rosell Ortiz, F.; Ostojic, M.; Welsh, R.C.; et al. Fibrinolysis or Primary PCI in ST-Segment Elevation Myocardial Infarction. N. Engl. J. Med. 2013, 368, 1379–1387. [Google Scholar] [CrossRef]
  11. Gaudino, M.; Angelini, G.D.; Antoniades, C.; Bakaeen, F.; Benedetto, U.; Calafiore, A.M.; Di Franco, A.; Di Mauro, M.; Fremes, S.E.; Girardi, L.N.; et al. Off-Pump Coronary Artery Bypass Grafting: 30 Years of Debate. J. Am. Heart Assoc. 2018, 7, e009934. [Google Scholar] [CrossRef]
  12. Kozora, E.; Kongs, S.; Collins, J.F.; Hattler, B.; Baltz, J.; Hampton, M.; Grover, F.L.; Novitzky, D.; Shroyer, A.L. Cognitive Outcomes After On- Versus Off-Pump Coronary Artery Bypass Surgery. Ann. Thorac. Surg. 2010, 90, 1134–1141. [Google Scholar] [CrossRef]
  13. Zhang, B.; Zhou, J.; Li, H.; Liu, Z.; Chen, A.; Zhao, Q. Comparison of Graft Patency Between Off-Pump and On-Pump Coronary Artery Bypass Grafting: An Updated Meta-Analysis. Ann. Thorac. Surg. 2014, 97, 1335–1341. [Google Scholar] [CrossRef] [PubMed]
  14. Khalaji, A.; Behnoush, A.H.; Jameie, M.; Sharifi, A.; Sheikhy, A.; Fallahzadeh, A.; Sadeghian, S.; Pashang, M.; Bagheri, J.; Ahmadi Tafti, S.H.; et al. Machine Learning Algorithms for Predicting Mortality after Coronary Artery Bypass Grafting. Front. Cardiovasc. Med. 2022, 9, 977747. [Google Scholar] [CrossRef] [PubMed]
  15. Momin, A.; Ranjan, R.; Valencia, O.; Jacques, A.; Lim, P.; Fluck, D.; Chua, T.P.; Chandrasekaran, V. Survival and Independent Predictors of Mortality Following Coronary Artery Bypass Graft Surgery in a Single-Unit Practice in the United Kingdom Over 20 Years. Cureus 2023, 15, e38413. [Google Scholar] [CrossRef] [PubMed]
  16. Podsiadło, P.; Brožek, T.; Balik, M.; Nowak, E.; Mendrala, K.; Hymczak, H.; Dąbrowski, W.; Miazgowski, B.; Rutkiewicz, A.; Burysz, M.; et al. Predictors of Cardiac Arrest in Severe Accidental Hypothermia. Am. J. Emerg. Med. 2024, 78, 145–150. [Google Scholar] [CrossRef] [PubMed]
  17. Zhu, H.-M.; Xiong, Y.-Y.; Chen, Y.-B.; Xiao, J. Serum Platelet Distribution Width Predicts Cardiovascular and All-Cause Mortality in Patients Undergoing Peritoneal Dialysis. Postgrad. Med. 2023, 135, 394–401. [Google Scholar] [CrossRef]
  18. Wang, J.; Ma, X.; Si, X.; Wu, M.; Han, W. Mean Platelet Volume and the Association with All-Cause Mortality and Cardiovascular Mortality among Incident Peritoneal Dialysis Patients. BMC Cardiovasc. Disord. 2023, 23, 543. [Google Scholar] [CrossRef]
  19. Oylumlu, M.; Oylumlu, M.; Arslan, B.; Polat, N.; Özbek, M.; Demir, M.; Yildiz, A.; Toprak, N. Platelet-to-Lymphocyte Ratio Is a Predictor of Long-Term Mortality in Patients with Acute Coronary Syndrome. Postep. Kardiol. Interwencyjnej 2020, 16, 170–176. [Google Scholar] [CrossRef]
  20. Bazick, H.S.; Chang, D.; Mahadevappa, K.; Gibbons, F.K.; Christopher, K.B. Red Cell Distribution Width and All-Cause Mortality in Critically Ill Patients. Crit. Care Med. 2011, 39, 1913–1921. [Google Scholar] [CrossRef]
  21. Larsen, M.K.; Skov, V.; Kjær, L.; Eickhardt-Dalbøge, C.S.; Knudsen, T.A.; Kristiansen, M.H.; Sørensen, A.L.; Wienecke, T.; Andersen, M.; Ottesen, J.T.; et al. Neutrophil-to-Lymphocyte Ratio and All-Cause Mortality with and without Myeloproliferative Neoplasms—A Danish Longitudinal Study. Blood Cancer J. 2024, 14, 28. [Google Scholar] [CrossRef]
  22. Msaouel, P.; Lam, A.P.; Gundabolu, K.; Chrysofakis, G.; Yu, Y.; Mantzaris, I.; Friedman, E.; Verma, A. Abnormal Platelet Count Is an Independent Predictor of Mortality in the Elderly and Is Influenced by Ethnicity. Haematologica 2014, 99, 930–936. [Google Scholar] [CrossRef]
  23. Haran, C.; Gimpel, D.; Clark, H.; McCormack, D.J. Preoperative Neutrophil and Lymphocyte Ratio as a Predictor of Mortality and Morbidity After Cardiac Surgery. Heart Lung Circ. 2021, 30, 414–418. [Google Scholar] [CrossRef]
  24. Preeshagul, I.; Gharbaran, R.; Jeong, K.H.; Abdel-Razek, A.; Lee, L.Y.; Elman, E.; Suh, K.S. Potential Biomarkers for Predicting Outcomes in CABG Cardiothoracic Surgeries. J. Cardiothorac. Surg. 2013, 8, 176. [Google Scholar] [CrossRef]
  25. Wu, L.; Zou, S.; Wang, C.; Tan, X.; Yu, M. Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratio in Chinese Han Population from Chaoshan Region in South China. BMC Cardiovasc. Disord. 2019, 19, 125. [Google Scholar] [CrossRef]
  26. Pruc, M.; Peacock, F.W.; Rafique, Z.; Swieczkowski, D.; Kurek, K.; Tomaszewska, M.; Katipoglu, B.; Koselak, M.; Cander, B.; Szarpak, L. The Prognostic Role of Platelet-to-Lymphocyte Ratio in Acute Coronary Syndromes: A Systematic Review and Meta-Analysis. J. Clin. Med. 2023, 12, 6903. [Google Scholar] [CrossRef]
  27. Tzikos, G.; Alexiou, I.; Tsagkaropoulos, S.; Menni, A.-E.; Chatziantoniou, G.; Doutsini, S.; Papavramidis, T.; Grosomanidis, V.; Stavrou, G.; Kotzampassi, K. Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio as Predictive Factors for Mortality and Length of Hospital Stay after Cardiac Surgery. J. Pers. Med. 2023, 13, 473. [Google Scholar] [CrossRef] [PubMed]
  28. Lee, S.I.; Lee, S.Y.; Choi, C.H.; Park, C.-H.; Park, K.Y.; Son, K.H. Relation between Changes in Red Blood Cell Distribution Width after Coronary Artery Bypass Grafting and Early Postoperative Morbidity. J. Thorac. Dis. 2018, 10, 4244–4254. [Google Scholar] [CrossRef] [PubMed]
  29. Joshi, D.; Chowdhury, M.A.T.; Alauddin, M.; Ranjan, R.; Khan, O.S.; Hoque, M.R. Role of Pre-Operative Red Cell Distribution Width Estimation in the Prediction of in-Hospital Mortality after off-Pump Coronary Artery Bypass Grafting. J. Cardiothorac. Surg. 2021, 16, 232. [Google Scholar] [CrossRef]
  30. Bujak, K.; Wasilewski, J.; Osadnik, T.; Jonczyk, S.; Kołodziejska, A.; Gierlotka, M.; Gąsior, M. The Prognostic Role of Red Blood Cell Distribution Width in Coronary Artery Disease: A Review of the Pathophysiology. Dis. Markers 2015, 2015, 824624. [Google Scholar] [CrossRef] [PubMed]
  31. Gurbuz, O.; Kumtepe, G.; Ozkan, H.; Karal, I.H.; Ercan, A.; Ener, S. Red Blood Cell Distribution Width Predicts Long Term Cardiovascular Event after On-Pump Beating Coronary Artery Bypass Grafting. J. Cardiothorac. Surg. 2016, 11, 48. [Google Scholar] [CrossRef]
  32. Lim, H.A.; Kang, J.K.; Kim, H.W.; Song, H.; Lim, J.Y. The Neutrophil-to-Lymphocyte Ratio as a Predictor of Postoperative Outcomes in Patients Undergoing Coronary Artery Bypass Grafting. J. Chest Surg. 2023, 56, 99–107. [Google Scholar] [CrossRef] [PubMed]
  33. ŞAHIN, A.; Sisli, E. Retrospective Evaluation of the Pre- and Postoperative Neutrophil-Lymphocyte Ratio as a Predictor of Mortality in Patients Who Underwent Coronary Artery Bypass Grafting. Heart Surg. Forum 2021, 24, E814–E820. [Google Scholar] [CrossRef] [PubMed]
  34. Silberman, S.; Abu-Yunis, U.; Tauber, R.; Shavit, L.; Grenader, T.; Fink, D.; Bitran, D.; Merin, O. Neutrophil-Lymphocyte Ratio: Prognostic Impact in Heart Surgery. Early Outcomes and Late Survival. Ann. Thorac. Surg. 2018, 105, 581–586. [Google Scholar] [CrossRef]
  35. Navani, R.; Huang, K.; Jin, D.; Baradi, A.; Paleri, S.; Nguyen, J.; Ellis, Z.; Newcomb, A.; Wilson, A. Is Preoperative Platelet-to-Lymphocyte Ratio a Predictive Biomarker of Postoperative Atrial Fibrillation in Patients Following Coronary Artery Bypass Graft Surgery? Heart Lung Circ. 2019, 28, S337. [Google Scholar] [CrossRef]
  36. Montagnana, M.; Cervellin, G.; Meschi, T.; Lippi, G. The Role of Red Blood Cell Distribution Width in Cardiovascular and Thrombotic Disorders. Clin. Chem. Lab. Med. 2012, 50, 635–641. [Google Scholar] [CrossRef]
  37. Polat, V.; Iscan, S.; Etli, M.; El Kılıc, H.; Gürsu, Ö.; Eker, E.; Ozdemir, F. Red Cell Distribution Width as a Prognostic Indicator in Pediatric Heart Disease and after Surgery. Biomed. Res. Int. 2014, 2014, 681679. [Google Scholar] [CrossRef] [PubMed]
  38. Benedetto, U.; Angeloni, E.; Melina, G.; Pisano, C.; Lechiancole, A.; Roscitano, A.; Pooley, M.; Comito, C.; Codispoti, M.; Sinatra, R. Red Blood Cell Distribution Width Predicts Mortality after Coronary Artery Bypass Grafting. Int. J. Cardiol. 2013, 165, 369–371. [Google Scholar] [CrossRef]
  39. Danese, E.L.G.M.M. Red Blood Cell Distribution Width and Cardiovascular Diseases. J. Thorac. Dis. 2015, 7, E402–E411. [Google Scholar]
  40. Mortaz, E.; Alipoor, S.D.; Adcock, I.M.; Mumby, S.; Koenderman, L. Update on Neutrophil Function in Severe Inflammation. Front. Immunol. 2018, 9, 2171. [Google Scholar] [CrossRef]
  41. Fest, J.; Ruiter, T.R.; Groot Koerkamp, B.; Rizopoulos, D.; Ikram, M.A.; van Eijck, C.H.J.; Stricker, B.H. The Neutrophil-to-Lymphocyte Ratio Is Associated with Mortality in the General Population: The Rotterdam Study. Eur. J. Epidemiol. 2019, 34, 463–470. [Google Scholar] [CrossRef] [PubMed]
  42. Adamstein, N.H.; MacFadyen, J.G.; Rose, L.M.; Glynn, R.J.; Dey, A.K.; Libby, P.; Tabas, I.A.; Mehta, N.N.; Ridker, P.M. The Neutrophil–Lymphocyte Ratio and Incident Atherosclerotic Events: Analyses from Five Contemporary Randomized Trials. Eur. Heart J. 2021, 42, 896–903. [Google Scholar] [CrossRef] [PubMed]
  43. Lee, M.-J.; Park, S.-D.; Kwon, S.W.; Woo, S.-I.; Lee, M.-D.; Shin, S.-H.; Kim, D.-H.; Kwan, J.; Park, K.-S. Relation Between Neutrophil-to-Lymphocyte Ratio and Index of Microcirculatory Resistance in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Am. J. Cardiol. 2016, 118, 1323–1328. [Google Scholar] [CrossRef]
  44. Li, W.; Hou, M.; Ding, Z.; Liu, X.; Shao, Y.; Li, X. Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Stroke: A Systematic Review and Meta-Analysis. Front. Neurol. 2021, 12, 686983. [Google Scholar] [CrossRef] [PubMed]
  45. Song, M.; Graubard, B.I.; Rabkin, C.S.; Engels, E.A. Neutrophil-to-Lymphocyte Ratio and Mortality in the United States General Population. Sci. Rep. 2021, 11, 464. [Google Scholar] [CrossRef]
  46. Suwalski, P.; Dąbrowski, E.J.; Batko, J.; Pasierski, M.; Litwinowicz, R.; Kowalówka, A.; Jasiński, M.; Rogowski, J.; Deja, M.; Bartus, K.; et al. Additional Bypass Graft or Concomitant Surgical Ablation? Insights from the HEIST Registry. Surgery 2024, 175, 974–983. [Google Scholar] [CrossRef]
  47. Pasierski, M.; Batko, J.; Kuźma, Ł.; Wańha, W.; Jasiński, M.; Widenka, K.; Deja, M.; Bartuś, K.; Hirnle, T.; Wojakowski, W.; et al. Surgical Ablation, Left Atrial Appendage Occlusion or Both? Nationwide Registry Analysis of Cardiac Surgery Patients with Underlying Atrial Fibrillation. Eur. J. Cardio-Thorac. Surg. 2024, 65, ezae014. [Google Scholar] [CrossRef]
  48. Rajtar-Salwa, R.; Bobrowska, B.; Batko, J.; Bartuś, S.; Petkow-Dimitrow, P.; Krawczyk-Ożóg, A. Lipid-Lowering Therapy after Acute Coronary Syndrome in Outpatient Practice—How to Achieve Goal. J. Clin. Med. 2023, 12, 6579. [Google Scholar] [CrossRef]
  49. Rams, D.; Batko, J.; Bartuś, K.; Filip, G.; Kowalewski, M.; Litwinowicz, R. Left Internal Mammary Artery Operative Topography for MIDCAB and TECAB Procedures. Innov. Technol. Tech. Cardiothorac. Vasc. Surg. 2022, 17, 499–505. [Google Scholar] [CrossRef]
  50. Batko, J.; Jakiel, R.; Krawczyk–Ożóg, A.; Litwinowicz, R.; Hołda, J.; Bartuś, S.; Bartuś, K.; Hołda, M.K.; Konieczyńska, M. Definition and Anatomical Description of the Left Atrial Appendage Neck. Clin. Anat. 2023, 37, 201–209. [Google Scholar] [CrossRef]
  51. Batko, J.; Rusinek, J.; Słomka, A.; Litwinowicz, R.; Burysz, M.; Bartuś, M.; Lakkireddy, D.R.; Lee, R.J.; Natorska, J.; Ząbczyk, M.; et al. Postoperative Coagulation Changes in Patients after Epicardial Left Atrial Appendage Occlusion Varies Based on the Left Atrial Appendage Size. Diseases 2023, 12, 8. [Google Scholar] [CrossRef] [PubMed]
  52. Perone, F.; Ancona, R.; di Stasio, F.; La Gambina, V.; Comenale Pinto, S. Myocardial Work Assessment in Patients after Coronary Artery Bypass Grafting during Cardiac Rehabilitation. J. Clin. Med. 2023, 12, 7540. [Google Scholar] [CrossRef] [PubMed]
  53. Krawczyk-Ożóg, A.; Batko, J.; Zdzierak, B.; Dziewierz, A.; Tyrak, K.; Bolechała, F.; Kopacz, P.; Strona, M.; Gil, K.; Hołda, J.; et al. Morphology of the Mural and Commissural Atrioventricular Junction of the Mitral Valve. Heart 2023, 110, 517–522. [Google Scholar] [CrossRef] [PubMed]
  54. Litwinowicz, R.; Batko, J.; Rusinek, J.; Olejek, W.; Rams, D.; Kowalewski, M.; Bartuś, K.; Burysz, M. LARIAT or AtriClip: Complications Profile and Comparison in Patients with Atrial Fibrillations Based on Manufacturer and User Facility Device Experience Database. Medicina 2023, 59, 2055. [Google Scholar] [CrossRef] [PubMed]
Jcdd 11 00375 g001
Figure 1. Flow chart for the inclusion of patients. The number of patients excluded based on each inclusion criterion is shown in white circles.
Figure 1. Flow chart for the inclusion of patients. The number of patients excluded based on each inclusion criterion is shown in white circles.
Jcdd 11 00375 g001
Jcdd 11 00375 g002
Figure 2. Survival curves of patients with blood parameters within and outside the normal range. The number of patients at risk is indicated for each survival curve. (A) Survival curve according to OPCAB, CABG and general population. (B) Survival curve of patients with RDW-SD within and outside the normal range over five years. (C) Patients with NLR within and outside the normal range of the five-year survival curve. (D) Patients with PLR within and outside the normal range of the five-year survival curve. CABG—coronary artery bypass graft, OPCAB—off-pump coronary artery bypass grafting, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio.
Figure 2. Survival curves of patients with blood parameters within and outside the normal range. The number of patients at risk is indicated for each survival curve. (A) Survival curve according to OPCAB, CABG and general population. (B) Survival curve of patients with RDW-SD within and outside the normal range over five years. (C) Patients with NLR within and outside the normal range of the five-year survival curve. (D) Patients with PLR within and outside the normal range of the five-year survival curve. CABG—coronary artery bypass graft, OPCAB—off-pump coronary artery bypass grafting, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio.
Jcdd 11 00375 g002
Table 1. Blood parameters with their units and normal ranges.
Table 1. Blood parameters with their units and normal ranges.
Blood ParameterUnitLaboratory/Experimental Normal Range
white blood cells103/μL4–10
neutrocyes103/μL1800–8000
lymphocytes103/μL1500–4500
neutrocytes to lymphocytes ratio-0.43–2.75 male and 0.37–2.87 female
red cells distribution width standard deviationfL36–47
platelets103/μL150–400
platelets to lymphocytes ratio-36.63–149.13 male and 43.36–172.68 female
mean platelet volumefL7.5–10.5
platelet distribution widthfL6.1–11
Table 2. Baseline characteristics and details of hospitalization of analyzed study group. CABG—Coronary Artery Bypass Grafting, OPCAB—Off-Pump Coronary Artery Bypass Grafting, BMI—Body Mass Index, CCS—Canadian Cardiovascular Society, MI—Myocardial Infarction, PCI—Percutaneous Coronary Intervention, ICU—Intensive Care Unit.
Table 2. Baseline characteristics and details of hospitalization of analyzed study group. CABG—Coronary Artery Bypass Grafting, OPCAB—Off-Pump Coronary Artery Bypass Grafting, BMI—Body Mass Index, CCS—Canadian Cardiovascular Society, MI—Myocardial Infarction, PCI—Percutaneous Coronary Intervention, ICU—Intensive Care Unit.
CABG (404)OPCAB (116)p
Age (years)65.6 ± 7.468.1 ± 8.2<0.001
BMI29.1 ± 4.528.8 ± 4.10.558
Male301 (74.5%)90 (77.6%)0.498
CCS Class125 (6.2%)8 (6.9%)0.871
2257 (63.6%)75 (64.7%)
3120 (29.7%)33 (28.4%)
42 (0.5%)0 (0%)
Previous MI194 (48%)62 (53.4%)0.092
Previous PCI18 (4.5%)3 (2.6%)0.367
Ever smokeractive107 (26.5%)30 (25.9%)0.926
previous201 (49.8%)60 (51.7%)
Diabetes183 (45.3%)58 (50%)0.371
Diabetes treatmentdiet33 (18%)11 (19%)0.977
oral88 (48.1%)27 (46.6%)
insulin62 (33.9%)20 (34.5%)
Hypertension369 (91.3%)106 (91.4%)0.989
Hyperlipidemia376 (93.1%)110 (94.8%)0.500
Chronic kidney injury11 (2.7%)6 (5.2%)0.232
Vascular diseasesperipheral66 (16.3%)19 (16.4%)0.885
cerebral50 (12.4%)12 (10.3%)
both69 (17.1%)18 (15.5%)
Left coronary artery main stem obstruction24 (5.9%)6 (5.2%)0.754
Hospital stay (days)12.7 ± 7.310.7 ± 2.70.001
Operation time (min)173.4 ± 32.2140.2 ± 30.1<0.001
ICU time (hours)31.2 ± 89.130.4 ± 67.10.253
Extubation time (hours)17.5 ± 98.127.6 ± 117.30.510
Table 3. Detailed preoperative blood parameters. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, WBC—white blood cell count, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio, MPV—mean platelet volume, PDW—platelet distribution width.
Table 3. Detailed preoperative blood parameters. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, WBC—white blood cell count, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio, MPV—mean platelet volume, PDW—platelet distribution width.
Parameter CABG (404)OPCAB (116)p
WBCmean ± SD7.9 ± 2.27.6 ± 2.80.177
out of range (%)60 (14.9%)10 (8.6%)0.083
Neutrocytesmean ± SD5 ± 1.74.7 ± 1.30.216
out of range (%)20 (5%)1 (0.9%)0.058
Lymphocytesmean ± SD2.1 ± 0.72.2 ± 2.50.52
out of range (%)93 (23%)23 (19.8%)0.528
NLRmean ± SD2.7 ± 1.62.6 ± 1.20.788
out of range (%)78 (19.3%)20 (17.2%)0.616
RDW-SDmean ± SD44.3 ± 4.745.1 ± 50.216
out of range (%)96 (23.8%)33 (28.4%)0.303
Plateletsmean ± SD241 ± 71229.6 ± 62.80.192
out of range (%)44 (10.9%)13 (11.2%)0.872
PLRmean ± SD130.8 ± 65.9127.2 ± 62.70.56
out of range (%)109 (27%)31 (26.7%)0.956
MPVmean ± SD10.5 ± 110.3 ± 10.081
out of range (%)181 (44.8%)43 (37.1%)0.138
PDWmean ± SD13 ± 2.212.5 ± 2.30.007
out of range (%)318 (78.7%)81 (69.8%)0.046
Table 4. Baseline characteristics and details of hospitalization of standardized study group. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, BMI—body mass index, CCS—Canadian Cardiovascular Society, MI—myocardial infarction, PCI—percutaneous coronary intervention, ICU—intensive care unit.
Table 4. Baseline characteristics and details of hospitalization of standardized study group. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, BMI—body mass index, CCS—Canadian Cardiovascular Society, MI—myocardial infarction, PCI—percutaneous coronary intervention, ICU—intensive care unit.
CABG (503)OPCAB (507)p
Age (years)66.3 ± 7.566 ± 8.80.746
BMI29.1 ± 4.429.2 ± 40.332
Male376 (74.8%)380 (75%)0.942
CCS Class130 (6%)35 (6.9%)0.911
2323 (64.2%)311 (61.3%)
3147 (29.2%)161 (31.8%)
43 (0.6%)0 (0%)
Previous MI237 (47.1%)260 (51.3%)0.186
Previous PCI22 (4.4%)13 (2.6%)0.117
Ever smokeractive132 (26.2%)146 (28.8%)0.560
previous253 (50.3%)256 (50.5%)
Diabetes236 (46.9%)231 (45.6%)0.687
Diabetes treatmentdiet40 (8%)41 (8.1%)0.943
oral116 (23.1%)110 (21.7%)
insulin80 (15.9%)80 (15.8%)
Hypertension458 (91.1%)461 (90.9%)0.943
Hyperlipidemy471 (93.6%)473 (93.3%)0.825
Chronic kidney injury15 (3%)15 (3%)0.982
Vascular diseasesperipheral84 (16.7%)90 (17.8%)0.889
cerebral60 (11.9%)55 (10.8%)
both84 (16.7%)90 (17.8%)
Left coronary artery main stem obstruction30 (6%)24 (4.7%)0.385
Hospital stay (days)12.9 ± 7.110.6 ± 2.7<0.001
Operation time (min)179.8 ± 41.2140.3 ± 31<0.001
ICU time (hours)32.3 ± 89.630 ± 63.30.091
Extubation time (hours)17.9 ± 96.840.7 ± 149.70.111
Table 5. Detailed preoperative blood parameters. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, WBC—white blood white blood cell count, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio, MPV—mean platelet volume, PDW—platelet distribution width.
Table 5. Detailed preoperative blood parameters. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, WBC—white blood white blood cell count, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio, MPV—mean platelet volume, PDW—platelet distribution width.
Parameter CABG (503)OPCAB (507)p
WBCmean ± SD7.8 ± 2.17.8 ± 2.70.779
out of range (%)71 (14.1%)47 (9.3%)0.016
Neutrocytesmean ± SD5 ± 1.74.8 ± 1.40.555
out of range (%)23 (4.6%)8 (1.5%)0.006
Lymphocytesmean ± SD2 ± 0.72.2 ± 2.30.241
out of range (%)118 (23.5%)80 (15.9%)0.002
NLRmean ± SD2.8 ± 1.62.6 ± 1.20.293
out of range (%)96 (19.1%)92 (18.2%)0.712
RDW-SDmean ± SD44.4 ± 4.644.7 ± 4.90.484
out of range (%)117 (23.3%)134 (26.5%)0.244
Plateletsmean ± SD239 ± 71229.1 ± 65.30.054
out of range (%)57 (11.3%)60 (11.9%)0.803
PLRmean ± SD132.7 ± 68.8122.9 ± 59.70.007
out of range (%)136 (27%)123 (24.3%)0.303
MPVmean ± SD10.4 ± 0.910.3 ± 0.90.010
out of range (%)225 (44.7%)193 (38%)0.032
PDWmean ± SD12.9 ± 2.212.4 ± 2.2<0.001
out of range (%)393 (78.1%)357 (70.5%)0.006
Table 6. Odds ratio for 30-day, 1-year and 5-year mortality in patients with blood results within and outside of standard range. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, WBC—white blood cells, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio, MPV—mean platelet volume, PDW—platelet distribution width. Statistically significant results bolded. “-”—value cant be calculated.
Table 6. Odds ratio for 30-day, 1-year and 5-year mortality in patients with blood results within and outside of standard range. CABG—coronary artery bypass grafting, OPCAB—off-pump coronary artery bypass grafting, WBC—white blood cells, NLR—neutrocyte-to-lymphocyte ratio, RDW-SD—red blood cell distribution width standard deviation, PLR—platelet-to-lymphocyte ratio, MPV—mean platelet volume, PDW—platelet distribution width. Statistically significant results bolded. “-”—value cant be calculated.
WBC Out of Range
OR (95% CI)		Neutrocytes Out of Range
OR (95% CI)		Lymphocytes Out of Range OR (95% CI)
OPCAB30 days mortality---
1 year mortality1.425 (0.409–4.969)-3.425 (1.444–8.122)
5 years mortality2.243 (1.017–4.946)-1.822 (0.927–3.581)
CABG30 days mortality0.657 (0.149–2.895)4.331 (1.166–16.082)1.533 (0.570–4.126)
1 year mortality1.261 (0.536–2.963)2.735 (0.879–8.511)1.637 (0.816–3.285)
5 years mortality1.225 (0.648–2.316)1.734 (0.663–4.533)1.330 (0.787–2.247)
General Population30 days mortality0.574 (0.134–2.448)4.067 (1.159–14.269)2.098 (0.928–4.743)
1 year mortality1.255 (0.603–2.611)2.344 (0.792–6.933)2.119 (1.227–3.661)
5 years mortality1.689 (1.036–2.753)1.492 (0.601–3.703)1.580 (1.045–2.387)
Platelets out of range
OR (95% CI)		MPV out of range
OR (95% CI)		PDW out of range
OR (95% CI)
OPCAB30 days mortality---
1 year mortality-1.171 (0.510–2.691)3.033 (0.891–10.326)
5 years mortality0.400 (0.121–1.323)0.660 (0.357–1.220)1.096 (0.584–2.055)
CABG30 days mortality3.630 (1.336–9.862)1.735 (0.686–4.389)0.595 (0.221–1.602)
1 year mortality2.112 (0.922–4.841)1.328 (0.701–2.517)0.952 (0.439–2.064)
5 years mortality1.691 (0.880–3.252)1.298 (0.816–2.067)0.736 (0.432–1.252)
General Population30 days mortality2.138 (0.848–5.386)0.916 (0.425–1.977)1.274 (0.511–3.178)
1 year mortality1.096 (0.509–2.360)1.272 (0.766–2.114)1.533 (0.805–2.920)
5 years mortality1.053 (0.609–1.821)1.040 (0.724–1.493)0.926 (0.619–1.384)
RDW-SD out of range
OR (95% CI)		NLR out of range
OR (95% CI)		PLR out of range
OR (95% CI)
OPCAB30 days mortality4.767 (1.124–20.229)--
1 year mortality2.477 (1.081–5.672)4.189 (1.813–9.681)3.342 (1.461–7.648)
5 years mortality2.958 (1.663–5.261)1.497 (0.767–2.924)2.396 (1.333–4.306)
CABG30 days mortality2.525 (0.991–6.436)2.015 (0.746–5.446)3.148 (1.251–7.925)
1 year mortality2.288 (1.176–4.449)2.199 (1.089–4.441)1.900 (0.976–3.698)
5 years mortality1.448 (0.861–2.435)1.849 (1.083–3.159)1.778 (1.089–2.903)
General Population30 days mortality3.139 (1.475–6.680)5.358 (2.504–11.463)6.149 (2.727–13.868)
1 year mortality2.349 (1.399–3.944)2.836 (1.664–4.834)2.390 (1.427–4.003)
5 years mortality1.896 (1.299–2.766)1.678 (1.109–2.539)2.071 (1.427–3.008)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Greberski, K.; Batko, J.; Bugajski, P.; Łuczak, M.; Brzeziński, M.; Bartuś, K. Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery. J. Cardiovasc. Dev. Dis. 2024, 11, 375. https://doi.org/10.3390/jcdd11110375

AMA Style

Greberski K, Batko J, Bugajski P, Łuczak M, Brzeziński M, Bartuś K. Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery. Journal of Cardiovascular Development and Disease. 2024; 11(11):375. https://doi.org/10.3390/jcdd11110375

Chicago/Turabian Style

Greberski, Krzysztof, Jakub Batko, Paweł Bugajski, Maciej Łuczak, Maciej Brzeziński, and Krzysztof Bartuś. 2024. "Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery" Journal of Cardiovascular Development and Disease 11, no. 11: 375. https://doi.org/10.3390/jcdd11110375

APA Style

Greberski, K., Batko, J., Bugajski, P., Łuczak, M., Brzeziński, M., & Bartuś, K. (2024). Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery. Journal of Cardiovascular Development and Disease, 11(11), 375. https://doi.org/10.3390/jcdd11110375

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop