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REVIEW ARTICLE
Year : 2023 | Volume
: 24
| Issue : 4 | Page : 188-193
The overall prevalence and main determinants of prolonged mechanical ventilation in patients undergoing coronary artery bypass grafting: A systematic review
Farzad Shahsanaei, Soudabeh Behrooj, Nima Rahimi Petrudi, Fahim Khajehbahrami
Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
Correspondence Address:
Dr. Fahim Khajehbahrami
Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas
Iran
Source of Support: None, Conflict of Interest: None
CheckDOI: 10.4103/heartviews.heartviews_71_23
Abstract
Background: Although respiratory support is necessary to maintain hemodynamic stability in patients undergoing major surgeries, prolonging the time of mechanical ventilation is considered a major complication following these procedures. The identification of potential factors related to this phenomenon should be identified. In the present systematic review, we first assess the pooled prevalence of prolonged mechanical ventilation (PMV) in patients undergoing coronary artery bypass grafting (CABG) surgery and also determine the main predictors for PMV by deeply reviewing the literature.
Materials and Methods: The manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the relevant keywords. Based on the titles and abstracts, 88 records were initially included and of those, 15 articles were eligible for the final analysis.
Results: The pooled prevalence of PMV in the studies that defined PMV as ventilation >24 h was 6.5% (95% confidence interval [CI]: 4.1%–10.2%) and in the studies, PMV as ventilation >48 h was 2.8% (95% CI: 1.7%–4.7%). Demographics (gender and advanced age), obesity, underlying comorbidities (hypertension, chronic kidney disease, cerebrovascular accident, high New York Heart Association class, history of chronic obstructive pulmonary disease, and history of acute coronary syndrome), emergency surgery, intraoperative characteristics (needing intra-aortic balloon pump, increased peak airway pressure, using cardiopulmonary bypass, the type of dose of anesthetics, cross-clamp time, increased units of blood transfusion, occurring cardiac ischemic events within an operation, fluid imbalance, and some anastomoses), and some postoperative outcome such as lowering O2 saturation, sequential organ failure assessment score, inotrope use, pleural effusion, delirium, and prolonged intensive care unit stay were found to be the main determinants for PMV.
Conclusion: Depending on the definition of PMV, the prevalence of PMV varied from 1.7% to 10.2%. Various factors before, during, and after surgery are the predictors of PMV in these patients, which can be used to design new scoring systems to predict it.
Keywords: Coronary artery bypass grafting, prolonged mechanical ventilation, weaning mechanical ventilation
Introduction 
Mechanical ventilation support is a major arm for respiratory support to maintain ventilator function in different medical and surgical conditions. This supportive strategy has a vital role in patients undergoing major surgeries such as coronary artery bypass grafting (CABG) surgery.[1],[2] Although the use of mechanical ventilation is needed to admit the patient to the intensive care unit (ICU), its absence may result in adverse consequences for such patients.[3]
In most patients, respiratory support is required for <24 h and leads to a respiratory stability state and transfers the patients to the general ward; however, in some patients with respiratory instability, short-time ventilation may not result in respiratory and hemodynamic recovery needing prolonged mechanical ventilation (PMV) with an overall incidence which ranged from 2.9% to 8.6%.[4],[5]
In different studies, the definition considered for PMV refers to ventilation time ranged 12–72 h.[6],[7] Although PMV is a routine protocol for providing vital conditions in such patients, it may also lead to adverse postoperative sequels.
PMV is a major complication after major surgeries and may be associated with significant morbidity and even mortality.[8] Thus, identifying its main determinants and trying to control these indicators can be very vital. Different preoperative, intraoperative, and postoperative factors have been introduced in the literature as potential predictors for PMV. However, both the prevalence of PMV and its related determinants may widely vary due to different reasons such as the type of study, the cutoff value considered to define PMV, and the targeted patients' population. Thus, systematically identifying such determinants can be very helpful to design a comprehensive strategy for minimizing the need for PMV and its proper management.
In the present systematic review, we first assess the pooled prevalence of PMV in patients undergoing CABG and also determine the main predictors for PMV by deeply reviewing the literature.
Materials and Methods Study selection
The present systematic review and meta-analysis followed the guidelines for the Preferred Reporting Items for Systematic Review and Meta-Analysis. First, two questions were suggested based on the author's purposes “What is the overall prevalence of PMV following CABG?” and “What are the related and predictive factors for PMV in patients undergoing CABG?”
In the next step, the manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords including “mechanical ventilation,” “coronary artery bypass,” “prevalence,” and “prediction.” The inclusion criteria were considered to retrieve the studies: (1) the studies finally assessed were those evaluated the rate of PMV after CABG, (2) due to the potential effects of other concomitant cardiovascular surgeries, only the studies including isolated CABG surgeries were included in the study, (3) the studies were restricted to the English language, (4) the studies with unclear or irreproducible results were all excluded, (5) lack of access to the manuscripts full texts were also considered as the inclusion criteria unless the abstracts had enough data for our analysis, and (6) case reports, case series, and review papers were all excluded. As shown in the flow diagram of the study selection [Figure 1], 88 articles were initially collected by database searching. After removing 3 articles due to evidence of duplication, 85 records were primarily under-screened. Based on the titles and abstracts, 58 records were excluded and the remaining 27 citations were assessed for further eligibility. Of those, 8 were also excluded due to the incompleteness of the data and contents. 15 articles were eligible for the final analysis[6],[9],[10],[11],[12],[13],[14],[15],[16],[17],[18],[19],[20],[21],[22][Table 1].
Data abstraction and validity assessment
Data abstraction was independently performed by two un-blinded reviewers on structure collection forms without divergences in data collection. We resolved disagreements by consensus or by involving a third person. The study quality was evaluated based on the following criteria: (1) the systematic review and meta-analysis based on the questions primarily described and formulated; (2) inclusion and exclusion criteria predefined in the studies as eligibility criteria; (3) searching the literature performed on a systematic and comprehensive approach; (4) to minimize the bias, the full texts of the article were dually reviewed; (5) the quality of included studies were rated independently by the reviewers for appraising internal validity; (6) studies' characteristics and findings were comprehensively listed; (7) the publication and risk of bias were listed; and (8) heterogeneity was also assessed. The risk of bias for each study was assessed using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions and also according to the QUADAS-2 tool. Any disagreement was resolved by discussion with the whole study team.
Statistical analysis
Dichotomous variables are reported as proportions and percentages. The pooled prevalence for PMV following CABG was assessed and presented by the prevalence rate and 95% confidence interval (CI). Cochran's Q-test was used to determine the statistical heterogeneity. This test was complemented with the I2 statistics, which quantifies the proportion of total variation across studies that is due to heterogeneity rather than chance. Publication bias was assessed by the rank correlation test and also confirmed by the funnel plot analysis. Reported values were two-tailed, and hypothesis testing results were considered statistically significant at P = 0.05. Statistical analysis was performed using the Comprehensive Meta-Analysis software version 3.0 (Biostat, Englewood, NJ 07631, USA).
Results To assess the prevalence and main correlates of PMV in patients undergoing CABG based on applied keywords, in total, 15 studies were finally assessed that were published from different countries between 1996 and 2022 [Table 1]. According to our risk of bias assessment, all 15 studies yielded good quality and none of the citations was determined to have a high risk of bias; therefore, the pooled results should be persuasive [Figure 2]. In total, 36,257 patients were assessed. Different cutoff values were defined in the studies for PMV of 15 studies, 10 defined PMV as ventilation longer than 48 h and 5 defined it as ventilation longer than 24 h. Despite focusing the study on PMV, the data on the definition of PMV were not found in the two studies.
The pooled prevalence of PMV in the studies that defined PMV as ventilation >24 h was 6.5% (95% CI: 4.1%–10.2%) and in the studies, PMV as ventilation >48 h was 2.8% (95% CI: 1.7%–4.7%). The statistical heterogeneity was significant in both assessments with an I2 of 93.682% (P < 0.001) and an I2 of 97.269% (P < 0.001), respectively. There was no significant publication bias as evidenced by either funnel plot asymmetry or the Egger test (P = 0.165) and (P = 0.631).
The main determinants of PMV were categorized into three subgroups: preoperative, operative, and postoperative parameters. Among preoperative determinants, female gender, advanced age, history of chronic kidney disease, history of chronic obstructive pulmonary disease, high body mass index, low left ventricular ejection fraction, high New York Heart Association (NYHA) class (IV), history of hypertension, preoperative inotrope use, emergent surgery, history of unstable angina or myocardial infarction, previous CABG, and cerebrovascular accident were found the main predictors for PMV. Of intraoperative parameters, using an intra-aortic balloon pump, prolonged surgery, high peak airway pressure at the end of the surgery, use of cardiopulmonary bypass and its prolongation, duration of continuous sedation and its dosage, clamping time, intraoperative blood transfusion, perioperative myocardial infarction, fluid retention, and the number of anastomoses were considered as the main indicators for PMV. Among postoperative parameters, the following were determined as the predictors for PMV: venous O2 saturation <60%, mediastinitis, inotrope use, pleural effusion, delirium, fluid accumulation, sequential organ failure assessment (SOFA) score, and longer stay in the ICU.
The pooled prevalence of PMV in the studies defined PMV as ventilation >24 hours was 6.5% (95% CI: 4.1% to 10.2%) and in the studies PMV as ventilation >48 hours was 2.8% (95% CI: 1.7% to 4.7%) [Figure 3].
Figure 3: The overall prevalence of prolonged mechanical ventilation in the cutoff values of >24 h (a) and >48 h (b) for ventilation. CI: Confidence interval Discussion Despite different advances in ventilator support for patients undergoing CABG surgery, PMV is a major adverse sequel following this procedure. Such outcomes can be also a trigger for postoperative complications such as prolonged hospital stay, requiring ICU stay, postoperative complications, and ultimately high hospital costs. In total, mechanical ventilatory support is essential for the stability of vital conditions in patients scheduled for major surgeries. It has been demonstrated that early extubation of such patients (frequently within 12 h after transferring the patients to the general ward) can lead to a more favorable outcome. In this regard, prolonged intubation or mechanical ventilation certainly results in adverse procedural outcomes and postoperative hemodynamic instability. Therefore, all the efforts of specialists are spent in shortening the time required for respiratory support of these patients and returning patients to stable conditions as quickly as possible. The first step in this way is to identify the factors that potentially increase the risk. Several factors before, during, and after surgery affect the possibility of needing PMV. As shown in our systematic review, different baseline, intraoperative, and postoperative factors have been shown to increase the likelihood of PMV in patients who underwent CABG. In this regard, demographics (gender and advanced age), obesity, underlying comorbidities (hypertension, chronic kidney disease, cerebrovascular accident, high NYHA class, history of chronic obstructive pulmonary disease, and history of acute coronary syndrome), emergency surgery, intraoperative characteristics (needing intra-aortic balloon pump, increased peak airway pressure, using cardiopulmonary bypass, the type of dose of anesthetics, cross-clamp time, increased units of blood transfusion, occurring cardiac ischemic events within an operation, fluid imbalance, and some anastomoses), and some postoperative outcome such as lowering O2 saturation, SOFA score, inotrope use, pleural effusion, delirium, and prolonged ICU stay were found to be the main determinants for PMV.
Today, with the identification of factors related to PMV, an attempt is made to design and provide comprehensive scoring systems to predict this event, which, while being complete, are also practical at the clinical level. The scoring system will be more complete if it incorporates more strong predictive factors. It is obvious that due to the large number of factors presented based on the present study, classification and weighting of these factors and selection of the most effective factors will be considered for the design of these scoring systems. In particular, consideration of modifiable and reversible factors is more important. However, it should be noted that the results obtained from the examination of these factors, as shown in this study, are associated with high heterogeneity, which is due to differences in the type of study design, differences in patient selection criteria, different follow-up periods, and most importantly, There have been various definitions of PMV.
Conclusion It can be finally concluded that depending on the definition of PMV, the prevalence of PMV varied from 1.7% to 10.2%. Various factors before, during, and after surgery are the predictors of PMV in these patients, which can be used to design new scoring systems to predict it.
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Conflicts of interest
There are no conflicts of interest.
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