Identifying biomarkers of ventilator induced lung injury during one-lung ventilation surgery: a scoping review

Introduction

Thoracic surgery is part of curative therapy for lung cancer, which is the leading cause of cancer-related mortality worldwide (1). Thoracic surgery is also used to treat many other malignant and benign conditions, such as the respiratory failure induced by SARS-Cov-2 (2). The majority of thoracic surgery requires that the operated lung be isolated from the contralateral lung, and not ventilated during the operation. Thus, the non-operated lung experiences one-lung ventilation (OLV). It is known that undergoing mechanical ventilation puts patients at risk for ventilator induced lung injury (VILI) (3-5). This has been shown in patients undergoing prolonged two-lung ventilation (TLV) (i.e., many days), predominantly in the intensive care setting. Some evidence also exists for the role of VILI in patients undergoing surgery requiring shorter periods of exposure to TLV (4). Although the exact mechanisms that underlie VILI have yet to be elucidated, studies to date have suggested that the main risk factors for OLV-induced VILI are iatrogenic (6). These risk factors include high tidal volume (Vt), high airway pressures and high fraction of inspired oxygen (FiO₂) all of which can contribute to lung parenchyma stress and mechanical injury that induces inflammation (7). Common pro-inflammatory cytokines, such as TNFα and IL-6, are increased in patients that have undergone OLV, while anti-inflammatory cytokines like IL-10 are decreased in abundance (8-10). Based on current knowledge and understanding, we hypothesize that patients undergoing OLV are at greater risk for VILI compared to patients undergoing TLV for two major reasons: (I) OLV is primarily used in a population that already has pre-existing lung damage; and (II) in OLV, all the potentially injurious factors are exerted on one lung rather than being distributed between two lungs.

One of the major causes of mortality after thoracic surgery is acute lung injury (ALI) or ARDS. ALI can occur in up to 4% of patients after OLV, with a mortality rate as high as 70% (4,5,11-13). Thus, OLV has potential to be a major risk factor for ALI after thoracic surgery, but the pathophysiology and determinants of OLV-induced VILI are poorly understood (4,12).

Studying and identifying these biomarkers is important as it will facilitate identification of high-risk patients for pre-operative, intra-operative and post-operative interventions to reduce the risk of VILI and respiratory complications. It is also important as a method of monitoring/tracking the response to such interventions. Moreover, studying and identifying these biomarkers is also important for studies that aim to understand the mechanisms behind such interventions. The aim of this scoping review is to identify biomarkers that have been linked to post-OLV lung injury. By providing an analysis of the current state of the literature, we hope to identify knowledge gaps, and provide guidance for further identification of reliable biomarkers for post-OLV VILI. We present the following article in accordance with the PRISMA-ScR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-20-2301/rc).

Methods

Study inclusion was based on PIO criteria. Population: All human and animal studies that used OLV as an exposure and investigated any analyte response as an outcome were considered. Studies using neonate models were excluded. Only English language studies or studies with an English translation were included. Intervention: Studies must utilize OLV. Studies that induced lung injury unrelated to VILI (i.e., lipopolysaccharide-induced injury) were excluded. Outcome: Studies must measure the effect of one lung ventilation on a biomarker.

In collaboration with a librarian (TG), a highly sensitive search strategy was used (Tables S1,S2). Electronic databases EMBASE, SCOPUS, and Medline were searched (inception to January 7, 2021). A gray literature search was conducted by an expert in this research area (BK). Bibliographies of included studies were hand-searched for relevant studies.

After abstract eligibility screening the data extracted from each study was; Title, year, authors, citation count, sample size, population type (type of animal or human), VILI induction method, blood product tested, intraoperative or post-operative measurement, histological findings, biomarker quantification method, biomarkers quantified and biomarker response including direction, magnitude, and statistical significance.

A calibration exercise was conducted initially for screening. During this calibration, screening of 50 studies was performed independently by 2 authors (BK and AB). Once calibration was achieved, all further screening was performed by a single author (AB) who assessed titles and abstracts for inclusion eligibility. Full text analysis, assessment for final inclusion and data extraction of the included studies was conducted independently by 2 authors (AB and RM). Chance-corrected agreement was calculated. All disagreements were resolved through discussion and consensus without the need for a third reviewer (BK).

Results

The search yielded 5,609 results after duplicates were removed with 4 manual additions. Twenty-nine studies were included for data synthesis as summarized in Table 1; the most common reason for exclusion was lack of OLV as an exposure, followed by lack of biomarker measurement (Figure 1).

Figure 1 Flow diagram for flow of scoping review. OLV, one-lung ventilation.

Table 1 describes the study subjects, and the ventilatory parameters, ventilation mode (e.g., the methodological designs were limited to human observational studies, human interventional studies testing anaesthetic effects, and animal studies. The most common model was human patients (52% of studies). Of the animal studies 50% utilized rabbit and 22% used rat. Mouse, pig, sheep, and dog were each used by a single study (15,22,29,37). Of the human studies, the average sample size was 57, with an average Vt of 7.8 mL/kg in the VILI group. Of the animal studies, the average sample size was 25. A quarter of the human studies were interventional in nature, testing the effect of different anaesthetic methods on cytokine levels, or investigating the difference between two ventilation strategies.

Table 2 describes the analytical methodology used to detect markers and the major findings of each study. The most commonly used analytic method to detect biomarkers was enzyme-linked immunoassay, which was used in 69% (n=20) of studies. The most used sample type was blood (45%, n=13), followed by bronchoalveolar lavage fluid (BALF) (41%, n=12) and tissue (41%, n=12). Exhaled breath condensate was used in three studies (8,15,20) and plural fluid and perfusate were both used singularly by Gama de Abreu et al. (19).

Figures 2,3 describe the change in abundance of each analyte in blood and BAL, respectively. A total of 27 analytes were investigated in blood across included studies. The studies investigating these analytes primarily used human subjects (69%, n=9). Only 6 analytes were investigated by ≥2 studies: IL-1β, IL-6, IL-8, IL-10, IL-12, and TNF-α. The bulk of markers investigated in blood were pro-inflammatory (81%, n=22 markers). Across studies investigating these markers 68% (n=30) of the assays found an increase. There were only three anti-inflammatory markers investigated in the blood (SOD, IL-10, and HO-1). Across studies 70% (n=7) of assays showed an increase in these markers.

Figure 2 Summary of change in analyte abundance measured in human blood. The height of the bar corresponds with the number of studies that analyzed each biomarker in the blood. Green, red, grey, and orange segments of the bars indicates a study that found that the analyte increased, decreased, did not change, was undetected, or was dependent on the anaesthetic used, respectively. A multicolored bar is indicative of a lack of consensus between studies. CRP, C-reactive protein; CXCL-1, chemokine (C-X-C motif) ligand 1; ICAM-1, intercellular adhesion molecule-1; IL, interleukin; MDA, malondialdehyde; RAGE, receptor for advanced glycation end products; SOD, superoxide dismutase; SP-D, surfactant protein D; TNF, tumor necrosis factor; VEGF, vascular endothelial growth factor; vWF, von Willebrand factor.

Figure 3 Summary of changes in analyte abundance measured in human and animal BAL samples. The height of the bar corresponds with the number of studies that analyzed each biomarker in the blood. Green, red, grey, and orange segments of the bars indicates a study that found that the analyte increased, decreased, did not change, was undetected, or was dependent on the anaesthetic used, respectively. A multicolored bar is indicative of a lack of consensus between studies. CXCL-1, chemokine (C-X-C motif) ligand 1; EGF, epidermal growth factor; IL, interleukin; MCP-1, monocyte chemoattractant protein-1; sICAM, soluble intercellular adhesion molecule; SOD, superoxide dismutase; TNF, tumor necrosis factor; VEGF, vascular endothelial growth factor; BAL, bronchoalveolar lavage.

Twenty-nine analytes were investigated in BALF samples. There were 17 markers that were investigated by more than one study, and of those 10 had disagreement between studies. Much of this discordance is attributed to the 2018 study by Fiorelli et al. who found that all 12 of the cytokine analyzed in BALF had no change in abundance or were undetected after OLV (17). Eight out of 12 of the findings by Fiorelli et al. were in discordance with other studies, and they speculate that this is a result of positive end expiratory pressure (PEEP) playing a protective role against inflammation. Studies using BALF samples selected pro-inflammatory markers almost exclusively (90%, n=26). Across all studies, 66% (n=45) of findings showed an increase in inflammatory markers in lavage, with 24% of findings showing no significant change. Two anti-inflammatory markers were investigated (IL-10, SOD). Two studies found that IL-10 will either increase or decrease in BALF during OLV depending on the type of anaesthetic used (9,18), and three studies finding a decrease or no significant change (17,26,28).

Thirty-two markers were used in the analysis of lung tissue after OLV, but only nine of those markers were investigated by more than one study (arachidonic acid, C-PLA2, CCSP, IL-1β, IL-6, IL-8, MPO, NF-kB binding activity, and TNF-α). In those nine markers there was a 67% (n=6) consensus between studies regarding the effect of OLV on markers concentrations. The markers chosen by researchers to quantify in tissue were primarily inflammatory mediators (75%, n=24). Ninety-one percent (n=31) of the lung tissue inflammatory marker measurements across all studies found a statistically significant increase after OLV.

Twelve publications also used less common bio-samples (Table 3). Diverse biomarkers were assayed, including intracellular signaling pathways that control oxidative stress (HO-1, HIF-1α), gene transcription (NF-κB, HIF-1α), trophic and secretory cell responses (ERK, JNK, p38, AKT phosphorylation), as well as secreted cytokines, chemokines and growth factors (Table 3).

If one considers the same analyte (i.e., IL-6) in a different sample medium (i.e., blood versus BALF) as a unique biomarker, there were a total of 98 markers tested. If not, there were 73 truly unique biomarker candidates: 29 were analyzed in the BALF, 27 in blood, 32 in lung tissue, and six in both the exhaled breath condensate and cell culture, 3 in pleural fluid, 2 in perfusate. Thirty-three analytes were investigated in more than one study, leaving 65 analytes examined by a single study. The most studied analytes were TNF-α (n=14), followed by IL-6 (n=13), IL-8 (n=9), and IL-10 (n=7). The majority of human studies utilized blood samples, while only a single animal study tested for analytes in blood. Animal studies utilized tissue samples more often (43% vs. 8%).

Discussion

We sought to determine the state of the evidence regarding potential biomarkers of VILI secondary to OLV. Although we identified 93 unique biomarker candidates, only 33 of these were measured in more than one study. Thus, 65% of the analytes were identified in only a single study. Of the markers identified in more than one study, fewer than half (n=16) showed concordance with respect to their change in abundance after an OLV intervention. For example, our synthesis of data from multiple studies showed that IL-10 levels may be either elevated, decreased or unchanged after OLV. It is unclear whether this discordance is driven by differences in analytic techniques, differences in exposures during OLV between studies, or both factors. If analytes in different sample mediums are considered distinct (e.g., IL-6 in blood vs. IL-6 in BALF), there was little overlap between analytes investigated by animal and human studies. This is primarily a consequence of the tendency of animal studies to use predominantly tissue samples and of human studies to use predominantly blood samples. There were four examples when animal and human studies did investigate the same analyte in the same sample medium (IL-6, IL-8, TNF-α, and total protein concentration in BALF). In these, there was consensus between human and animal results. There is gap in the literature regarding assessment of both BALF and blood in the same experiment. This is an important gap to address for future research. There may be important changes in both the systemic and local stress response; focusing only on just local (i.e., BALF) or systemic (i.e., blood) responses may result in an incomplete understanding of the processes involved in VILI after OLV.

The analytes most frequently assayed and identified as reliable biomarkers of OLV-induced lung injury in the BALF included the pro-inflammatory cytokines TNF-α (increased in 8 out of 10 studies), IL-8 (increased in 7 out of 7 studies), and IL-6 (increased in 7 out of 8 studies). In the lavage fluid inflammatory markers were found to increase in 67% of investigations. Biomarker results assessing the systemic response to OLV appear to be more consistent; in blood analyses, IL-6 (increased in 9 of 9 studies), and TNF-α (increased in 6 of 8 studies) appeared to be the most consistent biomarker candidates. TNF-α is a part of the primary immune response and can induce synthesis and release of other proinflammatory cytokines in the lung during injury^. (40). IL-6 has been shown to have a dual nature in animal models of lung injury, as it’s effect can be inflammatory, or anti-inflammatory, depending on the model employed^. (40). Systemic IL-10 levels increased in 4 out of 6 studies that investigated it, while local findings were much less concordant. IL-10 was found to decrease or not significantly change in three studies, and in two studies it was found that the direction of change from baseline was dependent on the anaesthetic used during ventilation. IL-10 is an anti-inflammatory cytokine that can inhibit the expression of inflammatory mediators while having no effect on anti-inflammatory mediators (41,42). Studies of lung injury using animal models have shown that IL-10 can reduce lung injury and plays a protective role in systemic inflammation (43). The most commonly used analytical method was enzyme-linked immunosorbent assay (ELISA) (72% of studies), which lends itself well to protein biomarker detection, as it is highly sensitive and can be calibrated to determine absolute concentrations (44). The most common sample types analyzed were bronchoalveolar lavage (BAL) (55% of studies) and blood (43%), samples routinely taken during thoracic surgery, and that represent local and systemic responses to OLV-induced VILI, respectively.

Fifty-two percent of the studies were conducted in humans. Human biomarker studies may be more relevant for clinical applications but present their own challenges. For ethical reasons, the acceptable experimental exposure human patients may be subjected to is significantly less damaging than animal models, which may explain why only a third (5 of 15) of human studies compared a protective ventilation protocol to a more historical and damaging one (Vt 10–15 mL/kg) (3). Ethically, it is not possible to justify purposefully exerting anesthetic exposures that are thought to be injurious. Thus, the literature is limited to observational studies that have retrospectively assessed the effect of potentially injurious historical ventilation practices.

The effects of OLV exposure on the anti-inflammatory cytokine IL-10 in BALF was the most discordant finding of the studies we reviewed. This may be due to the varying immunomodulatory effects of the anaesthetics employed during mechanical ventilation. For example, de la Gala et al. found that IL-10 increased with inhaled sevoflurane, and decreased with propofol (18). The authors suggest that sevoflurane protects against mechanical forces on the lung tissue by reducing alveolar capillary permeability, in agreement with the findings of Voigtsberger et al. (45). De la Gala et al. note that propofol may induce less of an inflammatory response than volatile anaesthetic in non-thoracic surgery.

There are limitations to our findings. Some of these are limitations of the scoping review but many are inextricably linked to the limitations in the data synthesized for this review. Discordant findings between studies, a relatively small number of studies on this topic, small sample sizes of the existing studies, and the short period of observation of the studies all contribute to limiting the strength of conclusions, and in uncovering the critical gaps in knowledge. Discordant findings between studies may be due to differences in OLV procedure and/or analytical method. Variations in experimental design between studies, such as the difference between studies that only employed protective ventilation practices and studies that used traditional practices, may have led the researchers to different conclusions with or without the availability of a well-defined control. Furthermore, even in those studies that managed to have control groups, ethical limitations in purposefully exerting “harmful” ventilation practices may have resulted in “experimental” and “control” groups experiencing exposures that were too similar; this has the potential of dampening or suppressing a true effect of clinically relevant OLV. Another limitation is that included studies did not account for potential interaction between pre-ventilation lung function and each analyte.

Another important caveat is that our review reports the response of analytes to OLV-induced lung injury. The FDA’s Biomarker Qualification Program requires preclinical biomarkers to not only show correlation with changes induced in the biological process, but it must be demonstrated that the response of the biomarker is exclusive to the change placed on the process (46). Our study cannot fully conclude that these biomarker profiles are entirely driven by exposure to OLV, as some component of inflammatory changes may be driven by the surgical insult. This is an issue which needs to be addressed in the design of future studies.

Conclusions

We sought to identify the evidence regarding candidate biomarkers of VILI caused by OLV. The candidate biomarkers with the most evidence and greatest reliability are general markers of inflammation, such as IL-6 and TNF-α. There is a gap in the literature regarding assessment of both local and systemic response in the same experiment. There remains a substantial body of biomarkers that remain unknown, including the response of individual lungs to OLV. Future studies should assess both in order to obtain more complete understanding of the processes involved in VILI after OLV. A reliable constellation of biomarkers for OLV-induced VILI could allow for rapid preclinical diagnosis to better allocate resources after surgery. Such biomarkers may allow identification of patients at highest risk for developing VILI after thoracic surgery and therefore allocate nursing, monitoring and preventive resources to these patients. Biomarkers for OLV-induced VILI may be used to measure effect of and response to novel preventative or therapeutic interventions. Finally, reliable biomarkers may also one day provide targets for novel interventions, such as immune-modulating drugs that could be used to prevent or reduce the risk of ARDS/ALI after thoracic surgery.

Acknowledgments

This work was presented at the 2018 Annual Surgery Research Day in Winnipeg, Manitoba, Canada (January 17, 2018).

Funding: This work was supported by the Optimization of Perioperative Care in Thoracic Surgery Grant (University Medical Group, University of Manitoba), which served as start-up research funds for the Senior Investigator (BK).

Footnote

Reporting Checklist: The authors have completed the PRISMA-ScR reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-20-2301/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-20-2301/coif). AJGB, RM, THG, SKS, and LT report that this work was supported by the Optimization of Perioperative Care in Thoracic Surgery Grant (University Medical Group, University of Manitoba) Optimization of Perioperative Care in Thoracic Surgery Grant (University Medical Group, University of Manitoba). AJH participated in a Canadian Thoracic Society advisory panel for a topic unrelated to this publication, sponsored by Boehringer Ingelheim Canada Inc., and received honorarium for lecture facilitation in local research rounds featuring an external speaker from AstraZeneca Canada (topic of presentation not relevant to work in manuscript), and reports grants from Canada Research Chairs Program and Children’s Hospital Research Institute of Manitoba (CHRIM). BK and GB received honoraria from AstraZeneca as part of a consultancy board concerning lung cancer treatment and report Optimization of Perioperative Care in Thoracic Surgery Grant (University Medical Group, University of Manitoba). The other authors have no conflict 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.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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