Independent association of lung injury with lactate after pulmonary vascular intervention: a cross-sectional study

Independent association of lung injury with lactate after pulmonary vascular intervention: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Independent association of lung injury with lactate after pulmonary vascular intervention: a cross-sectional study Bo Li, Yanxia Yang, Xiaomei Xi, Chuanzhou Liu, Hong Ling Su, Jinjin liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7556516/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Postoperative hyperlactatemia is associated with organ dysfunction, but its independent role in lung injury remains underexplored, particularly relative to pulmonary hemodynamics. Objective To determine whether postoperative lactate independently correlates with lung injury after surgery, dissected from pulmonary hemodynamic parameters. Methods In this single-center cross-sectional study, 143 surgical patients (non-injury = 101, injury = 42) were analyzed. Baseline characteristics, pulmonary hemodynamics (SPAP, PVR, TPR), and lactate levels were collected. Univariate and multifactorial logistic regression evaluated risk factors. Additive/multiplicative interactions between lactate and hemodynamic variables were tested using RERI/AP and product-term models. Results Of the 143 patients studied, 42 (29.4%) developed lung injury. Univariate analysis identified that elevated postoperative lactate (≥ 1.97 mmol/L), pulse, systolic pulmonary artery pressure (sPAP), pulmonary vascular resistance (PVR), total pulmonary resistance (TPR), and preoperative CO₂ were significant risk factors for lung injury (all P < 0.05). In multivariate analysis, postoperative lactate remained an independent predictor of lung injury across all adjusted models (adjusted OR range: 1.915–2.040, all P < 0.05). Multiplicative interaction analysis revealed a significant interaction between PVR and postoperative lactate (OR = 4.590, 95% CI: 1.098–19.186, P = 0.037). However, measures of additive interaction (RERI, AP, S) for sPAP, PVR, and TPR with postoperative lactate were not statistically significant. Conclusion Elevated postoperative lactate independently correlates with lung injury without synergistic effects from pulmonary hemodynamic dysfunction. Lactate assessment may provide standalone value for early risk stratification. Introduction Pulmonary artery stent implantation or balloon angioplasty represents a pivotal therapeutic advancement for patients with pulmonary artery stenosis, offering a minimally invasive yet effective approach to alleviate vascular obstruction. These interventions significantly improve hemodynamic parameters, enhance right ventricular function, and ultimately provide a critical opportunity for patients to achieve better functional capacity and quality of life[ 1 , 2 ]. For individuals with congenital heart defects, chronic thromboembolic pulmonary hypertension, or acquired vascular lesions, such procedures serve as a lifeline, reducing symptoms of dyspnea, fatigue, and exercise intolerance where surgical options may be limited or high-risk[ 3 , 4 ]. Despite their clinical benefits, interventional procedures such as stent deployment or balloon dilation carry inherent risks of iatrogenic pulmonary vascular injury[ 5 ]. Potential mechanisms include mechanical trauma to the vessel wall during balloon inflation or stent expansion, leading to endothelial denudation, local inflammation, and vascular permeability[ 6 ], distal vessel overstretching or rupture causing alveolar hemorrhage[ 7 ], and ischemia-reperfusion injury triggered by abrupt restoration of blood flow to previously underperfused lung segments[ 8 ]. Additionally, microembolization of thrombotic or atherosclerotic debris during manipulation may occlude distal capillaries, exacerbating parenchymal damage and inflammatory responses[ 9 ]. Emerging evidence suggests that dynamic changes in serum lactate levels may serve as a sensitive biomarker reflecting the extent of acute lung injury following such interventions[ 10 ]. Lactate elevation can result from tissue hypoxia due to impaired perfusion secondary to vascular injury[ 11 ], inflammatory cytokine-driven aerobic glycolysis[ 12 ], or direct cellular metabolic dysfunction in the injured lung parenchyma[ 13 ]. Monitoring peri-procedural lactate trends could therefore provide real-time insight into the severity of pulmonary compromise, enabling timely clinical intervention[ 14 ]. In summary, this study highlights that alterations in lactate levels following pulmonary artery interventions are closely associated with the development and progression of iatrogenic pulmonary injury, underscoring its potential role in early detection and risk stratification. Materials and Methods Study Design and Participants This retrospective study analyzed patients who underwent interventional treatment for pulmonary vascular stenosis at Gansu Provincial Hospital between January 1, 2019 and December 31, 2022. Participants were divided into two groups based on the presence of lung injury (non-injury: n = 101, injury: n = 42). Baseline demographic, clinical, and laboratory data were collected, including age, sex, comorbidities (e.g., heart disease), and physiological parameters (e.g., Pulse, sPAP, PVR, TPR, lactate level). Data Collection Variables were extracted from electronic medical records and included: Demographics: Age, sex, height, weight, BMI. Clinical measures: Pulse rate, oximetry, systolic pulmonary artery pressure (sPAP), pulmonary vascular resistance (PVR), total pulmonary resistance (TPR). Laboratory values: Pre-operative and post-operative lactate, glucose, CO 2 , pH, and respiratory rates. Continuous variables were reported as mean ± standard deviation (mean ± SD) or median (interquartile range, IQR), while categorical variables were presented as counts (%). Ethical Considerations The study was approved by the Institutional Review Board of Gansu Provincial Hospital (approval number: 2025 − 539), with waived informed consent due to retrospective anonymized data. Statistical Analysis: Statistical analysis was performed using SPSS 27.0 software. Continuous variables were assessed for normality. Normally distributed continuous data are presented as the mean ± standard deviation (mean ± SD). Comparisons between groups were made using the t-test. Non-normally distributed continuous data are presented as the median and interquartile range M (P25, P75). Comparisons between groups for non-normally distributed data were made using the Mann-Whitney U test. Categorical data are presented as frequencies and percentages (n, %), and comparisons between groups were made using the chi-square (χ²) test. A multivariable logistic regression model was employed to calculate the relationship between Post Lactate and pulmonary injury, as well as to assess the multiplicative interaction effects of Post Lactate with TPR, SPAP, and RVR (individually) on pulmonary injury. Additive interaction was analyzed using the "interaction R" package in R version 4.3.2. The additive interaction was evaluated using the following metrics: the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S). Additive interaction was considered statistically significant if the 95% confidence interval (95% CI) for RERI and AP did not include 0, and the 95% CI for S did not include 1. The significance level (α) was set at 0.05. Results Baseline Characteristics A total of 143 patients were included in this study, comprising 42 patients who developed lung injury and 101 who did not. As shown in Table 1 , there were no significant differences between the two groups in terms of age, sex, height, weight, BMI, respiration rate, or most comorbidities (all P > 0.05). However, the injury group exhibited significantly higher values in pulse (P = 0.045), PVR (P = 0.012), TPR (P = 0.011), sPAP (P = 0.014), preoperative carbon dioxide partial pressure (Pre-CO₂, P = 0.013), and postoperative lactate levels (Post-lactate, P = 0.018). Table 1 Baseline information. Variant non-injury (n = 101) injury (n = 42) P Age (years) 63.53 ± 7.57 61.83 ± 10.07 0.328 Female, n (%) 69(70.40) 29(29.60) 0.932 Height, cm 161.93 ± 6.26 162.77 ± 7.63 0.529 Weight, Kg 59.81 ± 11.76 58.81 ± 11.15 0.640 BMI, Kg/m 2 22.71 ± 3.83 22.20 ± 3.62 0.465 Rate, n 84.14 ± 14.08 89.49 ± 18.67 0.063 Pulse, n 83.83 ± 13.88 89.56 ± 18.67 0.045 Pespirationrate, n 18.76 ± 6.07 18.73 ± 2.71 0.975 Comorbidity, n (%) D2TM 9(90.00) 1(10.00) 0.301 EHP 17(68.00) 8(32.00) 0.751 Heartdiseases 33(70.20) 14(29.80) 0.939 Hemodynamics Gradient, mmHg 42.00(30.00, 60.00) 45.59(34.25, 62.75) 0.329 PVR, Wood unit 7.30(5.04, 8.47) 7.70(5.48, 11.25) 0.012 TPR, Wood unit 8.20(6.00, 10.20) 9.33(7.39, 13.19) 0.011 sPAP, mmHg 66.01 ± 21.32 76.21 ± 24.40 0.014 Pre-CO 2 , mmHg 66.01 ± 21.32 76.21 ± 24.40 0.013 Pre-PH 7.42 ± 0.03 7.42 ± 0.04 0.851 Oximetry, mmHg 95.07 ± 4.52 93.99 ± 4.58 0.199 Post-CO 2 , mmHg 38.18 ± 4.46 38.96 ± 6.13 0.394 Post-PH 7.43 ± 0.02 7.43 ± 0.03 0.713 Pre-lactate, mmol/L 1.67(1.25, 1.90) 1.67(1.40, 1.90) 0.325 Pre-glucose, mmol/L 7.74(7.74, 7.74) 7.74(7.74, 7.74) 0.963 Post-lactate, mmol/L 1.97(1.50, 1.99) 1.97(1.68, 2.23) 0.018 Δlactate, mmol/L 1.33(1.06, 1.33) 1.33 (1.00, 1.37) 0.521 Data are numbers (%). mean ± SD, or median (interquartile range). D2TM = Type 2 diabetes; HTN = Hypertension; sPAP = Pulmonary Artery Systolic Pressure; PVR = Pulmonary vascular resistance; Pre-CO 2 = Preoperative carbon dioxide partial pressure; TPR = Total Lung Resistance; Post-CO 2 = Postoperative carbon dioxide partial pressure; Gradient = Pressure gradient across the stenotic pulmonary artery; Pre-lactate, Preoperative lactate levels; Pre-glucose, Preoperative blood glucose level; Post-lactate = Postoperative lactate levels; Δlactate, Difference in lactate levels between preoperative and postoperative periods. Univariate Logistic Regression Analysis Univariate logistic regression analysis identified several factors significantly associated with lung injury (Table 2 ). These included Post-lactate (OR = 1.943, 95% CI: 1.120–3.369, P = 0.018), pulse (OR = 1.023, 95% CI: 1.000–1.047, P = 0.050), sPAP (OR = 1.020, 95% CI: 1.004–1.037, P = 0.016), PVR (OR = 1.169, 95% CI: 1.048–1.305, P = 0.005), TPR (OR = 1.142, 95% CI: 1.039–1.256, P = 0.006), and Pre-CO₂ (OR = 1.105, 95% CI: 1.019–1.197, P = 0.015). Table 2 One-way logistic regression analysis of factors influencing lung injury. Variant ß OR 95% CI P Post-Lactate 0.664 1.943 1.120 ~ 3.369 0.018 Pulse 0.023 1.023 1.000 ~ 1.047 0.050 sPAP 0.020 1.020 1.004 ~ 1.037 0.016 PVR 0.157 1.169 1.048 ~ 1.305 0.005 TPR 0.133 1.142 1.039 ~ 1.256 0.006 PreCO 2 0.099 1.105 1.019 ~ 1.197 0.015 sPAP = Pulmonary Artery Systolic Pressure; PVR = Pulmonary vascular resistance; Pre-CO 2 = Preoperative carbon dioxide partial pressure; TPR = Total Lung Resistance; Post-lactate = Postoperative lactate levels. Multivariate Logistic Regression Analysis After adjusting for potential confounders in multivariate models, postoperative lactate remained significantly associated with lung injury across all models (Model 0: OR = 1.943, 95% CI: 1.120–3.369, P = 0.018; Model 1: OR = 1.915, 95% CI: 1.094–3.353, P = 0.023; Model 2: OR = 2.040, 95% CI: 1.072–3.883, P = 0.030) (Table 3 ). Table 3 Multifactorial logistic regression analysis of the association between post-lactate and lung injury. Model OR 95%CI P value Model0 1.943 1.120 ~ 3.369 0.018 Model1 1.915 1.094 ~ 3.353 0.023 Model2 2.040 1.072 ~ 3.883 0.030 Interaction Analysis Multiplicative interaction analysis revealed a significant interaction between PVR and Post-lactate (OR = 4.590, 95% CI: 1.098–19.186, *P* = 0.037), though no significant interactions were observed for TPR or sPAP with Post-lactate (Table 4 ). Additive interaction analysis further indicated no significant synergistic effects between Post-lactate and sPAP, PVR, or TPR, as evidenced by the measures of relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S), all with confidence intervals including the null value (Table 5 ). Table 4 Multiplicative interaction analysis of TPR, sPAP, RVR and Post-Lactate on lung injury. Interaction OR(95%CI) P值 PVR 4.590(1.098 ~ 19.186) 0.037 Post-Lactate 1.089(0.299 ~ 3.965) 0.897 RVR×Post-Lactate 0.335(0.062 ~ 1.799) 0.202 TPR 1.499(0.404 ~ 5.558) 0.545 PostLactate 0.563(0.199 ~ 1.592) 0.279 TPR×Post-Lactate 1.123(0.230 ~ 5.494) 0.886 sPAP 3.823(0.933 ~ 15.661) 0.062 Post-Lactate 1.144(0.356 ~ 3.675) 0.821 RVR×Post-Lactate 0..264(0..050 ~ 1.398) 0.117 sPAP = Pulmonary Artery Systolic Pressure; PVR = Pulmonary vascular resistance; TPR = Total Lung Resistance; Post-lactate = Postoperative lactate levels. Table 5 Additive interaction of TPR, sPAP, RVR and Post-Lactate on lung injury. Variant Post-Lactate(≥1.97) OR(95%CI) sPAP RERI -3.07(-8.83 ~ 2.7) AP -2.33(-6.17 ~ 1.51) S 0.09(0 ~ 4.9) PVR RERI -3.29(-9.78 ~ 3.21) AP -1.9(-5.01 ~ 1.21) S 0.18(0.02 ~ 1.35) TPR RERI 0.21(-3.18 ~ 3.59) AP 0.07 (-1.09 ~ 1.24) S 1.13 (0.15 ~ 8.49) sPAP = Pulmonary Artery Systolic Pressure; PVR = Pulmonary vascular resistance; TPR = Total Lung Resistance; Post-lactate = Postoperative lactate levels. Discussion This study found that Post-Lactate levels (Post-Lactate ≥ 1.97 mmol/L) are an independent predictor of lung injury (adjusted OR ≈ 2.0, P < 0.05). This result aligns with evidence from previous studies demonstrating that hyperlactatemia leads to hypoxic injury in pulmonary endothelial cells and triggers an inflammatory cascade. Elevated lactate levels typically reflect an imbalance between tissue oxygen supply and demand, particularly in cases of circulatory impairment. This imbalance may cause hypoxia at the cellular level, which in turn initiates a series of inflammatory responses. Notably, although pulmonary hemodynamic disturbances (such as elevated sPAP, PVR, and TPR) increase the risk of lung injury, this study found no multiplicative or additive synergistic effects between these factors and lactate levels. This suggests that lactate may mediate lung injury through pathways independent of hemodynamic derangements, such as mitochondrial dysfunction. The existence of such independent pathways indicates that when assessing the risk of lung injury, reliance solely on hemodynamic parameters is insufficient; instead, other biomarkers like lactate levels should be comprehensively considered. The independent predictive value of postoperative lactate (OR = 1.943, 95%CI:1.120–3.369) cannot be solely explained by traditional hypoxic theories. We observed absence of synergistic effects with pulmonary hypertension parameters and incomplete correlation with hemodynamic impairment severity, suggesting critical roles for non-hypoxic mechanisms. Recent studies reveal three complementary pathways: Microcirculatory Dysfunction and Endothelial Injury: Elevated left atrial pressure in pulmonary hypertension induces pulmonary capillary stress failure, disrupting the alveolar-capillary barrier and facilitating lactate extravasation into interstitial compartments[ 15 ]. Extravasated lactate activates pulmonary macrophage TLR4/HMGB1 signaling, triggering local inflammatory cascades independent of systemic hypoxia[ 16 ]. This "inflammatory-metabolic positive feedback loop" sustains lactate production via aerobic glycolysis (Warburg effect), establishing a self-amplifying injury pathway. Metabolic Reprogramming: Single-cell sequencing studies (2025) demonstrate that vulnerable pulmonary vascular endothelial cells exhibit enhanced glycolysis and suppressed oxidative phosphorylation. This metabolic shift enables sustained lactate generation under normoxic conditions, particularly in stenotic regions of chronic thromboembolic pulmonary hypertension (CTEPH) patients showing marked lactate dehydrogenase A (LDHA) upregulation[ 17 ]. Our lactate threshold (1.97 mmol/L) corresponds to levels inducing endothelial-mesenchymal transition in experimental models, suggesting profibrotic potential. Hepato-Splanchnic Suppression: Right ventricular dysfunction in pulmonary hypertension causes hepatic congestion, significantly impairing lactate clearance capacity. Clinical studies confirm negative correlation between DLCO% predicted and lactate levels (r=-0.28, P < 0.01) in COPD patients with pulmonary hypertension, predicting acute exacerbation risk[ 18 ]. This "metabolic bottleneck effect" explains divergent lactate levels under similar hemodynamic insults depending on hepatic perfusion status. Notably, the temporal dynamics of lactate enhance its predictive value. Unlike hemodynamic parameters reflecting instantaneous status, lactate integrates cumulative metabolic stress over hours. This accounts for its superiority in our univariate analysis and aligns with CTEPH research demonstrating that lactate clearance rates better predict right ventricular functional recovery than absolute hemodynamic values[ 19 ]. The observed antagonistic interaction between lactate and pulmonary hemodynamic parameters (PVR×Lactate RERI=-3.29; sPAP×Lactate RERI=-3.07) challenges conventional pathophysiological models. Based on emerging evidence, we propose three mechanistic explanations: Pressure-Induced Endothelial Adaptation: Severe pulmonary hypertension induces endothelial phenotypic switching that unexpectedly reduces lactate sensitivity. Specialized endothelial subpopulations co-expressing pulmonary and bronchial markers emerge in stenotic pulmonary arteries of CTEPH patients, exhibiting altered metabolic flux and lactate transport. This adaptive transcriptomic remodeling diminishes lactate-driven inflammation, rationally explaining the "sub-additive" injury risk when both factors coexist. This finding corresponds with 3D microfluidic models where endothelial cells under high shear stress (simulating PH) show 40–60% reduced lactate-induced IL-6 production versus static conditions. Ventricular Interdependence Effects: Right ventricular pressure overload in pulmonary hypertension impairs left ventricular filling through septal shift, reducing cardiac output and systemic perfusion pressure. This generates dual lactate sources—pulmonary tissue (hypoperfusion) and peripheral tissues (low cardiac output). Consequently, lactate elevation loses specificity for pulmonary injury severity, weakening its interaction with hemodynamic parameters. Post-endarterectomy studies confirm this phenomenon: right ventricular functional recovery typically precedes lactate normalization by 48–72 hours. Unmeasured Therapeutic Confounding: Clinicians often initiate early vasodilator therapy (e.g., inhaled NO) when both lactate and sPAP/PVR are elevated, artificially attenuating observed risk. This treatment bias is particularly relevant given recent evidence that pulmonary vasodilators enhance lactate clearance independently of hemodynamic improvement. Additionally, compartmentalization of lactate pools further elucidates this paradox. Pulmonary hypertension redistributes blood volume from systemic to pulmonary circulation, concentrating lactate within pulmonary vasculature. However, vascular compartments exhibit divergent lactate signaling responses: pulmonary endothelial cells increase nitric oxide production (vasoprotective), while systemic endothelia develop prothrombotic profiles. This compartment-specific signaling explains why systemic lactate measurements poorly reflect local pulmonary injury risk when pulmonary hypertension is present. Conclusion In conclusion, this study confirms postoperative lactate as a robust, independent predictor of lung injury after cardiac surgery, alongside pulmonary hemodynamic dysfunction. However, the absence of synergy, and evidence of antagonism, between these pathways indicates they contribute to lung injury risk via largely distinct mechanisms. While their combined assessment improves risk stratification, the negative additive interactions underscore the need for deeper investigation into the underlying pathophysiology to guide more precise therapeutic interventions. Declarations Author contributions Bo Li: Formal analysis; Writing – original draft; Writing – Review & Editing. Yanxia Yang: Writing – original draft; Writing – Review & Editing. Xiaomei Xi: Writing –Methodology; review & editing. Chuan zhou Liu: Writing – review & editing. Hongling Su: Writing – review & editing. Jinjin Liu: Methodology. Xing chang, Wang: Conceptualization; Writing – review & editing. Fu Zhang: Conceptualization; Funding acquisition; Writing – review & editing. Funding This work was supported by the Natural Science Foundation of Gansu Province of China (24JRRA1049) and the Intramural Fund of Gansu Provincial Hospital of China (23GSSYF-28) awarded to Bo Li. Ethics approval and consent to participate The study was approved by the Ethics Committee of Gansu Provincial Hospital (204 Donggang West Road, Chengguan District, Lanzhou, Gansu Province, 730000) that granted exemption from obtaining informed consent from patients. Consent for publication Not applicable. Conflict of interest statement The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Hong C, Zhou D, Chen H, Wu X, Guo W, Cui J, et al. Pulmonary Artery Stent Implantation for Fibrosing Mediastinitis: Our Clinical Experience. Pulmonary circulation. 2025;15(2):e70076.https://doi.org/10.1002/pul2.70076 Duan Y, Zhou X, Su H, Jiang K, Wu W, Pan X, et al. Balloon angioplasty or stent implantation for pulmonary vein stenosis caused by fibrosing mediastinitis: a systematic review. Cardiovascular diagnosis and therapy. 2019;9(5):520-528.https://doi.org/10.21037/cdt.2019.09.14 Pascall E, Tulloh RM. Pulmonary hypertension in congenital heart disease. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 10 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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13:46:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":682060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7556516/v1/840d367d-9697-4a62-918e-6e3579233315.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Independent association of lung injury with lactate after pulmonary vascular intervention: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary artery stent implantation or balloon angioplasty represents a pivotal therapeutic advancement for patients with pulmonary artery stenosis, offering a minimally invasive yet effective approach to alleviate vascular obstruction. These interventions significantly improve hemodynamic parameters, enhance right ventricular function, and ultimately provide a critical opportunity for patients to achieve better functional capacity and quality of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For individuals with congenital heart defects, chronic thromboembolic pulmonary hypertension, or acquired vascular lesions, such procedures serve as a lifeline, reducing symptoms of dyspnea, fatigue, and exercise intolerance where surgical options may be limited or high-risk[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite their clinical benefits, interventional procedures such as stent deployment or balloon dilation carry inherent risks of iatrogenic pulmonary vascular injury[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Potential mechanisms include mechanical trauma to the vessel wall during balloon inflation or stent expansion, leading to endothelial denudation, local inflammation, and vascular permeability[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], distal vessel overstretching or rupture causing alveolar hemorrhage[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and ischemia-reperfusion injury triggered by abrupt restoration of blood flow to previously underperfused lung segments[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, microembolization of thrombotic or atherosclerotic debris during manipulation may occlude distal capillaries, exacerbating parenchymal damage and inflammatory responses[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that dynamic changes in serum lactate levels may serve as a sensitive biomarker reflecting the extent of acute lung injury following such interventions[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Lactate elevation can result from tissue hypoxia due to impaired perfusion secondary to vascular injury[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], inflammatory cytokine-driven aerobic glycolysis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], or direct cellular metabolic dysfunction in the injured lung parenchyma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Monitoring peri-procedural lactate trends could therefore provide real-time insight into the severity of pulmonary compromise, enabling timely clinical intervention[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, this study highlights that alterations in lactate levels following pulmonary artery interventions are closely associated with the development and progression of iatrogenic pulmonary injury, underscoring its potential role in early detection and risk stratification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis retrospective study analyzed patients who underwent interventional treatment for pulmonary vascular stenosis at Gansu Provincial Hospital between January 1, 2019 and December 31, 2022. Participants were divided into two groups based on the presence of lung injury (non-injury: n\u0026thinsp;=\u0026thinsp;101, injury: n\u0026thinsp;=\u0026thinsp;42). Baseline demographic, clinical, and laboratory data were collected, including age, sex, comorbidities (e.g., heart disease), and physiological parameters (e.g., Pulse, sPAP, PVR, TPR, lactate level).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eVariables were extracted from electronic medical records and included: Demographics: Age, sex, height, weight, BMI. Clinical measures: Pulse rate, oximetry, systolic pulmonary artery pressure (sPAP), pulmonary vascular resistance (PVR), total pulmonary resistance (TPR). Laboratory values: Pre-operative and post-operative lactate, glucose, CO\u003csub\u003e2\u003c/sub\u003e, pH, and respiratory rates. Continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) or median (interquartile range, IQR), while categorical variables were presented as counts (%).\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Institutional Review Board of Gansu Provincial Hospital (approval number: 2025\u0026thinsp;\u0026minus;\u0026thinsp;539), with waived informed consent due to retrospective anonymized data.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis:\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS 27.0 software. Continuous variables were assessed for normality. Normally distributed continuous data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Comparisons between groups were made using the t-test. Non-normally distributed continuous data are presented as the median and interquartile range M (P25, P75). Comparisons between groups for non-normally distributed data were made using the Mann-Whitney U test. Categorical data are presented as frequencies and percentages (n, %), and comparisons between groups were made using the chi-square (χ\u0026sup2;) test. A multivariable logistic regression model was employed to calculate the relationship between Post Lactate and pulmonary injury, as well as to assess the multiplicative interaction effects of Post Lactate with TPR, SPAP, and RVR (individually) on pulmonary injury. Additive interaction was analyzed using the \"interaction R\" package in R version 4.3.2. The additive interaction was evaluated using the following metrics: the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S). Additive interaction was considered statistically significant if the 95% confidence interval (95% CI) for RERI and AP did not include 0, and the 95% CI for S did not include 1. The significance level (α) was set at 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e\u003cp\u003eA total of 143 patients were included in this study, comprising 42 patients who developed lung injury and 101 who did not. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no significant differences between the two groups in terms of age, sex, height, weight, BMI, respiration rate, or most comorbidities (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the injury group exhibited significantly higher values in pulse (P\u0026thinsp;=\u0026thinsp;0.045), PVR (P\u0026thinsp;=\u0026thinsp;0.012), TPR (P\u0026thinsp;=\u0026thinsp;0.011), sPAP (P\u0026thinsp;=\u0026thinsp;0.014), preoperative carbon dioxide partial pressure (Pre-CO₂, P\u0026thinsp;=\u0026thinsp;0.013), and postoperative lactate levels (Post-lactate, P\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline information.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-injury (n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003einjury (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.53\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69(70.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29(29.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162.77\u0026thinsp;\u0026plusmn;\u0026thinsp;7.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, Kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.81\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.81\u0026thinsp;\u0026plusmn;\u0026thinsp;11.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, Kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRate, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.14\u0026thinsp;\u0026plusmn;\u0026thinsp;14.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.49\u0026thinsp;\u0026plusmn;\u0026thinsp;18.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulse, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePespirationrate, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.76\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD2TM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(90.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEHP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(68.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(32.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeartdiseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33(70.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(29.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemodynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGradient, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.00(30.00, 60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.59(34.25, 62.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVR, Wood unit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.30(5.04, 8.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.70(5.48, 11.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPR, Wood unit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.20(6.00, 10.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.33(7.39, 13.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.01\u0026thinsp;\u0026plusmn;\u0026thinsp;21.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.21\u0026thinsp;\u0026plusmn;\u0026thinsp;24.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-CO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.01\u0026thinsp;\u0026plusmn;\u0026thinsp;21.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.21\u0026thinsp;\u0026plusmn;\u0026thinsp;24.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-PH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOximetry, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-CO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-PH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-lactate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.67(1.25, 1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.67(1.40, 1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-glucose, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.74(7.74, 7.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.74(7.74, 7.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-lactate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97(1.50, 1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.97(1.68, 2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔlactate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33(1.06, 1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33 (1.00, 1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are numbers (%). mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median (interquartile range). D2TM\u0026thinsp;=\u0026thinsp;Type 2 diabetes; HTN\u0026thinsp;=\u0026thinsp;Hypertension; sPAP\u0026thinsp;=\u0026thinsp;Pulmonary Artery Systolic Pressure; PVR\u0026thinsp;=\u0026thinsp;Pulmonary vascular resistance; Pre-CO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Preoperative carbon dioxide partial pressure; TPR\u0026thinsp;=\u0026thinsp;Total Lung Resistance; Post-CO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Postoperative carbon dioxide partial pressure; Gradient\u0026thinsp;=\u0026thinsp;Pressure gradient across the stenotic pulmonary artery; Pre-lactate, Preoperative lactate levels; Pre-glucose, Preoperative blood glucose level; Post-lactate\u0026thinsp;=\u0026thinsp;Postoperative lactate levels; Δlactate, Difference in lactate levels between preoperative and postoperative periods.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUnivariate Logistic Regression Analysis\u003c/p\u003e\u003cp\u003eUnivariate logistic regression analysis identified several factors significantly associated with lung injury (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These included Post-lactate (OR\u0026thinsp;=\u0026thinsp;1.943, 95% CI: 1.120\u0026ndash;3.369, P\u0026thinsp;=\u0026thinsp;0.018), pulse (OR\u0026thinsp;=\u0026thinsp;1.023, 95% CI: 1.000\u0026ndash;1.047, P\u0026thinsp;=\u0026thinsp;0.050), sPAP (OR\u0026thinsp;=\u0026thinsp;1.020, 95% CI: 1.004\u0026ndash;1.037, P\u0026thinsp;=\u0026thinsp;0.016), PVR (OR\u0026thinsp;=\u0026thinsp;1.169, 95% CI: 1.048\u0026ndash;1.305, P\u0026thinsp;=\u0026thinsp;0.005), TPR (OR\u0026thinsp;=\u0026thinsp;1.142, 95% CI: 1.039\u0026ndash;1.256, P\u0026thinsp;=\u0026thinsp;0.006), and Pre-CO₂ (OR\u0026thinsp;=\u0026thinsp;1.105, 95% CI: 1.019\u0026ndash;1.197, P\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOne-way logistic regression analysis of factors influencing lung injury.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-Lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.120\u0026thinsp;~\u0026thinsp;3.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u0026thinsp;~\u0026thinsp;1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.004\u0026thinsp;~\u0026thinsp;1.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.048\u0026thinsp;~\u0026thinsp;1.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.039\u0026thinsp;~\u0026thinsp;1.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.019\u0026thinsp;~\u0026thinsp;1.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003esPAP\u0026thinsp;=\u0026thinsp;Pulmonary Artery Systolic Pressure; PVR\u0026thinsp;=\u0026thinsp;Pulmonary vascular resistance; Pre-CO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Preoperative carbon dioxide partial pressure; TPR\u0026thinsp;=\u0026thinsp;Total Lung Resistance; Post-lactate\u0026thinsp;=\u0026thinsp;Postoperative lactate levels.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultivariate Logistic Regression Analysis\u003c/p\u003e\u003cp\u003eAfter adjusting for potential confounders in multivariate models, postoperative lactate remained significantly associated with lung injury across all models (Model 0: OR\u0026thinsp;=\u0026thinsp;1.943, 95% CI: 1.120\u0026ndash;3.369, P\u0026thinsp;=\u0026thinsp;0.018; Model 1: OR\u0026thinsp;=\u0026thinsp;1.915, 95% CI: 1.094\u0026ndash;3.353, P\u0026thinsp;=\u0026thinsp;0.023; Model 2: OR\u0026thinsp;=\u0026thinsp;2.040, 95% CI: 1.072\u0026ndash;3.883, P\u0026thinsp;=\u0026thinsp;0.030) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultifactorial logistic regression analysis of the association between post-lactate and lung injury.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.120\u0026thinsp;~\u0026thinsp;3.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.094\u0026thinsp;~\u0026thinsp;3.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.072\u0026thinsp;~\u0026thinsp;3.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eInteraction Analysis\u003c/p\u003e\u003cp\u003eMultiplicative interaction analysis revealed a significant interaction between PVR and Post-lactate (OR\u0026thinsp;=\u0026thinsp;4.590, 95% CI: 1.098\u0026ndash;19.186, *P* = 0.037), though no significant interactions were observed for TPR or sPAP with Post-lactate (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additive interaction analysis further indicated no significant synergistic effects between Post-lactate and sPAP, PVR, or TPR, as evidenced by the measures of relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S), all with confidence intervals including the null value (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiplicative interaction analysis of TPR, sPAP, RVR and Post-Lactate on lung injury.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteraction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP值\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.590(1.098\u0026thinsp;~\u0026thinsp;19.186)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-Lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.089(0.299\u0026thinsp;~\u0026thinsp;3.965)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRVR\u0026times;Post-Lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.335(0.062\u0026thinsp;~\u0026thinsp;1.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.499(0.404\u0026thinsp;~\u0026thinsp;5.558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostLactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.563(0.199\u0026thinsp;~\u0026thinsp;1.592)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPR\u0026times;Post-Lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.123(0.230\u0026thinsp;~\u0026thinsp;5.494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.823(0.933\u0026thinsp;~\u0026thinsp;15.661)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-Lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.144(0.356\u0026thinsp;~\u0026thinsp;3.675)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRVR\u0026times;Post-Lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0..264(0..050\u0026thinsp;~\u0026thinsp;1.398)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003esPAP\u0026thinsp;=\u0026thinsp;Pulmonary Artery Systolic Pressure; PVR\u0026thinsp;=\u0026thinsp;Pulmonary vascular resistance; TPR\u0026thinsp;=\u0026thinsp;Total Lung Resistance; Post-lactate\u0026thinsp;=\u0026thinsp;Postoperative lactate levels.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdditive interaction of TPR, sPAP, RVR and Post-Lactate on lung injury.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-Lactate(\u0026ge;1.97)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRERI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.07(-8.83\u0026thinsp;~\u0026thinsp;2.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.33(-6.17\u0026thinsp;~\u0026thinsp;1.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09(0\u0026thinsp;~\u0026thinsp;4.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRERI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.29(-9.78\u0026thinsp;~\u0026thinsp;3.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.9(-5.01\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18(0.02\u0026thinsp;~\u0026thinsp;1.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRERI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21(-3.18\u0026thinsp;~\u0026thinsp;3.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07 (-1.09\u0026thinsp;~\u0026thinsp;1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13 (0.15\u0026thinsp;~\u0026thinsp;8.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003esPAP\u0026thinsp;=\u0026thinsp;Pulmonary Artery Systolic Pressure; PVR\u0026thinsp;=\u0026thinsp;Pulmonary vascular resistance; TPR\u0026thinsp;=\u0026thinsp;Total Lung Resistance; Post-lactate\u0026thinsp;=\u0026thinsp;Postoperative lactate levels.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that Post-Lactate levels (Post-Lactate\u0026thinsp;\u0026ge;\u0026thinsp;1.97 mmol/L) are an independent predictor of lung injury (adjusted OR\u0026thinsp;\u0026asymp;\u0026thinsp;2.0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This result aligns with evidence from previous studies demonstrating that hyperlactatemia leads to hypoxic injury in pulmonary endothelial cells and triggers an inflammatory cascade. Elevated lactate levels typically reflect an imbalance between tissue oxygen supply and demand, particularly in cases of circulatory impairment. This imbalance may cause hypoxia at the cellular level, which in turn initiates a series of inflammatory responses. Notably, although pulmonary hemodynamic disturbances (such as elevated sPAP, PVR, and TPR) increase the risk of lung injury, this study found no multiplicative or additive synergistic effects between these factors and lactate levels. This suggests that lactate may mediate lung injury through pathways independent of hemodynamic derangements, such as mitochondrial dysfunction. The existence of such independent pathways indicates that when assessing the risk of lung injury, reliance solely on hemodynamic parameters is insufficient; instead, other biomarkers like lactate levels should be comprehensively considered.\u003c/p\u003e\u003cp\u003eThe independent predictive value of postoperative lactate (OR\u0026thinsp;=\u0026thinsp;1.943, 95%CI:1.120\u0026ndash;3.369) cannot be solely explained by traditional hypoxic theories. We observed absence of synergistic effects with pulmonary hypertension parameters and incomplete correlation with hemodynamic impairment severity, suggesting critical roles for non-hypoxic mechanisms. Recent studies reveal three complementary pathways: Microcirculatory Dysfunction and Endothelial Injury: Elevated left atrial pressure in pulmonary hypertension induces pulmonary capillary stress failure, disrupting the alveolar-capillary barrier and facilitating lactate extravasation into interstitial compartments[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Extravasated lactate activates pulmonary macrophage TLR4/HMGB1 signaling, triggering local inflammatory cascades independent of systemic hypoxia[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This \"inflammatory-metabolic positive feedback loop\" sustains lactate production via aerobic glycolysis (Warburg effect), establishing a self-amplifying injury pathway. Metabolic Reprogramming: Single-cell sequencing studies (2025) demonstrate that vulnerable pulmonary vascular endothelial cells exhibit enhanced glycolysis and suppressed oxidative phosphorylation. This metabolic shift enables sustained lactate generation under normoxic conditions, particularly in stenotic regions of chronic thromboembolic pulmonary hypertension (CTEPH) patients showing marked lactate dehydrogenase A (LDHA) upregulation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our lactate threshold (1.97 mmol/L) corresponds to levels inducing endothelial-mesenchymal transition in experimental models, suggesting profibrotic potential. Hepato-Splanchnic Suppression: Right ventricular dysfunction in pulmonary hypertension causes hepatic congestion, significantly impairing lactate clearance capacity. Clinical studies confirm negative correlation between DLCO% predicted and lactate levels (r=-0.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in COPD patients with pulmonary hypertension, predicting acute exacerbation risk[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This \"metabolic bottleneck effect\" explains divergent lactate levels under similar hemodynamic insults depending on hepatic perfusion status. Notably, the temporal dynamics of lactate enhance its predictive value. Unlike hemodynamic parameters reflecting instantaneous status, lactate integrates cumulative metabolic stress over hours. This accounts for its superiority in our univariate analysis and aligns with CTEPH research demonstrating that lactate clearance rates better predict right ventricular functional recovery than absolute hemodynamic values[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe observed antagonistic interaction between lactate and pulmonary hemodynamic parameters (PVR\u0026times;Lactate RERI=-3.29; sPAP\u0026times;Lactate RERI=-3.07) challenges conventional pathophysiological models. Based on emerging evidence, we propose three mechanistic explanations: Pressure-Induced Endothelial Adaptation: Severe pulmonary hypertension induces endothelial phenotypic switching that unexpectedly reduces lactate sensitivity. Specialized endothelial subpopulations co-expressing pulmonary and bronchial markers emerge in stenotic pulmonary arteries of CTEPH patients, exhibiting altered metabolic flux and lactate transport. This adaptive transcriptomic remodeling diminishes lactate-driven inflammation, rationally explaining the \"sub-additive\" injury risk when both factors coexist. This finding corresponds with 3D microfluidic models where endothelial cells under high shear stress (simulating PH) show 40\u0026ndash;60% reduced lactate-induced IL-6 production versus static conditions. Ventricular Interdependence Effects: Right ventricular pressure overload in pulmonary hypertension impairs left ventricular filling through septal shift, reducing cardiac output and systemic perfusion pressure. This generates dual lactate sources\u0026mdash;pulmonary tissue (hypoperfusion) and peripheral tissues (low cardiac output). Consequently, lactate elevation loses specificity for pulmonary injury severity, weakening its interaction with hemodynamic parameters. Post-endarterectomy studies confirm this phenomenon: right ventricular functional recovery typically precedes lactate normalization by 48\u0026ndash;72 hours. Unmeasured Therapeutic Confounding: Clinicians often initiate early vasodilator therapy (e.g., inhaled NO) when both lactate and sPAP/PVR are elevated, artificially attenuating observed risk. This treatment bias is particularly relevant given recent evidence that pulmonary vasodilators enhance lactate clearance independently of hemodynamic improvement. Additionally, compartmentalization of lactate pools further elucidates this paradox. Pulmonary hypertension redistributes blood volume from systemic to pulmonary circulation, concentrating lactate within pulmonary vasculature. However, vascular compartments exhibit divergent lactate signaling responses: pulmonary endothelial cells increase nitric oxide production (vasoprotective), while systemic endothelia develop prothrombotic profiles. This compartment-specific signaling explains why systemic lactate measurements poorly reflect local pulmonary injury risk when pulmonary hypertension is present.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study confirms postoperative lactate as a robust, independent predictor of lung injury after cardiac surgery, alongside pulmonary hemodynamic dysfunction. However, the absence of synergy, and evidence of antagonism, between these pathways indicates they contribute to lung injury risk via largely distinct mechanisms. While their combined assessment improves risk stratification, the negative additive interactions underscore the need for deeper investigation into the underlying pathophysiology to guide more precise therapeutic interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBo Li:\u0026nbsp;\u003c/strong\u003eFormal analysis; Writing \u0026ndash; original draft;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYanxia Yang:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; original draft;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaomei Xi:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash;Methodology; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChuan zhou Liu:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHongling Su:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJinjin Liu:\u003c/strong\u003e Methodology.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXing chang, Wang:\u003c/strong\u003e Conceptualization; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFu Zhang:\u003c/strong\u003e Conceptualization; Funding acquisition; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Gansu Province of China (24JRRA1049) and the Intramural Fund of Gansu Provincial Hospital of China (23GSSYF-28) awarded to Bo Li.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Gansu Provincial Hospital (204 Donggang West Road, Chengguan District, Lanzhou, Gansu Province, 730000) that granted exemption from obtaining informed consent from patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHong C, Zhou D, Chen H, Wu X, Guo W, Cui J, et al. Pulmonary Artery Stent Implantation for Fibrosing Mediastinitis: Our Clinical Experience. Pulmonary circulation. 2025;15(2):e70076.https://doi.org/10.1002/pul2.70076\u003c/li\u003e\n\u003cli\u003eDuan Y, Zhou X, Su H, Jiang K, Wu W, Pan X, et al. Balloon angioplasty or stent implantation for pulmonary vein stenosis caused by fibrosing mediastinitis: a systematic review. Cardiovascular diagnosis and therapy. 2019;9(5):520-528.https://doi.org/10.21037/cdt.2019.09.14\u003c/li\u003e\n\u003cli\u003ePascall E, Tulloh RM. Pulmonary hypertension in congenital heart disease. Future cardiology. 2018;14(4):343-353.https://doi.org/10.2217/fca-2017-0065\u003c/li\u003e\n\u003cli\u003eAl Abri Q, Lu AJ, Ramchandani MK. Chronic Thromboembolic Pulmonary Hypertension: A Comprehensive Review and Multidisciplinary Approach to Surgical Treatment. Methodist DeBakey cardiovascular journal. 2021;17(2):e18-e28.https://doi.org/10.14797/iqtu6714\u003c/li\u003e\n\u003cli\u003eMahmud E, Ang L. Percutaneous Interventional Therapy for Chronic Thromboembolic Pulmonary Hypertension. Structural heart : the journal of the Heart Team. 2023;7(2):100115.https://doi.org/10.1016/j.shj.2022.100115\u003c/li\u003e\n\u003cli\u003eConway C, Nezami FR, Rogers C, Groothuis A, Squire JC, Edelman ER. Acute Stent-Induced Endothelial Denudation: Biomechanical Predictors of Vascular Injury. Frontiers in cardiovascular medicine. 2021;8:733605.https://doi.org/10.3389/fcvm.2021.733605\u003c/li\u003e\n\u003cli\u003eVan der Heiden K, Gijsen FJ, Narracott A, Hsiao S, Halliday I, Gunn J, et al. The effects of stenting on shear stress: relevance to endothelial injury and repair. Cardiovascular research. 2013;99(2):269-275.https://doi.org/10.1093/cvr/cvt090\u003c/li\u003e\n\u003cli\u003eTalaie T, DiChiacchio L, Prasad NK, Pasrija C, Julliard W, Kaczorowski DJ, et al. Ischemia-reperfusion Injury in the Transplanted Lung: A Literature Review. Transplantation direct. 2021;7(2):e652.https://doi.org/10.1097/txd.0000000000001104\u003c/li\u003e\n\u003cli\u003eBossardi Ramos R, Adam AP. Molecular Mechanisms of Vascular Damage During Lung Injury. Advances in experimental medicine and biology. 2021;1304:95-107.https://doi.org/10.1007/978-3-030-68748-9_6\u003c/li\u003e\n\u003cli\u003eChang W, Liu X, Wang H, Zhu X, Huang W, Liu L, et al. Lactate aggravates inflammation through the upregulation of ICAM-1 via GPR81-HSPA12B-mediated signaling in acute lung injury. Chinese medical journal. 2024;137(15):1885-1887.https://doi.org/10.1097/cm9.0000000000003153\u003c/li\u003e\n\u003cli\u003eZhang T, Chen L, Kueth G, Shao E, Wang X, Ha T, et al. Lactate\u0026apos;s impact on immune cells in sepsis: unraveling the complex interplay. Frontiers in immunology. 2024;15:1483400.https://doi.org/10.3389/fimmu.2024.1483400\u003c/li\u003e\n\u003cli\u003ePeng TY, Lu JM, Zheng XL, Zeng C, He YH. The role of lactate metabolism and lactylation in pulmonary arterial hypertension. Respiratory research. 2025;26(1):99.https://doi.org/10.1186/s12931-025-03163-3\u003c/li\u003e\n\u003cli\u003eSun Z, He W, Meng H, Ji Z, Qu J, Yu G. Lactate activates ER stress to promote alveolar epithelial cells apoptosis in pulmonary fibrosis. Respiratory research. 2024;25(1):401.https://doi.org/10.1186/s12931-024-03016-5\u003c/li\u003e\n\u003cli\u003eMothes H, Wickel J, Sponholz C, Lehmann T, Kaluza M, Zanow J, et al. Monitoring of the Progression of the Perioperative Serum Lactate Concentration Improves the Accuracy of the Prediction of Acute Mesenteric Ischemia Development After Cardiovascular Surgery. Journal of cardiothoracic and vascular anesthesia. 2021;35(6):1792-1799.https://doi.org/10.1053/j.jvca.2021.02.007\u003c/li\u003e\n\u003cli\u003eWest JB, Mathieu-Costello O. Stress failure of pulmonary capillaries: role in lung and heart disease. (0140-6736 (Print))\u003c/li\u003e\n\u003cli\u003eLin L, Li J, Song Q, Cheng W, Chen PA-O. The role of HMGB1/RAGE/TLR4 signaling pathways in cigarette smoke-induced inflammation in chronic obstructive pulmonary disease. (2050-4527 (Electronic))\u003c/li\u003e\n\u003cli\u003ePeng TY, Lu JM, Zheng XL, Zeng C, He YH. The role of lactate metabolism and lactylation in pulmonary arterial hypertension. (1465-993X (Electronic))\u003c/li\u003e\n\u003cli\u003eLi Y, Zhang R, Shan H, Shi W, Feng X, Chen H, et al. FVC/D(LCO) identifies pulmonary hypertension and predicts 5-year all-cause mortality in patients with COPD. European journal of medical research. 2023;28(1):174.https://doi.org/10.1186/s40001-023-01130-6\u003c/li\u003e\n\u003cli\u003eLee TR, Kang MJ, Cha WC, Shin TG, Sim MS, Jo IJ, et al. Better lactate clearance associated with good neurologic outcome in survivors who treated with therapeutic hypothermia after out-of-hospital cardiac arrest. Critical care (London, England). 2013;17(5):R260.https://doi.org/10.1186/cc13090 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7556516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7556516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePostoperative hyperlactatemia is associated with organ dysfunction, but its independent role in lung injury remains underexplored, particularly relative to pulmonary hemodynamics.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo determine whether postoperative lactate independently correlates with lung injury after surgery, dissected from pulmonary hemodynamic parameters.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this single-center cross-sectional study, 143 surgical patients (non-injury\u0026thinsp;=\u0026thinsp;101, injury\u0026thinsp;=\u0026thinsp;42) were analyzed. Baseline characteristics, pulmonary hemodynamics (SPAP, PVR, TPR), and lactate levels were collected. Univariate and multifactorial logistic regression evaluated risk factors. Additive/multiplicative interactions between lactate and hemodynamic variables were tested using RERI/AP and product-term models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 143 patients studied, 42 (29.4%) developed lung injury. Univariate analysis identified that elevated postoperative lactate (\u0026ge;\u0026thinsp;1.97 mmol/L), pulse, systolic pulmonary artery pressure (sPAP), pulmonary vascular resistance (PVR), total pulmonary resistance (TPR), and preoperative CO₂ were significant risk factors for lung injury (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In multivariate analysis, postoperative lactate remained an independent predictor of lung injury across all adjusted models (adjusted OR range: 1.915\u0026ndash;2.040, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multiplicative interaction analysis revealed a significant interaction between PVR and postoperative lactate (OR\u0026thinsp;=\u0026thinsp;4.590, 95% CI: 1.098\u0026ndash;19.186, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). However, measures of additive interaction (RERI, AP, S) for sPAP, PVR, and TPR with postoperative lactate were not statistically significant.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated postoperative lactate independently correlates with lung injury without synergistic effects from pulmonary hemodynamic dysfunction. Lactate assessment may provide standalone value for early risk stratification.\u003c/p\u003e","manuscriptTitle":"Independent association of lung injury with lactate after pulmonary vascular intervention: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 13:30:27","doi":"10.21203/rs.3.rs-7556516/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-06T10:01:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T20:09:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T02:42:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T02:42:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-09-07T13:21:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"527ff671-5e5a-490e-9407-99cc77949324","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T13:30:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 13:30:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7556516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7556516","identity":"rs-7556516","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}