Impact of Postoperative Pulmonary Complications on Clinical Outcomes in Liver Transplantation: A Single-Center Retrospective Analysis

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Hanifi Çanakcı, Gülnur Kul, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9242796/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Liver transplantation (LT) is the definitive treatment for end-stage liver disease. However, postoperative pulmonary complications (PPCs) significantly contribute to morbidity and mortality. This study aims to analyze the incidence, risk factors, and clinical impact of PPCs in adult LT recipients. Methods We retrospectively analyzed 86 adult patients who underwent LT at our tertiary referral center. Patients were categorized based on the development of PPCs, including pleural effusion, atelectasis, ARDS, pneumonia, and the need for reintubation. Demographic data, perioperative variables, and clinical outcomes (ICU stay, hospital stay, and 30-day mortality) were compared. Results The overall incidence of PPCs was 46.5%. Atelectasis (32.6%) and reintubation (22.1%) were the most frequent complications. Patients with PPCs had a significantly longer median ICU stay (11.0 vs. 4.0 days, p < 0.001) and hospital stay (19.5 vs. 16.5 days, p = 0.045). Smoking history (pack-years) was significantly associated with PPC development (p = 0.027). The 30-day mortality rate was 19.8% for the entire cohort. Mortality was more frequent among patients with PPCs (25.0% vs. 15.2%), although this difference did not reach statistical significance (OR 1.86, 95% CI 0.63–5.46). Conclusion PPCs are highly prevalent after LT and are associated with prolonged resource utilization. Smoking is a modifiable risk factor that predicts PPCs. Early identification and aggressive perioperative respiratory management are crucial to improving outcomes in LT recipients. Liver transplantation Postoperative pulmonary complications Atelectasis Intensive care unit stay Mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Liver transplantation (LT) remains the gold standard for treating end-stage liver disease and acute liver failure [ 1 , 2 ]. Despite significant advancements in surgical techniques, anesthesia, and immunosuppression, the perioperative period is still fraught with complications [ 3 ]. Among these, postoperative pulmonary complications (PPCs) are particularly common, with reported incidences ranging from 35% to 87% in various cohorts [ 4 , 5 ]. PPCs, which encompass conditions such as pleural effusion, atelectasis, pneumonia, and acute respiratory distress syndrome (ARDS), contribute substantially to postoperative morbidity [ 6 , 7 ]. The pathophysiology of PPCs in LT is multifactorial, involving factors such as the proximity of the surgical site to the diaphragm, prolonged anesthesia, intraoperative fluid shifts, and the immunosuppressed state of the recipient [ 8 , 9 ]. Furthermore, the underlying severity of liver disease, often quantified by the Model for End-Stage Liver Disease (MELD) score, and comorbidities like diabetes and renal impairment may further predispose patients to respiratory failure [ 10 , 11 ]. While many studies have explored general complications after LT, there is a need for contemporary data on the specific impact of PPCs on resource utilization and short-term survival in high-volume centers. Understanding the modifiable risk factors, such as smoking history, and the burden of specific complications like atelectasis can help refine perioperative protocols. This study aims to investigate the incidence and types of PPCs in our adult LT population, identify potential risk factors, and evaluate their impact on ICU and hospital length of stay, as well as 30-day mortality. Materials and Methods Study Design and Population This retrospective cohort study was conducted at a tertiary referral center specializing in hepatobiliary surgery and liver transplantation. We reviewed all consecutive adult patients (≥ 18 years) who underwent orthotopic liver transplantation between August 2024 and January 2026. Patients with incomplete medical records or intraoperative mortality were excluded from the analysis. Data Collection Demographic, clinical, and perioperative data were extracted from the institutional electronic medical records and transplantation database. Preoperative Variables Age Sex Body mass index (BMI) Smoking exposure (pack-years) MELD score Child–Pugh classification Comorbidities (diabetes mellitus, renal impairment) Baseline laboratory parameters (albumin, bilirubin, WBC, hemoglobin) Intraoperative Variables Operation duration (hours) Cold ischemia time Intraoperative fluid administration Blood product transfusion requirements Postoperative Variables Time to extubation Peak lactate level ICU length of stay (days) Total hospital length of stay (days) 30-day mortality Definition of Postoperative Pulmonary Complications Postoperative pulmonary complications (PPCs) were defined according to standardized clinical and radiological criteria and included: Pleural effusion : Radiologically confirmed effusion requiring intervention or associated with respiratory compromise Atelectasis : Radiographic evidence of lung collapse Acute respiratory distress syndrome (ARDS) : Defined according to the Berlin criteria Pneumonia : New pulmonary infiltrates accompanied by clinical signs of infection Reintubation : Requirement for invasive mechanical ventilation following initial successful extubation Patients were categorized into two groups based on PPC development (PPC vs. No PPC). Statistical Analysis Statistical analyses were performed using SPSS version 26.0 and Python (pandas, scipy, statsmodels, and scikit-learn libraries). Continuous variables were assessed for normality using the Shapiro–Wilk test. Non-normally distributed variables were reported as median with interquartile range (IQR) and compared using the Mann–Whitney U test. Categorical variables were expressed as frequency (percentage) and compared using the Chi-square or Fisher’s exact test. Correlations were evaluated using Spearman’s rank correlation coefficient. A two-sided p-value < 0.05 was considered statistically significant. Missing data were assessed prior to analysis. Given the low proportion of missing values (< 5% for all variables), complete-case analysis was performed. Multivariable Logistic Regression Analysis To identify independent predictors of PPC development, a multivariable logistic regression model was constructed. Variables were selected based on: Clinical relevance (age, MELD score, smoking exposure, operation duration, diabetes mellitus, and renal impairment), and A univariable screening threshold of p < 0.10. Adjusted odds ratios (aOR) with 95% confidence intervals (CI) were calculated. Smoking exposure was modeled per 10 pack-year increase to improve interpretability. Linearity in the logit for continuous variables was assessed prior to model fitting. Multicollinearity was assessed using variance inflation factors (VIF), with values > 5 considered indicative of significant collinearity. Model performance was evaluated by: Discrimination: Area under the receiver operating characteristic curve (AUC) Calibration: Hosmer–Lemeshow goodness-of-fit test Explanatory power: Nagelkerke’s R² To evaluate whether postoperative pulmonary complications were independently associated with 30-day mortality, a parsimonious multivariable logistic regression model was constructed including PPC status and MELD score, given the limited number of mortality events (n = 17). Adjusted odds ratios (aOR) with 95% confidence intervals were calculated. Model calibration was assessed using the Hosmer–Lemeshow test, and discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Adjusted Analysis of ICU Length of Stay Given the right-skewed distribution of ICU length of stay, a negative binomial regression model was used to assess the independent effect of PPCs on ICU duration after adjustment for demographic and perioperative covariates. Incidence rate ratios (IRR) with 95% confidence intervals were reported Results Patient Characteristics A total of 86 adult liver transplant recipients were included in the final analysis. The median age of the cohort was 51.0 years (IQR: 44.0–61.0), and 66.3% were male. The median MELD score was 16.0 (IQR: 12.0–23.0). Preoperative diabetes was present in 30.2% of patients, and 14.0% had preoperative renal impairment. Incidence and Types of PPCs The overall incidence of PPCs was 46.5% (n = 40). The most frequent individual pulmonary complications were atelectasis (32.6%), followed by the need for reintubation (22.1%) and pleural effusion (17.4%). More severe complications such as ARDS (5.8%) and pneumonia (4.7%) were less common but clinically significant. The distribution of these complications is shown in Fig. 1 . Risk Factors for PPCs Comparison of patients with and without PPCs revealed that smoking history was a significant risk factor. Patients who developed PPCs had a significantly higher median smoking history compared to those who did not (5.5 vs. 0.0 pack-years, p = 0.027). Although patients with PPCs were slightly older (median 54.0 vs. 50.5 years), this did not reach statistical significance (p = 0.207). Preoperative MELD score, operation duration, and intraoperative fluid administration were comparable between the two groups (Table 1 ). Table 1 Comparison of clinical and perioperative variables between patients with and without PPCs. Data are presented as median [IQR] Variable No PPC (n = 46) PPC (n = 40) p-value Age (years) 50.5 [41.2–60.0] 54.0 [45.8–62.0] 0.207 MELD Score 16.5 [14.2–23.8] 16.0 [11.8–22.5] 0.385 Smoking (pack-years) 0.0 [0.0–5.0] 5.5 [0.0–30.0] 0.027 Operation Duration (hours) 8.0 [7.5–9.5] 8.0 [7.0–9.0] 0.364 ICU Stay (days) 4.0 [3.2-5.0] 11.0 [6.0–21.0] < 0.001 Hospital Stay (days) 16.5 [13.2–20.0] 19.5 [15.0-26.2] 0.045 30-day mortality, n (%) 7 (15.2%) 10 (25.0%) 0.27 Multivariable Analysis of PPC Development A multivariable logistic regression model was constructed to determine independent predictors of PPC development (Table 2 ). After adjustment for age, MELD score, smoking exposure (per 10 pack-years), operation duration, diabetes mellitus, and renal impairment, smoking exposure remained independently associated with PPC occurrence (adjusted OR 1.38, 95% CI 1.08–1.82, p = 0.021). Table 2 Multivariable Logistic Regression for PPC Development Variable Adjusted OR 95% CI p-value Age (per year) 1.02 0.97–1.07 0.41 MELD (per point) 1.01 0.94–1.08 0.73 Smoking (per 10 pack-years) 1.38 1.08–1.82 0.021 Operation duration (hours) 1.09 0.84–1.41 0.52 Diabetes mellitus 1.27 0.58–2.81 0.54 Renal impairment 1.44 0.61–3.46 0.39 No other variables were identified as independent predictors. The magnitude and precision of adjusted associations are illustrated in Fig. 2 . The model demonstrated acceptable discrimination with an AUC of 0.74 (95% CI 0.51–0.92), as shown in Fig. 3 . Impact on Clinical Outcomes The development of PPCs had a profound impact on resource utilization. Patients with PPCs required significantly longer ICU stays (median 11.0 vs. 4.0 days, p < 0.001) and hospital stays (median 19.5 vs. 16.5 days, p = 0.045) (Fig. 4 ). The overall 30-day mortality rate in the cohort was 19.8% (n = 17). Mortality was more frequent among patients who developed PPCs compared with those without PPCs (25.0% vs. 15.2%, p = 0.27; Table 1 ). The crude odds ratio for mortality associated with PPC was 1.86 (95% CI 0.63–5.46). In a parsimonious multivariable logistic regression model adjusted for MELD score ( Table 3 ) , PPC status remained directionally associated with increased odds of 30-day mortality (adjusted OR 1.74, 95% CI 0.55–5.32, p = 0.34). MELD score itself was not independently associated with mortality (adjusted OR 1.05 per point increase, 95% CI 0.97–1.14, p = 0.21). The model demonstrated moderate discrimination (AUC 0.69) and acceptable calibration (Hosmer–Lemeshow p = 0.62). Table 3 Multivariable Logistic Regression Analysis for 30-Day Mortality Variable Adjusted OR 95% CI p-value PPC (yes vs no) 1.74 0.55–5.32 0.34 MELD (per point) 1.05 0.97–1.14 0.21 Although PPCs were associated with increased odds of early mortality in unadjusted analysis, this association did not reach statistical significance. Mortality events were predominantly related to multi-organ failure rather than isolated respiratory complications. The broader interrelationships among PPCs, mortality, and perioperative variables are illustrated in the correlation matrix ( Fig. 5 ) . In a secondary adjusted analysis using negative binomial regression ( Table 4 ) , the presence of PPC was independently associated with prolonged ICU length of stay (IRR 2.41, 95% CI 1.78–3.26, p < 0.001) after controlling for demographic and perioperative variables. Table 4 Adjusted Association Between PPC and ICU Stay Variable IRR 95% CI p-value PPC (yes vs no) 2.41 1.78–3.26 < 0.001 Age 1.01 0.99–1.02 0.29 MELD 1.02 0.99–1.05 0.18 Smoking 1.03 1.00–1.06 0.07 Operation duration 1.06 0.98–1.15 0.12 Discussion The present study investigates the incidence, risk factors, and clinical ramifications of postoperative pulmonary complications (PPCs) in a cohort of adult liver transplant (LT) recipients. Our observed overall PPC incidence of 46.5% (Fig. 1 ) aligns with the upper range of previously reported figures in the literature, which typically fluctuate between 35% and 87% across different centers and patient populations [ 4 , 5 , 6 ]. This variability often reflects differences in patient selection, surgical techniques, perioperative management protocols, and definitions of PPCs. The high prevalence in our cohort underscores the persistent challenge PPCs pose in the post-LT period, demanding continuous refinement of preventive and therapeutic strategies. Such heterogeneity in reported PPC rates across transplant centers likely reflects differences in perioperative ventilatory strategies, transfusion thresholds, fluid management during the anhepatic phase, extubation policies, and the intensity of postoperative respiratory surveillance. In addition, variability in diagnostic definitions and thresholds for radiologic confirmation may further contribute to the broad incidence range reported in the literature. A key finding of our analysis is the significant association between smoking history (pack-years) and the development of PPCs (p = 0.027). Importantly, smoking exposure remained statistically significant after multivariable adjustment, with each 10 pack-year increase conferring a 38% increase in the odds of PPC development. This reinforces smoking as an independent and clinically relevant determinant of postoperative pulmonary vulnerability in liver transplant recipients. This is a crucial modifiable risk factor, consistent with established knowledge that chronic tobacco exposure leads to impaired mucociliary clearance, increased airway hyperreactivity, and reduced pulmonary reserve, thereby predisposing patients to atelectasis, pneumonia, and prolonged mechanical ventilation [ 12 , 13 ]. The correlation matrix (Fig. 5 ) further illustrates this relationship, showing a positive correlation between smoking and PPC occurrence. This highlights an actionable area for intervention: aggressive preoperative smoking cessation programs could potentially mitigate a substantial portion of PPCs, improving patient outcomes and optimizing resource utilization. While other factors such as age and MELD score are frequently implicated in the literature [ 10 , 11 ], their lack of statistical significance in our cohort might be attributed to the relatively homogenous nature of our patient population or the effectiveness of standardized perioperative care protocols in mitigating their impact. In the adjusted negative binomial regression model ( Table 3 ) , the presence of PPC was independently associated with prolonged ICU length of stay (IRR 2.41, 95% CI 1.78–3.26, p < 0.001), even after controlling for demographic and perioperative variables. The clinical impact of PPCs was clearly demonstrated by the significantly prolonged intensive care unit (ICU) and hospital lengths of stay (Table 1 , Fig. 4 ). Patients experiencing PPCs required nearly three times longer ICU stays (median 11.0 days vs. 4.0 days, p < 0.001) and notably longer hospital stays (median 19.5 days vs. 16.5 days, p = 0.045). These extended durations not only escalate healthcare costs but also increase the risk of secondary complications such as nosocomial infections, critical illness polyneuropathy, and long-term functional impairment [ 14 , 15 ]. The strong positive correlation between PPCs and both ICU and hospital stay durations (r = 0.52 and r = 0.28, respectively, as shown in Fig. 5 ) emphasizes that these complications are major drivers of resource consumption and patient morbidity. Atelectasis emerged as the most common individual PPC (32.6%), followed by reintubation (22.1%) and pleural effusion (17.4%) (Fig. 1 ). The high incidence of atelectasis is multifactorial, stemming from the diaphragmatic dysfunction inherent in upper abdominal surgery, the effects of general anesthesia, and often aggressive fluid resuscitation during the anhepatic phase, which can lead to interstitial edema and impaired lung mechanics [ 7 , 16 ]. Reintubation, a critical event, often signifies a cascade of further complications, including ventilator-associated pneumonia and increased mortality. This highlights the importance of meticulous intraoperative fluid management, early mobilization, and aggressive postoperative pulmonary physiotherapy to prevent lung collapse and facilitate timely extubation. In our center, several targeted strategies are routinely implemented to mitigate PPC risk. Intraoperatively, lung-protective ventilation strategies with individualized positive end-expiratory pressure are employed, alongside judicious fluid management during the anhepatic phase and restrictive transfusion practices when clinically feasible. Postoperatively, early extubation protocols are prioritized in hemodynamically stable recipients, combined with structured respiratory physiotherapy, incentive spirometry, optimized analgesia, and early mobilization. Clinically significant pleural effusions are evaluated using bedside ultrasonography and drained when indicated to facilitate lung re-expansion. While the overall 30-day mortality rate in our cohort was 19.8%, PPCs appear to contribute to mortality primarily within the broader context of multi-organ dysfunction rather than as isolated respiratory events. In unadjusted analysis, mortality was more frequent among patients who developed PPCs (25.0% vs. 15.2%), corresponding to a nearly twofold increase in the odds of early mortality (OR 1.86, 95% CI 0.63–5.46, p = 0.27). Although this association did not reach statistical significance, the observed effect size suggests a potentially meaningful clinical association. Given the relatively small sample size and only 17 observed deaths, the study may have been underpowered to detect moderate differences in mortality between groups. Importantly, adjustment for MELD score did not materially alter the direction of association between PPCs and mortality, further supporting the interpretation that pulmonary complications may reflect systemic physiological vulnerability rather than isolated causal drivers. Larger multicenter studies are warranted to more definitively determine the independent contribution of PPCs to short-term survival. Taken together with the adjusted ICU findings, these results indicate that PPCs substantially worsen postoperative recovery trajectories and impose a considerable burden on critical care resources, even when they are not the sole proximate cause of mortality. Postoperative pulmonary complications represent a substantial and potentially modifiable driver of critical care burden following liver transplantation. From a practical standpoint, these observations underscore the importance of proactive perioperative pulmonary optimization, particularly targeted smoking cessation strategies and standardized respiratory care pathways in the early post-transplant period. By focusing on modifiable risk factors and structured prevention protocols, transplant programs may meaningfully reduce PPC incidence, shorten ICU utilization, and enhance overall postoperative recovery in liver transplant recipients. Limitations This study has several limitations that should be acknowledged. First, its retrospective single-center design may limit the generalizability of the findings to other transplant programs with different patient populations, perioperative protocols, or institutional practices. Although our center follows standardized perioperative care pathways, variations in management across institutions may influence PPC incidence and outcomes. Second, the relatively modest sample size may have limited the statistical power to detect weaker associations and contributed to the wide confidence intervals observed in some analyses. While multivariable adjustment was performed, the possibility of residual confounding cannot be entirely excluded. Third, although internal model validation was undertaken, external validation in independent cohorts is necessary before broader clinical implementation of the predictive findings. Future multicenter prospective studies are warranted to confirm the independent role of smoking exposure and to further refine risk stratification strategies. Finally, certain perioperative variables—such as detailed ventilator parameters, frailty indices, or inflammatory biomarkers—were not systematically available and therefore could not be incorporated into the regression models. Conclusion Postoperative pulmonary complications remain highly prevalent following liver transplantation and significantly influence early postoperative recovery. In our cohort, smoking exposure emerged as the only independent predictor of PPC development, underscoring the importance of modifiable risk factor optimization in transplant candidates. Even after adjustment for demographic and perioperative variables, PPCs were independently associated with prolonged ICU stay, highlighting their substantial impact on critical care resource utilization. Although PPCs were associated with increased crude and adjusted odds of early mortality, statistical significance was not reached, and mortality appeared to be primarily driven by multi-organ dysfunction. Targeted preoperative smoking cessation strategies and consistent implementation of structured perioperative respiratory care pathways may represent practical and effective approaches to mitigating PPC burden and improving postoperative outcomes in liver transplant recipients. Abbreviations PPCs Postoperative pulmonary complications LT Liver transplantation ICU Intensive care unit ARDS Acute respiratory distress syndrome MELD Model for End-Stage Liver Disease AUC Area under the receiver operating characteristic curve IRR Incidence rate ratio CI Confidence interval Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Ethics Committee of Ankara Etlik City Hospital (AEŞH-BADEK2-2026-224, approval date: March 24, 2026) and conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the requirement for informed consent was waived. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this study. Authors’ contributions G.D. conceived and designed the study. G.D. and D.K. collected the data. G.D. performed the statistical analyses. G.D., S.A.D., G.K., Ş.B., and M.H.Ç. contributed to data interpretation and manuscript preparation. All authors read and approved the final manuscript. Acknowledgements The authors thank the transplant surgery team and intensive care unit staff for their contributions to patient care. References Hwang S, Lee SG, Lee YJ, et al. Lessons learned from 1,000 living donor liver transplantations in a single center: how to make living donations safe. Liver Transpl. 2006;12(6):920–7. 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Garutti I, Martinez P, Bertran S et al. Intrapulmonary shunt and hypoxemia during liver transplantation. Transplant Proc . 2003;35(5):1913–1915. Nafidi O, Letourneau R, Roy A, et al. Impact of smoking on liver transplant outcomes. Clin Transpl. 2008;22(2):147–52. Kia L, Carias S, Atiemo K, et al. MELD-Na and MELD score as predictors of pulmonary complications after liver transplantation. Liver Transpl. 2016;22(Suppl 1):S145. Bozbas SS, Eyuboglu FO. Pulmonary complications after liver transplantation. Ann Transplant. 2011;16(3):9–17. Plevak DJ, Southorn PA, Narr BJ et al. Smoking and liver transplantation. Transplant Proc . 1993;25(1 Pt 2):1135. Singh S, Watt KD. Smoking and outcomes after liver transplantation. Liver Transpl. 2012;18(12):1395–6. Weiss E, Dahmani S, Bert F, et al. Early-onset pneumonia after liver transplantation: microbiological findings and risk factors. Liver Transpl. 2010;16(7):875–81. Dimick JB, Chen SL, Taheri PA, et al. Hospital costs associated with surgical complications: a report from the private-sector National Surgical Quality Improvement Program. J Am Coll Surg. 2004;199(4):531–7. Cheng S, Cao J, Yao J, et al. Risk factors for pulmonary complications after liver transplantation. Hepatobiliary Pancreat Dis Int. 2015;14(3):256–61. Vlaar AP, Juffermans NP. Transfusion-related acute lung injury: a clinical review. Lancet. 2013;382(9896):984–94. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9242796","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614717078,"identity":"6749f604-febf-427a-a152-5f8748c8b2d6","order_by":0,"name":"Gürkan Değirmencioğlu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDCCA3CCGURKyJCihS0BpIWHFC08BmCSoA6+28evSVecscuTdz/z+dWNGgseBvbDRzfg0yJ5LqdM8syN5GLDM7nbrHOOAR3Gk5Z2A58WgzM8aZINH5gTN87g3WacwwbUIsFjRoyWeqAWnmfGOf+I0sJ+TLLhxuHE+RI8zI9z24jQInmGh9my4czxxA08aWbMuX0SPGyE/MJ3hv3hzYZj1Ynz2w8//pzzrU6On/3wMbxaYNHBYHCAgU0CxGDDrxwE2B+AKfkGBuYPhFWPglEwCkbBSAQAGY5MwPSPySAAAAAASUVORK5CYII=","orcid":"","institution":"Etlik City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Gürkan","middleName":"","lastName":"Değirmencioğlu","suffix":""},{"id":614717079,"identity":"59dc1813-b67e-4641-ab39-a11d536e8dcc","order_by":1,"name":"Deniz Kütük","email":"","orcid":"","institution":"Etlik City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Deniz","middleName":"","lastName":"Kütük","suffix":""},{"id":614717081,"identity":"e7086799-fa78-4563-8be6-d0146afcdc17","order_by":2,"name":"M. Hanifi Çanakcı","email":"","orcid":"","institution":"Etlik City Hospital","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Hanifi","lastName":"Çanakcı","suffix":""},{"id":614717083,"identity":"00f2b4e7-1039-419d-9dc8-38d519685778","order_by":3,"name":"Gülnur Kul","email":"","orcid":"","institution":"Etlik City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gülnur","middleName":"","lastName":"Kul","suffix":""},{"id":614717084,"identity":"f93c8993-1460-4310-8a8d-7a6d09b7f9d6","order_by":4,"name":"Serap Akçalı Duru","email":"","orcid":"","institution":"Etlik City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Serap","middleName":"Akçalı","lastName":"Duru","suffix":""},{"id":614717085,"identity":"b2b9d6b9-635f-41f9-bea3-851d233e24d2","order_by":5,"name":"Şener Balas","email":"","orcid":"","institution":"Etlik City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Şener","middleName":"","lastName":"Balas","suffix":""}],"badges":[],"createdAt":"2026-03-27 09:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9242796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9242796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106004778,"identity":"30e690de-a3dd-451a-ade8-f7ef273aec23","added_by":"auto","created_at":"2026-04-02 10:33:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137927,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Postoperative Pulmonary Complications in Liver Transplant Recipients\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9242796/v1/282ba793b5a2291510643e8a.png"},{"id":106004779,"identity":"749b1284-45d1-4d0e-9ac3-1bb26fc155ff","added_by":"auto","created_at":"2026-04-02 10:33:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39588,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted Odds Ratios for Predictors of Postoperative Pulmonary Complications\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9242796/v1/0962b2dbd252782463b6b8de.png"},{"id":106004780,"identity":"82274bf5-70be-4c1d-a63c-e2bca52beb9c","added_by":"auto","created_at":"2026-04-02 10:33:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50804,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic Curve of the Multivariable PPC Prediction Model\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9242796/v1/964f984270b3ca6517b1c203.png"},{"id":106094993,"identity":"c8bed579-4c2d-4e09-aea3-bc230d91bc4b","added_by":"auto","created_at":"2026-04-03 11:43:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176482,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ICU and Hospital Length of Stay According to PPC Status\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9242796/v1/31cb7118e3c0e3910e6264c7.png"},{"id":106004782,"identity":"b4cc5806-e8a5-4a5a-86ff-5b19a3195ec0","added_by":"auto","created_at":"2026-04-02 10:33:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":263159,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Matrix of PPCs and Clinical Outcomes\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9242796/v1/e99b5eecde95c0793379d083.png"},{"id":106095797,"identity":"0b060647-c454-4dd9-9ddd-1e782700aca0","added_by":"auto","created_at":"2026-04-03 11:51:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9242796/v1/1bd42ad2-8eef-4fad-9606-f82689f7124b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Postoperative Pulmonary Complications on Clinical Outcomes in Liver Transplantation: A Single-Center Retrospective Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver transplantation (LT) remains the gold standard for treating end-stage liver disease and acute liver failure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite significant advancements in surgical techniques, anesthesia, and immunosuppression, the perioperative period is still fraught with complications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among these, postoperative pulmonary complications (PPCs) are particularly common, with reported incidences ranging from 35% to 87% in various cohorts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePPCs, which encompass conditions such as pleural effusion, atelectasis, pneumonia, and acute respiratory distress syndrome (ARDS), contribute substantially to postoperative morbidity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The pathophysiology of PPCs in LT is multifactorial, involving factors such as the proximity of the surgical site to the diaphragm, prolonged anesthesia, intraoperative fluid shifts, and the immunosuppressed state of the recipient [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, the underlying severity of liver disease, often quantified by the Model for End-Stage Liver Disease (MELD) score, and comorbidities like diabetes and renal impairment may further predispose patients to respiratory failure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile many studies have explored general complications after LT, there is a need for contemporary data on the specific impact of PPCs on resource utilization and short-term survival in high-volume centers. Understanding the modifiable risk factors, such as smoking history, and the burden of specific complications like atelectasis can help refine perioperative protocols.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the incidence and types of PPCs in our adult LT population, identify potential risk factors, and evaluate their impact on ICU and hospital length of stay, as well as 30-day mortality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study was conducted at a tertiary referral center specializing in hepatobiliary surgery and liver transplantation. We reviewed all consecutive adult patients (\u0026ge;\u0026thinsp;18 years) who underwent orthotopic liver transplantation between August 2024 and January 2026. Patients with incomplete medical records or intraoperative mortality were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic, clinical, and perioperative data were extracted from the institutional electronic medical records and transplantation database.\u003c/p\u003e \u003cp\u003ePreoperative Variables\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBody mass index (BMI)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSmoking exposure (pack-years)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMELD score\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChild\u0026ndash;Pugh classification\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComorbidities (diabetes mellitus, renal impairment)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBaseline laboratory parameters (albumin, bilirubin, WBC, hemoglobin)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIntraoperative Variables\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOperation duration (hours)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCold ischemia time\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntraoperative fluid administration\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBlood product transfusion requirements\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePostoperative Variables\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTime to extubation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeak lactate level\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eICU length of stay (days)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTotal hospital length of stay (days)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eDefinition of Postoperative Pulmonary Complications\u003c/h3\u003e\n\u003cp\u003ePostoperative pulmonary complications (PPCs) were defined according to standardized clinical and radiological criteria and included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePleural effusion\u003c/b\u003e: Radiologically confirmed effusion requiring intervention or associated with respiratory compromise\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAtelectasis\u003c/b\u003e: Radiographic evidence of lung collapse\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAcute respiratory distress syndrome (ARDS)\u003c/b\u003e: Defined according to the Berlin criteria\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePneumonia\u003c/b\u003e: New pulmonary infiltrates accompanied by clinical signs of infection\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReintubation\u003c/b\u003e: Requirement for invasive mechanical ventilation following initial successful extubation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePatients were categorized into two groups based on PPC development (PPC vs. No PPC).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 26.0 and Python (pandas, scipy, statsmodels, and scikit-learn libraries).\u003c/p\u003e \u003cp\u003eContinuous variables were assessed for normality using the Shapiro\u0026ndash;Wilk test. Non-normally distributed variables were reported as median with interquartile range (IQR) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were expressed as frequency (percentage) and compared using the Chi-square or Fisher\u0026rsquo;s exact test. Correlations were evaluated using Spearman\u0026rsquo;s rank correlation coefficient. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eMissing data were assessed prior to analysis. Given the low proportion of missing values (\u0026lt;\u0026thinsp;5% for all variables), complete-case analysis was performed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariable Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify independent predictors of PPC development, a multivariable logistic regression model was constructed.\u003c/p\u003e \u003cp\u003eVariables were selected based on:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eClinical relevance (age, MELD score, smoking exposure, operation duration, diabetes mellitus, and renal impairment), and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA univariable screening threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAdjusted odds ratios (aOR) with 95% confidence intervals (CI) were calculated. Smoking exposure was modeled per 10 pack-year increase to improve interpretability.\u003c/p\u003e \u003cp\u003eLinearity in the logit for continuous variables was assessed prior to model fitting.\u003c/p\u003e \u003cp\u003eMulticollinearity was assessed using variance inflation factors (VIF), with values\u0026thinsp;\u0026gt;\u0026thinsp;5 considered indicative of significant collinearity.\u003c/p\u003e \u003cp\u003eModel performance was evaluated by:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDiscrimination: Area under the receiver operating characteristic curve (AUC)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCalibration: Hosmer\u0026ndash;Lemeshow goodness-of-fit test\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExplanatory power: Nagelkerke\u0026rsquo;s R\u0026sup2;\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo evaluate whether postoperative pulmonary complications were independently associated with 30-day mortality, a parsimonious multivariable logistic regression model was constructed including PPC status and MELD score, given the limited number of mortality events (n\u0026thinsp;=\u0026thinsp;17). Adjusted odds ratios (aOR) with 95% confidence intervals were calculated. Model calibration was assessed using the Hosmer\u0026ndash;Lemeshow test, and discrimination was evaluated using the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAdjusted Analysis of ICU Length of Stay\u003c/h2\u003e \u003cp\u003eGiven the right-skewed distribution of ICU length of stay, a negative binomial regression model was used to assess the independent effect of PPCs on ICU duration after adjustment for demographic and perioperative covariates.\u003c/p\u003e \u003cp\u003eIncidence rate ratios (IRR) with 95% confidence intervals were reported\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 86 adult liver transplant recipients were included in the final analysis. The median age of the cohort was 51.0 years (IQR: 44.0\u0026ndash;61.0), and 66.3% were male. The median MELD score was 16.0 (IQR: 12.0\u0026ndash;23.0). Preoperative diabetes was present in 30.2% of patients, and 14.0% had preoperative renal impairment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIncidence and Types of PPCs\u003c/h2\u003e \u003cp\u003eThe overall incidence of PPCs was 46.5% (n\u0026thinsp;=\u0026thinsp;40). The most frequent individual pulmonary complications were atelectasis (32.6%), followed by the need for reintubation (22.1%) and pleural effusion (17.4%). More severe complications such as ARDS (5.8%) and pneumonia (4.7%) were less common but clinically significant. The distribution of these complications is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factors for PPCs\u003c/h2\u003e \u003cp\u003eComparison of patients with and without PPCs revealed that smoking history was a significant risk factor. Patients who developed PPCs had a significantly higher median smoking history compared to those who did not (5.5 vs. 0.0 pack-years, p\u0026thinsp;=\u0026thinsp;0.027). Although patients with PPCs were slightly older (median 54.0 vs. 50.5 years), this did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.207). Preoperative MELD score, operation duration, and intraoperative fluid administration were comparable between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eComparison of clinical and perioperative variables between patients with and without PPCs. Data are presented as median [IQR]\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo PPC (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPC (n\u0026thinsp;=\u0026thinsp;40)\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.5 [41.2\u0026ndash;60.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.0 [45.8\u0026ndash;62.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.5 [14.2\u0026ndash;23.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0 [11.8\u0026ndash;22.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (pack-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0 [0.0\u0026ndash;5.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.5 [0.0\u0026ndash;30.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation Duration (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.0 [7.5\u0026ndash;9.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0 [7.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 [3.2-5.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0 [6.0\u0026ndash;21.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.5 [13.2\u0026ndash;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.5 [15.0-26.2]\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\u003e30-day mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Analysis of PPC Development\u003c/h2\u003e \u003cp\u003eA multivariable logistic regression model was constructed to determine independent predictors of PPC development (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjustment for age, MELD score, smoking exposure (per 10 pack-years), operation duration, diabetes mellitus, and renal impairment, smoking exposure remained independently associated with PPC occurrence (adjusted OR 1.38, 95% CI 1.08\u0026ndash;1.82, p\u0026thinsp;=\u0026thinsp;0.021).\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\u003eMultivariable Logistic Regression for PPC Development\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\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\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD (per point)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (per 10 pack-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.08\u0026ndash;1.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation duration (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026ndash;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026ndash;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\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\u003eNo other variables were identified as independent predictors. The magnitude and precision of adjusted associations are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The model demonstrated acceptable discrimination with an AUC of 0.74 (95% CI 0.51\u0026ndash;0.92), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact on Clinical Outcomes\u003c/h2\u003e \u003cp\u003eThe development of PPCs had a profound impact on resource utilization. Patients with PPCs required significantly longer ICU stays (median 11.0 vs. 4.0 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hospital stays (median 19.5 vs. 16.5 days, p\u0026thinsp;=\u0026thinsp;0.045) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall 30-day mortality rate in the cohort was 19.8% (n\u0026thinsp;=\u0026thinsp;17). Mortality was more frequent among patients who developed PPCs compared with those without PPCs (25.0% vs. 15.2%, p\u0026thinsp;=\u0026thinsp;0.27; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The crude odds ratio for mortality associated with PPC was 1.86 (95% CI 0.63\u0026ndash;5.46).\u003c/p\u003e \u003cp\u003eIn a parsimonious multivariable logistic regression model adjusted for MELD score \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, PPC status remained directionally associated with increased odds of 30-day mortality (adjusted OR 1.74, 95% CI 0.55\u0026ndash;5.32, p\u0026thinsp;=\u0026thinsp;0.34). MELD score itself was not independently associated with mortality (adjusted OR 1.05 per point increase, 95% CI 0.97\u0026ndash;1.14, p\u0026thinsp;=\u0026thinsp;0.21). The model demonstrated moderate discrimination (AUC 0.69) and acceptable calibration (Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.62).\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\u003eMultivariable Logistic Regression Analysis for 30-Day Mortality\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\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\u003ePPC (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD (per point)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\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\u003eAlthough PPCs were associated with increased odds of early mortality in unadjusted analysis, this association did not reach statistical significance. Mortality events were predominantly related to multi-organ failure rather than isolated respiratory complications. The broader interrelationships among PPCs, mortality, and perioperative variables are illustrated in the correlation matrix \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a secondary adjusted analysis using negative binomial regression \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, the presence of PPC was independently associated with prolonged ICU length of stay (IRR 2.41, 95% CI 1.78\u0026ndash;3.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after controlling for demographic and perioperative variables.\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\u003eAdjusted Association Between PPC and ICU Stay\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIRR\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\u003ePPC (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.78\u0026ndash;3.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study investigates the incidence, risk factors, and clinical ramifications of postoperative pulmonary complications (PPCs) in a cohort of adult liver transplant (LT) recipients. Our observed overall PPC incidence of 46.5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) aligns with the upper range of previously reported figures in the literature, which typically fluctuate between 35% and 87% across different centers and patient populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This variability often reflects differences in patient selection, surgical techniques, perioperative management protocols, and definitions of PPCs. The high prevalence in our cohort underscores the persistent challenge PPCs pose in the post-LT period, demanding continuous refinement of preventive and therapeutic strategies. Such heterogeneity in reported PPC rates across transplant centers likely reflects differences in perioperative ventilatory strategies, transfusion thresholds, fluid management during the anhepatic phase, extubation policies, and the intensity of postoperative respiratory surveillance. In addition, variability in diagnostic definitions and thresholds for radiologic confirmation may further contribute to the broad incidence range reported in the literature.\u003c/p\u003e \u003cp\u003eA key finding of our analysis is the significant association between smoking history (pack-years) and the development of PPCs (p\u0026thinsp;=\u0026thinsp;0.027). Importantly, smoking exposure remained statistically significant after multivariable adjustment, with each 10 pack-year increase conferring a 38% increase in the odds of PPC development. This reinforces smoking as an independent and clinically relevant determinant of postoperative pulmonary vulnerability in liver transplant recipients. This is a crucial modifiable risk factor, consistent with established knowledge that chronic tobacco exposure leads to impaired mucociliary clearance, increased airway hyperreactivity, and reduced pulmonary reserve, thereby predisposing patients to atelectasis, pneumonia, and prolonged mechanical ventilation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The correlation matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) further illustrates this relationship, showing a positive correlation between smoking and PPC occurrence. This highlights an actionable area for intervention: aggressive preoperative smoking cessation programs could potentially mitigate a substantial portion of PPCs, improving patient outcomes and optimizing resource utilization. While other factors such as age and MELD score are frequently implicated in the literature [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], their lack of statistical significance in our cohort might be attributed to the relatively homogenous nature of our patient population or the effectiveness of standardized perioperative care protocols in mitigating their impact.\u003c/p\u003e \u003cp\u003eIn the adjusted negative binomial regression model \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, the presence of PPC was independently associated with prolonged ICU length of stay (IRR 2.41, 95% CI 1.78\u0026ndash;3.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), even after controlling for demographic and perioperative variables. The clinical impact of PPCs was clearly demonstrated by the significantly prolonged intensive care unit (ICU) and hospital lengths of stay (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Patients experiencing PPCs required nearly three times longer ICU stays (median 11.0 days vs. 4.0 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and notably longer hospital stays (median 19.5 days vs. 16.5 days, p\u0026thinsp;=\u0026thinsp;0.045). These extended durations not only escalate healthcare costs but also increase the risk of secondary complications such as nosocomial infections, critical illness polyneuropathy, and long-term functional impairment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The strong positive correlation between PPCs and both ICU and hospital stay durations (r\u0026thinsp;=\u0026thinsp;0.52 and r\u0026thinsp;=\u0026thinsp;0.28, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) emphasizes that these complications are major drivers of resource consumption and patient morbidity.\u003c/p\u003e \u003cp\u003eAtelectasis emerged as the most common individual PPC (32.6%), followed by reintubation (22.1%) and pleural effusion (17.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The high incidence of atelectasis is multifactorial, stemming from the diaphragmatic dysfunction inherent in upper abdominal surgery, the effects of general anesthesia, and often aggressive fluid resuscitation during the anhepatic phase, which can lead to interstitial edema and impaired lung mechanics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Reintubation, a critical event, often signifies a cascade of further complications, including ventilator-associated pneumonia and increased mortality. This highlights the importance of meticulous intraoperative fluid management, early mobilization, and aggressive postoperative pulmonary physiotherapy to prevent lung collapse and facilitate timely extubation. In our center, several targeted strategies are routinely implemented to mitigate PPC risk. Intraoperatively, lung-protective ventilation strategies with individualized positive end-expiratory pressure are employed, alongside judicious fluid management during the anhepatic phase and restrictive transfusion practices when clinically feasible. Postoperatively, early extubation protocols are prioritized in hemodynamically stable recipients, combined with structured respiratory physiotherapy, incentive spirometry, optimized analgesia, and early mobilization. Clinically significant pleural effusions are evaluated using bedside ultrasonography and drained when indicated to facilitate lung re-expansion.\u003c/p\u003e \u003cp\u003eWhile the overall 30-day mortality rate in our cohort was 19.8%, PPCs appear to contribute to mortality primarily within the broader context of multi-organ dysfunction rather than as isolated respiratory events. In unadjusted analysis, mortality was more frequent among patients who developed PPCs (25.0% vs. 15.2%), corresponding to a nearly twofold increase in the odds of early mortality (OR 1.86, 95% CI 0.63\u0026ndash;5.46, p\u0026thinsp;=\u0026thinsp;0.27). Although this association did not reach statistical significance, the observed effect size suggests a potentially meaningful clinical association. Given the relatively small sample size and only 17 observed deaths, the study may have been underpowered to detect moderate differences in mortality between groups. Importantly, adjustment for MELD score did not materially alter the direction of association between PPCs and mortality, further supporting the interpretation that pulmonary complications may reflect systemic physiological vulnerability rather than isolated causal drivers. Larger multicenter studies are warranted to more definitively determine the independent contribution of PPCs to short-term survival.\u003c/p\u003e \u003cp\u003eTaken together with the adjusted ICU findings, these results indicate that PPCs substantially worsen postoperative recovery trajectories and impose a considerable burden on critical care resources, even when they are not the sole proximate cause of mortality.\u003c/p\u003e \u003cp\u003ePostoperative pulmonary complications represent a substantial and potentially modifiable driver of critical care burden following liver transplantation. From a practical standpoint, these observations underscore the importance of proactive perioperative pulmonary optimization, particularly targeted smoking cessation strategies and standardized respiratory care pathways in the early post-transplant period. By focusing on modifiable risk factors and structured prevention protocols, transplant programs may meaningfully reduce PPC incidence, shorten ICU utilization, and enhance overall postoperative recovery in liver transplant recipients.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. First, its retrospective single-center design may limit the generalizability of the findings to other transplant programs with different patient populations, perioperative protocols, or institutional practices. Although our center follows standardized perioperative care pathways, variations in management across institutions may influence PPC incidence and outcomes.\u003c/p\u003e \u003cp\u003eSecond, the relatively modest sample size may have limited the statistical power to detect weaker associations and contributed to the wide confidence intervals observed in some analyses. While multivariable adjustment was performed, the possibility of residual confounding cannot be entirely excluded.\u003c/p\u003e \u003cp\u003eThird, although internal model validation was undertaken, external validation in independent cohorts is necessary before broader clinical implementation of the predictive findings. Future multicenter prospective studies are warranted to confirm the independent role of smoking exposure and to further refine risk stratification strategies.\u003c/p\u003e \u003cp\u003eFinally, certain perioperative variables\u0026mdash;such as detailed ventilator parameters, frailty indices, or inflammatory biomarkers\u0026mdash;were not systematically available and therefore could not be incorporated into the regression models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePostoperative pulmonary complications remain highly prevalent following liver transplantation and significantly influence early postoperative recovery. In our cohort, smoking exposure emerged as the only independent predictor of PPC development, underscoring the importance of modifiable risk factor optimization in transplant candidates.\u003c/p\u003e \u003cp\u003eEven after adjustment for demographic and perioperative variables, PPCs were independently associated with prolonged ICU stay, highlighting their substantial impact on critical care resource utilization. Although PPCs were associated with increased crude and adjusted odds of early mortality, statistical significance was not reached, and mortality appeared to be primarily driven by multi-organ dysfunction.\u003c/p\u003e \u003cp\u003eTargeted preoperative smoking cessation strategies and consistent implementation of structured perioperative respiratory care pathways may represent practical and effective approaches to mitigating PPC burden and improving postoperative outcomes in liver transplant recipients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostoperative pulmonary complications\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver transplantation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute respiratory distress syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModel for End-Stage Liver Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncidence rate ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Ethics Committee of Ankara Etlik City Hospital (AEŞH-BADEK2-2026-224, approval date: March 24, 2026) and conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.D. conceived and designed the study. G.D. and D.K. collected the data. G.D. performed the statistical analyses. G.D., S.A.D., G.K., Ş.B., and M.H.\u0026Ccedil;. contributed to data interpretation and manuscript preparation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the transplant surgery team and intensive care unit staff for their contributions to patient care.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHwang S, Lee SG, Lee YJ, et al. Lessons learned from 1,000 living donor liver transplantations in a single center: how to make living donations safe. Liver Transpl. 2006;12(6):920\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller CM, Gondolesi GE, Florman S, et al. One hundred nine living donor liver transplants in adults and children: a single-center experience. Ann Surg. 2001;234(3):301\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeltracco P, Barbieri S, Galligioni H, et al. Early respiratory complications after liver transplantation. World J Gastroenterol. 2013;19(48):9271\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevesque E, Hoti E, Azoulay D et al. Pulmonary complications after elective liver transplantation-incidence, risk factors, and outcome. \u003cem\u003eTransplant Proc\u003c/em\u003e. 2012;44(9):2714\u0026ndash;2717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin YH, Lin CC, Wang CC, et al. Perioperative risk factors for pulmonary complications after liver transplantation. Transplantation. 2010;90(11):1234\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirat A, Ozgur S, Torgay A et al. Risk factors for postoperative respiratory complications in adult liver transplant recipients. \u003cem\u003eTransplant Proc\u003c/em\u003e. 2004;36(1):218\u0026ndash;220.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvolio AW, Kaneko G, Bianco G, et al. Postoperative respiratory failure in liver transplantation: Risk factors and effect on prognosis. PLoS ONE. 2019;14(2):e0211678.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarutti I, Martinez P, Bertran S et al. Intrapulmonary shunt and hypoxemia during liver transplantation. \u003cem\u003eTransplant Proc\u003c/em\u003e. 2003;35(5):1913\u0026ndash;1915.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNafidi O, Letourneau R, Roy A, et al. Impact of smoking on liver transplant outcomes. Clin Transpl. 2008;22(2):147\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKia L, Carias S, Atiemo K, et al. MELD-Na and MELD score as predictors of pulmonary complications after liver transplantation. Liver Transpl. 2016;22(Suppl 1):S145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBozbas SS, Eyuboglu FO. Pulmonary complications after liver transplantation. Ann Transplant. 2011;16(3):9\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlevak DJ, Southorn PA, Narr BJ et al. Smoking and liver transplantation. \u003cem\u003eTransplant Proc\u003c/em\u003e. 1993;25(1 Pt 2):1135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Watt KD. Smoking and outcomes after liver transplantation. Liver Transpl. 2012;18(12):1395\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss E, Dahmani S, Bert F, et al. Early-onset pneumonia after liver transplantation: microbiological findings and risk factors. Liver Transpl. 2010;16(7):875\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimick JB, Chen SL, Taheri PA, et al. Hospital costs associated with surgical complications: a report from the private-sector National Surgical Quality Improvement Program. J Am Coll Surg. 2004;199(4):531\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng S, Cao J, Yao J, et al. Risk factors for pulmonary complications after liver transplantation. Hepatobiliary Pancreat Dis Int. 2015;14(3):256\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVlaar AP, Juffermans NP. Transfusion-related acute lung injury: a clinical review. Lancet. 2013;382(9896):984\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Liver transplantation, Postoperative pulmonary complications, Atelectasis, Intensive care unit stay, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-9242796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9242796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLiver transplantation (LT) is the definitive treatment for end-stage liver disease. However, postoperative pulmonary complications (PPCs) significantly contribute to morbidity and mortality. This study aims to analyze the incidence, risk factors, and clinical impact of PPCs in adult LT recipients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 86 adult patients who underwent LT at our tertiary referral center. Patients were categorized based on the development of PPCs, including pleural effusion, atelectasis, ARDS, pneumonia, and the need for reintubation. Demographic data, perioperative variables, and clinical outcomes (ICU stay, hospital stay, and 30-day mortality) were compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall incidence of PPCs was 46.5%. Atelectasis (32.6%) and reintubation (22.1%) were the most frequent complications. Patients with PPCs had a significantly longer median ICU stay (11.0 vs. 4.0 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hospital stay (19.5 vs. 16.5 days, p\u0026thinsp;=\u0026thinsp;0.045). Smoking history (pack-years) was significantly associated with PPC development (p\u0026thinsp;=\u0026thinsp;0.027). The 30-day mortality rate was 19.8% for the entire cohort. Mortality was more frequent among patients with PPCs (25.0% vs. 15.2%), although this difference did not reach statistical significance (OR 1.86, 95% CI 0.63\u0026ndash;5.46).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePPCs are highly prevalent after LT and are associated with prolonged resource utilization. Smoking is a modifiable risk factor that predicts PPCs. Early identification and aggressive perioperative respiratory management are crucial to improving outcomes in LT recipients.\u003c/p\u003e","manuscriptTitle":"Impact of Postoperative Pulmonary Complications on Clinical Outcomes in Liver Transplantation: A Single-Center Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 10:33:03","doi":"10.21203/rs.3.rs-9242796/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T11:32:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T19:10:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T13:33:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T07:39:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T15:13:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157486766659751961261504717787282137477","date":"2026-04-05T15:51:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75865353165480463821683636955904967817","date":"2026-04-04T13:10:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105568888063984490593640280707144799772","date":"2026-04-03T13:59:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248963545320973091128844788420685577181","date":"2026-04-03T13:49:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T13:39:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T08:30:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T11:49:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T11:48:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-03-27T09:12:34+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":"21077e9e-a3d6-4f1c-9525-4554f535f7eb","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T19:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 10:33:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9242796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9242796","identity":"rs-9242796","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}