A Nomogram for Predicting Postoperative Pulmonary Complications after Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Study

A Nomogram for Predicting Postoperative Pulmonary Complications after Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective 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 A Nomogram for Predicting Postoperative Pulmonary Complications after Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Study Yefei Wu, Xiao Xu, Cibao Liu, Yanni Wang, Yunqian Wang, Zixuan Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8411708/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Postoperative pulmonary complications (PPCs) are among the most common complications following cardiac surgery with cardiopulmonary bypass (CPB) and can adversely affect patient outcomes. This study aimed to identify risk factors for PPCs following cardiac surgery with CPB and to develop and validate a nomogram for individualized risk prediction. Methods This retrospective cohort study enrolled patients undergoing cardiac surgery with cardiopulmonary bypass. Participants were grouped based on occurrence of PPCs. Univariate analysis identified candidate predictors, and multivariable logistic regression determined independent risk factors. A nomogram was developed and internally validated using bootstrap resampling. Model performance was assessed via receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results A total of 502 patients were included, of whom 277 (55.18%) developed PPCs. Multivariable logistic regression analysis identified the following independent risk factors for PPCs: age (OR = 1.117, 95% CI: 1.083–1.152, P < 0.001), preoperative atrial fibrillation (OR = 3.881, 95% CI: 1.846–8.159, P < 0.001), underlying lung diseases (OR = 5.524, 95% CI: 2.218–13.76, P < 0.001), ASA physical status (OR = 5.131, 95% CI: 1.601–16.441, P = 0.006), duration of CPB (OR = 1.021, 95% CI: 1.011–1.032, P < 0.001), and duration of intraoperative hypotension (OR = 1.006, 95% CI: 1.001–1.011, P = 0.026). A nomogram model constructed based on these factors demonstrated excellent discriminative ability upon internal validation (adjusted area under the curve (AUC) = 0.879). Calibration curves and decision curve analysis confirmed its good calibration and clinical utility, respectively. Conclusions This study identified six independent predictors for PPCs and developed an individualized prediction nomogram. This tool may assist clinicians in identifying high-risk patients, thereby optimizing perioperative management strategies to prevent and reduce the incidence of PPCs. Cardiac surgical procedures Extracorporeal circulation Postoperative pulmonary complications Risk factors Nomograms Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In recent years, the global incidence of cardiovascular diseases has continued to rise [1]. Cardiac surgery serves as a critical treatment modality for conditions such as valvular heart disease, coronary artery disease, and major vascular diseases, playing an indispensable role in clinical practice [2, 3]. Cardiopulmonary bypass is a fundamental prerequisite and safety assurance for most cardiac surgical procedures. However, it may contribute to various postoperative complications through mechanisms such as systemic inflammatory response activation and ischemia-reperfusion injury [4–6]. Postoperative pulmonary complications represent one of the most common types of complications. They not only prolong hospital stays and increase medical costs but also severely impair patient recovery and quality of life, and may even be life-threatening [7]. Previous studies have identified several risk factors, including age, EuroSCORE II, pre-existing chronic obstructive pulmonary disease (COPD), type of surgery, and serum albumin levels [8, 9]. However, findings remain inconsistent, and a readily applicable visual nomogram is still lacking. Therefore, Therefore, we hypothesize that a multivariate prediction model incorporating easily accessible preoperative and intraoperative clinical indicators can reliably assess the individual risk of postoperative pulmonary complications in patients undergoing cardiac surgery with cardiopulmonary bypass. To validate this hypothesis, this study aims to: (1) identify independent risk factors for postoperative pulmonary complications; (2) develop and internally validate a nomogram that can be intuitively applied for bedside risk assessment. Methods Study Design This retrospective cohort study enrolled patients who underwent cardiac surgery under general anesthesia at Nanjing First Hospital between January 2024 and December 2024. The study protocol was approved by the Ethics Committee of Nanjing First Hospital (No.: KY20250922-KS-01). Owing to the retrospective design of this study, informed consent from participants was exempted. Inclusion and Exclusion Criteria A total of 502 eligible patients were included in this study. The inclusion criteria comprised adult patients, both male and female, who underwent cardiac surgery. The exclusion criteria were as follows: (1) patients with preoperative pulmonary complications such as pneumonia or atelectasis; (2) pregnant patients; (3) patients who underwent surgery without cardiopulmonary bypass; (4) patients requiring re-sternotomy; (5) patients with missing critical data. Data Collection Based on literature review, clinical guidelines, and expert experience, the following potential risk factors were collected: (1) Patient demographics and history: sex, age, body mass index (BMI), and history of hypertension, atrial fibrillation, diabetes, cerebral infarction, pre-existing pulmonary disease, or hypothyroidism. (2) Preoperative and intraoperative parameters: emergency surgery, combined cardiac procedures, ASA physical status, preoperative cardiac ejection fraction, preoperative hemoglobin level, preoperative aspartate aminotransferase (AST) level, preoperative blood urea nitrogen (BUN) level, preoperative creatinine level, duration of surgery, duration of anesthesia, duration of CPB, intraoperative fluid load, intraoperative transfusion volume, and duration of intraoperative hypotension. Definition of Key Variables Underlying pulmonary disease was defined as a chronic pulmonary condition diagnosed before surgery, primarily including COPD, asthma, interstitial lung disease, and giant pulmonary bullae. The diagnosis was confirmed by a documented medical history, pulmonary function test reports, or corresponding imaging evidence. Outcomes The primary outcome of this study was the occurrence of PPCs within 7 days after cardiac surgery. PPCs were defined according to the guidelines for standardized endpoints and definitions in perioperative clinical trials issued by the European Joint Task Force. Respiratory infection was defined as the administration of antibiotics for suspected respiratory infection, accompanied by at least one of the following criteria: new or worsened change in sputum characteristics, new or progressive pulmonary infiltrates on imaging, fever (body temperature > 38°C), or white blood cell count > 12×10⁹/L. Respiratory failure was defined as postoperative arterial partial pressure of oxygen (PaO₂) < 8 kPa (60 mmHg) while breathing room air, or a ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO₂/FiO₂) < 40 kPa (300 mmHg), or arterial oxygen saturation < 90% as measured by pulse oximetry requiring oxygen therapy. Pleural effusion was radiologically defined as the presence of one or more of the following findings: blunting of the costophrenic angle, loss of the sharp diaphragmatic contour on the upright view with evidence of displacement of adjacent anatomical structures, or (in the supine position) generalized opacification of a hemithorax with preserved vascular shadows. Atelectasis was defined as pulmonary opacification accompanied by displacement of the mediastinum, hilum, or hemidiaphragm toward the affected region, with compensatory hyperinflation of adjacent non-atelectatic lung areas. Pneumothorax was defined as the presence of air within the pleural space in the absence of vascular markings surrounding the visceral pleura. Bronchospasm was defined as the new onset of expiratory wheezing requiring treatment with bronchodilators. Aspiration pneumonia was defined as acute lung injury resulting from the inhalation of regurgitated gastric contents [10]. Statistical Analysis Statistical analyses were performed using SPSS (version 27.0) and R software (version 4.5.1). The normality of continuous variables was assessed using the Shapiro-Wilk test along with graphical evaluation. Normally distributed continuous data were presented as mean ± standard deviation (x̄ ± s) and compared using the independent samples t-test. Non-normally distributed continuous data were expressed as median (interquartile range) and compared using the Mann-Whitney U test. Categorical data were presented as frequency (percentage) and compared using the chi-square test or Fisher's exact test, as appropriate. Univariate logistic regression analysis was first performed, and variables with a significance level of P < 0.05 were subsequently entered into a multivariate logistic regression model to identify independent risk factors for PPCs. Adjusted odds ratios (OR) with their 95% confidence intervals (CI) were calculated, and the results of the multivariate analysis were visually presented using a forest plot. Based on the multivariate logistic regression results, a nomogram was constructed using the "rms" package in R software. Internal validation of the model was subsequently performed using the bootstrap resampling method with 1,000 replicates. The discriminative ability of the model was evaluated by plotting the ROC curve and calculating the area under the curve. Calibration was assessed by plotting calibration curves and performing the Hosmer-Lemeshow goodness-of-fit test to evaluate the agreement between predicted probabilities and observed outcomes. Decision curve analysis was applied to evaluate the clinical utility of the nomogram. In all analyses, a two-tailed P < 0.05 was considered statistically significant. Results A total of 502 patients were included in this study. Postoperative pulmonary complications occurred in 277 patients, corresponding to an incidence of 55.18%. Baseline characteristics and univariate analysis results are presented in Table 1 . Regarding baseline characteristics, patients who developed PPCs were significantly older, had higher American Society of Anesthesiologists (ASA) physical statuses, and showed a greater prevalence of atrial fibrillation and pre-existing pulmonary disease. Regarding preoperative physiological parameters, patients who developed PPCs demonstrated significantly lower cardiac ejection fraction alongside elevated levels of blood urea nitrogen and creatinine. For surgical and perioperative factors, patients with PPCs were more likely to undergo emergency surgery and combined procedures. Additionally, they experienced significantly longer duration of surgery, anesthesia, and cardiopulmonary bypass, prolonged intraoperative hypotension, and required larger volume blood transfusions. No statistically significant differences were observed in the remaining indicators. Table 1 Univariate analysis of variables related to postoperative pulmonary complications. Characteristic All patients(N = 502) Non-PPCs(n = 225) PPCs(n = 277) P value Age, median (IQR) 64(57,71) 60(53,66) 68(60,73) < 0.001 Male sex, n (%) 331(65.90) 151(67.10) 180(65.00) 0.617 BMI, median (IQR) 24.17(22.21,26.47) 24(22.21,26.35) 24.24(22.22,26.57) 0.771 ASA physical status, n (%) < 0.001 3 443(88.20) 216(96.00) 227(81.95) 4 56(11.20) 9(4.00) 47(16.97) 5 3(0.60) 0(0.00) 3(1.08) Atrial fibrillation, n (%) 90(17.90) 21(9.30) 69(24.90) < 0.001 Underlying lung diseases, n (%) 54(10.80) 10(4.40) 44(15.90) < 0.001 hypertension, n (%) 307(61.20) 127(56.40) 180(65.00) 0.051 diabetes, n (%) 122(24.30) 63(28.00) 59(21.30) 0.082 Cerebral infarction, n (%) 84(16.70) 32(14.20) 52(18.80) 0.174 Hypothyroidism, n (%) 11(2.20) 4(1.80) 7(2.50) 0.568 Cardiac EF, median (IQR) 62(52,64) 63(53,65) 61(49.5,64) 0.012 AST, median (IQR) 20(17,26) 20(17,24.4) 21(17,27.5) 0.026 Hemoglobin, median (IQR) 133(123,144) 135(126,145) 132(121,142) 0.034 Urea, median (IQR) 6.41(5.39,8.14) 6.06(5.135,7.52) 6.74(5.585,8.615) < 0.001 Creatinine, median (IQR) 72.90(61.58,86.73) 70.8(60.45,83.45) 75(62.5,91.05) 0.044 Emergency surgery, n (%) 49(9.8) 9(4.00) 40(14.40) < 0.001 Combined surgery, n (%) 204(40.6) 60(26.70) 144(52.00) < 0.001 Duration of CPB, median (IQR) 114(91,152) 95(78,111) 140(109.5,171.5) < 0.001 Duration of surgery, median (IQR) 275(230,330) 240(210,280) 310(270,365) < 0.001 Duration of anesthesia, median (IQR) 310(265,365) 275(242.5,315) 350(300,400) < 0.001 Fluid load, median (IQR) 2000(2000,2500) 2000(2000,2500) 2000(2000,2500) 0.002 Volume of blood transfusion, median (IQR) 1300(950,1800) 1150(800,1537.5) 1500(1050,2000) < 0.001 Duration of hypotension, median (IQR) 135(100,185) 110(85,145) 165(120,225) < 0.001 After adjustment for all potential confounding factors, multivariate logistic regression analysis identified the following as independent risk factors for PPCs, as visually summarized in the forest plot (Fig. 1 ): age (OR = 1.117, 95% CI: 1.083–1.152, P < 0.001), preoperative atrial fibrillation (OR = 3.881, 95% CI: 1.846–8.159, P < 0.001), underlying lung diseases (OR = 5.524, 95% CI: 2.218–13.76, P < 0.001), ASA physical status (OR = 5.131, 95% CI: 1.601–16.441, P = 0.006), duration of CPB (OR = 1.021, 95% CI: 1.011–1.032, P < 0.001), and duration of intraoperative hypotension (OR = 1.006, 95% CI: 1.001–1.011, P = 0.026). Based on the six independent risk factors identified, we developed a nomogram (Fig. 2 ) to predict the probability of PPCs in patients undergoing cardiac surgery with cardiopulmonary bypass. Each predictor is assigned a score on a points scale according to its value. The total points, obtained by summing the individual scores of all factors, can then be used to estimate the probability of PPCs. The predictive performance of the nomogram was subsequently evaluated. In the original cohort, the nomogram demonstrated an AUC of 0.886 (95% CI: 0.857–0.915) (Fig. 3 ). Following internal validation with 1000 bootstrap replicates, the model exhibited a corrected C-statistic of 0.879. These results indicate that the model possesses good and well-calibrated discriminatory ability. The calibration curve (Fig. 4 ) closely aligned with the ideal diagonal line, indicating excellent agreement between the predicted probabilities by the nomogram and the actual observed probabilities. The Hosmer-Lemeshow test (χ² = 11.86, df = 8, P = 0.157) further confirmed good model calibration. The decision curve (Fig. 5 ) demonstrated that the nomogram provided higher net clinical benefit compared with both the "no intervention" and "intervention for all" strategies across a wide range of threshold probabilities. Therefore, the decision curve analysis confirms the clinical utility of this predictive model. Discussion In this study, the incidence of PPCs reached 55.18%, which is consistent with recent reports on pulmonary complications following cardiac surgery [8]. Multivariable logistic regression analysis identified six independent risk factors for pulmonary complications following cardiac surgery with cardiopulmonary bypass: age, preoperative atrial fibrillation, ASA physical status, underlying lung diseases, duration of CPB, and duration of intraoperative hypotension. Based on these findings, we developed a nomogram to predict the risk of PPCs. The model demonstrated excellent predictive performance and possesses significant clinical applicability. The present study demonstrated a significantly increased risk of PPCs in elderly patients, which aligns with findings from multiple previous studies. For instance, Fischer et al. identified age, Euroscore II, chronic obstructive pulmonary disease, preoxygenation method, intraoperative positive end-expiratory pressure, absence of ventilation during cardiopulmonary bypass, lack of lung recruitment maneuvers, and neuromuscular blockade as factors associated with the occurrence of PPCs [9]. Da-Shuai Wang et al. identified ten predictors for postoperative pneumonia following heart valve surgery, among which advanced age was a significant factor [11]. Furthermore, age has also been incorporated as a key predictor in a nomogram developed to assess the risk of pulmonary complications following cardiac and major vascular surgery [12]. With advancing age, patients experience a comprehensive decline in physiological functional reserve. When this decline becomes severe, it clinically manifests as frailty, a condition potentially linked to underlying inflammatory processes and immunosenescence [13]. Elderly patients often present with varying degrees of frailty [14], a clinical state characterized by increased vulnerability and significantly diminished capacity to cope with surgical stress. Consequently, frail elderly patients demonstrate substantially higher rates of postoperative complications and adverse outcomes, following both cardiac and non-cardiac surgical procedures [15, 16]. The present study identified preoperative atrial fibrillation as an independent risk factor for PPCs. Although research specifically exploring the relationship between preoperative atrial fibrillation and PPCs remains limited, existing evidence has demonstrated its association with higher in-hospital mortality and increased incidence of postoperative complications, including stroke, renal failure, prolonged ventilation, reoperation, and deep sternal wound infection [17]. Concurrent performance of cardiac surgery with ablation procedures may potentially improve long-term survival in these patients [18, 19]. The potential mechanisms by which preoperative atrial fibrillation increases the risk of PPCs are multifactorial. Atrial fibrillation induces hemodynamic instability, impairs ventricular filling, and exacerbates pulmonary hypertension[20–22]. Simultaneously, it reflects underlying vascular endothelial dysfunction and a pro-inflammatory state [23], predisposing patients to increased capillary leakage. Furthermore, anticoagulation therapy associated with atrial fibrillation elevates the risk of perioperative bleeding and transfusion-related lung injury [24]. ASA physical status also emerged as a strong predictor of PPCs. It represents more than a simple anesthesia risk score; its core significance lies in integrating and summarizing the patient's preoperative comorbidity burden, physiological functional reserve, and overall frailty status [25]. However, the ASA physical status has inherent limitations due to its subjective and non-quantitative nature [25, 26]. Despite these constraints, studies have consistently demonstrated its robust predictive value in clinical practice [27–29]. Underlying lung disease was also identified as a significant predictor of PPCs. Previous studies have consistently shown that underlying lung disease substantially increases the risk of PPCs in both cardiac and non-cardiac surgical settings [9, 30, 31]. Patients with COPD present with persistent airflow limitation, reduced lung elastic recoil, and impaired mucociliary clearance [32], whereas asthmatic patients demonstrate airway hyperresponsiveness and reversible airflow obstruction [33]. These pathophysiological characteristics result in compromised pulmonary function even before surgery. Consequently, when such patients undergo cardiopulmonary bypass, their already vulnerable lung tissue becomes more susceptible to pulmonary complications following the additional insult. Duration of CPB is a well-established independent risk factor for PPCs, as consistently demonstrated in previous research [11, 34, 35]. CPB represents a controlled, non-physiological state of systemic perfusion whose duration directly determines the intensity and extent of physiological insult. Consequently, a clear dose-response relationship exists between CPB duration and the degree of systemic injury. The mechanisms whereby CPB contributes to PPCs primarily involve systemic inflammatory response syndrome and pulmonary ischemia-reperfusion injury. During CPB, blood contact with the non-physiological surfaces of the circuit and oxygenator, ischemia-reperfusion injury, and administration of medications such as heparin and protamine activate the coagulation, fibrinolytic, and complement systems. These processes simultaneously activate leukocytes, platelets, and endothelial cells, promoting the production and release of endogenous inflammatory mediators that ultimately lead to endothelial damage, microthrombus formation, capillary leakage, and organ dysfunction [36, 37]. These pathophysiological insights highlight the importance of minimizing CPB duration, whenever surgically feasible, as a key lung protection strategy. We also identified that the duration of hypotension served as an independent risk factor for pulmonary complications following cardiac surgery. Currently, research on the association between intraoperative hypotension during cardiac surgery and target organ injury has primarily focused on the kidneys and the brain [38, 39]. This is primarily because hypoperfusion and ischemic hypoxia resulting from hypotension exert more pronounced effects on organs with high metabolic demands and poor tolerance to ischemia. Intraoperative hypotension may increase the risk of PPCs through multiple mechanisms. CPB itself can activate the systemic inflammatory response syndrome (SIRS), and hypotension can exacerbate tissue hypoperfusion, which in turn promotes the release of inflammatory mediators and induces or aggravates lung injury. Furthermore, hypotension reduces pulmonary perfusion pressure, potentially leading to pulmonary ischemia. Subsequent reperfusion can then generate oxygen free radicals, damaging the pulmonary capillary endothelium and alveolar epithelium, thereby increasing the risk of acute lung injury [40]. Furthermore, tissue hypoperfusion resulting from intraoperative hypotension may increase the requirement for intraoperative blood transfusion, which in turn may lead to transfusion-related acute lung injury (TRALI) [41]. As a commonly used clinical prediction model, our nomogram exhibits robust predictive performance. It assists clinicians in the early identification of patients at high risk for PPCs, allowing for timely preventive interventions such as optimizing preoperative cardiopulmonary function, creating personalized fluid management plans, adopting earlier weaning protocols, and enhancing postoperative pulmonary rehabilitation and monitoring. Secondly, the model acts as a valuable communication aid, presenting surgical risks intuitively to patients and their families, thereby supporting collaborative decision-making. This study has several limitations. First, the data were derived from a single-center database, resulting in a relatively small study population and a limited set of included variables. Therefore, further investigation using multi-center, large-sample data is required to explore more potential risk factors. Second, although the nomogram was validated internally via bootstrap resampling, the lack of external validation may affect its generalizability and broader applicability. Furthermore, we only included patients with complete medical records. While this ensured data quality for model development, it may have introduced selection bias. A prospective study is warranted in the future to validate these findings. Conclusion Independent risk factors for postoperative pulmonary complications in patients undergoing cardiopulmonary bypass surgery included age, preoperative atrial fibrillation, underlying lung disease, ASA physical status, duration of CPB, and duration of intraoperative hypotension. The nomogram based on these six factors demonstrated favorable predictive performance and clinical utility. Serving as a simple yet effective tool, it facilitates individualized risk assessment and identification of high-risk patients, thereby assisting clinicians in refining clinical decision-making. Abbreviations PPCs Postoperative pulmonary complications CPB Cardiopulmonary bypass ROC Receiver operating characteristic DCA Decision curve analysis COPD Chronic obstructive pulmonary disease AST Aspartate aminotransferase BUN Blood urea nitrogen OR Odds ratios CI Confidence intervals ASA American Society of Anesthesiologists SIRS Systemic inflammatory response syndrome TRALI Transfusion-related acute lung injury EF Ejection Fraction IQR Interquartile Range BMI Body mass index Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Nanjing First Hospital (No.: KY20250922-KS-01). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed 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 None. Authors' contributions Yefei Wu: Conceptualization, Writing - original draft, Investigation. Xiao Xu: Writing - original draft, Investigation. Cibao Liu: Investigation, Formal analysis. Yanni Wang: Investigation, Validation. Yunqian Wang: Investigation, Visualization. Zixuan Li: Investigation, Visualization. Xue Jiang: Investigation, Visualization. Duanqi Zhu: Software. Feier Chen: Software. Wenwen Zhang: Software, Writing - Reviewing and Editing. Xiaoliang Wang: Writing - Reviewing and Editing. Yan Dong: Writing - Reviewing and Editing. Lei Xu: Writing - Reviewing and Editing. Acknowledgements Not applicable. References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Apr, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor assigned by journal 25 Dec, 2025 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 20 Dec, 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. 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-8411708","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586927562,"identity":"398d6efd-cd58-4321-9add-5921cce2118c","order_by":0,"name":"Yefei Wu","email":"","orcid":"","institution":"Nanjing First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yefei","middleName":"","lastName":"Wu","suffix":""},{"id":586927564,"identity":"849fe61c-2d17-4ed6-b7bc-b9fac674d946","order_by":1,"name":"Xiao Xu","email":"","orcid":"","institution":"Nanjing First 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-12-20 11:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8411708/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8411708/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102211776,"identity":"472e46d7-6a48-4d96-90d9-46e4738c4b52","added_by":"auto","created_at":"2026-02-09 12:28:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140432,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariable logistic regression for postoperative pulmonary complications after cardiac surgery with cardiopulmonary bypass.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8411708/v1/687a0bf2bd6999afb608bd8b.png"},{"id":102211668,"identity":"fa0e641b-1948-4a9b-b0dd-99814acfeb87","added_by":"auto","created_at":"2026-02-09 12:28:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172312,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the risk of postoperative pulmonary complications in patients undergoing cardiac surgery with cardiopulmonary bypass.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8411708/v1/94adfc251acc53000685edc8.png"},{"id":102211689,"identity":"6e9d3b54-76f1-4181-9f80-9c56ebcc27f1","added_by":"auto","created_at":"2026-02-09 12:28:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102382,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the nomogram.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8411708/v1/5fe5d9611b41a796e2a9778b.png"},{"id":102211704,"identity":"43a9f940-7977-4ca2-b01a-addc92dc3014","added_by":"auto","created_at":"2026-02-09 12:28:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139317,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot of nomogram.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8411708/v1/18152068e4ba7626f273fad5.png"},{"id":102211691,"identity":"6fb1a6ac-3564-4624-bf87-f13a99a42d75","added_by":"auto","created_at":"2026-02-09 12:28:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":175897,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curve of the nomogram.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8411708/v1/fe3cd6a1400f3a6ef516506b.png"},{"id":102211819,"identity":"84b46ad4-cd8f-4e92-a0f2-1cb649cc55e5","added_by":"auto","created_at":"2026-02-09 12:28:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8411708/v1/eb5b2861-37f4-488b-9bf3-418e94849516.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram for Predicting Postoperative Pulmonary Complications after Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the global incidence of cardiovascular diseases has continued to rise [1]. Cardiac surgery serves as a critical treatment modality for conditions such as valvular heart disease, coronary artery disease, and major vascular diseases, playing an indispensable role in clinical practice [2, 3]. Cardiopulmonary bypass is a fundamental prerequisite and safety assurance for most cardiac surgical procedures. However, it may contribute to various postoperative complications through mechanisms such as systemic inflammatory response activation and ischemia-reperfusion injury [4\u0026ndash;6]. Postoperative pulmonary complications represent one of the most common types of complications. They not only prolong hospital stays and increase medical costs but also severely impair patient recovery and quality of life, and may even be life-threatening [7].\u003c/p\u003e \u003cp\u003ePrevious studies have identified several risk factors, including age, EuroSCORE II, pre-existing chronic obstructive pulmonary disease (COPD), type of surgery, and serum albumin levels [8, 9]. However, findings remain inconsistent, and a readily applicable visual nomogram is still lacking. Therefore, Therefore, we hypothesize that a multivariate prediction model incorporating easily accessible preoperative and intraoperative clinical indicators can reliably assess the individual risk of postoperative pulmonary complications in patients undergoing cardiac surgery with cardiopulmonary bypass. To validate this hypothesis, this study aims to: (1) identify independent risk factors for postoperative pulmonary complications; (2) develop and internally validate a nomogram that can be intuitively applied for bedside risk assessment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e \u003cp\u003eThis retrospective cohort study enrolled patients who underwent cardiac surgery under general anesthesia at Nanjing First Hospital between January 2024 and December 2024. The study protocol was approved by the Ethics Committee of Nanjing First Hospital (No.: KY20250922-KS-01). Owing to the retrospective design of this study, informed consent from participants was exempted.\u003c/p\u003e \u003cp\u003eInclusion and Exclusion Criteria\u003c/p\u003e \u003cp\u003eA total of 502 eligible patients were included in this study. The inclusion criteria comprised adult patients, both male and female, who underwent cardiac surgery. The exclusion criteria were as follows: (1) patients with preoperative pulmonary complications such as pneumonia or atelectasis; (2) pregnant patients; (3) patients who underwent surgery without cardiopulmonary bypass; (4) patients requiring re-sternotomy; (5) patients with missing critical data.\u003c/p\u003e \u003cp\u003eData Collection\u003c/p\u003e \u003cp\u003e Based on literature review, clinical guidelines, and expert experience, the following potential risk factors were collected: (1) Patient demographics and history: sex, age, body mass index (BMI), and history of hypertension, atrial fibrillation, diabetes, cerebral infarction, pre-existing pulmonary disease, or hypothyroidism. (2) Preoperative and intraoperative parameters: emergency surgery, combined cardiac procedures, ASA physical status, preoperative cardiac ejection fraction, preoperative hemoglobin level, preoperative aspartate aminotransferase (AST) level, preoperative blood urea nitrogen (BUN) level, preoperative creatinine level, duration of surgery, duration of anesthesia, duration of CPB, intraoperative fluid load, intraoperative transfusion volume, and duration of intraoperative hypotension.\u003c/p\u003e \u003cp\u003eDefinition of Key Variables\u003c/p\u003e \u003cp\u003eUnderlying pulmonary disease was defined as a chronic pulmonary condition diagnosed before surgery, primarily including COPD, asthma, interstitial lung disease, and giant pulmonary bullae. The diagnosis was confirmed by a documented medical history, pulmonary function test reports, or corresponding imaging evidence.\u003c/p\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003cp\u003eThe primary outcome of this study was the occurrence of PPCs within 7 days after cardiac surgery. PPCs were defined according to the guidelines for standardized endpoints and definitions in perioperative clinical trials issued by the European Joint Task Force. Respiratory infection was defined as the administration of antibiotics for suspected respiratory infection, accompanied by at least one of the following criteria: new or worsened change in sputum characteristics, new or progressive pulmonary infiltrates on imaging, fever (body temperature\u0026thinsp;\u0026gt;\u0026thinsp;38\u0026deg;C), or white blood cell count\u0026thinsp;\u0026gt;\u0026thinsp;12\u0026times;10⁹/L. Respiratory failure was defined as postoperative arterial partial pressure of oxygen (PaO₂)\u0026thinsp;\u0026lt;\u0026thinsp;8 kPa (60 mmHg) while breathing room air, or a ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO₂/FiO₂)\u0026thinsp;\u0026lt;\u0026thinsp;40 kPa (300 mmHg), or arterial oxygen saturation\u0026thinsp;\u0026lt;\u0026thinsp;90% as measured by pulse oximetry requiring oxygen therapy. Pleural effusion was radiologically defined as the presence of one or more of the following findings: blunting of the costophrenic angle, loss of the sharp diaphragmatic contour on the upright view with evidence of displacement of adjacent anatomical structures, or (in the supine position) generalized opacification of a hemithorax with preserved vascular shadows. Atelectasis was defined as pulmonary opacification accompanied by displacement of the mediastinum, hilum, or hemidiaphragm toward the affected region, with compensatory hyperinflation of adjacent non-atelectatic lung areas. Pneumothorax was defined as the presence of air within the pleural space in the absence of vascular markings surrounding the visceral pleura. Bronchospasm was defined as the new onset of expiratory wheezing requiring treatment with bronchodilators. Aspiration pneumonia was defined as acute lung injury resulting from the inhalation of regurgitated gastric contents [10].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS (version 27.0) and R software (version 4.5.1). The normality of continuous variables was assessed using the Shapiro-Wilk test along with graphical evaluation. Normally distributed continuous data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) and compared using the independent samples t-test. Non-normally distributed continuous data were expressed as median (interquartile range) and compared using the Mann-Whitney U test. Categorical data were presented as frequency (percentage) and compared using the chi-square test or Fisher's exact test, as appropriate. Univariate logistic regression analysis was first performed, and variables with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were subsequently entered into a multivariate logistic regression model to identify independent risk factors for PPCs. Adjusted odds ratios (OR) with their 95% confidence intervals (CI) were calculated, and the results of the multivariate analysis were visually presented using a forest plot. Based on the multivariate logistic regression results, a nomogram was constructed using the \"rms\" package in R software. Internal validation of the model was subsequently performed using the bootstrap resampling method with 1,000 replicates. The discriminative ability of the model was evaluated by plotting the ROC curve and calculating the area under the curve. Calibration was assessed by plotting calibration curves and performing the Hosmer-Lemeshow goodness-of-fit test to evaluate the agreement between predicted probabilities and observed outcomes. Decision curve analysis was applied to evaluate the clinical utility of the nomogram. In all analyses, a two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 502 patients were included in this study. Postoperative pulmonary complications occurred in 277 patients, corresponding to an incidence of 55.18%. Baseline characteristics and univariate analysis results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Regarding baseline characteristics, patients who developed PPCs were significantly older, had higher American Society of Anesthesiologists (ASA) physical statuses, and showed a greater prevalence of atrial fibrillation and pre-existing pulmonary disease. Regarding preoperative physiological parameters, patients who developed PPCs demonstrated significantly lower cardiac ejection fraction alongside elevated levels of blood urea nitrogen and creatinine. For surgical and perioperative factors, patients with PPCs were more likely to undergo emergency surgery and combined procedures. Additionally, they experienced significantly longer duration of surgery, anesthesia, and cardiopulmonary bypass, prolonged intraoperative hypotension, and required larger volume blood transfusions. No statistically significant differences were observed in the remaining indicators.\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\u003eUnivariate analysis of variables related to postoperative pulmonary complications.\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=\"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=\"left\" 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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients(N\u0026thinsp;=\u0026thinsp;502)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PPCs(n\u0026thinsp;=\u0026thinsp;225)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPCs(n\u0026thinsp;=\u0026thinsp;277)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(57,71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(53,66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(60,73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331(65.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151(67.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180(65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.17(22.21,26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(22.21,26.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.24(22.22,26.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA physical status, 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 \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443(88.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216(96.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227(81.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(16.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(17.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(9.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(24.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eUnderlying lung diseases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(15.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ehypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307(61.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(56.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180(65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122(24.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(21.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(14.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(18.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac EF, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(52,64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(53,65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(49.5,64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(17,26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(17,24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(17,27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133(123,144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135(126,145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132(121,142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.41(5.39,8.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.06(5.135,7.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.74(5.585,8.615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCreatinine, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.90(61.58,86.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.8(60.45,83.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(62.5,91.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency surgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(14.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCombined surgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204(40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(26.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144(52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDuration of CPB, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(91,152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(78,111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(109.5,171.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDuration of surgery, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275(230,330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240(210,280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310(270,365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDuration of anesthesia, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310(265,365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275(242.5,315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350(300,400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eFluid load, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000(2000,2500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000(2000,2500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2000(2000,2500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume of blood transfusion, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1300(950,1800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1150(800,1537.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1500(1050,2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDuration of hypotension, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(100,185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110(85,145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165(120,225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eAfter adjustment for all potential confounding factors, multivariate logistic regression analysis identified the following as independent risk factors for PPCs, as visually summarized in the forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): age (OR\u0026thinsp;=\u0026thinsp;1.117, 95% CI: 1.083\u0026ndash;1.152, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), preoperative atrial fibrillation (OR\u0026thinsp;=\u0026thinsp;3.881, 95% CI: 1.846\u0026ndash;8.159, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underlying lung diseases (OR\u0026thinsp;=\u0026thinsp;5.524, 95% CI: 2.218\u0026ndash;13.76, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ASA physical status (OR\u0026thinsp;=\u0026thinsp;5.131, 95% CI: 1.601\u0026ndash;16.441, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), duration of CPB (OR\u0026thinsp;=\u0026thinsp;1.021, 95% CI: 1.011\u0026ndash;1.032, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and duration of intraoperative hypotension (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI: 1.001\u0026ndash;1.011, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the six independent risk factors identified, we developed a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to predict the probability of PPCs in patients undergoing cardiac surgery with cardiopulmonary bypass. Each predictor is assigned a score on a points scale according to its value. The total points, obtained by summing the individual scores of all factors, can then be used to estimate the probability of PPCs. The predictive performance of the nomogram was subsequently evaluated. In the original cohort, the nomogram demonstrated an AUC of 0.886 (95% CI: 0.857\u0026ndash;0.915) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Following internal validation with 1000 bootstrap replicates, the model exhibited a corrected C-statistic of 0.879. These results indicate that the model possesses good and well-calibrated discriminatory ability. The calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) closely aligned with the ideal diagonal line, indicating excellent agreement between the predicted probabilities by the nomogram and the actual observed probabilities. The Hosmer-Lemeshow test (χ\u0026sup2; = 11.86, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.157) further confirmed good model calibration. The decision curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated that the nomogram provided higher net clinical benefit compared with both the \"no intervention\" and \"intervention for all\" strategies across a wide range of threshold probabilities. Therefore, the decision curve analysis confirms the clinical utility of this predictive model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the incidence of PPCs reached 55.18%, which is consistent with recent reports on pulmonary complications following cardiac surgery [8]. Multivariable logistic regression analysis identified six independent risk factors for pulmonary complications following cardiac surgery with cardiopulmonary bypass: age, preoperative atrial fibrillation, ASA physical status, underlying lung diseases, duration of CPB, and duration of intraoperative hypotension. Based on these findings, we developed a nomogram to predict the risk of PPCs. The model demonstrated excellent predictive performance and possesses significant clinical applicability.\u003c/p\u003e \u003cp\u003eThe present study demonstrated a significantly increased risk of PPCs in elderly patients, which aligns with findings from multiple previous studies. For instance, Fischer et al. identified age, Euroscore II, chronic obstructive pulmonary disease, preoxygenation method, intraoperative positive end-expiratory pressure, absence of ventilation during cardiopulmonary bypass, lack of lung recruitment maneuvers, and neuromuscular blockade as factors associated with the occurrence of PPCs [9]. Da-Shuai Wang et al. identified ten predictors for postoperative pneumonia following heart valve surgery, among which advanced age was a significant factor [11]. Furthermore, age has also been incorporated as a key predictor in a nomogram developed to assess the risk of pulmonary complications following cardiac and major vascular surgery [12]. With advancing age, patients experience a comprehensive decline in physiological functional reserve. When this decline becomes severe, it clinically manifests as frailty, a condition potentially linked to underlying inflammatory processes and immunosenescence [13]. Elderly patients often present with varying degrees of frailty [14], a clinical state characterized by increased vulnerability and significantly diminished capacity to cope with surgical stress. Consequently, frail elderly patients demonstrate substantially higher rates of postoperative complications and adverse outcomes, following both cardiac and non-cardiac surgical procedures [15, 16].\u003c/p\u003e \u003cp\u003eThe present study identified preoperative atrial fibrillation as an independent risk factor for PPCs. Although research specifically exploring the relationship between preoperative atrial fibrillation and PPCs remains limited, existing evidence has demonstrated its association with higher in-hospital mortality and increased incidence of postoperative complications, including stroke, renal failure, prolonged ventilation, reoperation, and deep sternal wound infection [17]. Concurrent performance of cardiac surgery with ablation procedures may potentially improve long-term survival in these patients [18, 19]. The potential mechanisms by which preoperative atrial fibrillation increases the risk of PPCs are multifactorial. Atrial fibrillation induces hemodynamic instability, impairs ventricular filling, and exacerbates pulmonary hypertension[20\u0026ndash;22]. Simultaneously, it reflects underlying vascular endothelial dysfunction and a pro-inflammatory state [23], predisposing patients to increased capillary leakage. Furthermore, anticoagulation therapy associated with atrial fibrillation elevates the risk of perioperative bleeding and transfusion-related lung injury [24].\u003c/p\u003e \u003cp\u003eASA physical status also emerged as a strong predictor of PPCs. It represents more than a simple anesthesia risk score; its core significance lies in integrating and summarizing the patient's preoperative comorbidity burden, physiological functional reserve, and overall frailty status [25]. However, the ASA physical status has inherent limitations due to its subjective and non-quantitative nature [25, 26]. Despite these constraints, studies have consistently demonstrated its robust predictive value in clinical practice [27\u0026ndash;29].\u003c/p\u003e \u003cp\u003eUnderlying lung disease was also identified as a significant predictor of PPCs. Previous studies have consistently shown that underlying lung disease substantially increases the risk of PPCs in both cardiac and non-cardiac surgical settings [9, 30, 31]. Patients with COPD present with persistent airflow limitation, reduced lung elastic recoil, and impaired mucociliary clearance [32], whereas asthmatic patients demonstrate airway hyperresponsiveness and reversible airflow obstruction [33]. These pathophysiological characteristics result in compromised pulmonary function even before surgery. Consequently, when such patients undergo cardiopulmonary bypass, their already vulnerable lung tissue becomes more susceptible to pulmonary complications following the additional insult.\u003c/p\u003e \u003cp\u003eDuration of CPB is a well-established independent risk factor for PPCs, as consistently demonstrated in previous research [11, 34, 35]. CPB represents a controlled, non-physiological state of systemic perfusion whose duration directly determines the intensity and extent of physiological insult. Consequently, a clear dose-response relationship exists between CPB duration and the degree of systemic injury. The mechanisms whereby CPB contributes to PPCs primarily involve systemic inflammatory response syndrome and pulmonary ischemia-reperfusion injury. During CPB, blood contact with the non-physiological surfaces of the circuit and oxygenator, ischemia-reperfusion injury, and administration of medications such as heparin and protamine activate the coagulation, fibrinolytic, and complement systems. These processes simultaneously activate leukocytes, platelets, and endothelial cells, promoting the production and release of endogenous inflammatory mediators that ultimately lead to endothelial damage, microthrombus formation, capillary leakage, and organ dysfunction [36, 37]. These pathophysiological insights highlight the importance of minimizing CPB duration, whenever surgically feasible, as a key lung protection strategy.\u003c/p\u003e \u003cp\u003eWe also identified that the duration of hypotension served as an independent risk factor for pulmonary complications following cardiac surgery. Currently, research on the association between intraoperative hypotension during cardiac surgery and target organ injury has primarily focused on the kidneys and the brain [38, 39]. This is primarily because hypoperfusion and ischemic hypoxia resulting from hypotension exert more pronounced effects on organs with high metabolic demands and poor tolerance to ischemia. Intraoperative hypotension may increase the risk of PPCs through multiple mechanisms. CPB itself can activate the systemic inflammatory response syndrome (SIRS), and hypotension can exacerbate tissue hypoperfusion, which in turn promotes the release of inflammatory mediators and induces or aggravates lung injury. Furthermore, hypotension reduces pulmonary perfusion pressure, potentially leading to pulmonary ischemia. Subsequent reperfusion can then generate oxygen free radicals, damaging the pulmonary capillary endothelium and alveolar epithelium, thereby increasing the risk of acute lung injury [40]. Furthermore, tissue hypoperfusion resulting from intraoperative hypotension may increase the requirement for intraoperative blood transfusion, which in turn may lead to transfusion-related acute lung injury (TRALI) [41].\u003c/p\u003e \u003cp\u003eAs a commonly used clinical prediction model, our nomogram exhibits robust predictive performance. It assists clinicians in the early identification of patients at high risk for PPCs, allowing for timely preventive interventions such as optimizing preoperative cardiopulmonary function, creating personalized fluid management plans, adopting earlier weaning protocols, and enhancing postoperative pulmonary rehabilitation and monitoring. Secondly, the model acts as a valuable communication aid, presenting surgical risks intuitively to patients and their families, thereby supporting collaborative decision-making.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the data were derived from a single-center database, resulting in a relatively small study population and a limited set of included variables. Therefore, further investigation using multi-center, large-sample data is required to explore more potential risk factors. Second, although the nomogram was validated internally via bootstrap resampling, the lack of external validation may affect its generalizability and broader applicability. Furthermore, we only included patients with complete medical records. While this ensured data quality for model development, it may have introduced selection bias. A prospective study is warranted in the future to validate these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIndependent risk factors for postoperative pulmonary complications in patients undergoing cardiopulmonary bypass surgery included age, preoperative atrial fibrillation, underlying lung disease, ASA physical status, duration of CPB, and duration of intraoperative hypotension. The nomogram based on these six factors demonstrated favorable predictive performance and clinical utility. Serving as a simple yet effective tool, it facilitates individualized risk assessment and identification of high-risk patients, thereby assisting clinicians in refining clinical decision-making.\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\"\u003eCPB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiopulmonary bypass\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratios\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 intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Society of Anesthesiologists\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystemic inflammatory response syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRALI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransfusion-related acute lung injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEjection Fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Nanjing First Hospital (No.: KY20250922-KS-01).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eYefei Wu: Conceptualization, Writing - original draft, Investigation. Xiao Xu: Writing - original draft, Investigation. Cibao Liu: Investigation, Formal analysis. Yanni Wang: Investigation, Validation. Yunqian Wang: Investigation, Visualization. Zixuan Li: Investigation, Visualization. Xue Jiang: Investigation, Visualization. Duanqi Zhu: Software. Feier Chen: Software. Wenwen Zhang: Software, Writing - Reviewing and Editing. Xiaoliang Wang: Writing - Reviewing and Editing. Yan Dong: Writing - Reviewing and Editing. Lei Xu: Writing - Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982-3021.\u003c/li\u003e\n\u003cli\u003eBoskovski MT, Gleason TG. Current Therapeutic Options in Aortic Stenosis. Circ Res. 2021;128(9):1398-417.\u003c/li\u003e\n\u003cli\u003eDoenst T, Thiele H, Haasenritter J, Wahlers T, Massberg S, Haverich A. The Treatment of Coronary Artery Disease. Dtsch Arztebl Int. 2022;119(42):716-23.\u003c/li\u003e\n\u003cli\u003eMassoth C, Zarbock A, Meersch M. Acute Kidney Injury in Cardiac Surgery. Crit Care Clin. 2021;37(2):267-78.\u003c/li\u003e\n\u003cli\u003eMcDonagh DL, Berger M, Mathew JP, Graffagnino C, Milano CA, Newman MF. Neurological complications of cardiac surgery. Lancet Neurol. 2014;13(5):490-502.\u003c/li\u003e\n\u003cli\u003eTanner TG, Colvin MO. Pulmonary Complications of Cardiac Surgery. Lung. 2020;198(6):889-96.\u003c/li\u003e\n\u003cli\u003eHadaya J, Verma A, Marzban M, Sanaiha Y, Shemin RJ, Benharash P. Impact of Pulmonary Complications on Outcomes and Resource Use After Elective Cardiac Surgery. Ann Surg. 2023;278(3):e661-e6.\u003c/li\u003e\n\u003cli\u003eSetlers K, Jurcenko A, Arklina B, Zvaigzne L, Sabelnikovs O, Stradins P, et al. 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Permanent pacemaker implantation after valve and arrhythmia surgery in patients with preoperative atrial fibrillation. Heart Rhythm. 2022;19(9):1442-9.\u003c/li\u003e\n\u003cli\u003eMercurio V, Peloquin G, Bourji KI, Diab N, Sato T, Enobun B, et al. Pulmonary arterial hypertension and atrial arrhythmias: incidence, risk factors, and clinical impact. Pulm Circ. 2018;8(2):2045894018769874.\u003c/li\u003e\n\u003cli\u003eNaveed A, Azam H, Murtaza HG, Ahmad RA, Baig MAR. Incidence and risk factors of Pulmonary Complications after Cardiopulmonary bypass. Pak J Med Sci. 2017;33(4):993-6.\u003c/li\u003e\n\u003cli\u003eSultan D, Brundel B, Kurakula K. The Interplay Between Pulmonary Hypertension and Atrial Fibrillation: A Comprehensive Overview. Cells. 2025;14(11).\u003c/li\u003e\n\u003cli\u003eGuo Y, Lip GY, Apostolakis S. Inflammation in atrial fibrillation. J Am Coll Cardiol. 2012;60(22):2263-70.\u003c/li\u003e\n\u003cli\u003eGettel C, Goldberg EM. Is the Risk of Bleeding Among Older Adults With Atrial Fibrillation Lower With Antiplatelet Compared With Oral Anticoagulants? Ann Emerg Med. 2018;72(5):547-9.\u003c/li\u003e\n\u003cli\u003eHorvath B, Kloesel B, Todd MM, Cole DJ, Prielipp RC. The Evolution, Current Value, and Future of the American Society of Anesthesiologists Physical Status Classification System. Anesthesiology. 2021;135(5):904-19.\u003c/li\u003e\n\u003cli\u003eMayhew D, Mendonca V, Murthy BVS. A review of ASA physical status - historical perspectives and modern developments. Anaesthesia. 2019;74(3):373-9.\u003c/li\u003e\n\u003cli\u003eLi G, Walco JP, Mueller DA, Wanderer JP, Freundlich RE. Reliability of the ASA Physical Status Classification System in Predicting Surgical Morbidity: a Retrospective Analysis. J Med Syst. 2021;45(9):83.\u003c/li\u003e\n\u003cli\u003eHackett NJ, De Oliveira GS, Jain UK, Kim JY. ASA class is a reliable independent predictor of medical complications and mortality following surgery. Int J Surg. 2015;18:184-90.\u003c/li\u003e\n\u003cli\u003eSankar A, Johnson SR, Beattie WS, Tait G, Wijeysundera DN. Reliability of the American Society of Anesthesiologists physical status scale in clinical practice. Br J Anaesth. 2014;113(3):424-32.\u003c/li\u003e\n\u003cli\u003eNumata T, Nakayama K, Fujii S, Yumino Y, Saito N, Yoshida M, et al. Risk factors of postoperative pulmonary complications in patients with asthma and COPD. BMC Pulm Med. 2018;18(1):4.\u003c/li\u003e\n\u003cli\u003eZainab A, Nguyen DT, Graviss EA, Fatima S, Masud FN, MacGillivray TE. Development and Validation of a Risk Score for Respiratory Failure After Cardiac Surgery. Ann Thorac Surg. 2022;113(2):577-84.\u003c/li\u003e\n\u003cli\u003eFerrera MC, Labaki WW, Han MK. Advances in Chronic Obstructive Pulmonary Disease. Annu Rev Med. 2021;72:119-34.\u003c/li\u003e\n\u003cli\u003eVarricchi G, Ferri S, Pepys J, Poto R, Spadaro G, Nappi E, et al. Biologics and airway remodeling in severe asthma. Allergy. 2022;77(12):3538-52.\u003c/li\u003e\n\u003cli\u003eAksoy R, Karakoc AZ, Cevirme D, Elibol A, Yigit F, Yilmaz \u0026Uuml;, et al. Predictive Factors of Prolonged Ventilation Following Cardiac Surgery with Cardiopulmonary Bypass. Braz J Cardiovasc Surg. 2021;36(6):780-7.\u003c/li\u003e\n\u003cli\u003eJi Q, Mei Y, Wang X, Feng J, Cai J, Ding W. Risk factors for pulmonary complications following cardiac surgery with cardiopulmonary bypass. Int J Med Sci. 2013;10(11):1578-83.\u003c/li\u003e\n\u003cli\u003eDay JR, Taylor KM. The systemic inflammatory response syndrome and cardiopulmonary bypass. Int J Surg. 2005;3(2):129-40.\u003c/li\u003e\n\u003cli\u003eLi X, Zhan F, Qiu G, Lu P, Shen Z, Qi Y, et al. MOTS-c attenuates lung ischemia-reperfusion injury via MYH9-Dependent nuclear translocation and transcriptional activation of antioxidant genes. Redox Biol. 2025;84:103681.\u003c/li\u003e\n\u003cli\u003ede la Hoz MA, Rangasamy V, Bastos AB, Xu X, Novack V, Saugel B, et al. Intraoperative Hypotension and Acute Kidney Injury, Stroke, and Mortality during and outside Cardiopulmonary Bypass: A Retrospective Observational Cohort Study. Anesthesiology. 2022;136(6):927-39.\u003c/li\u003e\n\u003cli\u003eSun LY, Chung AM, Farkouh ME, van Diepen S, Weinberger J, Bourke M, et al. Defining an Intraoperative Hypotension Threshold in Association with Stroke in Cardiac Surgery. Anesthesiology. 2018;129(3):440-7.\u003c/li\u003e\n\u003cli\u003eShen Z, Lu P, Jin W, Wen Z, Qi Y, Li X, et al. MOTS-c Promotes Glycolysis via AMPK-HIF-1\u0026alpha;-PFKFB3 Pathway to Ameliorate Cardiopulmonary Bypass-induced Lung Injury. Am J Respir Cell Mol Biol. 2025;73(3):353-68.\u003c/li\u003e\n\u003cli\u003eRaphael J, Chae A, Feng X, Shotwell MS, Mazzeffi MA, Bollen BA, et al. Red Blood Cell Transfusion and Pulmonary Complications: The Society of Thoracic Surgeons Adult Cardiac Surgery Database Analysis. Ann Thorac Surg. 2024;117(4):839-46.\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":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiac surgical procedures, Extracorporeal circulation, Postoperative pulmonary complications, Risk factors, Nomograms","lastPublishedDoi":"10.21203/rs.3.rs-8411708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8411708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePostoperative pulmonary complications (PPCs) are among the most common complications following cardiac surgery with cardiopulmonary bypass (CPB) and can adversely affect patient outcomes. This study aimed to identify risk factors for PPCs following cardiac surgery with CPB and to develop and validate a nomogram for individualized risk prediction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis retrospective cohort study enrolled patients undergoing cardiac surgery with cardiopulmonary bypass. Participants were grouped based on occurrence of PPCs. Univariate analysis identified candidate predictors, and multivariable logistic regression determined independent risk factors. A nomogram was developed and internally validated using bootstrap resampling. Model performance was assessed via receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 502 patients were included, of whom 277 (55.18%) developed PPCs. Multivariable logistic regression analysis identified the following independent risk factors for PPCs: age (OR\u0026thinsp;=\u0026thinsp;1.117, 95% CI: 1.083\u0026ndash;1.152, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), preoperative atrial fibrillation (OR\u0026thinsp;=\u0026thinsp;3.881, 95% CI: 1.846\u0026ndash;8.159, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underlying lung diseases (OR\u0026thinsp;=\u0026thinsp;5.524, 95% CI: 2.218\u0026ndash;13.76, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ASA physical status (OR\u0026thinsp;=\u0026thinsp;5.131, 95% CI: 1.601\u0026ndash;16.441, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), duration of CPB (OR\u0026thinsp;=\u0026thinsp;1.021, 95% CI: 1.011\u0026ndash;1.032, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and duration of intraoperative hypotension (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI: 1.001\u0026ndash;1.011, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). A nomogram model constructed based on these factors demonstrated excellent discriminative ability upon internal validation (adjusted area under the curve (AUC)\u0026thinsp;=\u0026thinsp;0.879). Calibration curves and decision curve analysis confirmed its good calibration and clinical utility, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study identified six independent predictors for PPCs and developed an individualized prediction nomogram. This tool may assist clinicians in identifying high-risk patients, thereby optimizing perioperative management strategies to prevent and reduce the incidence of PPCs.\u003c/p\u003e","manuscriptTitle":"A Nomogram for Predicting Postoperative Pulmonary Complications after Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:25:34","doi":"10.21203/rs.3.rs-8411708/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"96476116289391128129238212355517431325","date":"2026-04-17T16:34:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T01:51:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99509250346769704958233778488691212908","date":"2026-02-09T23:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T10:56:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T03:45:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-26T03:45:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2025-12-20T10:53:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"accbef30-5a6b-4fd5-a42c-8790a7b6609e","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T12:25:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:25:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8411708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8411708","identity":"rs-8411708","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}