Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy

Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy | 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 Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy TanTan Fang, ChuanFei Liu, XiaoPing Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8127609/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in BMC Anesthesiology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Delayed extubation after anesthesia can lead to adverse clinical outcomes. An investigation was undertaken to identify the risk factors contributing to a delay extubation of patients undergoing robot-assisted radical prostatectomy and to develop a visualized nomogram prediction model for clinical use. Methods A total of 624 patients were included and divided into training group, validation group, and external validation group. The training group was utilized to develop a nomogram, whereas the validation group and external validation group was used to assess its performance. LASSO regression was employed to refine variables and choose predictors, and a nomogram was constructed using multivariate logistic regression. The performance of the model was internally validated using calibration and receiver operating characteristic curves. Additionally, decision curve analysis and clinical impact curves were used to assess the clinical utility of the model. Results Patients between January 2022 and April 2024 were included and divided into training group (n = 389), validation group (n = 98), and external validation group (n = 137). Logistic regression identified cerebral infarction, pulmonary disease, coronary heart disease, age, and intraoperative hypotension as independent predictors of delayed extubation. A nomogram constructed based on these factors demonstrated excellent predictive performance, with area under the curve values of 0.763 (95% CI: 0.717–0.810) in the train group, 0.811 (95% CI: 0.726–0.897) in the validation group, and 0.769 (95% CI: 0.689–0.848) in the external validation group. Across all three group, the model demonstrated a good fit, as indicated by a non-significant Hosmer-Lemeshow test statistic, and the calibration curves indicated a strong alignment between the predictions and actual observations. Furthermore, decision curve analysis and clinical impact curve demonstrated the clinical efficiency and benefits of the prediction model. Conclusion This study identified key risk factors for delayed extubation and established an effective predictive nomogram with high discriminative ability and clinical applicability for predictive the risk of extubation delay in patients undergoing robot-assisted radical prostatectomy. Trial registration The Medical Ethics Committee of Nanjing Drum Tower Hospital granted ethical approval for this research(grant number: 2024-742-01) Anesthesia Nomogram Robotic-assisted radical prostatectomy Prostate cancer Airway extubation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Tracheal extubation at the conclusion of surgery is a crucial step in recovery from general anesthesia. Delayed extubation after surgery can increase the risk of respiratory complications, including laryngeal injury, dysphagia, pneumonia, and glottic stenosis[ 1 – 4 ]. Previous research has substantiated that early removal of tracheal catheters in patients undergoing esophagectomy or cardiac surgery can reduce lung complications and promote hemodynamic stability[ 5 , 6 ]. Moreover, delayed extubation imposes a heavier workload on medical staff and reduces the turnover rate in the operating room [ 7 ]. Therefore, identifying the risk factors associated with delayed extubation and establishing a clinical prediction model are vital. The timely recognition of high-risk individuals and subsequent administration of tailored interventions can effectively reduce the incidence of delayed extubation and prevent perioperative complications. Prostate cancer significantly affects the male population globally, and is the second most prevalent cancer in men [ 8 , 9 ]. It accounts for 7% of newly diagnosed cancer cases in men worldwide and increases to 15% in developed regions. Furthermore, the annual number of newly diagnosed cases exceeds 1.2 million, with over 350,000 deaths attributable to prostate cancer annually [ 10 , 11 ]. These findings suggest that prostate cancer stands as a major contributor to cancer-specific deaths in men. The treatment strategy with the highest efficacy for prostate cancer is currently radical resection, which is considered a gold-standard approach. Robot-assisted radical resection of prostate cancer is an emerging minimally invasive surgical technique that can effectively reduce operation time, surgical trauma, intraoperative blood loss, and postoperative complications compared with traditional laparoscopic radical resection; therefore, it is widely used in clinics [ 12 , 13 ]. There is a strong correlation between advancing age and the incidence of prostate cancer, as evidenced by the fact that over 85% of new diagnoses are concentrated in individuals over 60 years of age [ 14 ]. A positive correlation has been observed between advanced patient age and an increased incidence of postoperative extubation delay. [ 15 ]. However, to date, no study has investigated the predictive factors for delayed extubation after robot-assisted radical resection of prostate cancer. Therefore, a retrospective analysis was conducted on the clinical data of patients who underwent robot-assisted radical prostatectomy, with the objectives of identifying risk factors for delayed extubation, developing a predictive nomogram, and internally validating the model to aid in clinical prognostication. Materials and methods Study design and patients This study is a single-center retrospective study, which was approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital (grant number: 2024-742-01). We retrospectively collected data from 750 patients who underwent robot-assisted radical resection for prostate cancer between January 2022 and April 2024. After exclusion, 624 patients were included in the study to investigate the factors associated with delayed extubation. Among them, 4according to a predefined 4:1 ratio, the cohort of 487 patients (January 2022 to October 2023) was randomly divided into training and validation groups and 112 patients from November 2023 to April 2024 were divided into the external validation group. The inclusion criteria for this study were as follows: 1) No severe hearing or visual impairment prior to surgery. 2) No dementia, coma, or disturbance of consciousness prior to surgery. 3) The patient was diagnosed with prostate cancer and underwent robot-assisted radical resection of prostate cancer. 4) Patients were extubated in the operating room. The exclusion criteria were as follows: 1) Incomplete clinical data. 2) Patients who died within 24 h after surgery.The study flow diagram is shown in Fig. 1 . Delayed extubation Extubation time was defined as the interval from the cessation of anesthetic administration to removal of the tracheal tube. The timing of extubation was comprehensively evaluated by a registered anesthesiologist and Post-anesthetic care unit nurses considering the patient's spontaneous breathing efforts, neurological status, motor function, and other relevant clinical indicators for extubation readiness[ 16 ]. Ultimately, the determination regarding tracheal catheter management (removal or retention) was made by a registered anesthesiologist Previous studies [ 17 , 18 ] have shown that extubation time exceeding 1 h can lead to increased clinical complications. Therefore, the patients were divided into two groups: the conventional (extubation time ≤ 1 h) and delayed (extubation time > 1 h) groups. Data collection We collected patient data after screening for the inclusion and exclusion criteria. Preoperative examination data and basic patient information included preoperative white blood cell count, neutrophil percentage, glucose, creatinine, serum potassium, serum calcium, and blood pressure; ASA grade; age; body mass index; smoking status; anemia (preoperative Hb < 120 g/L); hypoproteinemia (total serum protein < 60 g/L or albumin < 35 g/L); alanine transaminase levels; and presence of hypertension, diabetes, cerebral infarction, pulmonary disease, and coronary heart disease. Intraoperative data included the volume of crystalloid and colloid infusions, intraoperative blood transfusion, urine volume, intraoperative blood loss, intraoperative glucose levels, intraoperative serum potassium levels, and MAP. Statistical analysis Continuous variables were summarized according to their distribution: normally distributed data as mean ± standard deviation and compared using the independent samples t-test, while non-normally distributed data as median with interquartile range and compared using the Mann-Whitney U test. Categorical variables were expressed as percentages (%) and compared with the chi-square test or Fisher’s exact test, as appropriate[ 19 ]. All variables were subjected to a LASSO regression analysis for variable selection and risk factor identification. Variables selected by LASSO were incorporated into a multivariable logistic regression model, and a nomogram was developed based on the results. We assessed the model's discriminatory power using ROC analysis, with performance quantified by AUC. Internal validation of the model was performed employing the bootstrapping technique with 500 replicates. Simultaneously, the Hosmer-Lemeshow test was utilized to evaluate the model's goodness-of-fit.The clinical utility of the model was analyzed using decision curve DCA and CIC. The data analysis was carried out in R (version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria). P-value below 0.05 was considered to indicate statistical significance. Results Demographics of the training group The study site was a tertiary hospital in China. A total of 750 patients undergoing robot-assisted radical prostatectomy between January 2022 and April 2024 were retrospectively collected. Because this was an observational study, 126 patients with incomplete clinical data were excluded from the group. After exclusion, 624 patients were included in the study, among them 487 patients from January 2022 to October 2023 were subsequently randomized into training and validation group at a 4:1 ratio using the random sampling function in R software. Of the training group, 204 (52.6%) experienced delayed extubation after surgery. Table 1 summarizes the characteristics of the patients who participated in the study, comparing those who experienced delayed extubation with those who did not. Patients with delayed extubation were older than those without delayed extubation (P < 0.001) and had a higher incidence of anemia (P = 0.022), cerebral infarction (P = 0.003), pulmonary disease (P < 0.001), and coronary heart disease (P < 0.001). Simultaneously, in comparison to patients without delayed extubation, the incidence of intraoperative hypotension was significantly higher among patients with delayed extubation (P = 0.004). Table 1 — Patients’ characteristics in the training group Variable Total(n = 389) Delayed extubation p No(n = 184) Yes(n = 205) ASA, n (%) 0.23 2 11 (3%) 8 (4%) 3 (1%) 3 361 (93%) 168 (91%) 193 (94%) 4 17 (4%) 8 (4%) 9 (4%) preoperative white blood cell count (×10 9 /L) Median (Q1,Q3) 5.6 (4.6, 6.5) 5.7 (4.77, 6.8) 5.5 (4.5, 6.4) 0.089 preoperative neutrophil percentage (%), Mean ± SD 59.6 (54.5, 65) 59.8 (53.8, 64.82) 59.4 (55.5, 65.1) 0.774 anemia, n (%) 0.022 No 343 (88%) 170 (92%) 173 (84%) Yes 46 (12%) 14 (8%) 32 (16%) hypoproteinemia, n (%) 0.581 No 331 (85%) 159 (86%) 172 (84%) Yes 58 (15%) 25 (14%) 33 (16%) Preoperative glucose (mmol/L), Median (Q1,Q3) 4.78 (4.41, 5.39) 4.74 (4.45, 5.32) 4.8 (4.38, 5.56) 0.945 preoperative creatinine (µmol/L), Median (Q1,Q3) 70 (62, 78) 69 (62, 76) 71 (62.1, 81) 0.159 Preoperative serum potassium (mmol/L), Median (Q1,Q3) 3.92 (3.71, 4.15) 3.92 (3.71, 4.14) 3.94 (3.71, 4.16) 0.677 Preoperative serum calcium (mmol/L), Median (Q1,Q3) 2.34 (2.26, 2.42) 2.35 (2.29, 2.42) 2.33 (2.25, 2.43) 0.129 alanine transaminase (U/L), Median (Q1,Q3) 17.5 (13, 23.8) 18.1 (13, 24) 16.8 (13, 23.7) 0.714 hypertension, n (%) 0.034 No 157 (40%) 85 (46%) 72 (35%) Yes 232 (60%) 99 (54%) 133 (65%) diabetes, n (%) 0.244 No 315 (81%) 154 (84%) 161 (79%) Yes 74 (19%) 30 (16%) 44 (21%) cerebral infarction, n (%) 0.003 No 315 (81%) 161 (88%) 154 (75%) Yes 74 (19%) 23 (12%) 51 (25%) pulmonary disease, n (%) < 0.001 No 226 (58%) 133 (72%) 93 (45%) Yes 163 (42%) 51 (28%) 112 (55%) coronary heart disease, n (%) < 0.001 No 345 (89%) 177 (96%) 168 (82%) Yes 44 (11%) 7 (4%) 37 (18%) age, Median (Q1,Q3) 71 (66, 76) 69 (63.75, 74) 73 (68, 78) < 0.001 BMI (Kg/m 2 ), Median (Q1,Q3) 24.02 (22.49, 25.71) 24.3 (22.52, 25.95) 23.67 (22.49, 25.34) 0.356 smoking, n (%) 1 No 274 (70%) 130 (71%) 144 (70%) Yes 115 (30%) 54 (29%) 61 (30%) crystalloid infusion volume (ml), Median (Q1,Q3) 1000 (1000, 1500) 1000 (1000, 1300) 1000 (1000, 1500) 0.337 colloids infusion volume (ml), Median (Q1,Q3) 1000 (500, 1000) 500 (500, 1000) 1000 (500, 1000) 0.051 Intraoperative infusion volume (ml), Median (Q1,Q3) 2000 (1500, 2000) 2000 (1500, 2000) 2000 (1500, 2000) 0.633 intraoperative blood transfusion (ml), n (%) 0.063 No 384 (99%) 184 (100%) 200 (98%) Yes 5 (1%) 0 (0%) 5 (2%) urine volume (ml), Median (Q1,Q3) 400 (300, 700) 400 (300, 600) 400 (250, 800) 0.74 intraoperative blood loss (ml), Median (Q1,Q3) 100 (100, 100) 100 (100, 100) 100 (100, 100) 0.855 intraoperative glucose (mmol/L), Median (Q1,Q3) 5.9 (5.23, 6.73) 6 (5.2, 6.82) 5.75 (5.23, 6.7) 0.473 intraoperative serum potassium (mmol/L), Median (Q1,Q3) 3.57 (3.37, 3.8) 3.6 (3.35, 3.8) 3.55 (3.4, 3.8) 0.791 intraoperative hypotension, n (%) 0.004 MAP > 80% of baseline 103 (26%) 57 (31%) 46 (22%) MAP 70%-80༅ of baseline 139 (36%) 70 (38%) 69 (34%) MAP < 70% of baseline 113 (29%) 50 (27%) 63 (31%) MAP < 65mmHg 34 (9%) 7 (4%) 27 (13%) Model development This study utilized the LASSO regression model to analyze 27 variables obtained from patients, and 5 variables were identified as significant predictors due to non-zero coefficients. These selected variables were age, cerebral infarction, pulmonary disease, coronary heart disease and intraoperative hypotension (Fig. 2 ). The five variables selected from the LASSO regression analysis were used to performed a multivariate logistic regression for build a predictive model for delayed extubation. The results from this model identified that age (OR: 1.064, 95% CI: 1.031–1.098, P < 0.001), cerebral infarction (OR: 1.875, 95% CI: 1.035–3.399, P < 0.038), pulmonary disease (OR: 3.213, 95% CI: 2.027–5.092, P < 0.001), coronary heart disease (OR: 4.878, 95% CI: 2.029–11.728, P < 0.001), MAP 70%–80% of baseline (OR: 1.219, 95% CI: 0.689–2.154, P = 0.496), MAP < 70% of baseline (OR: 1.576, 95% CI: 0.867–2.863, P = 0.135), and MAP < 65 mmHg (OR: 3.802, 95% CI: 1.356–10.657, P = 0.011) were independent predictive variables of delayed extubation (Table 2 ). Based on the multivariate logistic regression, a clinical prediction nomogram was developed (Fig. 3 A). Each patient received a points assignment for the presence of each risk factor, with the aggregate score then being transformed into an estimated probability of delayed extubation. The probability of delayed extubation for a randomly selected patient was 83.9%, and the patient actually encountered extubation delay clinically, suggesting that the clinical applicability of the scoring system is favorable (Fig. 3 B) . Table 2 Multivariate analysis of the predictive factors in the training group. Variable B OR(95%CI) P cerebral infarction 0.629 1.875(1.035–3.399) 0.038 pulmonary disease 1.167 3.213(2.027–5.092) <0.001 coronary heart disease 1.585 4.878(2.029–11.728) <0.001 age 0.062 1.064(1.031–1.098) <0.001 intraoperative hypotension MAP 70%-80༅ of baseline 0.198 1.219(0.689–2.154) 0.496 MAP < 70% of baseline 0.455 1.576(0.867–2.863) 0.135 MAP < 65mmHg 1.336 3.802(1.356–10.657) 0.011 Evaluation and validation of the nomogram model To evaluate the predictive performance and clinical utility of the model, ROC curve analysis was employed. AUC was 0.763 (95% CI: 0.717–0.810) for the training group (Fig. 4 A), 0.811 (95% CI: 0.726–0.897) for the validation group ( Fig. 4 B), and 0.769 (95% CI: 0.689–0.848) for the external validation group (Fig. 4 C). These results indicate that the nomogram model exhibits strong predictive accuracy for delayed extubation. The Hosmer–Lemeshow test yielded non-significant P-values of 0.707 in the training group, 0.401 in the validation group, and 0.735 in the external validation group, indicating a satisfactory goodness of fit for the model. After 500 bootstrap resampling iterations, the mean absolute error between the simulated and actual curves was 0.011 in the training group (Fig. 5 A), 0.047 in the validation group (Fig. 5 A), and 0.046 in the external validation group (Fig. 5 A). These results suggest that the trend trajectories of the two curves were closely aligned and exhibited strong consistency. DCA demonstrated that the nomogram model yielded higher net benefits compared to the treat-none and treat-all strategies across the training (Fig. 6 A), validation (Fig. 6 B), and external validation (Fig. 6 C) groups. Across all group, the nomogram showed a consistently high net benefit (training: 23%-99%; validation: 21%-89%; external validation: 18%-90%). This supports its utility as an effective predictor of delayed extubation risk and a guide for clinical decision-making. CIC (Fig. 7 A-C) was generated by predicting risk stratification among 1000 individuals using bootstrap resampling. The red curve traces the count of patients classified as high-risk for delayed extubation at varying threshold probabilities, whereas the blue curve tracks the concomitant number of true positives. Across all three group, the model demonstrated consistent effectiveness in identifying high-risk patients throughout nearly the entire spectrum of threshold probabilities, indicating robust clinical applicability. Discussion Prolonged mechanical ventilation is associated with adverse clinical outcomes, higher morbidity, and longer hospital stay [ 20 ]. Prostate cancer is the most common cancer among men, particularly older men [ 21 ]. Some studies have indicated that advanced age may lead to delayed extubation [ 15 ], However, to the best of our knowledge, no study has investigated delayed extubation after anesthesia in patients undergoing robot-assisted radical prostatic cancer surgery. Therefore, a preoperative assessment system is required to predict the risk of delayed extubation in patients undergoing robot-assisted radical prostate cancer surgery. We developed and validated a predictive model for delayed extubation, which showed good discrimination and calibration for individualized prediction. Our predictive model was based on 5 predictive factors: age, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension. Consistent with previous studies [ 1 , 22 – 25 ], our research also identified age, anemia, coronary heart disease, colloid infusion volume, and intraoperative blood transfusion as risk factors associated with delayed extubation. Although anemia did not reach statistical significance in the multivariate analysis, a significant association was observed in univariate analysis. Similarly, colloid infusion volume showed a suggestive difference in univariate analysis but failed to achieve statistical significance. A trend was also noted for intraoperative blood transfusion; however, likely due to the limited number of positive cases, it did not reach a statistically significant level. Compared with previous studies, our results also identified pulmonary disease, cerebral infarction, and intraoperative hypotension as significant predictors of delayed extubation. Tong et al [ 15 ] reported that a lower FEV 1 /FVC ratio was an independent risk factor for delayed extubation, while Rasera et al [ 26 ] found that elevated end-tidal carbon dioxide was associated with extubation failure, collectively underscoring the impact of pulmonary function on extubation outcomes. In line with these findings, our study confirmed pulmonary disease as an independent predictor. Chen et al [ 27 ] found that patients with a history of cerebral infarction before surgery had a higher risk of extubation failure, highlighting the role of central nervous system in postoperative extubation. Our analysis similarly showed an association between previous cerebral infarction and delayed extubation. Additionally, intraoperative hypotension may contribute to impaired cerebral perfusion and disrupted neural regulation, thereby increasing the risk of delayed extubation. The nomogram was developed to enhance the visualization of the model and promote its clinical applicability. The predictive model exhibited strong discriminatory ability, with AUC of 0.763, 0.811, and 0.769 in the training, validation, and external validation groups, respectively. Both the Hosmer-Lemeshow test and calibration curves indicated good model consistency across all three groups. Furthermore, the DCA and CIC graphically confirmed that a significant net clinical benefit for the nomogram. This finding further validated the model's applicability for clinical decision-making. The majority of patients undergoing robot-assisted radical prostatectomy are elderly men, who demonstrate a higher probability of delayed extubation. However, there is currently a scarcity of research focused on this specific clinical scenario. To address this gap, this study developed a nomogram prediction model for delayed extubation based on the following predictors: age, anemia, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension. Despite its contributions, this study has several limitations. First, as a single-center investigation, it may be influenced by center-specific biases, and the generalizability of our findings could be limited. Therefore, future multicenter prospective studies are warranted to validate these results. Second, the relatively small number of cases involving intraoperative blood transfusion may have reduced the statistical power to detect significant associations, potentially affecting the reliability of this specific result. Conclusions This study showed that age, anemia, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension were significantly associated with delayed extubation. Furthermore, the developed nomogram facilitated the prediction of the risk of delayed extubation, which can assist clinical practitioners in formulating personalized treatment plans for each patient. Abbreviations MAP Intraoperative mean arterial pressure LASSO Least absolute shrinkage and selection operator ROC Receiver operating characteristic AUC Area under the curve DCA Decision curve analysis CIC Clinical impact curve FEV 1 Forced expiratory volume in 1 second FVC Forced vital capacity Declarations Acknowledgements We extend our sincere gratitude to our colleagues in the Department of Anesthesiology, Nanjing Drum Tower Hospital, for their support of this work. Contributions Xiaoping Gu contributed to the study protocol and manuscript preparation. Tantan Fang was responsible for data collection and performed the statistical analysis. ChuanFei Liu assisted in the statistical analysis and was involved in interpreting the results. Animal studies Not applicable. Funding Not applicable. Author details 1 Department of Anesthesiology, Nanjing Drum Tower Hospital, Nanjing, China; 2 Department of Anesthesiology,The Affiliated Hospital of Nanjing University Medical School, Nanjing, China Corresponding authors Xiao-Ping Gu , Department of Anesthesiology, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, 210008, Nanjing, China. Data availability Upon reasonable request, the corresponding author can provide the datasets used and/or analyzed in this study. Ethics declarations Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital with the reference number 2024-742-01, and in compliance with the Helsinki Declaration. Given that this study was conducted using anonymized observational data, individual informed consent for participation was waived by the Committee on Medical Ethics of Nanjing Drum Tower Hospital. Consent for publication Not applicable. 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Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in BMC Anesthesiology → Version 1 posted Editorial decision: Revision requested 02 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers invited by journal 02 Jan, 2026 Editor assigned by journal 02 Jan, 2026 Editor invited by journal 29 Dec, 2025 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 23 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. 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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-8127609","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568423304,"identity":"182b2495-9d68-4595-b32a-1c26efcc3fff","order_by":0,"name":"TanTan Fang","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital","correspondingAuthor":false,"prefix":"","firstName":"TanTan","middleName":"","lastName":"Fang","suffix":""},{"id":568423305,"identity":"d8e602f9-e6a2-4aaf-ad7b-4b847383a02e","order_by":1,"name":"ChuanFei Liu","email":"","orcid":"","institution":"The Affiliated Hospital of Nanjing University Medical School","correspondingAuthor":false,"prefix":"","firstName":"ChuanFei","middleName":"","lastName":"Liu","suffix":""},{"id":568423306,"identity":"df481d99-03b3-4c75-b3ef-c6975a5104d3","order_by":2,"name":"XiaoPing Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACZhBhAGYdYJAA0QeI18KWQKQWBOAxgNCEtBgcZ374uKDgjt38iJxvDyzbGOT4biQwfi7Ao0Wymc3YeIbBs+SNN3K3G0i2MRhL3khglp6BRws/M4OZNI/B4WTDGbnbJIBaEjfcSGBj5sGjhY2Z/RtUS84zkJZ6glr4mXnAttjJS+SwgbQkGBDSItnMUwz0y+EEA55nZhIS5yQMZ5552CyNT4vB+eMbHxf8OWwv3578TFqizEae73jywc/4tIAAKDYTNxwAMiTAkcnYQEADRIu9PFAd4weCakfBKBgFo2AkAgBdIETY3QIv0AAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Hospital of Nanjing University Medical School","correspondingAuthor":true,"prefix":"","firstName":"XiaoPing","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2025-11-16 13:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8127609/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8127609/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12871-026-03693-3","type":"published","date":"2026-02-21T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99792030,"identity":"7a9e5fdf-090d-47c9-b699-63dbe9ea9ccf","added_by":"auto","created_at":"2026-01-08 13:12:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8381859,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/28c9eb8ceaa344eb9d49dc6b.docx"},{"id":99513819,"identity":"a48db34f-c61b-46eb-9b7c-c27fa1099574","added_by":"auto","created_at":"2026-01-05 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09:58:04","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110050,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/61ae473655622b4bac276bf7.html"},{"id":99790541,"identity":"66b62df7-ba07-4c2b-aa48-e7cf607f69ec","added_by":"auto","created_at":"2026-01-08 12:58:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287068,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient selection, grouping, and model development\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/89dbd5694990414db626ce78.jpeg"},{"id":99790793,"identity":"d279ac5c-3ff6-4339-9dfe-728ba88669dd","added_by":"auto","created_at":"2026-01-08 12:58:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":229656,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection was performed utilizing LASSO logistic regression. (A) The coefficient profile plot displays the trajectories of the 27 selected variables (B) The optimal tuning parameter (λ) was determined via cross-validation, with the vertical dashed lines representing mean-square error criterion (left dashed line) and the standard error criterion (right dashed line)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/6964c418316b39295b7b0b46.jpeg"},{"id":99513821,"identity":"6aa46501-ae28-4444-ab40-b9900a5ed8a7","added_by":"auto","created_at":"2026-01-05 09:58:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":244793,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram used for predicting the risk of delayed extubation in patients undergoing robot-assisted radical prostatic cancer surgery. (A) Based on multivariable logistic regression results, nomogram was constructed using age, cerebral infarction, pulmonary disease, coronary heart disease and intraoperative hypotension. (B) Each patient was assigned a point score based on their specific risk factor profile as defined by the nomogram. This cumulative score was then converted into a final probability estimate for delayed extubation.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/cff3f37108fd00da6e97e7ed.jpeg"},{"id":99791761,"identity":"979946c7-b3d0-4ec2-8a5e-3aa369bbc98d","added_by":"auto","created_at":"2026-01-08 13:09:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119824,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for model evaluation. A-C: ROC curves were generated for the training group, validation group, and external validation group to assess the model’s predictive performance and clinical utilit.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/3ef0526629adb80b575ad514.jpeg"},{"id":99513829,"identity":"cdc764aa-ce21-4b41-a707-b74f40c8cab8","added_by":"auto","created_at":"2026-01-05 09:58:04","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124118,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for model evaluation. A-C: Calibration curves in the training group, validation group, and external validation group to evaluate the performance of the prediction model. The 45-degree line (dashed) serves as the benchmark for perfect calibration. The model's initial fit within the development cohort is shown by the apparent calibration curve (dotted line), and the bias-corrected curve (solid line) quantifies the expected performance in new populations after applying a bootstrap-based correction for optimism (500 resamples).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/0721e2bbdcd159fa08b43159.jpeg"},{"id":99792111,"identity":"55a7a390-df65-434b-b9db-996c36dfaec1","added_by":"auto","created_at":"2026-01-08 13:15:30","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":138718,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for model evaluation. A-C: DCA curves in the training group, validation group, and external validation group to assess the model's net benefit and clinical applicability across different threshold probabilities. The DCA demonstrates the clinical value of the nomogram. The bold solid blue line denotes the nomogram's predicted risk of delayed extubation, contrasted with two reference scenarios-universal occurrence (solid gray line) and non-occurrence (fine solid black line) of the event.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/45dc39a600a1e512cd059b50.jpeg"},{"id":99513824,"identity":"4397f41b-a2d3-46b7-87fc-d36396360dff","added_by":"auto","created_at":"2026-01-05 09:58:04","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":174799,"visible":true,"origin":"","legend":"\u003cp\u003eCIC for model evaluation. A-C: CIC curves in the training group, validation group, and external validation group to visually evaluate the potential clinical utility of a prediction model.CIC shows the number of individuals classified as high-risk for delayed extubation (red curve) and the number of true positives (blue curve) at different threshold probabilities.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/9ebff1d1213332dc94b7c39e.jpeg"},{"id":103252009,"identity":"051992e5-35dc-4e74-8390-2372ebe46659","added_by":"auto","created_at":"2026-02-23 16:12:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2152869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8127609/v1/9c1a9bfe-053d-412a-a3c0-8241ba414685.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTracheal extubation at the conclusion of surgery is a crucial step in recovery from general anesthesia. Delayed extubation after surgery can increase the risk of respiratory complications, including laryngeal injury, dysphagia, pneumonia, and glottic stenosis[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous research has substantiated that early removal of tracheal catheters in patients undergoing esophagectomy or cardiac surgery can reduce lung complications and promote hemodynamic stability[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, delayed extubation imposes a heavier workload on medical staff and reduces the turnover rate in the operating room [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, identifying the risk factors associated with delayed extubation and establishing a clinical prediction model are vital. The timely recognition of high-risk individuals and subsequent administration of tailored interventions can effectively reduce the incidence of delayed extubation and prevent perioperative complications.\u003c/p\u003e \u003cp\u003eProstate cancer significantly affects the male population globally, and is the second most prevalent cancer in men [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It accounts for 7% of newly diagnosed cancer cases in men worldwide and increases to 15% in developed regions. Furthermore, the annual number of newly diagnosed cases exceeds 1.2\u0026nbsp;million, with over 350,000 deaths attributable to prostate cancer annually [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings suggest that prostate cancer stands as a major contributor to cancer-specific deaths in men.\u003c/p\u003e \u003cp\u003eThe treatment strategy with the highest efficacy for prostate cancer is currently radical resection, which is considered a gold-standard approach. Robot-assisted radical resection of prostate cancer is an emerging minimally invasive surgical technique that can effectively reduce operation time, surgical trauma, intraoperative blood loss, and postoperative complications compared with traditional laparoscopic radical resection; therefore, it is widely used in clinics [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There is a strong correlation between advancing age and the incidence of prostate cancer, as evidenced by the fact that over 85% of new diagnoses are concentrated in individuals over 60 years of age [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A positive correlation has been observed between advanced patient age and an increased incidence of postoperative extubation delay. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, to date, no study has investigated the predictive factors for delayed extubation after robot-assisted radical resection of prostate cancer. Therefore, a retrospective analysis was conducted on the clinical data of patients who underwent robot-assisted radical prostatectomy, with the objectives of identifying risk factors for delayed extubation, developing a predictive nomogram, and internally validating the model to aid in clinical prognostication.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003e This study is a single-center retrospective study, which was approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital (grant number: 2024-742-01). We retrospectively collected data from 750 patients who underwent robot-assisted radical resection for prostate cancer between January 2022 and April 2024. After exclusion, 624 patients were included in the study to investigate the factors associated with delayed extubation. Among them, 4according to a predefined 4:1 ratio, the cohort of 487 patients (January 2022 to October 2023) was randomly divided into training and validation groups and 112 patients from November 2023 to April 2024 were divided into the external validation group.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for this study were as follows: 1) No severe hearing or visual impairment prior to surgery. 2) No dementia, coma, or disturbance of consciousness prior to surgery. 3) The patient was diagnosed with prostate cancer and underwent robot-assisted radical resection of prostate cancer. 4) Patients were extubated in the operating room. The exclusion criteria were as follows: 1) Incomplete clinical data.\u003c/p\u003e \u003cp\u003e2) Patients who died within 24 h after surgery.The study flow diagram 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\n\u003ch3\u003eDelayed extubation\u003c/h3\u003e\n\u003cp\u003eExtubation time was defined as the interval from the cessation of anesthetic administration to removal of the tracheal tube. The timing of extubation was comprehensively evaluated by a registered anesthesiologist and Post-anesthetic care unit nurses considering the patient's spontaneous breathing efforts, neurological status, motor function, and other relevant clinical indicators for extubation readiness[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ultimately, the determination regarding tracheal catheter management (removal or retention) was made by a registered anesthesiologist Previous studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have shown that extubation time exceeding 1 h can lead to increased clinical complications. Therefore, the patients were divided into two groups: the conventional (extubation time\u0026thinsp;\u0026le;\u0026thinsp;1 h) and delayed (extubation time\u0026thinsp;\u0026gt;\u0026thinsp;1 h) groups.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eWe collected patient data after screening for the inclusion and exclusion criteria. Preoperative examination data and basic patient information included preoperative white blood cell count, neutrophil percentage, glucose, creatinine, serum potassium, serum calcium, and blood pressure; ASA grade; age; body mass index; smoking status; anemia (preoperative Hb\u0026thinsp;\u0026lt;\u0026thinsp;120 g/L); hypoproteinemia (total serum protein\u0026thinsp;\u0026lt;\u0026thinsp;60 g/L or albumin\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L); alanine transaminase levels; and presence of hypertension, diabetes, cerebral infarction, pulmonary disease, and coronary heart disease. Intraoperative data included the volume of crystalloid and colloid infusions, intraoperative blood transfusion, urine volume, intraoperative blood loss, intraoperative glucose levels, intraoperative serum potassium levels, and MAP.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized according to their distribution: normally distributed data as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using the independent samples t-test, while non-normally distributed data as median with interquartile range and compared using the Mann-Whitney U test. Categorical variables were expressed as percentages (%) and compared with the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All variables were subjected to a LASSO regression analysis for variable selection and risk factor identification. Variables selected by LASSO were incorporated into a multivariable logistic regression model, and a nomogram was developed based on the results. We assessed the model's discriminatory power using ROC analysis, with performance quantified by AUC. Internal validation of the model was performed employing the bootstrapping technique with 500 replicates. Simultaneously, the Hosmer-Lemeshow test was utilized to evaluate the model's goodness-of-fit.The clinical utility of the model was analyzed using decision curve DCA and CIC. The data analysis was carried out in R (version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria). P-value below 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographics of the training group\u003c/h2\u003e \u003cp\u003eThe study site was a tertiary hospital in China. A total of 750 patients undergoing robot-assisted radical prostatectomy between January 2022 and April 2024 were retrospectively collected. Because this was an observational study, 126 patients with incomplete clinical data were excluded from the group. After exclusion, 624 patients were included in the study, among them 487 patients from January 2022 to October 2023 were subsequently randomized into training and validation group at a 4:1 ratio using the random sampling function in R software. Of the training group, 204 (52.6%) experienced delayed extubation after surgery.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the characteristics of the patients who participated in the study, comparing those who experienced delayed extubation with those who did not. Patients with delayed extubation were older than those without delayed extubation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a higher incidence of anemia (P\u0026thinsp;=\u0026thinsp;0.022), cerebral infarction (P\u0026thinsp;=\u0026thinsp;0.003), pulmonary disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and coronary heart disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Simultaneously, in comparison to patients without delayed extubation, the incidence of intraoperative hypotension was significantly higher among patients with delayed extubation (P\u0026thinsp;=\u0026thinsp;0.004).\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\u003e\u0026mdash; Patients\u0026rsquo; characteristics in the training group\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDelayed extubation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo(n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes(n\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1%)\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (94%)\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\u003e17 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4%)\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\u003epreoperative white blood cell count (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L) Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6 (4.6, 6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (4.77, 6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5 (4.5, 6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative neutrophil percentage (%), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.6 (54.5, 65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.8 (53.8, 64.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.4 (55.5, 65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanemia, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (84%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (16%)\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\u003ehypoproteinemia, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (84%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (16%)\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\u003ePreoperative glucose (mmol/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.78 (4.41, 5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.74 (4.45, 5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8 (4.38, 5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative creatinine (\u0026micro;mol/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (62, 78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (62, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (62.1, 81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative serum potassium (mmol/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92 (3.71, 4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.92 (3.71, 4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.94 (3.71, 4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative serum calcium (mmol/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34 (2.26, 2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35 (2.29, 2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (2.25, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealanine transaminase (U/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.5 (13, 23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1 (13, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.8 (13, 23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.714\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\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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (35%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (65%)\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\u003ediabetes, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161 (79%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (21%)\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\u003ecerebral infarction, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (75%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (25%)\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\u003epulmonary disease, 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=\"left\" 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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (45%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (55%)\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\u003ecoronary heart disease, 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=\"left\" 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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (82%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (18%)\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\u003eage, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (66, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (63.75, 74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (68, 78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.02 (22.49, 25.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3 (22.52, 25.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.67 (22.49, 25.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (70%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (30%)\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\u003ecrystalloid infusion volume (ml), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (1000, 1500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000 (1000, 1300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 (1000, 1500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecolloids infusion volume (ml), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (500, 1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 (500, 1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 (500, 1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative infusion volume (ml), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000 (1500, 2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000 (1500, 2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2000 (1500, 2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintraoperative blood transfusion (ml), 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200 (98%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2%)\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\u003eurine volume (ml), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (300, 700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 (300, 600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (250, 800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintraoperative blood loss (ml), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (100, 100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (100, 100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (100, 100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintraoperative glucose (mmol/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9 (5.23, 6.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.2, 6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.75 (5.23, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintraoperative serum potassium (mmol/L), Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.57 (3.37, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (3.35, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.55 (3.4, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintraoperative hypotension, 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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP \u0026gt; 80% of baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (22%)\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\u003eMAP 70%-80༅ of baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (34%)\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\u003eMAP \u0026lt; 70% of baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (31%)\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\u003eMAP \u0026lt; 65mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel development\u003c/h3\u003e\n\u003cp\u003eThis study utilized the LASSO regression model to analyze 27 variables obtained from patients, and 5 variables were identified as significant predictors due to non-zero coefficients. These selected variables were age, cerebral infarction, pulmonary disease, coronary heart disease and intraoperative hypotension (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe five variables selected from the LASSO regression analysis were used to performed a multivariate logistic regression for build a predictive model for delayed extubation. The results from this model identified that age (OR: 1.064, 95% CI: 1.031\u0026ndash;1.098, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cerebral infarction (OR: 1.875, 95% CI: 1.035\u0026ndash;3.399, P\u0026thinsp;\u0026lt;\u0026thinsp;0.038), pulmonary disease (OR: 3.213, 95% CI: 2.027\u0026ndash;5.092, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), coronary heart disease (OR: 4.878, 95% CI: 2.029\u0026ndash;11.728, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MAP 70%\u0026ndash;80% of baseline (OR: 1.219, 95% CI: 0.689\u0026ndash;2.154, P\u0026thinsp;=\u0026thinsp;0.496), MAP\u0026thinsp;\u0026lt;\u0026thinsp;70% of baseline (OR: 1.576, 95% CI: 0.867\u0026ndash;2.863, P\u0026thinsp;=\u0026thinsp;0.135), and MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg (OR: 3.802, 95% CI: 1.356\u0026ndash;10.657, P\u0026thinsp;=\u0026thinsp;0.011) were independent predictive variables of delayed extubation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the multivariate logistic regression, a clinical prediction nomogram was developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Each patient received a points assignment for the presence of each risk factor, with the aggregate score then being transformed into an estimated probability of delayed extubation. The probability of delayed extubation for a randomly selected patient was 83.9%, and the patient actually encountered extubation delay clinically, suggesting that the clinical applicability of the scoring system is favorable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) .\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\u003eMultivariate analysis of the predictive factors in the training group.\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\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.875(1.035\u0026ndash;3.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.213(2.027\u0026ndash;5.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.878(2.029\u0026ndash;11.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\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\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064(1.031\u0026ndash;1.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintraoperative hypotension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP 70%-80༅ of baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.219(0.689\u0026ndash;2.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u0026thinsp;\u0026lt;\u0026thinsp;70% of baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.576(0.867\u0026ndash;2.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u0026thinsp;\u0026lt;\u0026thinsp;65mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.802(1.356\u0026ndash;10.657)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvaluation and validation of the nomogram model\u003c/h3\u003e\n\u003cp\u003eTo evaluate the predictive performance and clinical utility of the model, ROC curve analysis was employed. AUC was 0.763 (95% CI: 0.717\u0026ndash;0.810) for the training group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), 0.811 (95% CI: 0.726\u0026ndash;0.897) for the validation group ( Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and 0.769 (95% CI: 0.689\u0026ndash;0.848) for the external validation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These results indicate that the nomogram model exhibits strong predictive accuracy for delayed extubation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Hosmer\u0026ndash;Lemeshow test yielded non-significant P-values of 0.707 in the training group, 0.401 in the validation group, and 0.735 in the external validation group, indicating a satisfactory goodness of fit for the model.\u003c/p\u003e \u003cp\u003eAfter 500 bootstrap resampling iterations, the mean absolute error between the simulated and actual curves was 0.011 in the training group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), 0.047 in the validation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and 0.046 in the external validation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These results suggest that the trend trajectories of the two curves were closely aligned and exhibited strong consistency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDCA demonstrated that the nomogram model yielded higher net benefits compared to the treat-none and treat-all strategies across the training (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), and external validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) groups. Across all group, the nomogram showed a consistently high net benefit (training: 23%-99%; validation: 21%-89%; external validation: 18%-90%). This supports its utility as an effective predictor of delayed extubation risk and a guide for clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCIC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C) was generated by predicting risk stratification among 1000 individuals using bootstrap resampling. The red curve traces the count of patients classified as high-risk for delayed extubation at varying threshold probabilities, whereas the blue curve tracks the concomitant number of true positives. Across all three group, the model demonstrated consistent effectiveness in identifying high-risk patients throughout nearly the entire spectrum of threshold probabilities, indicating robust clinical applicability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eProlonged mechanical ventilation is associated with adverse clinical outcomes, higher morbidity, and longer hospital stay [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Prostate cancer is the most common cancer among men, particularly older men [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Some studies have indicated that advanced age may lead to delayed extubation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], However, to the best of our knowledge, no study has investigated delayed extubation after anesthesia in patients undergoing robot-assisted radical prostatic cancer surgery. Therefore, a preoperative assessment system is required to predict the risk of delayed extubation in patients undergoing robot-assisted radical prostate cancer surgery.\u003c/p\u003e \u003cp\u003eWe developed and validated a predictive model for delayed extubation, which showed good discrimination and calibration for individualized prediction. Our predictive model was based on 5 predictive factors: age, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension.\u003c/p\u003e \u003cp\u003eConsistent with previous studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], our research also identified age, anemia, coronary heart disease, colloid infusion volume, and intraoperative blood transfusion as risk factors associated with delayed extubation. Although anemia did not reach statistical significance in the multivariate analysis, a significant association was observed in univariate analysis. Similarly, colloid infusion volume showed a suggestive difference in univariate analysis but failed to achieve statistical significance. A trend was also noted for intraoperative blood transfusion; however, likely due to the limited number of positive cases, it did not reach a statistically significant level.\u003c/p\u003e \u003cp\u003eCompared with previous studies, our results also identified pulmonary disease, cerebral infarction, and intraoperative hypotension as significant predictors of delayed extubation. Tong et al [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that a lower FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio was an independent risk factor for delayed extubation, while Rasera et al [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found that elevated end-tidal carbon dioxide was associated with extubation failure, collectively underscoring the impact of pulmonary function on extubation outcomes. In line with these findings, our study confirmed pulmonary disease as an independent predictor. Chen et al [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] found that patients with a history of cerebral infarction before surgery had a higher risk of extubation failure, highlighting the role of central nervous system in postoperative extubation. Our analysis similarly showed an association between previous cerebral infarction and delayed extubation. Additionally, intraoperative hypotension may contribute to impaired cerebral perfusion and disrupted neural regulation, thereby increasing the risk of delayed extubation.\u003c/p\u003e \u003cp\u003eThe nomogram was developed to enhance the visualization of the model and promote its clinical applicability. The predictive model exhibited strong discriminatory ability, with AUC of 0.763, 0.811, and 0.769 in the training, validation, and external validation groups, respectively. Both the Hosmer-Lemeshow test and calibration curves indicated good model consistency across all three groups. Furthermore, the DCA and CIC graphically confirmed that a significant net clinical benefit for the nomogram. This finding further validated the model's applicability for clinical decision-making.\u003c/p\u003e \u003cp\u003eThe majority of patients undergoing robot-assisted radical prostatectomy are elderly men, who demonstrate a higher probability of delayed extubation. However, there is currently a scarcity of research focused on this specific clinical scenario. To address this gap, this study developed a nomogram prediction model for delayed extubation based on the following predictors: age, anemia, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension.\u003c/p\u003e \u003cp\u003eDespite its contributions, this study has several limitations. First, as a single-center investigation, it may be influenced by center-specific biases, and the generalizability of our findings could be limited. Therefore, future multicenter prospective studies are warranted to validate these results. Second, the relatively small number of cases involving intraoperative blood transfusion may have reduced the statistical power to detect significant associations, potentially affecting the reliability of this specific result.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study showed that age, anemia, cerebral infarction, pulmonary disease, coronary heart disease, and intraoperative hypotension were significantly associated with delayed extubation. Furthermore, the developed nomogram facilitated the prediction of the risk of delayed extubation, which can assist clinical practitioners in formulating personalized treatment plans for each patient.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraoperative mean arterial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast absolute shrinkage and selection operator\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\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\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\"\u003eCIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical impact curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFEV\u003csub\u003e1\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced expiratory volume in 1 second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFVC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced vital capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to our colleagues in the Department of Anesthesiology, Nanjing Drum Tower Hospital, for their support of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoping Gu contributed to the study protocol and manuscript preparation. Tantan Fang was responsible for data collection and performed the statistical analysis. ChuanFei Liu assisted in the statistical analysis and was involved in interpreting the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Anesthesiology, Nanjing Drum Tower Hospital, Nanjing, China; \u003csup\u003e2\u003c/sup\u003eDepartment of Anesthesiology,The Affiliated Hospital of Nanjing University Medical School, Nanjing, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao-Ping Gu , Department of Anesthesiology, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, 210008, Nanjing, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon reasonable request, the corresponding author can provide the datasets used and/or analyzed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of Nanjing Drum Tower Hospital with the reference number 2024-742-01, and in compliance with the Helsinki Declaration. Given that this study was conducted using anonymized observational data, individual informed consent for participation was waived by the Committee on Medical Ethics of Nanjing Drum Tower Hospital.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZirka HA, John GG, Laura CL, Joanna LM, Eric JH, Mitchell F. Factors That Correlate With the Decision to Delay Extubation After Multilevel Prone Spine Surgery. J Neurosurg Anesthesiol. 2014;26(2):167\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawkins DB. Glottic and subglottic stenosis from endotracheal intubation. Laryngoscope. 2009;87(3):339\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuidelines for the Management. of Adults with Hospital-acquired, Ventilator-associated, and Healthcare-associated Pneumonia. Am J Respir Crit Care Med. 2005;171(4):388\u0026ndash;416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarker J, Martino R, Reichardt B, Hickey EJ, Ralph-Edwards A. Incidence and impact of dysphagia in patients receiving prolonged endotracheal intubation after cardiac surgery. Can J Surg. 2009;52(2):119\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon MC, Abdoh A, Hamilton GA, Lindsay WG, Duke PC, Pascoe EA, Del Rizzo DF. Safety and Efficacy of Fast Track in Patients Undergoing Coronary Artery Bypass Surgery. J Card Surg. 2001;16(4):319\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenberg H, Antognini JF, Muldoon S. Test Malignant Hyperth Anesthesiology. 2002;96(1):232\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpstein RH, Dexter F, Cajigas I, Mahavadi AK, Shah AH, Abitbol N, Komotar RJ. Prolonged tracheal extubation time after glioma surgery was associated with lack of familiarity between the anesthesia provider and the operating neurosurgeon. A retrospective, observational study. J Clin Anesth. 2020;60(0):118\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A, Cancer statistics. 2018. CA: A Cancer Journal for Clinicians 2018; 68: 7\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer. J Clin. 2018;68(6):394\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDy GW, Gore JL, Forouzanfar MH, Naghavi M, Fitzmaurice C. Global Burden of Urologic Cancers, 1990\u0026ndash;2013. Eur Urol. 2017;71(3):437\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong MCS, Goggins WB, Wang HHX, Fung FDH, Leung C, Wong SYS, Ng CF, Sung J. J.Y. Global incidence and mortality for prostate cancer: analysis of temporal patterns and trends in 36 countries. Eur Urol. 2016;70(5):862\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes T, Rai B, Madaan S, Chedgy E, Somani B. The Availability, Cost, Limitations, Learning Curve and Future of Robotic Systems in Urology and Prostate Cancer Surgery. J Clin Med. 2023;12(6):2268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel VR, Coelho RF, Chauhan S, Orvieto MA, Palmer KJ, Rocco B, Sivaraman A, Coughlin G. Continence, potency and oncological outcomes after robotic-assisted radical prostatectomy: early trifecta results of a high‐volume surgeon. BJU Int. 2010;106(5):696\u0026ndash;702.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimura T, Egawa S. Epidemiology of prostate cancer in Asian countries. Int J Urol. 2018;25(6):524\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong C, Miao Q, Zheng J, Wu J. A novel nomogram for predicting the decision to delayed extubation after thoracoscopic lung cancer surgery. Ann Med. 2023;55(1):800\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeyckemans F, von Ungern-Sternberg B. Tracheal extubation in children: Planning, technique, and complications. Pediatr Anesth. 2020;30(3):331\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVannucci A, Riordan IR, Prifti K, Sebastiani A, Helsten DL, Lander DP, Kallogjeri D, Cavallone L. Prolonged time to extubation after general anaesthesia is associated with early escalation of care. Eur J Anaesthesiol. 2021;38(5):494\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandell MS, Stoner TJ, Barnett R, Shaked A, Bellamy M, Biancofiore G, Niemann C, Walia A, Vater Y. Tran ZV and Kam I. A multicenter evaluation of safety of early extubation in liver transplant recipients. Liver Transpl. 2007;13(11):1557\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z. Univariate description and bivariate statistical inference: the first step delving into data. Annals Translational Med. 2016;4(5):91\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu S-S, Tian Y, Ma Y-J, Zhou Y-M, Tian Y, Gao R, Yang Y-L, Zhang L, Zhou J-X. Development of a Prediction Score for Evaluation of Extubation Readiness in Neurosurgical Patients with Mechanical Ventilation. Anesthesiology. 2023;139(5):614\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang LG, Monta\u0026ntilde;o AR, Masillati AM, Jones JA, Barth CW, Combs JR, Kumarapeli SU, Shams NA, van den Berg NS, Antaris AL, Galvis SN, McDowall I, Rizvi SZH, Alani AWG, Sorger JM, Gibbs SL. Nerve Visualization using Phenoxazine-Based Near‐Infrared Fluorophores to Guide Prostatectomy. Adv Mater. 2024;36(16):e2304724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnastasian ZH, Kim M, Heyer EJ, Wang S, Berman MF. Attending Handoff Is Correlated with the Decision to Delay Extubation After Surgery. Anesth Analgesia. 2016;122(3):758\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Liu J, Xu Z, Wang Y, Chen L, Bai Y, Xie W, Wu Q. Early identification of delayed extubation following cardiac surgery: Development and validation of a risk prediction model. Front Cardiovasc Med. 2022;9(0):1002768.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Tan R, Lin J-W, Li G, Xie J. Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study. BMC Anesthesiol. 2023;23(1):83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang R, Liu Y, Li W, Zhang C, Zhang X, Wang F, Li Y, Yang X, Tan B. Tunan Chen and Jishu Xian. Analysis of factors affecting smooth extubation in delayed extubation patients following intracranial surgery: an ambispective cohort study. BMC Anesthesiol. 2025;25(1):531.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasera CC, Gewehr PM, Domingues AMT. PET(CO2) measurement and feature extraction of capnogram signals for extubation outcomes from mechanical ventilation. Physiol Meas. 2015;36(2):231\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Zhang Y, Che L, Shen L, Huang Y. Risk factors for unplanned reintubation caused by acute airway compromise after general anesthesia: a case-control study. BMC Anesthesiol. 2021;21(1):17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anesthesia, Nomogram, Robotic-assisted radical prostatectomy, Prostate cancer, Airway extubation","lastPublishedDoi":"10.21203/rs.3.rs-8127609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8127609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDelayed extubation after anesthesia can lead to adverse clinical outcomes. An investigation was undertaken to identify the risk factors contributing to a delay extubation of patients undergoing robot-assisted radical prostatectomy and to develop a visualized nomogram prediction model for clinical use.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 624 patients were included and divided into training group, validation group, and external validation group. The training group was utilized to develop a nomogram, whereas the validation group and external validation group was used to assess its performance. LASSO regression was employed to refine variables and choose predictors, and a nomogram was constructed using multivariate logistic regression. The performance of the model was internally validated using calibration and receiver operating characteristic curves. Additionally, decision curve analysis and clinical impact curves were used to assess the clinical utility of the model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients between January 2022 and April 2024 were included and divided into training group (n\u0026thinsp;=\u0026thinsp;389), validation group (n\u0026thinsp;=\u0026thinsp;98), and external validation group (n\u0026thinsp;=\u0026thinsp;137). Logistic regression identified cerebral infarction, pulmonary disease, coronary heart disease, age, and intraoperative hypotension as independent predictors of delayed extubation. A nomogram constructed based on these factors demonstrated excellent predictive performance, with area under the curve values of 0.763 (95% CI: 0.717\u0026ndash;0.810) in the train group, 0.811 (95% CI: 0.726\u0026ndash;0.897) in the validation group, and 0.769 (95% CI: 0.689\u0026ndash;0.848) in the external validation group. Across all three group, the model demonstrated a good fit, as indicated by a non-significant Hosmer-Lemeshow test statistic, and the calibration curves indicated a strong alignment between the predictions and actual observations. Furthermore, decision curve analysis and clinical impact curve demonstrated the clinical efficiency and benefits of the prediction model.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified key risk factors for delayed extubation and established an effective predictive nomogram with high discriminative ability and clinical applicability for predictive the risk of extubation delay in patients undergoing robot-assisted radical prostatectomy.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003e The Medical Ethics Committee of Nanjing Drum Tower Hospital granted ethical approval for this research(grant number: 2024-742-01)\u003c/p\u003e","manuscriptTitle":"Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 09:57:59","doi":"10.21203/rs.3.rs-8127609/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-02T12:27:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T12:26:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96892516073998454432362656978663874927","date":"2026-01-02T12:10:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198084718078264943309200202219110702624","date":"2026-01-02T11:44:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T10:23:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240640900914238260363303983754821256189","date":"2026-01-02T10:14:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T09:47:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301089985385968338156620102630167722966","date":"2026-01-02T09:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-02T08:13:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T08:13:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-29T10:51:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-24T04:02:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2025-12-24T03:56:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e49e9541-2d2b-4166-a747-25ff0d5988f0","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:07:31+00:00","versionOfRecord":{"articleIdentity":"rs-8127609","link":"https://doi.org/10.1186/s12871-026-03693-3","journal":{"identity":"bmc-anesthesiology","isVorOnly":false,"title":"BMC Anesthesiology"},"publishedOn":"2026-02-21 15:57:29","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2026-01-05 09:57:59","video":"","vorDoi":"10.1186/s12871-026-03693-3","vorDoiUrl":"https://doi.org/10.1186/s12871-026-03693-3","workflowStages":[]},"version":"v1","identity":"rs-8127609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8127609","identity":"rs-8127609","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}