Nomogram based on preoperative systemic immune-inflammation index for Post-Anesthesia Care Unit duration predication in renal cancer patients underwent Robot-assisted partial nephrectomy

Nomogram based on preoperative systemic immune-inflammation index for Post-Anesthesia Care Unit duration predication in renal cancer patients underwent Robot-assisted partial nephrectomy | 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 based on preoperative systemic immune-inflammation index for Post-Anesthesia Care Unit duration predication in renal cancer patients underwent Robot-assisted partial nephrectomy Ying Ke, Wanshu Liu, Yi He, Bo Yang, Meihong Lv, Meiyu Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8150211/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is crucial for preventing postoperative complications and optimizing hospital resource utilization. This study aimed to develop a preoperative nomogram incorporating inflammatory markers and clinical characteristics to predict anesthesia recovery time in patients with renal cell carcinoma (RCC) undergoing robot-assisted partial nephrectomy (RAPN). Methods A nomogram was developed to identify significant predictors of anesthesia recovery time in a retrospective cohort of 218 patients under general anesthesia for RCC between December 2023 and April 2025. Based on preoperative covariates and multivariable Cox proportional hazards regression, the model estimates the probability of discharge from the post-anesthesia care unit (PACU) at 35, 45, and 55 minutes. Validation of the nomogram was performed using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Results Age, RENAL score, monocyte count, and systemic immune-inflammation index (SII) were identified as independent predictors and incorporated into the nomogram. The model demonstrated strong predictive performance for PACU duration at all three time points. The C-index values were 0.816 for the training cohort and 0.835 for the validation cohort. ROC analysis, calibration plots, and DCA all indicated satisfactory discrimination and clinical utility. Furthermore, the nomogram showed superior predictive performance compared to a model based solely on age and RENAL score. Risk stratification based on nomogram scores categorized patients into two distinct groups with significantly different recovery times. Conclusions The nomogram developed in this study exhibits strong predictive accuracy for estimating anesthesia recovery time, showing promise as a practical tool for assessing PACU discharge readiness. Further research is warranted to validate its clinical utility and potential to enhance patient outcomes and operational efficiency. Trial registration This study is a retrospective study. Monocyte count systemic immune-inflammation index PACU stay Robot-assisted partial nephrectomy nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Post-Anesthesia Care Unit (PACU), commonly known as the recovery room, provides immediate postoperative care for patients recovering from anesthesia, with continuous monitoring of their physiological status and management of any emerging complications. Inadequate or premature discharge from the PACU may result in serious postoperative issues, such as respiratory, cardiovascular, and neurological complications [ 1 ]. The length of stay in the PACU plays a critical role in operating room management, significantly affecting operational efficiency, hospital costs, and staffing demands [ 2 ]. This metric is essential for ensuring a safe transition of patients from the PACU to their subsequent postoperative destinations. Extended PACU stays can diminish patient satisfaction and increase institutional expenses, whereas discharge that is too early may lead to anesthesia- or surgery-related complications, potentially raising morbidity and mortality rates [ 3 , 4 ]. Thus, timely and appropriate discharge from the PACU is vital for patient safety, reduction of complications, and improvement of hospital operational efficiency. Conventionally, PACU discharge decisions rely on standardized assessment tools such as the Aldrete score, which evaluates vital signs including respiration, circulation, consciousness, mobility, and oxygen saturation [ 5 ]. Although various clinical prediction models have been developed for different patient groups [ 4 , 6 , 7 ], existing models are not suitable for those undergoing partial nephrectomy. Furthermore, many previously proposed models incorporate intraoperative variables [ 4 , 7 ]. While some demonstrate strong predictive ability, their reliance on parameters unavailable preoperatively limits their utility in clinical forecasting. As a result, they cannot support early prediction of PACU stay or facilitate proactive optimization of workflow and resource allocation. Anesthetic agents are frequently metabolized via the CYP450 enzyme system [ 8 ]. Evidence suggests that inflammation and pathological states can downregulate CYP450 activity, thereby impairing drug metabolism [ 9 ]. The magnitude of this suppression is often correlated with the severity of inflammation [ 10 ]. We therefore hypothesized that patients exhibiting elevated systemic inflammatory markers would experience delayed recovery from anesthesia and prolonged PACU stays. Despite ongoing research efforts [ 11 ], significant knowledge gaps remain in this field. Accordingly, this study aimed to evaluate the predictive utility of peripheral inflammatory markers—including the systemic immune-inflammation index (SII)—in estimating PACU duration for patients with renal cell carcinoma (RCC) undergoing robot-assisted partial nephrectomy (RAPN). Methods and materials Patient recruitment and clinical variables collection This retrospective study analyzed data from 218 patients diagnosed with renal cell carcinoma (RCC) who underwent robot-assisted partial nephrectomy (RAPN) under general anesthesia between December 2023 and April 2025. The study protocol received approval from the Ethics Committee of the Second Hospital of Dalian Medical University (Approval No. : 2023064), and all participants provided written informed consent for the use of their medical records for research purposes. All patient data were anonymized prior to analysis. Inclusion criteria comprised adult patients (≥ 18 years) scheduled for elective RAPN. Patients with incomplete medical records or those who experienced significant intraoperative complications (e.g., major hemorrhage or cardiac events) that could confound standard postoperative recovery were excluded. PACU discharge readiness was determined based on Aldrete scores consistently reaching 9 or higher [ 12 ]. We collected a comprehensive set of preoperative variables, including demographic characteristics (age, sex), tumor complexity assessed by the RENAL nephrometry score [ 13 ], and hematological parameters. The latter included absolute counts of neutrophils, lymphocytes, platelets, and monocytes, as well as derived inflammatory indices: the systemic immune-inflammation index (SII, calculated as platelet count × neutrophil count / lymphocyte count), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). The primary outcome was the duration of PACU stay, defined as the time from PACU admission to meeting discharge criteria. Nomogram development and validation The study cohort was randomly partitioned into a training set (n = 154, 70%) for model development and an internal validation set (n = 64, 30%) using the 'caret' package in R. To identify predictors of PACU stay duration, we performed univariate Cox regression analyses on all preoperative variables. Variables showing significant association (P < 0.05) in the univariate analysis were subsequently entered into a multivariate Cox proportional hazards regression model within the training cohort. A nomogram was then constructed using the 'rms' package in R, incorporating the independent predictors identified from the multivariate analysis. This nomogram was designed to estimate the probability of PACU discharge at three specific time points: 35, 45, and 55 minutes. Each patient's cumulative risk score, derived from the nomogram, was calculated to stratify patients into different risk categories for PACU stay duration. The model's predictive performance was evaluated using several metrics. Discrimination was assessed with the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves. Calibration plots were used to visualize the agreement between predicted and observed discharge probabilities. Furthermore, decision curve analysis (DCA) was employed to evaluate the clinical net benefit of the nomogram across a range of threshold probabilities. Risk stratification based on nomogram scores Using the 'maxstat' package in R, an optimal cut-off value for the nomogram-derived risk score was determined to categorize patients into distinct risk groups. The analysis ensured that each group contained between 25% and 75% of the total cohort. Kaplan-Meier survival curves were subsequently generated for these risk groups, and the statistical significance of the differences in PACU stay duration was compared using the log-rank test. Statistical analysis Continuous variables with a normal distribution were dichotomized using optimal cut-off values determined by X-tile software (v3.6.1). All statistical analyses were conducted using SPSS (version 24.0), with categorical variables summarized as frequencies and percentages. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were derived from the Cox regression models. ROC analyses were performed using the 'pROC' package in R (version 1.17.0.1) to calculate the area under the curve (AUC) at the specified time points. A two-sided P-value of less than 0.05 was considered statistically significant for all analyses. Results Baseline patient characteristics A total of 218 patients who met the inclusion criteria were included in the final analysis. The median age was 60 years (range: 23-85), and the majority were male (62.39%). Tumor complexity, as measured by the RENAL score, was distributed as follows: 25.69% low complexity (4-6), 44.95% moderate complexity (7-9), and 29.36% high complexity (10-12). The median PACU stay duration was 45 minutes, with a range from 25 to 140 minutes. Based on the optimal cut-off values determined by X-tile software, continuous hematological variables were transformed into categorical variables for subsequent analysis. A detailed summary of the baseline clinicopathological characteristics of the entire cohort is provided in Table 1. Table 1. Representativeness of study participants Clinical characteristics Number Age (years old) Median [min-max] 60 [23-85] ≤ 48 46 (21.10%) > 48 172 (78.90%) Sex Male 136 (62.39%) Female 82 (37.61%) RENAL score 10-12 56 (25.69%) 7-9 98 (44.95%) 4-6 64 (29.36%) Neutrophil count (10 9 /L) Median [min-max] 3.32 [1.04 - 6.58] ≤ 3.47 122 (55.96%) > 3.47 96 (44.04%) lymphocyte count (10 9 /L) Median [min-max] 1.79 [0.38 - 3.88] ≤ 1.80 112 (51.38%) > 1.80 106 (48.62%) White cell count (10 9 /L) Median [min-max] 5.83 [2.63 - 9.72] ≤ 5.00 62 (28.44%) > 5.00 156 (71.56%) Platelet count (10 9 /L) Median [min-max] 228 [112 - 606] ≤ 260 156 (71.56%) > 260 62 (28.44%) Monocyte count (10 9 /L) Median [min-max] 0.40 [0.15 - 1.23] ≤ 0.36 80 (36.70%) > 0.36 138 (63.30%) NLR Median [min-max] 1.77 [0.53 - 8.02] ≤ 1.63 92 (42.20%) > 1.63 126 (57.80%) PLR Median [min-max] 125.52 [40.94 - 497.83] ≤ 162.20 156 (71.56%) > 162.20 62 (28.44%) LMR Median [min-max] 4.38 [1.00 - 11.85] ≤ 3.06 39 (17.89%) > 3.06 179 (82.11%) SII Median [min-max] 405.18 [111.69 - 2058.89] ≤ 457.82 131 (60.09%) > 457.82 87 (39.91%) PACU duration (minutes) Median [min-max] 45 [25- 140] Abbreviations: NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio; SII: systemic immune-inflammation index; PACU: Post-Anesthesia Care Unit. Identification of independent predictors and nomogram construction The cohort was randomly divided into training (n=154) and validation (n=64) sets. Univariate Cox regression analysis in the training set identified several variables potentially associated with PACU duration. Subsequent multivariate analysis revealed that older age (>48 years), higher tumor complexity (RENAL score), elevated monocyte count (>0.36 × 10⁹/L), and a higher systemic immune-inflammation index (SII > 457.82) were independent predictors of a prolonged PACU stay (all P < 0.001; see Table 2). These four independent predictors were incorporated into the construction of a nomogram (Figure 1A). The nomogram allows for the calculation of a total point score for an individual patient, which corresponds to the predicted probability of PACU discharge at 35, 45, and 55 minutes. Calibration curves for both the training and validation cohorts demonstrated excellent agreement between the nomogram's predictions and actual observed outcomes (Figure 1B, 1C). To illustrate the added value of the inflammatory markers, a baseline clinical model was built using only age and RENAL score. The comprehensive nomogram exhibited superior predictive performance, with a C-index of 0.816 (95% CI: 0.783–0.849) in the training set, compared to 0.746 (95% CI: 0.701–0.789) for the baseline model. Decision curve analysis (Figure 2A) confirmed that the full nomogram provided a greater net clinical benefit across a wide range of threshold probabilities than the baseline model. Similarly, ROC analysis (Figure 2B) showed that the nomogram achieved higher AUC values for predicting PACU discharge at all three time points. Table 2. Univariate and multivariate analyses of factors associated with PACU duration Variables Univariate analysis Multivariate analysis HR (95%CI) P value HR (95%CI) P value Age ≤ 48 - - - - > 48 0.36 (0.24-0.56） ) <0.001*** 0.44 (0.28-0.67) <0.001*** Sex Female - - - - Male 0.88 (1.134-0.63) 2.78) 0.471 - - RENAL score 4-6 - - - - 7-9 1.71 (1.16-2.53) 0.006** 1.81 (1.21-2.72) 0.004** 10-12 4.37 (2.78-6.89) <0.001*** 5.48 (3.33-9.02) 3.47 - - - - ≤ 3.47 0.98 (0.70-1.36) 0.892 - - lymphocyte count (10 9 /L) ≤ 1.80 - - - - > 1.80 0.79 (0.57-1.09) 0.159 - - White cell count (10 9 /L) > 5.00 - - - - ≤ 5.00 1.21 (0.84-1.74) 0.316 - - Platelet count (10 9 /L) > 260 - - - - ≤ 260 1.77 (1.24-2.52) 0.002** 1.71 (0.61-4.82) 0.272 Monocyte count (10 9 /L) > 0.36 - - - - ≤ 0.36 1.88 (1.34-2.64) <0.001*** 2.25 (1.56-3.23) 1.63 - - - - ≤ 1.63 1.14 (0.81-1.59) 0.433 - - PLR > 162.20 - - - - ≤ 162.20 1.77 (1.24-2.52) 0.002** 0.48 (0.17-1.49) 0.179 LMR ≤ 3.06 - - - - > 3.06 1 (0.6619-1.511) 0.999 - - SII > 457.82 - - - - ≤ 457.82 3.23 (2.23-4.61) <0.001*** 3.62 (2.33-5.61) <0.001*** Abbreviations: SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio, PLR: platelet-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio. *P < 0.05, **P < 0.01, ***P < 0.001 Figure 1. Development and validation of a predictive nomogram. (A) Nomogram based on age, RENAL score, monocyte count (MON), and systemic immune-inflammation index (SII) for estimating PACU discharge probability at 35, 45, and 55 minutes. (B, C) Calibration curves demonstrating the agreement between predicted and observed outcomes in the training (B) and validation (C) cohorts. Figure 2. Validation of the nomogram in the training cohort. (A) Comparison of time-dependent decision curve analysis between the nomogram and a clinical model. (B) ROC curves for predicting PACU duration at 35, 45, and 55 minutes. Validation of the nomogram's predictive accuracy The robust performance of the nomogram was confirmed in the internal validation cohort. It achieved a C-index of 0.835 (95% CI: 0.790–0.880), which was substantially higher than the 0.695 (95% CI: 0.615–0.775) of the baseline model. The decision curve analysis in the validation set (Figure 3A) reiterated the superior clinical utility of the nomogram. The AUC values for the nomogram at 35, 45, and 55 minutes were 0.989, 0.975, and 0.878, respectively, consistently outperforming the baseline model (Figure 3B). Clinical application through risk stratification Using the nomogram-generated risk scores for the entire cohort, an optimal cut-off value was applied to stratify patients into high-risk (n=92, 42.2%) and low-risk (n=126, 57.8%) groups. The PACU discharge probability of patients in the low-risk group (median: 50min) was considerably superior than that of the high-risk group (median: 35min, P <0.0001), according to the K-M survival curves (Fig 4). Figure 3 ． Validation of the nomogram in the test set. (A) Decision curve analysis comparing the nomogram and clinical model. (B) ROC curves for predicting PACU discharge at 35, 45, and 55 minutes. Figure 4. Predictive validation of the nomogram. PACU discharge probability curves stratified by the nomogram-based risk score in the entire cohort. Discussion This study establishes a significant association between preoperative systemic inflammation—measured SII and monocyte count—and the duration of PACU stay in patients undergoing RAPN for RCC. By integrating these readily available hematologic markers with established clinical predictors such as patient age and tumor complexity (as quantified by the RENAL score), we developed and validated a novel preoperative nomogram. This tool demonstrates robust performance in predicting the probability of PACU discharge at key time thresholds (35, 45, and 55 minutes), showing considerable potential to optimize perioperative workflow and resource allocation. The central hypothesis of this study—that elevated systemic inflammation delays emergence from anesthesia and prolongs PACU stay—is strongly supported by the identification of SII and monocyte count as independent predictors. This finding is consistent with growing evidence underscoring the interplay between inflammation, drug metabolism, and neurological recovery [14]. A key mechanistic pathway involves the downregulation of cytochrome P450 (CYP450) enzymes by inflammatory mediators. Many commonly used anesthetic agents (e.g., propofol, midazolam, opioids such as fentanyl and sufentanil) are metabolized via this pathway [8, 9]. Pro-inflammatory cytokines—including IL-6, IL-1β, and TNF-α, which are frequently elevated in cancer and systemic inflammatory states—can suppress the expression [15, 16]. An elevated SII, which reflects neutrophilia and lymphopenia often driven by these cytokines, serves as a surrogate marker of this inflammatory state [17]. Similarly, monocytes are primary producers of IL-1β and TNF-α [18]. Thus, patients with high preoperative SII and monocyte counts are likely to exhibit slower clearance of anesthetics, resulting prolonged drug effects, delayed awakening, reduced respiratory drive, and impaired recovery of protective reflexes and cognitive function—all critical factors determining safe PACU discharge. Inflammation may also alter pharmacodynamic responses to anesthetics [19]. Cytokines can modulate neurotransmitter systems and neuronal excitability within the central nervous system (CNS), potentially changing sensitivity to sedatives and analgesics. Systemic inflammation may increase blood-brain barrier permeability, permitting greater entry of inflammatory mediators into the CNS, where they can exert direct sedative or delirium-inducing effects [20]. This compromised CNS environment likely contributes to slower neurological recovery in patients with higher inflammatory burden. Furthermore, chronic inflammation—a hallmark of cancer—is often associated with underlying organ dysfunction and greater comorbidity burden [21, 22]. For instance, inflammation can exacerbate conditions such as coronary artery disease or chronic obstructive pulmonary disease [22, 23], rendering patients more susceptible to hemodynamic and respiratory instability during emergence, thereby necessitating prolonged PACU monitoring or intervention. The inclusion of age as a predictor is physiologically well-founded. Aging is associated with reduced hepatic and renal function, altered body composition, diminished neuronal density and neurotransmitter activity, lower cardiopulmonary reserve, and higher prevalence of comorbidities [24]. Older patients typically experience slower emergence, increased risk of postoperative delirium, and require more cautious medication management—all contributing to extended PACU stays. The RENAL nephrometry score, which assesses tumor complexity based on size, contour, proximity to collecting system, and location, also proved to be a strong predictor [13]. Higher scores indicate more technically challenging surgeries, often associated with longer operative times, greater blood loss, more extensive renal ischemia, and complex renorrhaphy. These factors amplify surgical stress, fluid shifts, analgesic requirements, and physiological disturbance during emergence, thereby justifying the score’s predictive value for PACU duration. Additionally, complex tumors may correlate with a more aggressive tumor biology and higher baseline inflammatory status [25]. Our nomogram offers several advantages over conventional PACU discharge assessments and existing predictive models. Its foremost strength is its preoperative applicability. By relying solely on preoperative data—demographics, imaging-based RENAL score, and routine blood tests—the model provides predictive insights before surgery, enabling proactive resource allocation and clinical planning. For instance, identifying patients likely to require prolonged PACU stay helps managers anticipate bed needs, optimize nursing schedules, and streamline bed turnover. Setting realistic expectations with patients and families may further improve satisfaction and reduce anxiety. Although age and tumor complexity are known predictors, incorporating SII and monocyte count significantly enhances predictive accuracy by leveraging inexpensive, routinely available biomarkers, underscoring the value of integrating inflammatory biology into perioperative risk assessment. The nomogram exhibited excellent discrimination (C-index ≈ 0.80 in both training and validation cohorts) and good calibration across all time points. Decision curve analysis confirmed its clinical utility across a wide range of threshold probabilities. Moreover, it outperformed a model based solely on age and RENAL score, highlighting the added value of inflammatory markers. Our findings align with and extend previous studies linking inflammatory markers to prolonged recovery in other surgical settings [26]. However, our study is the first to focus specifically on PACU stay following RAPN for RCC. Although other models predict prolonged PACU stay, many incorporate intraoperative or postoperative variables, limiting their utility for preoperative planning—a gap our model directly addresses [4,7]. Furthermore, our use of simple, objective, and widely available preoperative data contrasts with models reliant on complex comorbidity indices or functional assessments. Nomograms are increasingly valued for personalized surgical risk prediction due to their user-friendly graphical format, which facilitates quick estimation of individual patient risks. Monitoring actual versus predicted PACU times by risk group could serve as a quality improvement metric. This study has several limitations. Its single-center, retrospective design introduces risks of selection bias and unmeasured confounding. Generalizability requires validation in larger, prospective, multi-center cohorts. The model is specific to RAPN for RCC and its applicability to other surgeries remains unknown. Variations in surgical technique, anesthetic management, and intraoperative factors were not controlled for. Although discharge decisions followed institutional protocols, some subjectivity remains; further standardization may improve consistency. While SII and monocytes are practical surrogates, direct cytokine or CYP450 activity measurement could offer deeper mechanistic insight. Finally, although we predicted a process measure (PACU duration), future research should assess whether using the nomogram improves patient outcomes (e.g., fewer complications, greater satisfaction) or operational efficiency (e.g., faster turnover, lower costs). Conclusion In conclusion, we developed and validated a preoperative nomogram that combines age, RENAL score, monocyte count, and SII to predict PACU stay in RAPN patients. The model demonstrates strong performance, underpinned by the association between systemic inflammation and delayed recovery. It represents a meaningful advance toward personalized, data-driven care, with the potential to enhance patient safety, optimize resources, and reduce costs. Future efforts should focus on external validation, preoperative anti-inflammatory interventions, and seamless integration into clinical workflows. Abbreviations PACU Post-Anesthesia Care Unit RCC Renal cell carcinoma RAPN Robot-assisted partial nephrectomy C-index Concordance index ROC Receiver operating characteristic DCA Decision curve analysis SII Systemic immune-inflammation index NLR Neutrophil-to-lymphocyte ratio PLR Platelet-to-lymphocyte ratio LMR Lymphocyte-to-monocyte ratio DCAs Decision curve analyses HRs Hazard ratios CIs Confidence intervals Declarations Ethics approval and consent to participate This randomized controlled trial adhered to the Declaration of Helsinki and was approved by the Ethics Committee of The Dalian Medical University Second Hospital (certificate number: 2023064). This study obtained informed consent from all participants. Consent for publication Not Applicable. Availability of data and materials The datasets used and analyzed during the study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This study was supported by the “1+X” Research Project of the Second Hospital of Dalian Medical University (CYQH2024016) to Y.H; Dalian Municipal Guidance Plan Initiative for the Life and Health Sector (2024ZDJH01PT113) to Y.H.; Liaoning Provincial Department of Education Basic Scientific Research Project. (LJ212510161015) to B.Y. Authors' contributions YK: Conceptualization, Investigation, Methodology, Analysis, Visualization, Writing original draft, Review and Editing. WL: Conceptualization, Data curation, Analysis, Investigation. YH: Conceptualization, Analysis, Investigation, Writing original draft. BY: Conceptualization, Project administration, Supervision, Review and Editing. ML & MW: Investigation, Resources. All authors read and approved the final manuscript. Acknowledgements We wish to thank all the anesthesiologists and surgical teams for their support in this study. References Zhang Y, Dai Q, Xu K, et al. 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Prognostic Significance of Preoperative Inflammation Markers on the Long-Term Outcomes in Peritoneal Carcinomatosis from Ovarian Cancer. Cancers. 2024;16:254. https://doi.org/10.3390/cancers16020254 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 24 Dec, 2025 Editor assigned by journal 22 Dec, 2025 Editor invited by journal 27 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 26 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8150211","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565106645,"identity":"3844de66-285c-452e-a58c-e39e03c473bf","order_by":0,"name":"Ying Ke","email":"","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Ke","suffix":""},{"id":565106646,"identity":"dde9f5a0-a0fe-4e3f-a0ee-60e0310b0c7d","order_by":1,"name":"Wanshu Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wanshu","middleName":"","lastName":"Liu","suffix":""},{"id":565106647,"identity":"6867b989-e35a-4955-80a6-834bce713476","order_by":2,"name":"Yi He","email":"","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"He","suffix":""},{"id":565106648,"identity":"b2b6454c-9afe-499f-b16d-5a8922eb4f10","order_by":3,"name":"Bo Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie3PsUoDMRzH8ZRAXP7XW3NYrj5CIFARD918jhyFmw65sWPKQVx8gPgizv8jg8vprQVHRzukuAraw00w7eiQ7xh+H/iHkFjsn0YJKYCdrDUqUUCa6qNIlU/BdeibapZZPIo4mfObZWe9K4RW4bl4enbvzYqWhoNwIAYQBCd+VwdIf1td2p6V5vR+JK9wTjXNHh4DBOuFTAyUZtb/kAuNjCYhMmxHwveH1SN5AYHqANnU8i0xQjJeqc4KPEyyzXZBba9yBg7RiyVktmuDf5kOtfxoVl8wv1u3Xn1eXadp2/ldgJwhYfzX20T/vd8314T64CIWi8Vi3xD4WPAGdYvvAAAAAElFTkSuQmCC","orcid":"","institution":"Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yang","suffix":""},{"id":565106650,"identity":"d4ce8f81-8f29-49b5-9595-d38b05a3979e","order_by":4,"name":"Meihong Lv","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical 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10:00:34","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113159,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8150211/v1/ef8e964c282d9b5cee07fbac.html"},{"id":99317592,"identity":"b9b6c829-61d1-4a47-b1f0-33bf57bbb1e9","added_by":"auto","created_at":"2025-12-31 16:30:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":469673,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment and validation of a predictive nomogram. (A) Nomogram based on age, RENAL score, monocyte count (MON), and systemic immune-inflammation index (SII) for estimating PACU discharge probability at 35, 45, and 55 minutes. (B, C) Calibration curves demonstrating the agreement between predicted and observed outcomes in the training (B) and validation (C) cohorts.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8150211/v1/23b2b8c7fa905693ce50ba1f.jpeg"},{"id":99318733,"identity":"ba0d1925-d073-4cbd-94af-9107b4850716","added_by":"auto","created_at":"2025-12-31 16:34:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133734,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the nomogram in the training cohort. (A) Comparison of time-dependent decision curve analysis between the nomogram and a clinical model. (B) ROC curves for predicting PACU duration at 35, 45, and 55 minutes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8150211/v1/d06b579879dee1845f5493d4.png"},{"id":99223440,"identity":"ddb677e2-e901-4a29-8841-3dfd1cb569f6","added_by":"auto","created_at":"2025-12-30 10:00:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145197,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the nomogram in the test set. (A) Decision curve analysis comparing the nomogram and clinical model. (B) ROC curves for predicting PACU discharge at 35, 45, and 55 minutes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8150211/v1/17450d3922b63065c3219306.png"},{"id":99317198,"identity":"494d9428-2261-40fe-b5cb-728ad7060cb1","added_by":"auto","created_at":"2025-12-31 16:29:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74448,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive validation of the nomogram. PACU discharge probability curves stratified by the nomogram-based risk score in the entire cohort.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8150211/v1/551eab1cd92abc0524ab1203.png"},{"id":99323469,"identity":"00222cad-be17-4aff-aaf9-444ec680b19d","added_by":"auto","created_at":"2025-12-31 16:45:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1581469,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8150211/v1/ca52a384-ffe2-4467-aa90-ab38ba5bc3b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram based on preoperative systemic immune-inflammation index for Post-Anesthesia Care Unit duration predication in renal cancer patients underwent Robot-assisted partial nephrectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Post-Anesthesia Care Unit (PACU), commonly known as the recovery room, provides immediate postoperative care for patients recovering from anesthesia, with continuous monitoring of their physiological status and management of any emerging complications. Inadequate or premature discharge from the PACU may result in serious postoperative issues, such as respiratory, cardiovascular, and neurological complications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe length of stay in the PACU plays a critical role in operating room management, significantly affecting operational efficiency, hospital costs, and staffing demands [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This metric is essential for ensuring a safe transition of patients from the PACU to their subsequent postoperative destinations. Extended PACU stays can diminish patient satisfaction and increase institutional expenses, whereas discharge that is too early may lead to anesthesia- or surgery-related complications, potentially raising morbidity and mortality rates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, timely and appropriate discharge from the PACU is vital for patient safety, reduction of complications, and improvement of hospital operational efficiency.\u003c/p\u003e \u003cp\u003eConventionally, PACU discharge decisions rely on standardized assessment tools such as the Aldrete score, which evaluates vital signs including respiration, circulation, consciousness, mobility, and oxygen saturation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although various clinical prediction models have been developed for different patient groups [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], existing models are not suitable for those undergoing partial nephrectomy. Furthermore, many previously proposed models incorporate intraoperative variables [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While some demonstrate strong predictive ability, their reliance on parameters unavailable preoperatively limits their utility in clinical forecasting. As a result, they cannot support early prediction of PACU stay or facilitate proactive optimization of workflow and resource allocation.\u003c/p\u003e \u003cp\u003eAnesthetic agents are frequently metabolized via the CYP450 enzyme system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Evidence suggests that inflammation and pathological states can downregulate CYP450 activity, thereby impairing drug metabolism [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The magnitude of this suppression is often correlated with the severity of inflammation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We therefore hypothesized that patients exhibiting elevated systemic inflammatory markers would experience delayed recovery from anesthesia and prolonged PACU stays. Despite ongoing research efforts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], significant knowledge gaps remain in this field. Accordingly, this study aimed to evaluate the predictive utility of peripheral inflammatory markers\u0026mdash;including the systemic immune-inflammation index (SII)\u0026mdash;in estimating PACU duration for patients with renal cell carcinoma (RCC) undergoing robot-assisted partial nephrectomy (RAPN).\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient recruitment and clinical variables collection\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed data from 218 patients diagnosed with renal cell carcinoma (RCC) who underwent robot-assisted partial nephrectomy (RAPN) under general anesthesia between December 2023 and April 2025. The study protocol received approval from the Ethics Committee of the Second Hospital of Dalian Medical University (Approval No. : 2023064), and all participants provided written informed consent for the use of their medical records for research purposes. All patient data were anonymized prior to analysis.\u003c/p\u003e \u003cp\u003eInclusion criteria comprised adult patients (\u0026ge;\u0026thinsp;18 years) scheduled for elective RAPN. Patients with incomplete medical records or those who experienced significant intraoperative complications (e.g., major hemorrhage or cardiac events) that could confound standard postoperative recovery were excluded. PACU discharge readiness was determined based on Aldrete scores consistently reaching 9 or higher [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe collected a comprehensive set of preoperative variables, including demographic characteristics (age, sex), tumor complexity assessed by the RENAL nephrometry score [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and hematological parameters. The latter included absolute counts of neutrophils, lymphocytes, platelets, and monocytes, as well as derived inflammatory indices: the systemic immune-inflammation index (SII, calculated as platelet count \u0026times; neutrophil count / lymphocyte count), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). The primary outcome was the duration of PACU stay, defined as the time from PACU admission to meeting discharge criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNomogram development and validation\u003c/h3\u003e\n\u003cp\u003eThe study cohort was randomly partitioned into a training set (n\u0026thinsp;=\u0026thinsp;154, 70%) for model development and an internal validation set (n\u0026thinsp;=\u0026thinsp;64, 30%) using the 'caret' package in R. To identify predictors of PACU stay duration, we performed univariate Cox regression analyses on all preoperative variables. Variables showing significant association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the univariate analysis were subsequently entered into a multivariate Cox proportional hazards regression model within the training cohort.\u003c/p\u003e \u003cp\u003eA nomogram was then constructed using the 'rms' package in R, incorporating the independent predictors identified from the multivariate analysis. This nomogram was designed to estimate the probability of PACU discharge at three specific time points: 35, 45, and 55 minutes. Each patient's cumulative risk score, derived from the nomogram, was calculated to stratify patients into different risk categories for PACU stay duration.\u003c/p\u003e \u003cp\u003eThe model's predictive performance was evaluated using several metrics. Discrimination was assessed with the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves. Calibration plots were used to visualize the agreement between predicted and observed discharge probabilities. Furthermore, decision curve analysis (DCA) was employed to evaluate the clinical net benefit of the nomogram across a range of threshold probabilities.\u003c/p\u003e\n\u003ch3\u003eRisk stratification based on nomogram scores\u003c/h3\u003e\n\u003cp\u003eUsing the 'maxstat' package in R, an optimal cut-off value for the nomogram-derived risk score was determined to categorize patients into distinct risk groups. The analysis ensured that each group contained between 25% and 75% of the total cohort. Kaplan-Meier survival curves were subsequently generated for these risk groups, and the statistical significance of the differences in PACU stay duration was compared using the log-rank test.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables with a normal distribution were dichotomized using optimal cut-off values determined by X-tile software (v3.6.1). All statistical analyses were conducted using SPSS (version 24.0), with categorical variables summarized as frequencies and percentages. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were derived from the Cox regression models. ROC analyses were performed using the 'pROC' package in R (version 1.17.0.1) to calculate the area under the curve (AUC) at the specified time points. A two-sided P-value of less than 0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline patient\u0026nbsp;characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 218 patients who met the inclusion criteria were included in the final analysis. The median age was 60 years (range: 23-85), and the majority were male (62.39%). Tumor complexity, as measured by the RENAL score, was distributed as follows: 25.69% low complexity (4-6), 44.95% moderate complexity (7-9), and 29.36% high complexity (10-12). The median PACU stay duration was 45 minutes, with a range from 25 to 140 minutes. Based on the optimal cut-off values determined by X-tile software, continuous hematological variables were transformed into categorical variables for subsequent analysis. A detailed summary of the baseline clinicopathological characteristics of the entire cohort is provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eRepresentativeness of study participants\u003c/p\u003e\n\u003ctable width=\"529\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"315\"\u003e\n\u003cp\u003eClinical characteristics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003eNumber\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eAge (years old)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e60 [23-85]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e46 (21.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e172 (78.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"132\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e136 (62.39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e82 (37.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eRENAL score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e56 (25.69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e7-9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e98 (44.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e4-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e64 (29.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eNeutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e3.32 [1.04 - 6.58]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e122 (55.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e96 (44.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003elymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e1.79 [0.38 - 3.88]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e112 (51.38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e106 (48.62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eWhite cell count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e5.83 [2.63 - 9.72]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 5.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e62 (28.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 5.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e156 (71.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003ePlatelet count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e228 [112 - 606]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e156 (71.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e62 (28.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eMonocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e0.40 [0.15 - 1.23]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e80 (36.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e138 (63.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eNLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e1.77 [0.53 - 8.02]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 1.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e92 (42.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 1.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e126 (57.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003ePLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e125.52 [40.94 - 497.83]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 162.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e156 (71.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 162.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e62 (28.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eLMR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e4.38 [1.00 - 11.85]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e39 (17.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e179 (82.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"132\"\u003e\n\u003cp\u003eSII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e405.18 [111.69 - 2058.89]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026le; 457.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e131 (60.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003e\u0026gt; 457.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e87 (39.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003ePACU duration (minutes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"183\"\u003e\n\u003cp\u003eMedian [min-max]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e45 [25- 140]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: NLR: neutrophil-to-lymphocyte ratio; PLR: platelet-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio; SII: systemic immune-inflammation index; PACU: Post-Anesthesia Care Unit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of independent predictors and nomogram construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort was randomly divided into training (n=154) and validation (n=64) sets. Univariate Cox regression analysis in the training set identified several variables potentially associated with PACU duration. Subsequent multivariate analysis revealed that older age (\u0026gt;48 years), higher tumor complexity (RENAL score), elevated monocyte count (\u0026gt;0.36 \u0026times; 10⁹/L), and a higher systemic immune-inflammation index (SII \u0026gt; 457.82) were independent predictors of a prolonged PACU stay (all P \u0026lt; 0.001; see Table 2).\u003c/p\u003e\n\u003cp\u003eThese four independent predictors were incorporated into the construction of a nomogram (Figure 1A). The nomogram allows for the calculation of a total point score for an individual patient, which corresponds to the predicted probability of PACU discharge at 35, 45, and 55 minutes. Calibration curves for both the training and validation cohorts demonstrated excellent agreement between the nomogram's predictions and actual observed outcomes (Figure 1B, 1C).\u003c/p\u003e\n\u003cp\u003eTo illustrate the added value of the inflammatory markers, a baseline clinical model was built using only age and RENAL score. The comprehensive nomogram exhibited superior predictive performance, with a C-index of 0.816 (95% CI: 0.783\u0026ndash;0.849) in the training set, compared to 0.746 (95% CI: 0.701\u0026ndash;0.789) for the baseline model. Decision curve analysis (Figure 2A) confirmed that the full nomogram provided a greater net clinical benefit across a wide range of threshold probabilities than the baseline model. Similarly, ROC analysis (Figure 2B) showed that the nomogram achieved higher AUC values for predicting PACU discharge at all three time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u003c/strong\u003eUnivariate and multivariate analyses of factors associated with PACU duration\u003c/p\u003e\n\u003ctable width=\"682\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" width=\"236\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"232\"\u003e\n\u003cp\u003eUnivariate analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"215\"\u003e\n\u003cp\u003eMultivariate analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eHR (95%CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003eHR (95%CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e0.36 (0.24-0.56）\u003c/p\u003e\n\u003cp\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e0.44 (0.28-0.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;0.88 (1.134-0.63)\u003c/p\u003e\n\u003cp\u003e2.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"124\"\u003e\n\u003cp\u003eRENAL score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e4-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e7-9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.71 (1.16-2.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.006**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e1.81 (1.21-2.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.004**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e4.37 (2.78-6.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e5.48 (3.33-9.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eNeutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e0.98 (0.70-1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003elymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e0.79 (0.57-1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eWhite cell count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 5.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 5.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.21 (0.84-1.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003ePlatelet count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.77 (1.24-2.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e1.71 (0.61-4.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.272\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eMonocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.88 (1.34-2.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e2.25 (1.56-3.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eNLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 1.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 1.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.14 (0.81-1.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.433\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003ePLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 162.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 162.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.77 (1.24-2.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e0.48 (0.17-1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.179\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eLMR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1 (0.6619-1.511)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"124\"\u003e\n\u003cp\u003eSII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026gt; 457.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026le; 457.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e3.23 (2.23-4.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e3.62 (2.33-5.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio, PLR: platelet-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e Development and validation of a predictive nomogram. (A) Nomogram based on age, RENAL score, monocyte count (MON), and systemic immune-inflammation index (SII) for estimating PACU discharge probability at 35, 45, and 55 minutes. (B, C) Calibration curves demonstrating the agreement between predicted and observed outcomes in the training (B) and validation (C) cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e Validation of the nomogram in the training cohort. (A) Comparison of time-dependent decision curve analysis between the nomogram and a clinical model. (B) ROC curves for predicting PACU duration at 35, 45, and 55 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the nomogram's predictive accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe robust performance of the nomogram was confirmed in the internal validation cohort. It achieved a C-index of 0.835 (95% CI: 0.790\u0026ndash;0.880), which was substantially higher than the 0.695 (95% CI: 0.615\u0026ndash;0.775) of the baseline model. The decision curve analysis in the validation set (Figure 3A) reiterated the superior clinical utility of the nomogram. The AUC values for the nomogram at 35, 45, and 55 minutes were 0.989, 0.975, and 0.878, respectively, consistently outperforming the baseline model (Figure 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical application through risk stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the nomogram-generated risk scores for the entire cohort, an optimal cut-off value was applied to stratify patients into high-risk (n=92, 42.2%) and low-risk (n=126, 57.8%) groups. The PACU discharge probability of patients in the low-risk group (median: 50min) was considerably superior than that of the high-risk group (median: 35min, P \u0026lt;0.0001), according to the K-M survival curves (Fig 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003eValidation of the nomogram in the test set. (A) Decision curve analysis comparing the nomogram and clinical model. (B) ROC curves for predicting PACU discharge at 35, 45, and 55 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e Predictive validation of the nomogram. PACU discharge probability curves stratified by the nomogram-based risk score in the entire cohort.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study establishes a significant association between preoperative systemic inflammation—measured SII and monocyte count—and the duration of PACU stay in patients undergoing RAPN for RCC. By integrating these readily available hematologic markers with established clinical predictors such as patient age and tumor complexity (as quantified by the RENAL score), we developed and validated a novel preoperative nomogram. This tool demonstrates robust performance in predicting the probability of PACU discharge at key time thresholds (35, 45, and 55 minutes), showing considerable potential to optimize perioperative workflow and resource allocation.\u003c/p\u003e\n\u003cp\u003eThe central hypothesis of this study—that elevated systemic inflammation delays emergence from anesthesia and prolongs PACU stay—is strongly supported by the identification of SII and monocyte count as independent predictors. This finding is consistent with growing evidence underscoring the interplay between inflammation, drug metabolism, and neurological recovery [14]. A key mechanistic pathway involves the downregulation of cytochrome P450 (CYP450) enzymes by inflammatory mediators. Many commonly used anesthetic agents (e.g., propofol, midazolam, opioids such as fentanyl and sufentanil) are metabolized via this pathway [8, 9]. Pro-inflammatory cytokines—including IL-6, IL-1β, and TNF-α, which are frequently elevated in cancer and systemic inflammatory states—can suppress the expression [15, 16]. An elevated SII, which reflects neutrophilia and lymphopenia often driven by these cytokines, serves as a surrogate marker of this inflammatory state [17]. Similarly, monocytes are primary producers of IL-1β and TNF-α [18]. Thus, patients with high preoperative SII and monocyte counts are likely to exhibit slower clearance of anesthetics, resulting prolonged drug effects, delayed awakening, reduced respiratory drive, and impaired recovery of protective reflexes and cognitive function—all critical factors determining safe PACU discharge.\u003c/p\u003e\n\u003cp\u003eInflammation may also alter pharmacodynamic responses to anesthetics [19]. Cytokines can modulate neurotransmitter systems and neuronal excitability within the central nervous system (CNS), potentially changing sensitivity to sedatives and analgesics. Systemic inflammation may increase blood-brain barrier permeability, permitting greater entry of inflammatory mediators into the CNS, where they can exert direct sedative or delirium-inducing effects [20]. This compromised CNS environment likely contributes to slower neurological recovery in patients with higher inflammatory burden. Furthermore, chronic inflammation—a hallmark of cancer—is often associated with underlying organ dysfunction and greater comorbidity burden [21, 22]. For instance, inflammation can exacerbate conditions such as coronary artery disease or chronic obstructive pulmonary disease [22, 23], rendering patients more susceptible to hemodynamic and respiratory instability during emergence, thereby necessitating prolonged PACU monitoring or intervention.\u003c/p\u003e\n\u003cp\u003eThe inclusion of age as a predictor is physiologically well-founded. Aging is associated with reduced hepatic and renal function, altered body composition, diminished neuronal density and neurotransmitter activity, lower cardiopulmonary reserve, and higher prevalence of comorbidities [24]. Older patients typically experience slower emergence, increased risk of postoperative delirium, and require more cautious medication management—all contributing to extended PACU stays. The RENAL nephrometry score, which assesses tumor complexity based on size, contour, proximity to collecting system, and location, also proved to be a strong predictor [13]. Higher scores indicate more technically challenging surgeries, often associated with longer operative times, greater blood loss, more extensive renal ischemia, and complex renorrhaphy. These factors amplify surgical stress, fluid shifts, analgesic requirements, and physiological disturbance during emergence, thereby justifying the score’s predictive value for PACU duration. Additionally, complex tumors may correlate with a more aggressive tumor biology and higher baseline inflammatory status [25].\u003c/p\u003e\n\u003cp\u003eOur nomogram offers several advantages over conventional PACU discharge assessments and existing predictive models. Its foremost strength is its preoperative applicability. By relying solely on preoperative data—demographics, imaging-based RENAL score, and routine blood tests—the model provides predictive insights before surgery, enabling proactive resource allocation and clinical planning. For instance, identifying patients likely to require prolonged PACU stay helps managers anticipate bed needs, optimize nursing schedules, and streamline bed turnover. Setting realistic expectations with patients and families may further improve satisfaction and reduce anxiety. Although age and tumor complexity are known predictors, incorporating SII and monocyte count significantly enhances predictive accuracy by leveraging inexpensive, routinely available biomarkers, underscoring the value of integrating inflammatory biology into perioperative risk assessment.\u003c/p\u003e\n\u003cp\u003eThe nomogram exhibited excellent discrimination (C-index ≈ 0.80 in both training and validation cohorts) and good calibration across all time points. Decision curve analysis confirmed its clinical utility across a wide range of threshold probabilities. Moreover, it outperformed a model based solely on age and RENAL score, highlighting the added value of inflammatory markers.\u003c/p\u003e\n\u003cp\u003eOur findings align with and extend previous studies linking inflammatory markers to prolonged recovery in other surgical settings [26]. However, our study is the first to focus specifically on PACU stay following RAPN for RCC. Although other models predict prolonged PACU stay, many incorporate intraoperative or postoperative variables, limiting their utility for preoperative planning—a gap our model directly addresses [4,7]. Furthermore, our use of simple, objective, and widely available preoperative data contrasts with models reliant on complex comorbidity indices or functional assessments. Nomograms are increasingly valued for personalized surgical risk prediction due to their user-friendly graphical format, which facilitates quick estimation of individual patient risks. Monitoring actual versus predicted PACU times by risk group could serve as a quality improvement metric.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Its single-center, retrospective design introduces risks of selection bias and unmeasured confounding. Generalizability requires validation in larger, prospective, multi-center cohorts. The model is specific to RAPN for RCC and its applicability to other surgeries remains unknown. Variations in surgical technique, anesthetic management, and intraoperative factors were not controlled for. Although discharge decisions followed institutional protocols, some subjectivity remains; further standardization may improve consistency. While SII and monocytes are practical surrogates, direct cytokine or CYP450 activity measurement could offer deeper mechanistic insight. Finally, although we predicted a process measure (PACU duration), future research should assess whether using the nomogram improves patient outcomes (e.g., fewer complications, greater satisfaction) or operational efficiency (e.g., faster turnover, lower costs).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we developed and validated a preoperative nomogram that combines age, RENAL score, monocyte count, and SII to predict PACU stay in RAPN patients. The model demonstrates strong performance, underpinned by the association between systemic inflammation and delayed recovery. It represents a meaningful advance toward personalized, data-driven care, with the potential to enhance patient safety, optimize resources, and reduce costs. Future efforts should focus on external validation, preoperative anti-inflammatory interventions, and seamless integration into clinical workflows.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePACU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePost-Anesthesia Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAPN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRobot-assisted partial nephrectomy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConcordance index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLymphocyte-to-monocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis randomized controlled trial adhered to the Declaration of Helsinki and was approved by the Ethics Committee of The Dalian Medical University Second Hospital (certificate number: 2023064). This study obtained informed consent from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the study are available from the corresponding author upon reasonable request.\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the “1+X” Research Project of the Second Hospital of Dalian Medical University (CYQH2024016) to Y.H; Dalian Municipal Guidance Plan Initiative for the Life and Health Sector (2024ZDJH01PT113) to Y.H.; Liaoning Provincial Department of Education Basic Scientific Research Project. (LJ212510161015) to B.Y.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYK: Conceptualization, Investigation, Methodology, Analysis, Visualization, Writing original draft, Review and Editing. WL: Conceptualization, Data curation, Analysis, Investigation. YH: Conceptualization, Analysis, Investigation, Writing original draft. BY: Conceptualization, Project administration, Supervision, Review and Editing. ML \u0026amp; MW: Investigation, Resources. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank all the anesthesiologists and surgical teams for their support in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Dai Q, Xu K, et al. 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Cancers. 2024;16:254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers16020254\u003c/span\u003e\u003cspan address=\"10.3390/cancers16020254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":false,"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":"Monocyte count, systemic immune-inflammation index, PACU stay, Robot-assisted partial nephrectomy, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-8150211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8150211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is crucial for preventing postoperative complications and optimizing hospital resource utilization. This study aimed to develop a preoperative nomogram incorporating inflammatory markers and clinical characteristics to predict anesthesia recovery time in patients with renal cell carcinoma (RCC) undergoing robot-assisted partial nephrectomy (RAPN).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA nomogram was developed to identify significant predictors of anesthesia recovery time in a retrospective cohort of 218 patients under general anesthesia for RCC between December 2023 and April 2025. Based on preoperative covariates and multivariable Cox proportional hazards regression, the model estimates the probability of discharge from the post-anesthesia care unit (PACU) at 35, 45, and 55 minutes. Validation of the nomogram was performed using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAge, RENAL score, monocyte count, and systemic immune-inflammation index (SII) were identified as independent predictors and incorporated into the nomogram. The model demonstrated strong predictive performance for PACU duration at all three time points. The C-index values were 0.816 for the training cohort and 0.835 for the validation cohort. ROC analysis, calibration plots, and DCA all indicated satisfactory discrimination and clinical utility. Furthermore, the nomogram showed superior predictive performance compared to a model based solely on age and RENAL score. Risk stratification based on nomogram scores categorized patients into two distinct groups with significantly different recovery times.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe nomogram developed in this study exhibits strong predictive accuracy for estimating anesthesia recovery time, showing promise as a practical tool for assessing PACU discharge readiness. Further research is warranted to validate its clinical utility and potential to enhance patient outcomes and operational efficiency.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eThis study is a retrospective study.\u003c/p\u003e","manuscriptTitle":"Nomogram based on preoperative systemic immune-inflammation index for Post-Anesthesia Care Unit duration predication in renal cancer patients underwent Robot-assisted partial nephrectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 10:00:28","doi":"10.21203/rs.3.rs-8150211/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-24T05:01:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T13:43:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-27T10:59:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T07:58:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2025-11-26T07:51:11+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":"168f2217-51a1-453e-a498-bddbcae2d55e","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T10:00:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 10:00:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8150211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8150211","identity":"rs-8150211","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}