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).