Analysis of Risk Factors for Delayed Discharge from the Post-Anesthesia Care Unit in Patients Undergoing General Anesthesia and Establishment and Validation of a Predictive Model: A Retrospective Case-Control Study

Analysis of Risk Factors for Delayed Discharge from the Post-Anesthesia Care Unit in Patients Undergoing General Anesthesia and Establishment and Validation of a Predictive Model: A Retrospective Case-Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Risk Factors for Delayed Discharge from the Post-Anesthesia Care Unit in Patients Undergoing General Anesthesia and Establishment and Validation of a Predictive Model: A Retrospective Case-Control Study Yongwen Lai, Chunying Zhu, Cheng Lin, Yongcheng Liu, XiuQin Lu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8291467/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Delayed discharge from the post-anesthesia care unit (PACU) after general anesthesia is a common complication in clinical anesthesia, resulting from the combined effect of multiple risk factors. It compromises the quality of postoperative recovery while diminishing the efficiency of perioperative turnover. Our study attempts to determine the risk factors for delayed PACU discharge and to create and validate a nomogram predictive model. Methods : A total of 746 patients with delayed PACU discharge after general anesthesia were enrolled. Using a 1:1 matching design (consistent gender and age ±2 years), 746 eligible patients without delayed discharge were selected as controls. Both the delayed and non-delayed discharge groups were split into a training set (n=1046) and a test set (n=446) at a 7:3 ratio. Logistic regression analysis was performed in the training set to develop a risk prediction model, which was then validated in the test set. The discriminative ability, model calibration, and clinical utility were assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), correspondingly.The goodness-of-fit for the calibration curves was determined using the Hosmer-Lemeshow (HL) test. Results : A predictive nomogram model was developed using eight significant variables identified through multivariate logistic regression analysis: Body Mass Index (BMI), rocuronium dosage, Fasting blood glucose (FBG) , blood loss, PACU agitation, PACU pain, PACU hypoxemia, and PACU blood gas analysis. For the training set, the area under the ROC curve (AUC) was 0.888 (95% confidence interval [CI]: 0.868–0.908), and the corresponding value in the test set was 0.887 (95% CI: 0.856–0.918). Calibration curves indicated a high degree of agreement between predicted probabilities and actual probabilities. The P-values of the HL test in the training set and test set were 0.53 and 0.15, respectively, indicating good goodness-of-fit. DCA demonstrated that when the predicted probability exceeded 10%, using this model to predict delayed PACU discharge and implement intervention measures would yield greater benefits. Conclusion : This study created and validated a predictive model to estimate the likelihood of delayed PACU discharge in patients following general anesthesia. General anesthesia PACU Delayed discharge Risk factors Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction For patients in the anesthesia recovery period following surgery, the post-anesthesia care unit (PACU) delivers short-term monitoring. It not only improves surgical turnover rate but also ensures the awakening and early recovery of patients after anesthesia, including the detection and treatment of early anesthesia and surgical complications, until their vital signs and consciousness return to a level suitable for transfer to a general ward or discharge [1, 2]. Delayed discharge from the PACU following general anesthesia is a frequent complication during the recovery phase，which not only hinders the PACU's ability to admit new patients but also creates bottlenecks at different stages of the perioperative process. More importantly, it leads to prolonged hospital stay, increased medical costs, compromised patient safety and nursing quality, and patient dissatisfaction [3, 4]. At present, the enhanced recovery after surgery (ERAS)protocol has been applied in multiple surgical subspecialties during perioperative management.The goal of the project is to decrease surgical stress and problems by working in a number of different ways and to speed the patient recovery process after surgery by having a variety of well-coordinated teamwork.As the first stop for patient awakening and postoperative recovery, the PACU plays a crucial role in ensuring timely patient awakening and early recovery, which is essential for the successful implementation of ERAS [5, 6]. Therefore, it is necessary to identify the risk factors for delayed PACU discharge and establish and validate a predictive model. In recent years, relevant studies have been reported in this field, but these studies are limited to specific surgical types, specific populations, or the impact of single factors, resulting in limited applicability in predicting other surgical types, other patient populations, or other influencing factors [7-11]. Thus, on the basis of previous studies, this study collected three types of indicators (preoperative general conditions, intraoperative surgical and anesthetic conditions, and postoperative complications) from 1492 patients, covering various surgical types and a broader patient population, to further identify other unknown risk factors and establish and validate a predictive model. This aims to provide an innovative predictive tool for anesthesiologists and nurses in future clinical work and research to identify high-risk patients for delayed PACU discharge. Materials and Methods Study Population This retrospective study was conducted at the First Affiliated Hospital of Guilin Medical University,with data collected from patients admitted to the PACU for recovery between January 2023 and December 2024. Its Ethics Committee granted approval for this study (Ethics Approval No.: 2024YJSLL-107), and all participating patients provided written informed consent. Eligibility criteria were as follows: ① patients transferred to the PACU for recovery post-surgery; ② patients receiving endotracheal intubation general anesthesia; ③ patients ≥ 18 years of age. The exclusion criteria were: ① patients undergoing neurosurgery; ② patients undergoing cardiac and great vessel surgery; ③ missing data. Three sections were used to gather the variables that were obtained using the electronic medical record system: preoperative indicators (age, gender, albumin, total bilirubin, fasting blood glucose concentration, creatinine, urea nitrogen, hemoglobin, white blood cells, smoking history, alcoholism history, hypertension history, diabetes mellitus history, respiratory system disease history, cardiovascular system disease history, cerebrovascular system disease history, American Society of Anesthesiologists (ASA) classification, Body Mass Index (BMI) classification), intraoperative indicators (surgical time, surgical grade, elective/emergency surgery, surgical type, surgical position, total fluid infusion volume, leukocyte-depleted red blood cell transfusion, plasma transfusion, autologous blood transfusion, anesthesia time, recovery time, anesthesia type, sufentanil dosage, rocuronium dosage, regional block, use of vasoactive drugs, intraoperative hypothermia, blood loss, intraoperative blood gas analysis, bispectral index (BIS) monitoring), and postoperative indicators (PACU hypothermia, PACU hypertension, PACU hypotension, PACU agitation, PACU pain, PACU hypoxemia, PACU blood gas analysis, prognosis and transfer). Surgical grades were categorized into Grade 1, 2, 3 and 4, while surgical types were classified as minor,moderate and major surgery. Minor surgery included superficial masses, thyroid nodules, breast nodules, debridement, and endoscopic surgeries such as hysteroscopy and ureteroscopy. Moderate surgery included cholecystolithiasis, hepatocellular cysts, renal cysts, hernia, appendicitis, gastrointestinal fistula, uterine fibroids, thyroid cancer, breast cancer, wedge resection of the lung, joint replacement, etc. Major surgery included liver cancer, gallbladder cancer, gastric cancer, intestinal cancer, pancreatic cancer, renal cancer, lobectomy, segmentectomy, spinal surgery. Surgical positions were grouped as follows:supine position;prone position;right posterior position;left lateral position;left posterior position and right lateral position. ASA classification was separated into two groups:ASA≤II and ASA＞II. The types of anesthesia were categorized into total intravenous anesthesia and combined intravenous-inhalation anesthesia. BMI was categorized into three groups:low BMI group(less than 18 kg/m²), normal BMI group(18-24 kg/m²), and high BMI group(greater than 24 kg/m²). Preoperative albumin levels were categorized into two groups the hypoalbuminemia group(less than 35 g/L)and the normal albuminemia group(35 g/L or greater). Preoperative fasting blood glucose was separated into hypoglycemia group(＜3.9mmol/L), normal blood glucose group(3.9-6.1mmol/L), and hyperglycemia group(＞6.1mmol/L). The following were the diagnostic standards for intraoperative and PACU hypothermia:body temperature was 36 ℃. A diagnosis of PACU hypertension was established if blood pressure rose by over 20% compared with the pre-anesthetic baseline or reached a peak of 160/95 mmHg. The diagnostic criterion for PACU hypotension was a decrease in blood pressure exceeding 20% of the pre-anesthetic level or a systolic blood pressure decrease to 80 mmHg. For the purpose of this study, pain in the PACU was diagnosed if the numerical rating scale (NRS) score reached 4 or above. The standard for diagnosing agitation in the PACU was established as a Richmond Agitation-Sedation Scale (RASS) score ≥ +2. The diagnostic criterion for PACU hypoxemia was an arterial partial pressure of oxygen (PaO2) ＜60 mmHg or a pulse oxygen saturation (SpO2) ＜90% when breathing room air. PACU discharge destinations include general wards and intensive care unit (ICU). Definition of Delayed PACU Discharge At present, there is no consensus on a standard definition for delayed discharge from the PACU.According to the latest National Medical Quality Control Indicators for Anesthesiology in China (2022 Edition), Guowei Ban Yi Han〔2022〕No. 161, delayed PACU discharge is defined as a stay time in the PACU exceeding 120 minutes. Outcome Indicators The primary endpoint was the risk factors for delayed PACU discharge in patients after general anesthesia. The secondary endpoint was the incidence of delayed PACU discharge among patients following general anesthesia. Statistical Analysis All statistical analyses were performed using R software version 4.3.1. Continuous variables following a normal distribution were presented as mean ± standard deviation, with intergroup comparisons conducted via the independent samples t-test. Continuous variables not following a normal distribution were presented as median and interquartile range, with the Wilcoxon rank-sum test employed for intergroup comparisons. Frequency and percentage were used to describe categorical variables, while the chi-square test was employed for comparisons between groups. All statistical tests were two-sided, and statistical significance was defined as P < 0.05. This was a case-control study with a 1:1 matching design. Based on consistent gender and age ±2 years of patients in the delayed PACU discharge group, eligible patients were matched from the non-delayed PACU discharge group. Additionally, the aforementioned two patient groups were split into a training set and a test set at a 7:3 ratio. The training set was subjected to Logistic regression analysis to pinpoint potential risk factors of delayed PACU discharge, followed by the inclusion of variables with P < 0.05 into multivariate Logistic regression analysis to identify independent risk factors. On this basis, a nomogram, a graphical representation of the risk prediction model for delayed PACU discharge in patients after general anesthesia, was established using R software version 4.3.1. Longer line segments in the nomogram indicate higher scores, meaning that the prediction coefficient has a more significant impact on the outcome. The way of this study was to assess the predictive model's performance in the training set and validation set by means of calibration, discriminant, and discriminating analyses. Calibration curves were used to assess the calibration ability of the predictive model, i.e., the consistency between the probability predicted by the model and the actual event incidence [12]. Additionally，the Hosmer-Lemeshow test was conducted to determine the statistical significance of the consistency of the curves. The area under the receiver operating characteristic (ROC) curve (AUC) is used to assess the model’s discriminative ability [13]. In addition, decision curve analysis (DCA) was used to test clinical applicability, and the threshold probability interval of the net benefit (NB) value that the nomogram could obtain was observed to evaluate the clinical application range of the nomogram [14]. Results Characteristics of patients From January 2023 to December 2024, a total of 22,726 patients were transferred to the PACU for recovery after surgery at our hospital. After screening according to the inclusion and exclusion criteria, 17,032 patients remained, among whom 746 patients had a recovery time in the PACU exceeding 120 minutes. Figure 1 displays the entire procedure for gathering data. According to the gender and age characteristics of these 746 patients, eligible cases were matched 1:1 from 16,286 cases with a recovery time of less than 120 minutes based on equal gender and age ±2 years. Table 1 displays the clinical and demographic information of the two patient groups following matching. Before matching, there were significant differences in gender and age distribution between the 746 patients in the delayed PACU discharge group and the 16,286 patients with a recovery time of less than 120 minutes (P＜0.05). Table 2 displays the clinical and demographic information in the test and training sets; there were no statistically significant differences in each of the indicators, suggesting that there was consistency in the data distribution in both the test and training sets. Table 1 should be placed here Table 2 should be placed here Incidence of Delayed PACU Discharge Figure 2 shows the incidence of delayed PACU discharge from 2023 to 2024 after excluding 5694 patients who did not undergo endotracheal intubation general anesthesia, were aged ＜18 years, underwent neurosurgery or cardiac and great vessel surgery, or had missing data. The incidence rates were 5.79% (95%CI:5.29%-6.29%) and 3.02% (95%CI:2.66%-3.38%) in the years 2023 and 2024，respectively，which indicated a declining trend in the last two years. The total incidence of delayed PACU discharge from 2023 to 2024 was 4.38% (95% CI: 4.07%-4.69%). Table 3 displays the incidence of delayed PACU discharge in each department. In each department，incidence rates were as follows: Department of Spinal Surgery 11.13% (95% CI: 8.48%-14.29%), Department of Joint and Sports Medicine 9.61% (95% CI: 7.75%-11.77%), Department of Gastrointestinal Surgery 8.38% (95% CI: 7.35%-9.52%), Department of Hepatobiliary and Pancreatic Surgery 7.46% (95% CI: 6.23%-8.85%), Department of Urology 4.92% (95% CI: 4.04%-5.95%), Department of Gastroenterology 3.92% (95% CI: 1.99%-6.98%), Department of Extremity Trauma and Hand Surgery 3.56% (95% CI: 1.69%-6.65%), Department of Oral and Maxillofacial Surgery 3.19% (95% CI: 1.97%-4.99%), Department of Emergency Trauma Surgery 2.88% (95% CI: 0.60%-8.23%), Department of Gynecology 2.26% (95% CI: 1.68%-2.98%), Department of Thoracic Surgery 2.08% (95% CI: 1.19%-3.47%), Department of Breast and Thyroid Surgery 1.68% (95% CI: 1.20%-2.29%), Department of Ophthalmology 1.36% (95% CI: 0.45%-3.16%), other departments (Including the Department of Vascular Interventional Surgery, Department of Cardiovascular Medicine, and other departments, with fewer than 100 cases in each department.) 1.34% (95% CI: 0.37%-3.39%), Department of Otorhinolaryngology-Head and Neck Surgery 1.24% (95% CI: 0.86%-1.73%). Table 3 should be placed here Independent Risk Factors for Delayed PACU Discharge Table 4 displays the independent risk factors identified by multivariate Logistic regression analysis in the training set: BMI [ OR, 0.645, 95% CI (0.486-0.869), P＝0.004], rocuronium dosage [OR, 1.015, 95% CI (1.009-1.022), P＜0.001], fasting blood glucose [OR, 1.446, 95% CI (1.030-2.032), P＝0.033], blood loss [OR, 1.003, 95% CI (1.001-1.004), P＜0.001], PACU agitation [OR, 8.088, 95% CI (2.164-39.381), P＝0.004], PACU pain [OR, 4.842, 95% CI (1.629-16.493), P＝0.007], PACU hypoxemia [OR, 47.878, 95% CI (8.609-900.458), P＜0.001], and PACU blood gas analysis [OR, 23.078, 95% CI (13.487-42.067), P＜0.001]. Table4 .Multivariable Logistic Regression Analysis for Predicting the Probability of delayed discharge from the PACU in the training set Variables Beta coefficient OR(95%CI) SE P-Value BMI -0.439 0.645 (0.476-0.869) 0.153 0.004 Rocuronium dosage 0.015 1.015 (1.009-1.022) 0.003 ＜0.001 FBG 0.369 1.446 (1.030-2.032) 0.173 0.033 Blood loss 0.003 1.003 (1.001-1.004) 0.001 ＜0.001 PACU blood gas analysis 3.139 23.078 (13.487-42.067) 0.289 ＜0.001 PACU hypoxemia 3.869 47.878 (8.609-900.458) 1.073 0.001 PACU agitation 2.091 8.088 (2.164-39.381) 0.718 0.004 PACU pain 1.577 4.842 (1.629-16.493) 0.580 0.007 Abbreviations:BMI,Body mass index, FBG Fasting blood glucose PACU,Post-Anesthesia Care Unit P＜0.05: statistically significant Establishment and Validation of the Nomogram A risk prediction model for delayed PACU discharge was developed using R software version 4.3.1, based on the eight independent risk factors identified from the multivariate logistic regression analysis in the training set. The nomogram clearly demonstrates the influence of risk factors on the model for medical staff, and the probability of delayed PACU discharge can be obtained by summing the scores of each risk factor. As shown in Figure 3, four risk factors—PACU blood gas analysis, rocuronium dosage, blood loss, and PACU hypoxemia—had higher scores in the model, significantly increasing the probability of delayed PACU discharge. BMI and preoperative fasting blood glucose had lower scores in the model, contributing less than one-tenth to delayed PACU discharge. The AUC was 0.888 (95% CI = 0.868-0.908) in the training set and 0.887 (95% CI = 0.856-0.918) in the test set(Figures 4). The AUC values for both data groups exceeded 0.85，suggesting that the nomogram demonstrated excellent discriminative ability. The calibration curves of the nomogram showed that the P-values of the Hosmer-Lemeshow test in the training set and test set were 0.53 and 0.15, respectively, both greater than 0.05 (Figures 5A, B). This indicates that the predictive model had good fit in both the training set and test set, with no significant difference between the predicted values and the actual values. Clinical Utility The decision curve analysis (DCA) of the predictive model showed (Figures 5C, D) that when the probability threshold exceeded 10%, using this predictive model to predict the probability of delayed PACU discharge in patients after general anesthesia and implementing intervention measures yielded more net benefit compared with the strategies of "intervening all patients" and "intervening no patients". Discussion This study analyzed 17,032 patients admitted to the PACU for recovery at the our hospital from 2023 to 2024 and found that the incidence of delayed PACU discharge was 4.38% (95% CI: 4.07%-4.69%). The previously reported incidence of delayed PACU discharge varied from 0.07 ‰ to 61.8% [15, 16], and the incidence observed in this study also lies within this spectrum.Among the incidence rates across different subspecialties, those of Spinal Surgery, Joint and Sports Medicine, Gastrointestinal Surgery, and Hepatobiliary and Pancreatic Surgery were relatively high, all exceeding 5%. This finding underscores the need for comprehensive preoperative assessment of patients, close intraoperative monitoring of vital signs, and prompt management of PACU complications when administering general anesthesia to patients from these departments. A nomogram predictive model was created by this work for patients having general anesthesia who were experiencing a delay in the discharge of PACU. Calibration analysis and discriminant analysis in the current study confirmed that the model has excellent calibration and discriminative ability. Anesthesiologists and nurses can use this nomogram to comprehensively assess the probability of delayed discharge in patients in the PACU in future clinical work, better implement individualized interventions for potential patients at risk of delayed PACU discharge, improve the turnover rate of operating rooms and the PACU, and further give play to the function of the anesthesiology department in ERAS. According to the nomogram predictive model, the independent risk factors for delayed PACU discharge were BMI ＜18 kg/m², rocuronium dosage, fasting blood glucose ＞6.1 mmol/L, blood loss, PACU agitation, PACU pain, PACU hypoxemia, and PACU blood gas analysis. The findings of this study suggest that a BMI of less than 18 kg/m² is an independent risk factor for delayed discharge from the PACU. This is consistent with the previous research conclusions of Fang and Cao et al, who reported a negative correlation between BMI and PACU discharge time [7, 17]. A possible reason is that compared with patients with normal BMI and high BMI, patients with low BMI are often accompanied by reduced muscle mass and malnutrition [18, 19], resulting in insufficient body energy reserves and thus reduced tolerance to surgery and anesthesia. Once anesthetized and the patient was given anesthesia，the PACU usually needs to take longer to recover. However, a study by Gabriel et al. showed that BMI ＞40 kg/m² is a risk factor for delayed PACU discharge in outpatient surgical patients. A plausible explanation is that patients who are morbidly obese have a greater chance of having sleep apnea after surgery. The number of people who are interested in this population is especially concerning because of the increased incidence of airway blockage [20]. Therefore, the impact of BMI on delayed PACU discharge in patients after general anesthesia is controversial and requires further in-depth research. The dose of rocuronium is an independent risk factor for delayed discharge from the PACU, which is not unexpected. The neuromuscular blocker Rocuronium，which is a non-depolarizing condition，is involved with general anesthesia in most cases. It can effectively block neuromuscular transmission, thereby relaxing muscles to meet the needs of surgical operations. While rocuronium does not directly impede the restoration of consciousness following anesthesia, its excessive use can lead to residual neuromuscular blockade in patients post-surgery, thereby impacting respiratory function and limb mobility even after the procedure has concluded. This will inevitably prolong the recovery time of patients in the PACU, leading to delayed PACU discharge [21]. Doshu-Kajiura et al. reported a case of a patient with chronic renal failure who experienced delayed onset and prolonged duration of neuromuscular block after accidental misuse of rocuronium. The onset and duration of action of rocuronium were significantly extended，as observed through train-of-four stimulation [22]. Therefore, in clinical application of rocuronium, the dosage must be accurately controlled, and an individualized medication strategy must be implemented, fully considering the individual differences of patients, so as to reduce the occurrence of delayed PACU discharge caused by improper rocuronium dosage. In this study, blood loss was included in the nomogram predictive model as an independent risk factor. According to the nomogram predictive model, the probability of delayed PACU discharge exceeds 50% when blood loss is greater than 1500 ml. As demonstrated by Samuel et al. in a cross-sectional study investigating delayed awakening after general anesthesia, their finding that blood loss over 1500 ml is an important risk factor for delayed postoperative awakening is consistent with this conclusion. A possible reason is that excessive blood loss causes hypotension and decreased cardiac output, leading to insufficient metabolism and elimination of anesthetic drugs [23]. Postoperative delayed PACU discharge is strongly correlated with fasting blood glucose level. Specifically, compared with patients who have a fasting blood glucose 6.1 mmol/L are more prone to delayed postoperative PACU discharge. In this study, preoperative diabetes mellitus did not emerge as a risk factor. This finding warns us that it is arbitrary to assess the prognosis of patients based solely on whether they have diabetes mellitus. Patients who had preoperative fasting blood glucose level cannot be directly determined by whether or not they had diabetes mellitus preoperatively. Xie et al. found in a study on establishing a postoperative predictive model for patients with delayed PACU discharge that patients with a fasting blood glucose ＞5.9 mmol/L (especially ＞9.1 mmol/L) are more likely to experience delayed PACU discharge, which matches the findings reported in the present research [24]. However, this study did not explain this phenomenon in depth and simply classified patients with high fasting blood glucose as diabetic patients, which could result in the disregard of preoperative hyperglycemia stemming from other causes. In patients, for instance, stress-related anxiety and the condition of those who are critically sick may result in hyperglycemia brought on by stress. In addition, long-term use of corticosteroids before surgery or receiving parenteral or enteral nutrition support with high sugar content may also lead to elevated blood glucose in patients [21]. Therefore, it is necessary to understand the causes of preoperative hyperglycemia in patients from multiple aspects. For preoperative hyperglycemia in non-diabetic patients, sufficient attention should also be paid, and corresponding intervention measures should be taken to reduce the risk of delayed PACU discharge and improve the quality of postoperative recovery and the efficiency of medical resource utilization. This study found that the occurrence of hypoxemia (PaO2＜60mmHg or SpO2 ＜90%) in the PACU can significantly increase the risk of delayed PACU discharge, whereas the immediate causes are airway obstruction and hypoventilation. Rose et al. discovered in a survey of adverse respiratory events in the PACU that extubation procedure, residual anesthetics, opioid effects, obesity, age, surgical type, and residual neuromuscular block can all lead to airway obstruction and hypoventilation in patients [25]. Among them, residual neuromuscular block requires special attention from anesthesiologists. Bucheery et al. pointed out that 36.6% of patients have residual neuromuscular block when admitted to the PACU, while this residual effect is linked to enhanced vulnerability to postoperative respiratory complications [26]. According to this study, PACU Pain is a separate risk factor for delayed PACU discharge. Patients suffering from pain require analgesic treatment and observation in the PACU, thereby increasing the length of stay in the PACU. A study by Mercado et al. on the relationship between intraoperative opioid administration and postoperative pain showed that increasing the intraoperative dosage of fentanyl and hydromorphone can not only effectively reduce the maximum pain score in the PACU, reduce the probability and total dosage of opioid administration in the PACU, but also significantly reduce the opioid usage within 30 days, 90 days, and 180 days after surgery without increasing adverse reactions [27]. In addition, Zhang et al. found in a study on the influencing factors of moderate-to-severe postoperative pain in cancer patients that in addition to opioids, the use of non-steroidal anti-inflammatory drugs and epidural analgesia techniques can reduce the incidence of moderate-to-severe postoperative pain [28]. Therefore, perioperative analgesic management must be properly performed to shorten the length of stay of patients in the PACU and implement the ERAS treatment plan from the perspective of anesthesia. Patients with PACU agitation (RASS ≥2) are more likely to experience delayed PACU discharge because agitated patients require sedative treatment and observation in the PACU. Previous research has indicated that, besides unmodifiable factors like patients' preoperative baseline conditions, the presence of postoperative indwelling invasive catheters—including endotracheal tubes, urinary catheters，nasogastric tubes, and thoracoabdominal drainage tubes—can continuously stimulate patients recovering in the PACU, resulting in pain or discomfort that may contribute to agitation in this setting [29, 30]. Therefore, anesthesiologists and nurses need to closely monitor the status of various postoperative invasive catheters in patients, assess the necessity of their indwelling, minimize unnecessary catheter indwelling, and take effective fixation and nursing measures for catheters that must be indwelled to reduce their stimulation to patients, thereby reducing the incidence of delayed PACU discharge and improving the quality of patient recovery and the efficiency of medical resource utilization. Patients who undergo blood gas analysis in the PACU are more likely to experience delayed PACU discharge, which is a thought-provoking result. Because blood gas analysis is not a direct risk factor for delayed PACU discharge, but a signal of potential disease risks in patients. First，all patients who are getting in the PACU do not routinely undergo blood gas analysis. Instead, when patients have abnormalities in respiration, circulation, or metabolism, blood gas analysis is considered to determine the internal environment of patients. Second, if the results of blood gas analysis are abnormal, anesthesiologists and nurses need to implement intervention and treatment for patients. Therefore, both the process of performing blood gas analysis and the subsequent intervention and treatment measures will increase the length of stay of patients in the PACU. This study has several limitations.First, there are a lot of variables influencing the discharge of delayed PACU. Although this study has analyzed from three aspects: preoperative general conditions, intraoperative surgical and anesthetic conditions, and postoperative complications, it is impossible to include all potential risk factors. Second, this study adopted the latest national anesthesia quality control indicators for the standard of delayed PACU discharge, and the diagnosis was made by professional anesthesiologists and nurses, but it may still be interfered by "non-clinical factors" and overestimate the incidence of delayed PACU discharge. Third, the predictive model has not been subjected to external validation. Future clinical research will determine whether the model is feasible in this regard. Conclusion In conclusion, through the analysis of eight independent risk factors, we developed a nomogram predictive model with significant discriminative ability and clinical utility, which can help identify high-risk patients for delayed PACU discharge. Future clinical research can use risk scoring to help patients who are admitted to the PACU for recovery, and high-risk patients can be screened and taken into account as soon as possible, which is helpful in putting the ERAS concept into practice and increasing the effectiveness of different perioperative procedures. Abbreviations PACU Post-anesthesia care unit ERAS Enhanced recovery after surgery NRS Numerical Rating Scale RASS Richmond agitation-sedation score ICU Intensive Care Unit ASA American Society of Anesthesiologists BMI Body mass index BIS bispectral index monitoring FBG Fasting blood glucose ROC Receiver operating characteristic AUC Area under the receiver operating characteristic curve DCA Decision curve analysis HL Hosmer-Lemeshow Declarations Acknowledgments Not applicable. Authors’ contributions Yongwen Lai and Chunying Zhu conducted a literature review, participated in the study design and data collection, and wrote the first draft of the paper, with both making equal contributions to this work. Cheng Lin and Yongchen Liu assisted in statistical analysis and manuscript editing. XiuQin Lu and Qingting Yang assisted in the ethical application. Weiqiang Deng and Jiaxin Huang helped with statistical analysis and made significant contributions to the revision of the manuscript. Chaosheng Qin initiated the study, participated in the study design, data interpretation, and manuscript revision. All authors reviewed the manuscript. Funding GuangxiMedical and health key discipline construction project Availability of data and materials The data and materials in this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study strictly adheres to the Declaration of Helsinki to ensure the life, health, personal privacy and dignity of research participants. The research protocol was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guilin Medical University on December 26, 2024 (Ethics Review Opinion No.: 2024YJSLL-107). Informed consent forms for participation in the study have been obtained from all participants or their legal guardians. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare no competing interests. References Ji XL, Li HB, Liu N, Li RH. [The history of post-anesthesia care units]. Zhonghua Yi Shi Za Zhi. 2022;52(2):100-4. https://doi.org/10.3760/cma.j.cn112155-20200121-00012. PMID: 35570345. Party: MotW, Whitaker Chair DK, Booth H, et al. Immediate post-anaesthesia recovery 2013: Association of Anaesthetists of Great Britain and Ireland. 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Change in body weight is positively related to the change in muscle mass of the quadriceps in older inpatients with severely low BMI according to the GLIM criteria. BMC Geriatr. 2024;24(1):711. https://doi.org/10.1186/s12877-024-05309-2. PMID: 39187769. Mistry T, Pal R, Ghosh S, et al. Impact of Low BMI and Nutritional Status on Quality of Life and Disease Outcome in Breast Cancer Patients: Insights From a Tertiary Cancer Center in India. Nutr Cancer. 2024;76(7):596-607. https://doi.org/10.1080/01635581.2024.2347396. PMID: 38836498. Gabriel RA, Waterman RS, Kim J, Ohno-Machado L. A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries. Anesth Analg. 2017;124(5):1529-36. https://doi.org/10.1213/ANE.0000000000001827. PMID: 28079580. Thomas E, Martin F, Pollard B. Delayed recovery of consciousness after general anaesthesia. BJA Educ. 2020;20(5):173-9. https://doi.org/10.1016/j.bjae.2020.01.007. PMID: 33456947. Doshu-Kajiura A, Suzuki J, Suzuki T. Prolonged onset and duration of action of rocuronium after accidental subcutaneous injection in a patient with chronic renal failure-a case report. JA Clin Rep. 2021;7(1):18. https://doi.org/10.1186/s40981-021-00421-3. PMID: 33638714. Bayable SD, Amberbir WD, Fetene MB. Delayed awakening and its associated factor following general anesthesia service, 2022: a cross-sectional study. Ann Med Surg (Lond). 2023;85(9):4321-8. https://doi.org/10.1097/MS9.0000000000001103. PMID: 37663712. Xie GH, Shen J, Li F, Yan HH, Qian Y. Development and Validation of a Clinical Model for Predicting Delay in Postoperative Transfer Out of the Post-Anesthesia Care Unit: A Retrospective Cohort Study. J Multidiscip Healthc. 2024;17:2535-50. https://doi.org/10.2147/JMDH.S458784. PMID: 38799012. Rose DK, Cohen MM, Wigglesworth DF, DeBoer DP. Critical respiratory events in the postanesthesia care unit. Patient, surgical, and anesthetic factors. Anesthesiology. 1994;81(2):410-8. https://doi.org/10.1097/00000542-199408000-00020. PMID: 8053592. Bucheery BA, Isa HM, Rafiq O, et al. Residual Neuromuscular Blockade and Postoperative Pulmonary Complications in the Post-anesthesia Care Unit: A Prospective Observational Study. Cureus. 2023;15(12):e51013. https://doi.org/10.7759/cureus.51013. PMID: 38264400. Mercado LASC, Liu R, Bharadwaj KM, et al. Association of Intraoperative Opioid Administration With Postoperative Pain and Opioid Use. JAMA Surg. 2023;158(8):854-64. https://doi.org/10.1001/jamasurg.2023.2009. PMID: 37314800. Zhang Y, Dai Q, Xu K, Fu H, Zhang A, Du W. Predictors and influence of postoperative moderate-to-severe pain of PACU in the patients with malignancy. BMC Anesthesiol. 2024;24(1):81. https://doi.org/10.1186/s12871-024-02464-2. PMID: 38413909. Kim HC, Kim E, Jeon YT, et al. Postanaesthetic emergence agitation in adult patients after general anaesthesia for urological surgery. J Int Med Res. 2015;43(2):226-35. https://doi.org/10.1177/0300060514562489. PMID: 25637216. Fields A, Huang J, Schroeder D, Sprung J, Weingarten T. Agitation in adults in the post-anaesthesia care unit after general anaesthesia. Br J Anaesth. 2018;121(5):1052-8. https://doi.org/10.1016/j.bja.2018.07.017. PMID: 30336849. Table Table 1 is available in the Supplementary Files section. Tables 2 and 3 are not available with this version Additional Declarations No competing interests reported. Supplementary Files TABLE1.doc Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":126768,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of our study. PACU, postoperative anesthesia care unit\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/024e3f9898efeefd2bdc6b50.png"},{"id":100004667,"identity":"5de68345-068e-4c3b-a839-0385ad953839","added_by":"auto","created_at":"2026-01-12 05:27:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11457,"visible":true,"origin":"","legend":"\u003cp\u003eThe incidence of delayed PACU discharge from 2023 to 2024\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/d1c95cba6f3964865ce18f11.png"},{"id":100360520,"identity":"d303e29d-dafa-4cc9-b32a-b05397d488ab","added_by":"auto","created_at":"2026-01-16 07:39:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249471,"visible":true,"origin":"","legend":"\u003cp\u003eA static nomogram showing a patient’s scoring process.First, identify the target predictive variable and its corresponding axis on the nomogram; then, pinpoint the exact spot on this axis that matches your actual data value.Next, refer to the adjacent scoring scale to find the score linked to the variable’s value, and note down this score for each variable in turn.Add up the scores of all the predictive variables you’ve noted to obtain a combined total score.Finally, locate this total score on the dedicated total score axis; trace from this point to the predicted outcome axis (typically by a vertical line) to get the final predicted result.BMI,body mass index.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/96dd0eaacfb07437ccbe2b1d.png"},{"id":100004672,"identity":"6fdd99c6-ac2e-48f1-84cb-9d06a0d309ea","added_by":"auto","created_at":"2026-01-12 05:27:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":966225,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the model.The blue line represents the training set with an AUC of 0.888, while the red line represents the test set with an AUC of 0.887.ROC, receiver operating characteristic curve; AUC, area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/2481c9a26e004ab8dde7e9ef.png"},{"id":100360782,"identity":"9dcdd096-610b-44cf-9d48-dcdfb2aa4721","added_by":"auto","created_at":"2026-01-16 07:43:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1681654,"visible":true,"origin":"","legend":"\u003cp\u003eA and B represent the Calibration curves of the nomogram for delayed discharge from PACU in the training set and test set,the p-values of the HL test in the training set and test set were 0.53 and 0.15, respectively,both indicating that the model has good consistency.C and D represent the DCA of the training set and the set set, respectively.HL,Hosmer-Lemeshow;DCA, decision curve analysis\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/2c40a8e4013fc95eeeb1cdcf.png"},{"id":103506382,"identity":"6ac3f9a9-a5c4-4804-b88c-5b6f09ad65b7","added_by":"auto","created_at":"2026-02-26 13:35:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3180137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/29636d44-0a61-494a-b30f-01475efaaedd.pdf"},{"id":100004665,"identity":"3c03d547-152e-4da9-b228-cf4851d89d51","added_by":"auto","created_at":"2026-01-12 05:27:48","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":559616,"visible":true,"origin":"","legend":"","description":"","filename":"TABLE1.doc","url":"https://assets-eu.researchsquare.com/files/rs-8291467/v1/879cccf10ddaefd3d38d0175.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Risk Factors for Delayed Discharge from the Post-Anesthesia Care Unit in Patients Undergoing General Anesthesia and Establishment and Validation of a Predictive Model: A Retrospective Case-Control Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFor patients in the anesthesia recovery period following surgery, the post-anesthesia care unit (PACU) delivers short-term monitoring. It not only improves surgical turnover rate but also ensures the awakening and early recovery of patients after anesthesia, including the detection and treatment of early anesthesia and surgical complications, until their vital signs and consciousness return to a level suitable for transfer to a general ward or discharge [1, 2]. Delayed discharge from the PACU following general anesthesia is a frequent complication during the recovery phase，which not only hinders the PACU\u0026apos;s ability to admit new patients but also creates bottlenecks at different stages of the perioperative process. More importantly, it leads to prolonged hospital stay, increased medical costs, compromised patient safety and nursing quality, and patient dissatisfaction [3, 4]. At present, the enhanced recovery after surgery (ERAS)protocol has been applied in multiple surgical subspecialties during perioperative management.The goal of the project is to decrease surgical stress and problems by working in a number of different ways and to speed the patient recovery process after surgery by having a variety of well-coordinated teamwork.As the first stop for patient awakening and postoperative recovery, the PACU plays a crucial role in ensuring timely patient awakening and early recovery, which is essential for the successful implementation of ERAS [5, 6]. Therefore, it is necessary to identify the risk factors for delayed PACU discharge and establish and validate a predictive model. In recent years, relevant studies have been reported in this field, but these studies are limited to specific surgical types, specific populations, or the impact of single factors, resulting in limited applicability in predicting other surgical types, other patient populations, or other influencing factors [7-11]. Thus, on the basis of previous studies, this study collected three types of indicators (preoperative general conditions, intraoperative surgical and anesthetic conditions, and postoperative complications) from 1492 patients, covering various surgical types and a broader patient population, to further identify other unknown risk factors and establish and validate a predictive model. This aims to provide an innovative predictive tool for anesthesiologists and nurses in future clinical work and research to identify high-risk patients for delayed PACU discharge.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted at the First Affiliated Hospital of Guilin Medical University,with data collected from patients admitted to the PACU for recovery between January 2023 and December 2024. Its Ethics Committee granted approval for this study (Ethics Approval No.: 2024YJSLL-107), and all participating patients provided written informed consent. Eligibility criteria were as follows: ① patients transferred to the PACU for recovery post-surgery; ② patients receiving endotracheal intubation general anesthesia; ③ patients \u0026ge; 18 years of age. The exclusion criteria were: ① patients undergoing neurosurgery; ② patients undergoing cardiac and great vessel surgery; ③ missing data. Three sections were used to gather the variables that were obtained using the electronic medical record system: preoperative indicators (age, gender, albumin, total bilirubin, fasting blood glucose concentration, creatinine, urea nitrogen, hemoglobin, white blood cells, smoking history, alcoholism history, hypertension history, diabetes mellitus history, respiratory system disease history, cardiovascular system disease history, cerebrovascular system disease history, American Society of Anesthesiologists (ASA) classification, Body Mass Index (BMI) classification), intraoperative indicators (surgical time, surgical grade, elective/emergency surgery, surgical type, surgical position, total fluid infusion volume, leukocyte-depleted red blood cell transfusion, plasma transfusion, autologous blood transfusion, anesthesia time, recovery time, anesthesia type, sufentanil dosage, rocuronium dosage, regional block, use of vasoactive drugs, intraoperative hypothermia, blood loss, intraoperative blood gas analysis, bispectral index (BIS) monitoring), and postoperative indicators (PACU hypothermia, PACU hypertension, PACU hypotension, PACU agitation, PACU pain, PACU hypoxemia, PACU blood gas analysis, prognosis and transfer). Surgical grades were categorized into Grade 1, 2, 3 and 4, while surgical types were classified as minor,moderate and major surgery. Minor surgery included superficial masses, thyroid nodules, breast nodules, debridement, and endoscopic surgeries such as hysteroscopy and ureteroscopy. Moderate surgery included cholecystolithiasis, hepatocellular cysts, renal cysts, hernia, appendicitis, gastrointestinal fistula, uterine fibroids, thyroid cancer, breast cancer, wedge resection of the lung, joint replacement, etc. Major surgery included liver cancer, gallbladder cancer, gastric cancer, intestinal cancer, pancreatic cancer, renal cancer, lobectomy, segmentectomy, spinal surgery. Surgical positions were grouped as follows:supine position;prone position;right posterior position;left lateral position;left posterior position and right lateral position. ASA classification was separated into two groups:ASA\u0026le;II and ASA＞II. The types of anesthesia were categorized into total intravenous anesthesia and combined intravenous-inhalation anesthesia. BMI was categorized into three groups:low BMI group(less than 18 kg/m\u0026sup2;), normal BMI group(18-24 kg/m\u0026sup2;), and high BMI group(greater than 24 kg/m\u0026sup2;). Preoperative albumin levels were categorized into two groups the hypoalbuminemia group(less than 35 g/L)and the normal albuminemia group(35 g/L or greater). Preoperative fasting blood glucose was separated into hypoglycemia group(＜3.9mmol/L), normal blood glucose group(3.9-6.1mmol/L), and hyperglycemia group(＞6.1mmol/L). The following were the diagnostic standards for intraoperative and PACU hypothermia:body temperature was 36 ℃. A diagnosis of PACU hypertension was established if blood pressure rose by over 20% compared with the pre-anesthetic baseline or reached a peak of 160/95 mmHg. The diagnostic criterion for PACU hypotension was a decrease in blood pressure exceeding 20% of the pre-anesthetic level or a systolic blood pressure decrease to 80 mmHg. For the purpose of this study, pain in the PACU was diagnosed if the numerical rating scale (NRS) score reached 4 or above. The standard for diagnosing agitation in the PACU was established as a Richmond Agitation-Sedation Scale (RASS) score \u0026ge; +2. The diagnostic criterion for PACU hypoxemia was an arterial partial pressure of oxygen (PaO2)\u0026nbsp;＜60 mmHg or a pulse oxygen saturation (SpO2)\u0026nbsp;＜90% when breathing room air. PACU discharge destinations include general wards and intensive care unit (ICU).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of Delayed PACU Discharge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt present, there is no consensus on a standard definition for delayed discharge from the PACU.According to the latest National Medical Quality Control Indicators for Anesthesiology in China (2022 Edition), Guowei Ban Yi Han〔2022〕No. 161, delayed PACU discharge is defined as a stay time in the PACU exceeding 120 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was the risk factors for delayed PACU discharge in patients after general anesthesia. The secondary endpoint was the incidence of delayed PACU discharge among patients following general anesthesia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software version 4.3.1. Continuous variables following a normal distribution were presented as mean \u0026plusmn; standard deviation, with intergroup comparisons conducted via the independent samples t-test. Continuous variables not following a normal distribution were presented as median and interquartile range, with the Wilcoxon rank-sum test employed for intergroup comparisons. Frequency and percentage were used to describe categorical variables, while the chi-square test was employed for comparisons between groups. All statistical tests were two-sided, and statistical significance was defined as P \u0026lt; 0.05. This was a case-control study with a 1:1 matching design. Based on consistent gender and age \u0026plusmn;2 years of patients in the delayed PACU discharge group, eligible patients were matched from the non-delayed PACU discharge group. Additionally, the aforementioned two patient groups were split into a training set and a test set at a 7:3 ratio. The training set was subjected to Logistic regression analysis to pinpoint potential risk factors of delayed PACU discharge, followed by the inclusion of variables with P \u0026lt; 0.05 into multivariate Logistic regression analysis to identify independent risk factors. On this basis, a nomogram, a graphical representation of the risk prediction model for delayed PACU discharge in patients after general anesthesia, was established using R software version 4.3.1. Longer line segments in the nomogram indicate higher scores, meaning that the prediction coefficient has a more significant impact on the outcome. The way of this study was to assess the predictive model\u0026apos;s performance in the training set and validation set by means of calibration, discriminant, and discriminating analyses. Calibration curves were used to assess the calibration ability of the predictive model, i.e., the consistency between the probability predicted by the model and the actual event incidence [12]. Additionally，the Hosmer-Lemeshow test was conducted to determine the statistical significance of the consistency of the curves. The area under the receiver operating characteristic (ROC) curve (AUC) is used to assess the model\u0026rsquo;s discriminative ability [13]. In addition, decision curve analysis (DCA) was used to test clinical applicability, and the threshold probability interval of the net benefit (NB) value that the nomogram could obtain was observed to evaluate the clinical application range of the nomogram [14].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom January 2023 to December 2024, a total of 22,726 patients were transferred to the PACU for recovery after surgery at our hospital. After screening according to the inclusion and exclusion criteria, 17,032 patients remained, among whom 746 patients had a recovery time in the PACU exceeding 120 minutes. Figure 1 displays the entire procedure for gathering data. According to the gender and age characteristics of these 746 patients, eligible cases were matched 1:1 from 16,286 cases with a recovery time of less than 120 minutes based on equal gender and age \u0026plusmn;2 years. Table 1 displays the clinical and demographic information of the two patient groups following matching. Before matching, there were significant differences in gender and age distribution between the 746 patients in the delayed PACU discharge group and the 16,286 patients with a recovery time of less than 120 minutes (P＜0.05). Table 2 displays the clinical and demographic information in the test and training sets; there were no statistically significant differences in each of the indicators, suggesting that there was consistency in the data distribution in both the test and training sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e should be placed here\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e should be placed here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncidence of Delayed PACU Discharge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the incidence of delayed PACU discharge from 2023 to 2024 after excluding 5694 patients who did not undergo endotracheal intubation general anesthesia, were aged ＜18 years, underwent neurosurgery or cardiac and great vessel surgery, or had missing data. The incidence rates were 5.79% (95%CI:5.29%-6.29%) and 3.02% (95%CI:2.66%-3.38%) in the years 2023 and 2024，respectively，which indicated a declining trend in the last two years. The total incidence of delayed PACU discharge from 2023 to 2024 was 4.38% (95% CI: 4.07%-4.69%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 displays the incidence of delayed PACU discharge in each department. In each department，incidence rates were as follows: Department of Spinal Surgery 11.13% (95% CI: 8.48%-14.29%), Department of Joint and Sports Medicine 9.61% (95% CI: 7.75%-11.77%), Department of Gastrointestinal Surgery 8.38% (95% CI: 7.35%-9.52%), Department of Hepatobiliary and Pancreatic Surgery 7.46% (95% CI: 6.23%-8.85%), Department of Urology 4.92% (95% CI: 4.04%-5.95%), Department of Gastroenterology 3.92% (95% CI: 1.99%-6.98%), Department of Extremity Trauma and Hand Surgery 3.56% (95% CI: 1.69%-6.65%), Department of Oral and Maxillofacial Surgery 3.19% (95% CI: 1.97%-4.99%), Department of Emergency Trauma Surgery 2.88% (95% CI: 0.60%-8.23%), Department of Gynecology 2.26% (95% CI: 1.68%-2.98%), Department of Thoracic Surgery 2.08% (95% CI: 1.19%-3.47%), Department of Breast and Thyroid Surgery 1.68% (95% CI: 1.20%-2.29%), Department of Ophthalmology 1.36% (95% CI: 0.45%-3.16%), other departments (Including the Department of Vascular Interventional Surgery, Department of Cardiovascular Medicine, and other departments, with fewer than 100 cases in each department.) 1.34% (95% CI: 0.37%-3.39%), Department of Otorhinolaryngology-Head and Neck Surgery 1.24% (95% CI: 0.86%-1.73%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e should be placed here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Risk Factors for Delayed PACU Discharge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 displays the independent risk factors identified by multivariate Logistic regression analysis in the training set: BMI [ OR, 0.645, 95% CI (0.486-0.869), P＝0.004], rocuronium dosage [OR, 1.015, 95% CI (1.009-1.022), P＜0.001], fasting blood glucose [OR, 1.446, 95% CI (1.030-2.032), P＝0.033], blood loss [OR, 1.003, 95% CI (1.001-1.004), P＜0.001], PACU agitation [OR, 8.088, 95% CI (2.164-39.381), P＝0.004], PACU pain [OR, 4.842, 95% CI (1.629-16.493), P＝0.007], PACU hypoxemia [OR, 47.878, 95% CI (8.609-900.458), P＜0.001], and PACU blood gas analysis [OR, 23.078, 95% CI (13.487-42.067), P＜0.001].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable4\u003c/strong\u003e.Multivariable Logistic Regression Analysis for Predicting the Probability of delayed discharge from the PACU in the training set\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 202px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eBeta coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.645 (0.476-0.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eRocuronium dosage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e1.015 (1.009-1.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e1.446 (1.030-2.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003eBlood loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e1.003 (1.001-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003ePACU blood gas analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e23.078 (13.487-42.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003ePACU hypoxemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e47.878 (8.609-900.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003ePACU agitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e8.088 (2.164-39.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 202px;\"\u003e\n \u003cp\u003ePACU pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e4.842 (1.629-16.493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:BMI,Body mass index, FBG Fasting blood glucose\u003c/p\u003e\n\u003cp\u003ePACU,Post-Anesthesia Care Unit\u003c/p\u003e\n\u003cp\u003eP＜0.05: statistically significant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment and Validation of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA risk prediction model for delayed PACU discharge was developed using R software version 4.3.1, based on the eight independent risk factors identified from the multivariate logistic regression analysis in the training set. The nomogram clearly demonstrates the influence of risk factors on the model for medical staff, and the probability of delayed PACU discharge can be obtained by summing the scores of each risk factor. As shown in Figure 3, four risk factors\u0026mdash;PACU blood gas analysis, rocuronium dosage, blood loss, and PACU hypoxemia\u0026mdash;had higher scores in the model, significantly increasing the probability of delayed PACU discharge. BMI and preoperative fasting blood glucose had lower scores in the model, contributing less than one-tenth to delayed PACU discharge. The AUC was 0.888 (95% CI = 0.868-0.908) in the training set and 0.887 (95% CI = 0.856-0.918) in the test set(Figures 4). The AUC values for both data groups exceeded 0.85，suggesting that the nomogram demonstrated excellent discriminative ability. The calibration curves of the nomogram showed that the P-values of the Hosmer-Lemeshow test in the training set and test set were 0.53 and 0.15, respectively, both greater than 0.05 (Figures 5A, B). This indicates that the predictive model had good fit in both the training set and test set, with no significant difference between the predicted values and the actual values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Utility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe decision curve analysis (DCA) of the predictive model showed (Figures 5C, D) that when the probability threshold exceeded 10%, using this predictive model to predict the probability of delayed PACU discharge in patients after general anesthesia and implementing intervention measures yielded more net benefit compared with the strategies of \u0026quot;intervening all patients\u0026quot; and \u0026quot;intervening no patients\u0026quot;.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed 17,032 patients admitted to the PACU for recovery at the our hospital from 2023 to 2024 and found that the incidence of delayed PACU discharge was 4.38% (95% CI: 4.07%-4.69%). The previously reported incidence of delayed PACU discharge varied from 0.07 \u0026permil; to 61.8%\u0026nbsp;[15, 16], and the incidence observed in this study also lies within this spectrum.Among the incidence rates across different subspecialties, those of Spinal Surgery, Joint and Sports Medicine, Gastrointestinal Surgery, and Hepatobiliary and Pancreatic Surgery were relatively high, all exceeding 5%. This finding underscores the need for comprehensive preoperative assessment of patients, close intraoperative monitoring of vital signs, and prompt management of PACU complications when administering general anesthesia to patients from these departments.\u003c/p\u003e\n\u003cp\u003eA nomogram predictive model was created by this work for patients having general anesthesia who were experiencing a delay in the discharge of PACU. Calibration analysis and discriminant analysis in the current study confirmed that the model has excellent calibration and discriminative ability. Anesthesiologists and nurses can use this nomogram to comprehensively assess the probability of delayed discharge in patients in the PACU in future clinical work, better implement individualized interventions for potential patients at risk of delayed PACU discharge, improve the turnover rate of operating rooms and the PACU, and further give play to the function of the anesthesiology department in ERAS. According to the nomogram predictive model, the independent risk factors for delayed PACU discharge were BMI\u0026nbsp;＜18\u0026nbsp;kg/m\u0026sup2;, rocuronium dosage, fasting blood glucose\u0026nbsp;＞6.1 mmol/L, blood loss, PACU agitation, PACU pain, PACU hypoxemia, and PACU blood gas analysis.\u003c/p\u003e\n\u003cp\u003eThe findings of this study suggest that a BMI of less than 18\u0026nbsp;kg/m\u0026sup2;\u0026nbsp;is an independent risk factor for delayed discharge from the PACU. This is consistent with the previous research conclusions of Fang and Cao et al, who reported a negative correlation between BMI and PACU discharge time\u0026nbsp;[7, 17]. A possible reason is that compared with patients with normal BMI and high BMI, patients with low BMI are often accompanied by reduced muscle mass and malnutrition\u0026nbsp;[18, 19], resulting in insufficient body energy reserves and thus reduced tolerance to surgery and anesthesia. Once anesthetized and the patient was given anesthesia，the PACU usually needs to take longer to recover. However, a study by Gabriel et al. showed that BMI\u0026nbsp;＞40\u0026nbsp;kg/m\u0026sup2;\u0026nbsp;is a risk factor for delayed PACU discharge in outpatient surgical patients. A plausible explanation is that patients who are morbidly obese have a greater chance of having sleep apnea after surgery. The number of people who are interested in this population is especially concerning because of the increased incidence of airway blockage\u0026nbsp;[20]. Therefore, the impact of BMI on delayed PACU discharge in patients after general anesthesia is controversial and requires further in-depth research.\u003c/p\u003e\n\u003cp\u003eThe dose of rocuronium is an independent risk factor for delayed discharge from the PACU, which is not unexpected. The neuromuscular blocker Rocuronium，which is a non-depolarizing condition，is involved with general anesthesia in most cases. It can effectively block neuromuscular transmission, thereby relaxing muscles to meet the needs of surgical operations. While rocuronium does not directly impede the restoration of consciousness following anesthesia, its excessive use can lead to residual neuromuscular blockade in patients post-surgery, thereby impacting respiratory function and limb mobility even after the procedure has concluded. This will inevitably prolong the recovery time of patients in the PACU, leading to delayed PACU discharge\u0026nbsp;[21]. Doshu-Kajiura et al. reported a case of a patient with chronic renal failure who experienced delayed onset and prolonged duration of neuromuscular block after accidental misuse of rocuronium. The onset and duration of action of rocuronium were significantly extended，as observed through train-of-four stimulation\u0026nbsp;[22]. Therefore, in clinical application of rocuronium, the dosage must be accurately controlled, and an individualized medication strategy must be implemented, fully considering the individual differences of patients, so as to reduce the occurrence of delayed PACU discharge caused by improper rocuronium dosage.\u003c/p\u003e\n\u003cp\u003eIn this study, blood loss was included in the nomogram predictive model as an independent risk factor. According to the nomogram predictive model, the probability of delayed PACU discharge exceeds 50% when blood loss is greater than 1500 ml. As demonstrated by Samuel et al. in a cross-sectional study investigating delayed awakening after general anesthesia, their finding that blood loss over 1500 ml is an important risk factor for delayed postoperative awakening is consistent with this conclusion. A possible reason is that excessive blood loss causes hypotension and decreased cardiac output, leading to insufficient metabolism and elimination of anesthetic drugs\u0026nbsp;[23].\u003c/p\u003e\n\u003cp\u003ePostoperative delayed PACU discharge is strongly correlated with fasting blood glucose level. Specifically, compared with patients who have a fasting blood glucose \u0026lt; 6.1 mmol/L, those with a fasting blood glucose \u0026gt; 6.1 mmol/L are more prone to delayed postoperative PACU discharge. In this study, preoperative diabetes mellitus did not emerge as a risk factor. This finding warns us that it is arbitrary to assess the prognosis of patients based solely on whether they have diabetes mellitus. Patients who had preoperative fasting blood glucose level cannot be directly determined by whether or not they had diabetes mellitus preoperatively. Xie et al. found in a study on establishing a postoperative predictive model for patients with delayed PACU discharge that patients with a fasting blood glucose\u0026nbsp;＞5.9 mmol/L (especially\u0026nbsp;＞9.1 mmol/L) are more likely to experience delayed PACU discharge, which matches the findings reported in the present research\u0026nbsp;[24]. However, this study did not explain this phenomenon in depth and simply classified patients with high fasting blood glucose \u0026nbsp;as diabetic patients, which could result in the disregard of preoperative hyperglycemia stemming from other causes. In patients, for instance, stress-related anxiety and the condition of those who are critically sick may result in hyperglycemia brought on by stress. In addition, long-term use of corticosteroids before surgery or receiving parenteral or enteral nutrition support with high sugar content may also lead to elevated blood glucose in patients\u0026nbsp;[21]. Therefore, it is necessary to understand the causes of preoperative hyperglycemia in patients from multiple aspects. For preoperative hyperglycemia in non-diabetic patients, sufficient attention should also be paid, and corresponding intervention measures should be taken to reduce the risk of delayed PACU discharge and improve the quality of postoperative recovery and the efficiency of medical resource utilization.\u003c/p\u003e\n\u003cp\u003eThis study found that the occurrence of hypoxemia (PaO2＜60mmHg or SpO2\u0026nbsp;＜90%) in the PACU can significantly increase the risk of delayed PACU discharge, whereas the immediate causes are airway obstruction and hypoventilation. Rose et al. discovered in a survey of adverse respiratory events in the PACU that extubation procedure, residual anesthetics, opioid effects, obesity, age, surgical type, and residual neuromuscular block can all lead to airway obstruction and hypoventilation in patients\u0026nbsp;[25]. Among them, residual neuromuscular block requires special attention from anesthesiologists. Bucheery et al. pointed out that 36.6% of patients have residual neuromuscular block when admitted to the PACU, while this residual effect is linked to enhanced vulnerability to postoperative respiratory complications\u0026nbsp;[26].\u003c/p\u003e\n\u003cp\u003eAccording to this study, PACU Pain is a separate risk factor for delayed PACU discharge. Patients suffering from pain require analgesic treatment and observation in the PACU, thereby increasing the length of stay in the PACU. A study by Mercado et al. on the relationship between intraoperative opioid administration and postoperative pain showed that increasing the intraoperative dosage of fentanyl and hydromorphone can not only effectively reduce the maximum pain score in the PACU, reduce the probability and total dosage of opioid administration in the PACU, but also significantly reduce the opioid usage within 30 days, 90 days, and 180 days after surgery without increasing adverse reactions\u0026nbsp;[27]. In addition, Zhang et al. found in a study on the influencing factors of moderate-to-severe postoperative pain in cancer patients that in addition to opioids, the use of non-steroidal anti-inflammatory drugs and epidural analgesia techniques can reduce the incidence of moderate-to-severe postoperative pain\u0026nbsp;[28]. Therefore, perioperative analgesic management must be properly performed to shorten the length of stay of patients in the PACU and implement the ERAS treatment plan from the perspective of anesthesia.\u003c/p\u003e\n\u003cp\u003ePatients with PACU agitation (RASS \u0026ge;2) are more likely to experience delayed PACU discharge because agitated patients require sedative treatment and observation in the PACU. Previous research has indicated that, besides unmodifiable factors like patients\u0026apos; preoperative baseline conditions, the presence of postoperative indwelling invasive catheters\u0026mdash;including endotracheal tubes, urinary catheters，nasogastric tubes, and thoracoabdominal drainage tubes\u0026mdash;can continuously stimulate patients recovering in the PACU, resulting in pain or discomfort that may contribute to agitation in this setting\u0026nbsp;[29, 30]. Therefore, anesthesiologists and nurses need to closely monitor the status of various postoperative invasive catheters in patients, assess the necessity of their indwelling, minimize unnecessary catheter indwelling, and take effective fixation and nursing measures for catheters that must be indwelled to reduce their stimulation to patients, thereby reducing the incidence of delayed PACU discharge and improving the quality of patient recovery and the efficiency of medical resource utilization.\u003c/p\u003e\n\u003cp\u003ePatients who undergo blood gas analysis in the PACU are more likely to experience delayed PACU discharge, which is a thought-provoking result. Because blood gas analysis is not a direct risk factor for delayed PACU discharge, but a signal of potential disease risks in patients. First，all patients who are getting in the PACU do not routinely undergo blood gas analysis. Instead, when patients have abnormalities in respiration, circulation, or metabolism, blood gas analysis is considered to determine the internal environment of patients. Second, if the results of blood gas analysis are abnormal, anesthesiologists and nurses need to implement intervention and treatment for patients. Therefore, both the process of performing blood gas analysis and the subsequent intervention and treatment measures will increase the length of stay of patients in the PACU.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations.First, there are a lot of variables influencing the discharge of delayed PACU. Although this study has analyzed from three aspects: preoperative general conditions, intraoperative surgical and anesthetic conditions, and postoperative complications, it is impossible to include all potential risk factors. Second, this study adopted the latest national anesthesia quality control indicators for the standard of delayed PACU discharge, and the diagnosis was made by professional anesthesiologists and nurses, but it may still be interfered by \u0026quot;non-clinical factors\u0026quot; and overestimate the incidence of delayed PACU discharge. Third, the predictive model has not been subjected to external validation. Future clinical research will determine whether the model is feasible in this regard.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, through the analysis of eight independent risk factors, we developed a nomogram predictive model with significant discriminative ability and clinical utility, which can help identify high-risk patients for delayed PACU discharge. Future clinical research can use risk scoring to help patients who are admitted to the PACU for recovery, and high-risk patients can be screened and taken into account as soon as possible, which is helpful in putting the ERAS concept into practice and increasing the effectiveness of different perioperative procedures.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePACU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Post-anesthesia care unit\u003c/p\u003e\n\u003cp\u003eERAS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Enhanced recovery after surgery\u003c/p\u003e\n\u003cp\u003eNRS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Numerical Rating Scale\u003c/p\u003e\n\u003cp\u003eRASS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Richmond agitation-sedation score\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eASA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;American Society of Anesthesiologists\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Body mass index\u003c/p\u003e\n\u003cp\u003eBIS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;bispectral index monitoring\u003c/p\u003e\n\u003cp\u003eFBG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Fasting blood glucose\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision curve analysis\u003c/p\u003e\n\u003cp\u003eHL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hosmer-Lemeshow\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYongwen Lai and Chunying Zhu conducted a literature review, participated in the study design and data collection, and wrote the first draft of the paper, with both making equal contributions to this work. Cheng Lin and Yongchen Liu assisted in statistical analysis and manuscript editing. XiuQin Lu and Qingting Yang assisted in the ethical application. Weiqiang Deng and Jiaxin Huang helped with statistical analysis and made significant contributions to the revision of the manuscript. Chaosheng Qin initiated the study, participated in the study design, data interpretation, and manuscript revision. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangxiMedical and health key discipline construction project\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials in this study are available from the corresponding author upon reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study strictly adheres to the \u003cem\u003eDeclaration of Helsinki\u003c/em\u003e to ensure the life, health, personal privacy and dignity of research participants. The research protocol was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guilin Medical University on December 26, 2024 (Ethics Review Opinion No.: 2024YJSLL-107). Informed consent forms for participation in the study have been obtained from all participants or their legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJi XL, Li HB, Liu N, Li RH. [The history of post-anesthesia care units]. Zhonghua Yi Shi Za Zhi. 2022;52(2):100-4. https://doi.org/10.3760/cma.j.cn112155-20200121-00012. PMID: 35570345.\u003c/li\u003e\n\u003cli\u003eParty: MotW, Whitaker Chair DK, Booth H, et al. 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J Perianesth Nurs. 2013;28(3):151-5. https://doi.org/10.1016/j.jopan.2012.06.009. PMID: 23711311.\u003c/li\u003e\n\u003cli\u003eFang F, Liu T, Li J, et al. A novel nomogram for predicting the prolonged length of stay in post-anesthesia care unit after elective operation. BMC Anesthesiol. 2023;23(1):404. https://doi.org/10.1186/s12871-023-02365-w. PMID: 38062380.\u003c/li\u003e\n\u003cli\u003eAkazawa N, Funai K, Hino T, et al. Change in body weight is positively related to the change in muscle mass of the quadriceps in older inpatients with severely low BMI according to the GLIM criteria. BMC Geriatr. 2024;24(1):711. https://doi.org/10.1186/s12877-024-05309-2. PMID: 39187769.\u003c/li\u003e\n\u003cli\u003eMistry T, Pal R, Ghosh S, et al. Impact of Low BMI and Nutritional Status on Quality of Life and Disease Outcome in Breast Cancer Patients: Insights From a Tertiary Cancer Center in India. Nutr Cancer. 2024;76(7):596-607. https://doi.org/10.1080/01635581.2024.2347396. PMID: 38836498.\u003c/li\u003e\n\u003cli\u003eGabriel RA, Waterman RS, Kim J, Ohno-Machado L. A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries. Anesth Analg. 2017;124(5):1529-36. https://doi.org/10.1213/ANE.0000000000001827. PMID: 28079580.\u003c/li\u003e\n\u003cli\u003eThomas E, Martin F, Pollard B. Delayed recovery of consciousness after general anaesthesia. BJA Educ. 2020;20(5):173-9. https://doi.org/10.1016/j.bjae.2020.01.007. PMID: 33456947.\u003c/li\u003e\n\u003cli\u003eDoshu-Kajiura A, Suzuki J, Suzuki T. Prolonged onset and duration of action of rocuronium after accidental subcutaneous injection in a patient with chronic renal failure-a case report. JA Clin Rep. 2021;7(1):18. https://doi.org/10.1186/s40981-021-00421-3. PMID: 33638714.\u003c/li\u003e\n\u003cli\u003eBayable SD, Amberbir WD, Fetene MB. Delayed awakening and its associated factor following general anesthesia service, 2022: a cross-sectional study. 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PMID: 38264400.\u003c/li\u003e\n\u003cli\u003eMercado LASC, Liu R, Bharadwaj KM, et al. Association of Intraoperative Opioid Administration With Postoperative Pain and Opioid Use. JAMA Surg. 2023;158(8):854-64. https://doi.org/10.1001/jamasurg.2023.2009. PMID: 37314800.\u003c/li\u003e\n\u003cli\u003eZhang Y, Dai Q, Xu K, Fu H, Zhang A, Du W. Predictors and influence of postoperative moderate-to-severe pain of PACU in the patients with malignancy. BMC Anesthesiol. 2024;24(1):81. https://doi.org/10.1186/s12871-024-02464-2. PMID: 38413909.\u003c/li\u003e\n\u003cli\u003eKim HC, Kim E, Jeon YT, et al. Postanaesthetic emergence agitation in adult patients after general anaesthesia for urological surgery. J Int Med Res. 2015;43(2):226-35. https://doi.org/10.1177/0300060514562489. PMID: 25637216.\u003c/li\u003e\n\u003cli\u003eFields A, Huang J, Schroeder D, Sprung J, Weingarten T. Agitation in adults in the post-anaesthesia care unit after general anaesthesia. Br J Anaesth. 2018;121(5):1052-8. https://doi.org/10.1016/j.bja.2018.07.017. PMID: 30336849.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n\u003cp\u003eTables 2 and 3 are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"General anesthesia, PACU, Delayed discharge, Risk factors, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-8291467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8291467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Delayed discharge from the post-anesthesia care unit (PACU) after general anesthesia is a common complication in clinical anesthesia, resulting from the combined effect of multiple risk factors. It compromises the quality of postoperative recovery while diminishing the efficiency of perioperative turnover. Our study attempts to determine the risk factors for delayed PACU discharge and to create and validate a nomogram predictive model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 746 patients with delayed PACU discharge after general anesthesia were enrolled. Using a 1:1 matching design (consistent gender and age ±2 years), 746 eligible patients without delayed discharge were selected as controls. Both the delayed and non-delayed discharge groups were split into a training set (n=1046) and a test set (n=446) at a 7:3 ratio. Logistic regression analysis was performed in the training set to develop a risk prediction model, which was then validated in the test set. The discriminative ability, model calibration, and clinical utility were assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), correspondingly.The goodness-of-fit for the calibration curves was determined using the Hosmer-Lemeshow (HL) test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A predictive nomogram model was developed using eight significant variables identified through multivariate logistic regression analysis: Body Mass Index (BMI), rocuronium dosage, Fasting blood glucose (FBG) , blood loss, PACU agitation, PACU pain, PACU hypoxemia, and PACU blood gas analysis. For the training set, the area under the ROC curve (AUC) was 0.888 (95% confidence interval [CI]: 0.868–0.908), and the corresponding value in the test set was 0.887 (95% CI: 0.856–0.918). Calibration curves indicated a high degree of agreement between predicted probabilities and actual probabilities. The P-values of the HL test in the training set and test set were 0.53 and 0.15, respectively, indicating good goodness-of-fit. DCA demonstrated that when the predicted probability exceeded 10%, using this model to predict delayed PACU discharge and implement intervention measures would yield greater benefits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study created and validated a predictive model to estimate the likelihood of delayed PACU discharge in patients following general anesthesia.\u003c/p\u003e","manuscriptTitle":"Analysis of Risk Factors for Delayed Discharge from the Post-Anesthesia Care Unit in Patients Undergoing General Anesthesia and Establishment and Validation of a Predictive Model: A Retrospective Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:27:43","doi":"10.21203/rs.3.rs-8291467/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b4dbbb8-fd51-4978-8360-07db7d819e71","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T10:41:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:27:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8291467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8291467","identity":"rs-8291467","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}