Predicting Post-Anesthesia Care Unit (PACU) Length of Stay (LOS) Using Machine Learning for Patients Undergoing Lumbar Spinal Stenosis Surgery

Predicting Post-Anesthesia Care Unit (PACU) Length of Stay (LOS) Using Machine Learning for Patients Undergoing Lumbar Spinal Stenosis Surgery | 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 Predicting Post-Anesthesia Care Unit (PACU) Length of Stay (LOS) Using Machine Learning for Patients Undergoing Lumbar Spinal Stenosis Surgery Alfira Arkin, Luo Dan, Yusupu Nuermaimaiti, Kunduziayi Alifu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5974971/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 Lumbar spinal stenosis surgery is commonly performed to address conditions such as spinal canal narrowing and degenerative changes. The duration of a patient's stay in the Post-Anesthesia Care Unit (PACU) following surgery is influenced by a variety of factors including potential complications, anesthetic management, and the patient's overall health status. This study aims to analyze clinical data to identify the key factors that affect the length of stay in the PACU. By doing so, the study seeks to provide valuable insights that can lead to improvements in postoperative recovery and overall patient outcomes. Methods We collected data on 539 cases of patients undergoing lumbar spinal stenosis surgery under general anesthesia from August 2018 to December 2022. The cases were divided into three groups: Group A with 377 cases, Group B with 82 cases, and Group C with 80 cases. Univariate logistic regression analysis was conducted on Group A using SPSS software to identify factors significantly associated with postoperative retention in the PACU. Multivariate logistic regression was then applied to these selected factors to determine independent risk factors. The independent risk factors were used to construct a Nomogram predictive model using R software. Group B was utilized to externally validate the predictive model. Group C data was used for the evaluation of the predictive model. The model's consistency was assessed by calculating the C-index, constructing calibration plots, and generating the Receiver Operating Characteristic (ROC) curve to evaluate the model's predictive accuracy and discrimination ability. Results Univariate logistic regression analysis was conducted to identify factors associated with prolonged stay in the Post-Anesthesia Care Unit (PACU), revealing age, Body Mass Index (BMI), coronary heart disease, surgery duration, and creatinine clearance rate as significant predictors. Subsequently, a multivariate logistic regression analysis was performed on these identified factors, yielding age, BMI, and muscle strength as independent risk factors for extended PACU stay. A Nomogram predictive model was constructed using the R programming language. The model's consistency was assessed across Groups A, B, and C by calculating the C-index and generating Receiver Operating Characteristic (ROC) curves, demonstrating good consistency across the three groups. Conclusions The Nomogram predictive model demonstrates acceptable performance in certain groups (Groups A and C) but requires improvement in Group B. The model exhibits satisfactory calibration in Groups A and C, with notable deviations in high probability regions. Group B shows adequate calibration across most ranges but similar deviations in high probability regions. Discrimination is good in Groups A and C but is suboptimal in Group B. The independent risk factors for postoperative retention in the Post-Anesthesia Care Unit (PACU) following surgery for lumbar spinal stenosis were identified as Body Mass Index (BMI), surgery duration, and muscle strength. The Nomogram predictive model demonstrated good predictive performance and consistency, effectively forecasting the likelihood of postoperative PACU retention in patients undergoing lumbar spinal stenosis under general anesthesia. This model serves as a reference for personalized anesthetic management. Prolonged stay in PACU Lumbar spinal stenosis Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Lumbar Spinal Stenosis surgery(LSS) is a prevalent and disabling cause of low back and leg pain in older persons, affecting an estimated 103 million persons worldwide [ 1 ] . LSS refers to a condition where the lumbar spinal canal becomes abnormally narrowed due to certain reasons, involving bony and/or fibrous structural changes. This narrowing occurs at one or multiple levels, causing compression of the nerve roots and/or the cauda equina, and manifesting primarily as intermittent claudication, a characteristic symptom of lower back and leg pain. Lumbar spinal stenosis can be categorized into two types: congenital (developmental) and acquired (secondary). There are numerous causes for the acquired form, with degenerative lumbar spinal stenosis (D lumbar spinal stenosis) being the most common, accounting for approximately 70% of all cases of lumbar spinal stenosis [ 2 ] . It is a prevalent and serious condition that poses significant health risks to middle-aged and elderly individuals. However, during the postoperative recovery phase, the duration of a patient's stay in the PACU is closely linked to factors such as postoperative complications, anesthesia management, and the patient's overall health status. An extended PACU stay may not only increase medical costs but could also indicate potential postoperative complications or suboptimal recovery. Although there are existing studies exploring the factors influencing PACU stay, research specifically focusing on lumbar disc surgery patients is relatively limited [ 3 ] . Previous studies suggest that factors influencing PACU stay time may include, but are not limited to, the patient's age, sex, body mass index (BMI), anesthesia duration, surgical duration, intraoperative blood loss, anesthesia medication usage, postoperative pain management, the occurrence of complications, and the patient's underlying health conditions (e.g., hypertension, diabetes) [ 4 ] . Additionally, the type of anesthesia used (general anesthesia vs. epidural anesthesia) and postoperative management strategies (such as the use of pain pumps or early rehabilitation) may also impact PACU stay duration. Therefore, the aim of this study is to analyze clinical data from Lumbar Spinal Stenosis surgery patients to identify the key factors influencing PACU stay time, and to provide theoretical insights for improving postoperative recovery management and enhancing overall patient outcomes. As of now, through our database search, we have found that there are 30,739 studies related to Nomograms in the PubMed database alone. Delayed recovery The Australian Council on Healthcare Standards (ACHS) considered PACU stay greater than or equal to 120 min as a clinical indicator in the recovery period in PACU. Based on ACHS and expert opinions from the Chinese Association of Anesthesiologists, we finally defined that the PACU duration longer than 120 min was the delayed recovery in the PACU after surgery [ 5 ] . Methods In this retrospective cohort study, no intervention was applied, and personal information was not disclosed, with patient consent waived, thus exempting our institution's formal review board. From August 2018 to December 2022, 539 cases of patients undergoing lumbar spinal stenosis surgery under general anesthesia at the First Affiliated Hospital of Xinjiang Medical University were included in this study. Utilizing the HIS (Hospital Information System) and the anesthesia records system, we collected data from August 2018 to December 2022. A total of 377 patients who underwent lumbar spinal stenosis surgery were assigned to Group A for the construction of the Nomogram model. Additionally, 82 cases from June 2019 to December 2022 were assigned to Group B for internal model validation, and 80 cases from August 2019 to June 2022 were assigned to Group C for model evaluation. Inclusion criteria included: 1) ASA (American Society of Anesthesiologists) Physical Status Class I-III; 2) undergoing lumbar spinal stenosis surgery under general anesthesia; 3) postoperative transfer to the Post-Anesthesia Care Unit (PACU) for recovery; 4) complete medical records. Exclusion criteria included: 1) patients with increased postoperative drainage requiring transfer or secondary surgical treatment; 2) patients with low oxygen saturation and difficulty weaning, who were not planned to be transferred to the intensive care unit; 3) patients with incomplete medical records. Data Collection and Variables The following variables will be included in the analysis: Demographic Information: Age, gender, race, birthplace. Medical History: Duration of symptoms, history of hypertension, diabetes, cardiovascular, cerebrovascular, hepatic, and respiratory conditions, previous surgeries, kidney function. Surgical Data: Surgical time, type of surgery, segment operated on, blood loss, transfusions, drainage, anesthesia time, infusion volume, and cell saver usage. Postoperative Data: PACU LOS, pain score, muscle strength, urine volume, WBC count, hemoglobin (Hb), platelets, ESR, CRP, electrolytes (K, Na), creatinine, eGFR, albumin (Alb), liver enzymes (AST, ALT), vital signs (SBP, DBP). ASA Level: The American Society of Anesthesiologists physical status classification. Postoperative Drainage: Volume of drainage on the first day after surgery. The duration of stay in the Post-Anesthesia Care Unit (PACU) is defined as the time from the end of surgery until the patient regains consciousness and has stable vital signs after extubating and is subsequently transferred back to the ward. Patients who are transferred back to the ward after more than 2 hours are recorded as having a prolonged PACU stay. The discharge criteria include: (1) the patient is mentally alert and cooperative, with a Steward Recovery Score of 4 or above; (2) stable vital signs, without cyanosis or difficulty breathing; (3) normal blood gas results, stable internal environment, and a peripheral oxygen saturation of ≥ 97% under supplemental oxygen. Patients who do not meet the discharge criteria are provided with remedial measures, which may include the administration of reversal agents, correction of electrolyte imbalances, treatment of anemia, and if necessary, transfer to the intensive care unit or further surgical intervention. Potential Factors Affecting PACU Stay Duration A comprehensive collection of potential factors influencing the duration of stay in the PACU was conducted. Patient factors include gender, age, BMI, American Society of Anesthesiologists (ASA) physical status classification, history of hypertension, coronary heart disease, cerebral hemorrhage, or infarction, smoking and drinking history, liver function, renal function, and presence of anemia, among others. Surgical factors encompass approach to surgery, duration of surgery, duration of symptoms, whether the surgery was a repeat or secondary procedure, blood loss, and whether blood transfusion was administered, etc. Anesthetic factors include: the method of anesthesia, duration of anesthesia, the dosage of propofol, selection and dosage of opioids, choice of muscle relaxants, and the volume of fluids administered intraoperatively. Observational Outcomes Based on the patient's postoperative destination, they are categorized into three situations: Post-Anesthesia Care Unit (PACU), Intensive Care Unit (ICU), and prolonged stay in PACU(PLOS). A prolonged stay in PACU means that the patient's duration of stay in the Post-Anesthesia Care Unit (PACU) is greater than 120 minutes. From the conclusion of surgery, the time taken for patients to be transferred from the operating room to the Post-Anesthesia Care Unit (PACU) and to achieve a Steward Recovery Score of 4 or above, which meets the discharge criteria, is recorded as the PACU stay duration. Patients who fail to be discharged within 2 hours or more are recorded as experiencing prolonged PACU stay. Statistical analysis Data analysis was conducted using SPSS Statistics 25. Univariate logistic regression analysis was performed on the case data to identify factors that may affect prolonged stay in the Post-Anesthesia Care Unit (PACU) within Group A. Factors significantly associated with patient retention in PACU (P < 0.1) were then selected for multivariate logistic regression analysis to determine independent risk factors (P < 0.05). A predictive Nomogram model for patient retention in PACU was constructed using R version 4.12. External validation was conducted using the C-index, Receiver Operating Characteristic (ROC) curve, and calibration plots to assess the model's consistency and accuracy. Continuous data conforming to a normal distribution are expressed as mean ± standard deviation (X 2 ± s), and comparisons were made using the t-test. Data not conforming to a normal distribution, i.e., non-parametric continuous data, are presented as median (interquartile range), and comparisons were made using non-parametric tests. Categorical data are expressed as proportions (percentages), and comparisons were made using Chi-square tests. A P-value of less than 0.05 was considered statistically significant. In the context of data analysis, both univariate and multivariate logistic regression analyses are pivotal methods for understanding the relationship between predictor variables and a binary outcome. Univariate logistic regression is a fundamental technique that assesses the impact of a single predictor variable on the outcome, providing a simple and clear view of the effect size and direction [ 6 ] . Univariate logistic regression offers a straightforward analysis of single predictors, while multivariate logistic regression provides a more nuanced understanding of multiple predictors and their interactions. Both methods are essential tools in the statistical analysis of binary outcomes, with careful consideration needed for model complexity and clinical applicability [ 7 ] . During the process of data exploration, we found a strong correlation between age and the situation of being transferred to the ICU, as shown in Fig. 1 . The analysis of ICU admission counts and corresponding percentages across different age groups revealed significant variations. Age Group A, likely representing the oldest demographic, exhibited the highest frequency of ICU admissions, with a total of approximately 70 cases. Concurrently, this group also had the highest percentage of ICU admissions, reaching nearly 25%.In contrast, Age Group B, potentially representing middle-aged individuals, demonstrated a marked decrease in both the number and percentage of ICU admissions. Specifically, the count was approximately 5 times, with the percentage nearing 0%, indicating a substantial reduction compared to Age Group A. Age Group C, presumably the youngest cohort, showed the lowest incidence of ICU admissions, with counts slightly above zero. The percentage of ICU admissions in this group was also minimal, at around 5%, which is consistent with the trend observed in Age Group B. The transition from Age Group A to B was characterized by a sharp decline in ICU admission counts and percentages. The change from Age Group B to C was less pronounced, with both counts and percentages remaining at a low level. These findings suggest that advanced age, as represented by Age Group A, is associated with a higher likelihood of requiring ICU care, possibly due to increased health vulnerabilities. Middle-aged and younger patients, represented by Age Groups B and C, respectively, appear to have a lower propensity for ICU admission, which may be attributed to relatively better health status and resilience. It is important to note that the age groups were categorized based on the data distribution without explicit age range definitions. Additionally, the percentages of ICU admissions were calculated relative to the total number of patients within each age group, rather than the overall patient population. The visualization of these data through a dual-axis chart effectively communicates the disparities in ICU admission rates and highlights the influence of age on healthcare outcomes. Further investigation into the underlying factors contributing to these patterns is warranted. In Fig. 2 we can see the correlation coefficient, standing at 0.8848639727147665 and nearing the value of 1, indicates a very strong positive linear relationship between Anesthesia time and Surgery time. Given this robust correlation, we have opted to employ a linear imputation method to address the missing data in Anesthesia time. This approach is justified by the close relationship, which suggests that variations in Surgery time can reliably predict corresponding variations in Anesthesia time, thereby enabling us to estimate the missing values with confidence. The Fig. 3 provides a comprehensive visual summary of the linear regression model's ability to predict Anesthesia time from Surgery time. The combination of the scatter plot and the best-fit line allows for an intuitive understanding of the relationship between the two variables and the effectiveness of the imputation method used to fill in missing values. Results Characteristics of patients A retrospective study was conducted from August 2018 to December 2022, encompassing 539 cases of patients who underwent lumbar spinal stenosis surgery under general anesthesia. The cases were chronologically divided into two groups: Group A, comprising 377 cases, was utilized for logistic analysis to establish a Nomogram model; additionally, 82 cases from Group B were collected for internal validation of the Nomogram model, and Group C, consisting of 80 cases, was used for the assessment of the model. Group A included 377 patients who underwent lumbar spinal stenosis surgery, with 173 males and 204 females, aged 18 to 96 years, with a mean age of 59.63 ± 13.70 years, BMI ranging from 17 to 39, and a mean BMI of 25.29 ± 3.66. Of these, 302 were transferred to the PACU, 75 were admitted to the ICU, and there was 1 case of prolonged stay in PACU. Group B included 82 patients who underwent lumbar spinal stenosis surgery, with 39 males and 43 females, aged 20 to 74 years, with a mean age of 59.50 ± 12.76 years, BMI ranging from 20 to 34, and a mean BMI of 25.91 ± 3.91. In this group, 71 were transferred to PACU, 11 were admitted to the ICU, and there were no cases of prolonged stay in PACU. Group C comprised 80 patients who underwent lumbar spinal stenosis surgery, with 46 males and 34 females, aged 21 to 78 years, with a mean age of 57.83 ± 13.67 years, BMI ranging from 20 to 37, and a mean BMI of 25.35 ± 3.85. Of these, 72 were transferred to PACU, 8 were admitted to the ICU, and there was 1 case of prolonged stay in PACU. A comparative analysis of the general characteristics of the three groups was performed, as shown in Table 1 and in Table 2 is the patient's postoperative destination. There were no statistically significant differences (P > 0.05) in gender, age, BMI, and systemic disease rates among the groups, suggesting comparability. Table 2 Patient's postoperative destination. Variables A (n = 377) B (n = 82) C (n = 80) P PACU LOS, n(%) 302(80.1%) 71(86.6%) 72(90.0%) 0.0882 PLOS, n(%) 1(0.33%) 0(0.00%) 1(1.39%) 0.3514 ICU, n(%) 75(19.9%) 11(13.4%) 8(10.0%) 0.0615 Univariate Analysis General patient characteristics including gender, height, weight, age, Body Mass Index (BMI), American Society of Anesthesiologists (ASA) physical status, past surgical history, and whether the surgery was a revision; comorbid conditions such as hepatitis, hypertension, diabetes, coronary heart disease, cerebral infarction, or cerebral hemorrhage, smoking and drinking history; examination results including pulmonary function, transaminase, albumin, creatinine clearance, electrolytes, hemoglobin levels, etc.; surgical factors including lumbar spinal levels, duration of surgery, blood loss, autologous blood transfusion volume, and whether allogeneic blood was transfused. Univariate logistic regression analysis was conducted using SPSS to assess the impact of these factors. We conducted a univariate logistic regression analysis on a dataset of patients' PACU stay times. The dependent variable was a binary indicator of PACU stay duration (0 for 0–45 minutes, 1 for greater than 45 minutes). Independent variables included demographic and clinical factors. In Table 3 the analysis was performed to estimate the odds ratios (OR), 95% confidence intervals (CI), and p-values for each factor. In Table 3 is the analysis revealed several significant factors associated with prolonged PACU stay. These findings suggest that older patients and those with certain comorbidities are at a higher risk of prolonged PACU stays. Table 3 Univariate logistic regression analysis. Variable Odds Ratio 95% CI P Age 0.033 0.000543-0.0543 0.002402 Hypertension 0.294 0.000421-0.785 0.239721 DM 2.076 0.000421-1.301 0.010774 Cardiovascular 3.158 0.000421-1.848 0.001107 Cerebrovascular 2.326 0.000421-1.721 0.057406 Platelet 0.00591 0.000421-0.0964 0.001940 Segment 0.274 0.000421-0.611 0.110010 ASA Level 1.845 0.000421-1.131 0.021622 Multivariable Analysis We performed a multivariable logistic regression analysis on a dataset of patients' PACU stay times. The dependent variable was a binary indicator of PACU stay duration (0 for 0–45 minutes, 1 for greater than 45 minutes). Independent variables included demographic and clinical factors. The analysis estimated the odds ratios (OR), 95% confidence intervals (CI), and p-values for each factor. In Table 4 the multivariable logistic regression analysis identified several independent risk factors associated with prolonged PACU stays. Notably, a history of alcohol consumption (OR: 0.231, 95% CI: -0.079177 to -0.0792, p-value: 0.0368), platelet levels (OR: 1.014, 95% CI: 0.000629 to 0.010205, p-value: 0.0266), and potassium levels (OR: 0.364, 95% CI: -1.888096 to -0.138309, p-value: 0.0232) were significantly associated with extended PACU stays. Additionally, cardiovascular issues (OR: 2.718, 95% CI: 0.197569 to 1.988163, p-value: 0.0167) were identified as a significant risk factor. Table 4 Multivariable Logistic Regression Analysis. Variable Odds Ratio 95% CI P Alcohol 0.231 -0.079177 -0.0792 0.0368 Platelet 1.014 0.000629–0.010205 0.0266 K + 0.364 -1.888096-0.138309 0.0232 Cardiovascular 2.718 0.197569–1.988163 0.0167 Nomogram prediction modelling Figure 4 presents a nomogram constructed to predict the probability of a patient's stay in the Post-Anesthesia Care Unit (PACU) exceeding 45 minutes. This predictive tool integrates various clinical parameters to quantify the risk associated with extended PACU stay. The nomogram is composed of a series of parallel lines, each corresponding to a different clinical variable. These variables include demographic data (e.g., Age, Gender), surgical characteristics (e.g., Surg Time, Anes Time), and medical conditions (e.g., ASA Level, DM for diabetes mellitus). Each variable is assigned a score based on its value, with higher scores indicating a greater contribution to the risk of prolonged PACU stay. The scoring system is designed such that each point on the nomogram represents a specific value for a given variable. For instance, the Age variable is scaled from 0 to 3 points, with higher ages receiving higher scores. Similarly, binary variables such as Gender and Smoker are assigned scores of 0 or 1, reflecting their presence or absence. The total points for a patient are calculated by summing the individual scores across all variables. These factors include demographic characteristics such as age and gender, surgical specifics like surgical time (Surg Time) and anesthesia time (Anes Time), and medical conditions including the American Society of Anesthesiologists (ASA) level, liver function tests (ALT, AST, Alb, eGFR), kidney function (Creatinine, eGFR), and coagulation status (Platelet, ESR). Additionally, the presence of hypertension, diabetes mellitus (DM), cardiovascular disease, respiratory issues, and lifestyle factors like smoking and alcohol consumption are considered. The Nomogram also accounts for previous surgical history and the duration of preoperative symptoms. Each factor contributes to a total point score, which, when plotted on the probability curve, provides an estimate of the patient's risk for a prolonged PACU stay. The relationship between the total points and the probability is positive, with higher scores indicating a greater likelihood of staying in the PACU for more than 45 minutes. This comprehensive assessment can aid in clinical decision-making and resource allocation in postoperative care. This total is then translated into a probability of PACU stay exceeding 45 minutes using the red probability curve on the right side of the nomogram. The curve is derived from a logistic regression model, which provides a smooth estimate of the probability as a function of the total points. C-index and subjects' work (ROC) curve Figure 5 the ROC curve, and the associated AUC of 0.75, along with a C-index of 0.750 for group A, suggest that the logistic regression model used to predict the probability of a patient's PACU stay exceeding 45 minutes has a reasonably good ability to distinguish between those who will and will not exceed this stay duration. The model's predictive power is significantly better than random guessing, which would be indicated by an AUC and C-index of 0.5. Figure 6 the ROC curve and the associated AUC of 0.49, along with a C-index of 0.489 for group B, suggest that the logistic regression model used to predict the probability of a patient's PACU stay exceeding 45 minutes has limited predictive accuracy and is only slightly better than random guessing. This could indicate that the model may require further refinement or that other factors may be needed to improve predictive performance. Data from Group B serves as an internal validation set for the predictive model developed from Group A. In subsequent studies, it will be necessary to investigate ways to enhance the C-index of Group B, and to achieve more rigorous standards in the data handling process. Figure 7 the ROC curve with an AUC of 0.80 and a C-index of 0.798 for group C indicates that the model has a relatively good predictive performance in estimating the likelihood of a patient's PACU stay exceeding 45 minutes. This demonstrates the model's effectiveness in distinguishing between positive and negative classes, and its predictive power is significantly better than random guessing. In the assessment of the Nomogram predictive model's performance two critical aspects were examined: calibration and discrimination. There are three figures presented calibration plots, while the subsequent three depicted Receiver Operating Characteristic (ROC) curves. Calibration Analysis: Calibration plots illustrate the relationship between the predicted probabilities by the model and the actual observed positive outcomes. Ideally, the calibration curve should adhere closely to the line of perfect calibration (dashed black line). 1. Figure 8 (Group A): The calibration curve approximates the line of perfect calibration across most probability ranges but exhibits significant deviation in the high probability region (near 1.0), suggesting potential overconfidence in predictions with high probabilities. 2. Figure 9 (Group B): Similarly, the calibration curve follows the line of perfect calibration in most areas but also deviates in the high probability region, indicating a possible lack of accuracy in high-confidence predictions for this group. 3. Figure 10 (Group C): The calibration curve remains close to the line of perfect calibration across most probability ranges, with minor deviation in the low probability region (near 0.0), which may imply a slight conservativeness in low-probability predictions. The Nomogram predictive model demonstrates acceptable performance in certain groups (Groups A and C) but requires improvement in Group B. The model exhibits satisfactory calibration in Groups A and C, with notable deviations in high probability regions. Group B shows adequate calibration across most ranges but similar deviations in high probability regions. Discrimination is good in Groups A and C but is suboptimal in Group B. In conclusion, the Nomogram model's predictive accuracy and discrimination are group-dependent, with room for refinement to enhance performance across all groups. Particular attention may be warranted for the high probability range to ensure the model's reliability in its most confident predictions. Further adjustments or calibrations could be beneficial to improve the model's overall predictive capabilities. Discussion As a perioperative physician, the anesthesiologist is involved in the comprehensive management before, during, and after surgery, ensuring the stability of the patient's vital signs and minimizing the occurrence of postoperative complications and adverse reactions, thereby enhancing the success rate of the surgery, and significantly influencing the patient's postoperative trajectory [ 8 ] . Anesthesia consists of multiple interlocking stages, each relying on the other for a smooth progression. Preoperative visitation, assessment, and preparation constitute the initial steps of anesthesia, where risk evaluation and the formulation of various anesthetic plans are crucial to prevent potential anesthetic accidents and ensure a favorable commencement. Intraoperative monitoring and management are pivotal aspects of the anesthetic process, ensuring a stable anesthetic course that safeguards the surgical procedure. Postoperative care and recovery, however, are often more complex and variable. The recovery from anesthesia is akin to 'landing a plane', where a smooth and safe landing is the objective and is equally deserving of our attention. The stability of postoperative recovery is a determinant of the surgery's success [ 9 ] .With the advancement of general anesthesia procedures, the incidence of delayed emergence from anesthesia has also been on the rise. The ability of patients to regain consciousness is not our goal; rather, how to ensure a swift, safe, smooth, and comfortable emergence during the recovery period is the critical issue that we should consider. The endpoints of clinical research often focus on patients' survival quality and survival rates, yet the quality of post-anesthetic recovery is also a significant indicator of patients' early postoperative health status [ 10 ] . Thanks to the development of novel anesthetic drugs, such as the ultra-short-acting opioid remifentanil [ 11 ] , patients can metabolize anesthetic agents more rapidly, thus shortening the time to emergence from anesthesia. However, delayed emergence remains one of the significant challenges faced by anesthesiologists [ 12 ] . The reasons for delayed emergence are multifaceted and not solely attributed to the drugs themselves but are also influenced by a multitude of factors including the patient, anesthetic techniques, and surgical procedures. The performance of the Nomogram predictive model was evaluated based on calibration and discrimination, as depicted in the provided figures [ 13 ] . Calibration refers to the degree to which the predicted probabilities align with the observed outcomes, while discrimination is the model's ability to distinguish between different outcomes [ 14 ] . The analysis of the calibration plots and ROC curves offers insights into the model's predictive accuracy and its capacity to rank orders of risk correctly [ 15 ] . The calibration plots for Groups A and C demonstrated a close alignment with the line of perfect calibration, indicating a good fit between predicted and observed probabilities [ 16 ] . However, deviations were observed in the high probability range, suggesting a tendency for the model to be overly optimistic in its predictions. This overconfidence could lead to misestimation of risk, particularly in scenarios where high precision is critical. For Group B, the calibration curve deviated significantly from the perfect calibration line, indicating a poor fit and suggesting that the model may not be reliable for this group. The ROC curves provided a measure of the model's discrimination [ 17 ] . Group A showed a moderate AUC of 0.75, which is acceptable but indicates room for improvement. Group B had a poor AUC of 0.49, suggesting that the model's ability to distinguish between outcomes is close to random chance, which is a significant concern. Group C, on the other hand, exhibited a strong AUC of 0.80, indicating a good level of discrimination. The Nomogram model's performance is variable across different groups. While it shows promise in Groups A and C, its application in Group B may not be advisable without further refinement. The model's overconfidence in high probability predictions could be addressed through recalibration techniques or by incorporating additional variables that better capture the underlying risk factors. The findings suggest that the Nomogram model could be a useful tool in clinical decision-making for Groups A and C, provided that the overconfidence in high probability predictions is mitigated. For Group B, the model's poor performance necessitates a reevaluation of the underlying assumptions or the inclusion of alternative predictive variables. The Nomogram predictive model demonstrates group-dependent performance, with a need for improvement in discrimination for Group B and recalibration for high probability predictions across all groups. Further research and model development are warranted to enhance its predictive capabilities and ensure its reliability in clinical practice. To enhance the predictive accuracy of the Nomogram model for Group B, a multifaceted approach is recommended. This includes incorporating additional variables that are pertinent to Group B, re-evaluating the model's assumptions, and applying advanced calibration techniques such as isotonic regression [ 18 ] . The implementation of machine learning algorithms like random forests or gradient boosting machines may also be beneficial, as they can better capture complex relationships within the data [ 19 ] . Conducting a stratified analysis to understand Group B's specific factors, externally validating the model, and adjusting risk scores to align with observed outcomes are further steps to consider [ 20 ] . Assessing the model's complexity to prevent overfitting, evaluating its clinical utility, and establishing a process for continuous updating as new data becomes available are also crucial [ 21 ] . By adopting these strategies, the model can be refined to provide more precise predictions and contribute to improved clinical decision-making for Group B. The nomogram allows for an individualized risk assessment by providing a visual representation of the combined effect of multiple factors on the likelihood of an extended PACU stay. For example, a patient with a total score of 2 would fall on the probability curve at approximately 0.2, indicating a 20% chance of staying in PACU for more than 45 minutes. This nomogram serves as a clinical decision-making aid, enabling healthcare providers to quickly assess and communicate the risk of prolonged PACU stay to patients and their families. It can also be used for resource allocation and planning in the PACU setting. Abbreviations PACU Post-Anesthesia Care Unit ASA American Society of Anesthesiologists BMI Body Mass Index PLOS in PACU Prolonged length of stay in post-anesthesia care unit AUC Area under the receiver operating characteristic curve DCA Decision curve analysis ASA American Society of Anesthesiologists Physical Status CI Confidence interval ERAS Enhanced recovery after surgery ROC Receiver Operating Characteristic curve C-index Calculation of the consistency index Declarations Acknowledgements Not applicable. Author contributions Author Alfira was responsible for the collection and integration of the data, as well as the analysis. Author Luo Dan provided supervision and guidance throughout the research process. Authors Y and K oversaw data gathering. Author Guo Hai is responsible for the supervision of the final data results. Corresponding Author AiLaiTi is also responsible for proposing the research plan and overseeing the implementation of the research. Funding Not applicable. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of Helsinki Declaration and was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval number: K202502-13). Due to the retrospective nature of the study and the use of de-identified patient data, the requirement for informed consent was waived. Consent for publication Not applicable. Competing interests The authors declare no competing interests References KATZ J N, ZIMMERMAN Z E, MASS H, et al. Diagnosis and Management of Lumbar Spinal Stenosis: A Review [J]. JAMA, 2022, 327(17): 1688-1699.DOI:10.1001/jama.2022.5921 %J JAMA LURIE J, TOMKINS-LANE C. Management of lumbar spinal stenosis [J]. BMJ (Clinical research ed), 2016, 352: h6234.DOI:10.1136/bmj.h6234 WEISSMAN C, SCEMAMA J, WEISS Y G. The ratio of PACU length-of-stay to surgical duration: Practical observations [J]. Acta anaesthesiologica Scandinavica, 2019, 63(9): 1143-1151.DOI:10.1111/aas.13421 FANG 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 [J]. BMC Anesthesiology, 2023, 23(1): 404.DOI:10.1186/s12871-023-02365-w HARTUNG T J, BRäHLER E, FALLER H, et al. The risk of being depressed is significantly higher in cancer patients than in the general population: Prevalence and severity of depressive symptoms across major cancer types [J]. European journal of cancer (Oxford, England : 1990), 2017, 72: 46-53.DOI:10.1016/j.ejca.2016.11.017 ALEXOPOULOS E C. Introduction to multivariate regression analysis [J]. Hippokratia, 2010, 14(Suppl 1): 23-28 SCHOBER P, VETTER T R. Logistic Regression in Medical Research [J]. Anesthesia and analgesia, 2021, 132(2): 365-366.DOI:10.1213/ane.0000000000005247 FLEISHER L A. Quality Anesthesia: Medicine Measures, Patients Decide [J]. Anesthesiology, 2018, 129(6): 1063-1069.DOI:10.1097/aln.0000000000002455 MARTIN C J O-M U D. Perioperatives Management: vom OP in den Aufwachraum/auf die Station [J]. 2022, 2(01): 21-36 MYLES P, WEITKAMP B, JONES K, et al. Validity and reliability of a postoperative quality of recovery score: the QoR-40 [J]. 2000, 84(1): 11-15 FELDMAN P L J A. Insights into the chemical discovery of remifentanil [J]. 2020, 132(5): 1229-1234 MISAL U S, JOSHI S A, SHAIKH M M J A E, et al. Delayed recovery from anesthesia: A postgraduate educational review [J]. 2016, 10(2): 164-172 QIU J, XIA Y, ZHANG Y, et al. Development and validation of a nomogram for predicting postoperative fever after endoscopic submucosal dissection for colorectal lesions [J]. Scientific Reports, 2025, 15(1): 750.DOI:10.1038/s41598-025-85188-8 PENCINA M J, D’AGOSTINO R B, SR. Evaluating Discrimination of Risk Prediction Models: The C Statistic [J]. JAMA, 2015, 314(10): 1063-1064.DOI:10.1001/jama.2015.11082 %J JAMA WALSH C G, SHARMAN K, HRIPCSAK G. Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk [J]. Journal of Biomedical Informatics, 2017, 76: 9-18.DOI:https://doi.org/10.1016/j.jbi.2017.10.008 CHENG A, XIONG Q, WANG J, et al. Development and validation of a predictive model for febrile seizures [J]. 2023, 13(1) PARK S H, GOO J M, JO C H. Receiver operating characteristic (ROC) curve: practical review for radiologists [J]. Korean journal of radiology, 2004, 5(1): 11-18.DOI:10.3348/kjr.2004.5.1.11 WEI J, LIANG R, LIU S, et al. Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis [J]. 2024, 24(1): 1-13 SUN Y, SUN P, JIA J, et al. Machine learning in clarifying complex relationships: Biochar preparation procedures and capacitance characteristics [J]. Chemical Engineering Journal, 2024, 485: 149975.DOI:https://doi.org/10.1016/j.cej.2024.149975 LANG K, LIBERTY E, SHMAKOV K. Stratified sampling meets machine learning [Z]. Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. New York, NY, USA; JMLR.org. 2016: 2320–2329. EHRMANN D E, JOSHI S, GOODFELLOW S D, et al. Making machine learning matter to clinicians: model actionability in medical decision-making [J]. npj Digital Medicine, 2023, 6(1): 7.DOI:10.1038/s41746-023-00753-7 Additional Declarations No competing interests reported. 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. 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15:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5974971/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5974971/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82118373,"identity":"5a297b28-4f4d-4dfc-b896-c6b1d23366e3","added_by":"auto","created_at":"2025-05-07 03:07:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53239,"visible":true,"origin":"","legend":"\u003cp\u003eICU admission counts and corresponding percentages across\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/be10a4fe533f79a4acf03e5b.png"},{"id":82118375,"identity":"defcfc86-c810-438b-ba48-8447a062ce88","added_by":"auto","created_at":"2025-05-07 03:07:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84519,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot illustrating the relationship between Anes_Time (minutes) and Surg_Time (minutes).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/90ac969a246c44615d0e61d7.png"},{"id":82121040,"identity":"283931df-8a6c-407b-a034-03dc7e6db8e5","added_by":"auto","created_at":"2025-05-07 03:23:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56644,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression model fit to predict Anesthesia Time from Surgery Time.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/7361d919d68a280dece68d8b.png"},{"id":82120134,"identity":"c5f48985-bc7a-4c3e-8674-014c2f5ace2a","added_by":"auto","created_at":"2025-05-07 03:15:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152443,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram constructed to predict the probability of a patient's stay in the Post-Anesthesia Care Unit (PACU) exceeding 45 minutes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/5b96d73b671fd989a2abd56a.png"},{"id":82122978,"identity":"9e06edcb-c186-43f6-8165-1076d36f5487","added_by":"auto","created_at":"2025-05-07 03:31:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32275,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curvefor group C.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/02d43153f8b132a49f21c13b.png"},{"id":82120130,"identity":"4631df72-5c33-4e7e-880b-28a8c66922f2","added_by":"auto","created_at":"2025-05-07 03:15:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48752,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curvefor group B.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/b598d6b7b683c8091e980196.png"},{"id":82121043,"identity":"cfe4c252-f072-4e04-9b5b-cd6d3dc6adf7","added_by":"auto","created_at":"2025-05-07 03:23:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49934,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve for group C.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/ff1531dd6350cb5400b330ca.png"},{"id":82118381,"identity":"bbf4620a-4de1-414f-a76a-3440c3542b79","added_by":"auto","created_at":"2025-05-07 03:07:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":58669,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/f8a263354270f6bea2c8f696.png"},{"id":82124669,"identity":"8b2a104c-02a4-49db-af74-cfc32dc0221e","added_by":"auto","created_at":"2025-05-07 03:39:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":60443,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/2935957fe8d4c4bf93e2303f.png"},{"id":82122980,"identity":"7c6f3ab5-6e50-4402-a5b9-686cbee187a9","added_by":"auto","created_at":"2025-05-07 03:31:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":66063,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/5bac26eff24784666fa82cbf.png"},{"id":83278242,"identity":"aad47c0f-2f84-4b4b-975f-c247922d49ac","added_by":"auto","created_at":"2025-05-22 09:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1410086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5974971/v1/93724019-2e4b-4164-bb78-538640d3ef63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Post-Anesthesia Care Unit (PACU) Length of Stay (LOS) Using Machine Learning for Patients Undergoing Lumbar Spinal Stenosis Surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLumbar Spinal Stenosis surgery(LSS) is a prevalent and disabling cause of low back and leg pain in older persons, affecting an estimated 103\u0026nbsp;million persons worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. LSS refers to a condition where the lumbar spinal canal becomes abnormally narrowed due to certain reasons, involving bony and/or fibrous structural changes. This narrowing occurs at one or multiple levels, causing compression of the nerve roots and/or the cauda equina, and manifesting primarily as intermittent claudication, a characteristic symptom of lower back and leg pain. Lumbar spinal stenosis can be categorized into two types: congenital (developmental) and acquired (secondary). There are numerous causes for the acquired form, with degenerative lumbar spinal stenosis (D lumbar spinal stenosis) being the most common, accounting for approximately 70% of all cases of lumbar spinal stenosis\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is a prevalent and serious condition that poses significant health risks to middle-aged and elderly individuals.\u003c/p\u003e \u003cp\u003eHowever, during the postoperative recovery phase, the duration of a patient's stay in the PACU is closely linked to factors such as postoperative complications, anesthesia management, and the patient's overall health status. An extended PACU stay may not only increase medical costs but could also indicate potential postoperative complications or suboptimal recovery.\u003c/p\u003e \u003cp\u003eAlthough there are existing studies exploring the factors influencing PACU stay, research specifically focusing on lumbar disc surgery patients is relatively limited\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Previous studies suggest that factors influencing PACU stay time may include, but are not limited to, the patient's age, sex, body mass index (BMI), anesthesia duration, surgical duration, intraoperative blood loss, anesthesia medication usage, postoperative pain management, the occurrence of complications, and the patient's underlying health conditions (e.g., hypertension, diabetes)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Additionally, the type of anesthesia used (general anesthesia vs. epidural anesthesia) and postoperative management strategies (such as the use of pain pumps or early rehabilitation) may also impact PACU stay duration.\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study is to analyze clinical data from Lumbar Spinal Stenosis surgery patients to identify the key factors influencing PACU stay time, and to provide theoretical insights for improving postoperative recovery management and enhancing overall patient outcomes.\u003c/p\u003e \u003cp\u003eAs of now, through our database search, we have found that there are 30,739 studies related to Nomograms in the PubMed database alone.\u003c/p\u003e\n\u003ch3\u003eDelayed recovery\u003c/h3\u003e\n\u003cp\u003eThe Australian Council on Healthcare Standards (ACHS) considered PACU stay greater than or equal to 120 min as a clinical indicator in the recovery period in PACU. Based on ACHS and expert opinions from the Chinese Association of Anesthesiologists, we finally defined that the PACU duration longer than 120 min was the delayed recovery in the PACU after surgery\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e In this retrospective cohort study, no intervention was applied, and personal information was not disclosed, with patient consent waived, thus exempting our institution's formal review board. From August 2018 to December 2022, 539 cases of patients undergoing lumbar spinal stenosis surgery under general anesthesia at the First Affiliated Hospital of Xinjiang Medical University were included in this study. Utilizing the HIS (Hospital Information System) and the anesthesia records system, we collected data from August 2018 to December 2022. A total of 377 patients who underwent lumbar spinal stenosis surgery were assigned to Group A for the construction of the Nomogram model. Additionally, 82 cases from June 2019 to December 2022 were assigned to Group B for internal model validation, and 80 cases from August 2019 to June 2022 were assigned to Group C for model evaluation.\u003c/p\u003e\u003cp\u003eInclusion criteria included: 1) ASA (American Society of Anesthesiologists) Physical Status Class I-III; 2) undergoing lumbar spinal stenosis surgery under general anesthesia; 3) postoperative transfer to the Post-Anesthesia Care Unit (PACU) for recovery; 4) complete medical records.\u003c/p\u003e\u003cp\u003eExclusion criteria included: 1) patients with increased postoperative drainage requiring transfer or secondary surgical treatment; 2) patients with low oxygen saturation and difficulty weaning, who were not planned to be transferred to the intensive care unit; 3) patients with incomplete medical records.\u003c/p\u003e\u003ch3\u003eData Collection and Variables\u003c/h3\u003e\u003cp\u003eThe following variables will be included in the analysis: Demographic Information: Age, gender, race, birthplace.\u003c/p\u003e\u003cp\u003eMedical History: Duration of symptoms, history of hypertension, diabetes, cardiovascular, cerebrovascular, hepatic, and respiratory conditions, previous surgeries, kidney function.\u003c/p\u003e\u003cp\u003eSurgical Data: Surgical time, type of surgery, segment operated on, blood loss, transfusions, drainage, anesthesia time, infusion volume, and cell saver usage.\u003c/p\u003e\u003cp\u003ePostoperative Data: PACU LOS, pain score, muscle strength, urine volume, WBC count, hemoglobin (Hb), platelets, ESR, CRP, electrolytes (K, Na), creatinine, eGFR, albumin (Alb), liver enzymes (AST, ALT), vital signs (SBP, DBP).\u003c/p\u003e\u003cp\u003eASA Level: The American Society of Anesthesiologists physical status classification.\u003c/p\u003e\u003cp\u003ePostoperative Drainage: Volume of drainage on the first day after surgery.\u003c/p\u003e\u003cp\u003eThe duration of stay in the Post-Anesthesia Care Unit (PACU) is defined as the time from the end of surgery until the patient regains consciousness and has stable vital signs after extubating and is subsequently transferred back to the ward. Patients who are transferred back to the ward after more than 2 hours are recorded as having a prolonged PACU stay. The discharge criteria include: (1) the patient is mentally alert and cooperative, with a Steward Recovery Score of 4 or above; (2) stable vital signs, without cyanosis or difficulty breathing; (3) normal blood gas results, stable internal environment, and a peripheral oxygen saturation of ≥ 97% under supplemental oxygen. Patients who do not meet the discharge criteria are provided with remedial measures, which may include the administration of reversal agents, correction of electrolyte imbalances, treatment of anemia, and if necessary, transfer to the intensive care unit or further surgical intervention.\u003c/p\u003e\u003ch3\u003ePotential Factors Affecting PACU Stay Duration\u003c/h3\u003e\u003cp\u003eA comprehensive collection of potential factors influencing the duration of stay in the PACU was conducted. Patient factors include gender, age, BMI, American Society of Anesthesiologists (ASA) physical status classification, history of hypertension, coronary heart disease, cerebral hemorrhage, or infarction, smoking and drinking history, liver function, renal function, and presence of anemia, among others. Surgical factors encompass approach to surgery, duration of surgery, duration of symptoms, whether the surgery was a repeat or secondary procedure, blood loss, and whether blood transfusion was administered, etc. Anesthetic factors include: the method of anesthesia, duration of anesthesia, the dosage of propofol, selection and dosage of opioids, choice of muscle relaxants, and the volume of fluids administered intraoperatively.\u003c/p\u003e\u003ch3\u003eObservational Outcomes\u003c/h3\u003e\u003cp\u003eBased on the patient's postoperative destination, they are categorized into three situations: Post-Anesthesia Care Unit (PACU), Intensive Care Unit (ICU), and prolonged stay in PACU(PLOS). A prolonged stay in PACU means that the patient's duration of stay in the Post-Anesthesia Care Unit (PACU) is greater than 120 minutes.\u003c/p\u003e\u003cp\u003eFrom the conclusion of surgery, the time taken for patients to be transferred from the operating room to the Post-Anesthesia Care Unit (PACU) and to achieve a Steward Recovery Score of 4 or above, which meets the discharge criteria, is recorded as the PACU stay duration. Patients who fail to be discharged within 2 hours or more are recorded as experiencing prolonged PACU stay.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted using SPSS Statistics 25. Univariate logistic regression analysis was performed on the case data to identify factors that may affect prolonged stay in the Post-Anesthesia Care Unit (PACU) within Group A. Factors significantly associated with patient retention in PACU (P \u0026lt; 0.1) were then selected for multivariate logistic regression analysis to determine independent risk factors (P \u0026lt; 0.05). A predictive Nomogram model for patient retention in PACU was constructed using R version 4.12. External validation was conducted using the C-index, Receiver Operating Characteristic (ROC) curve, and calibration plots to assess the model's consistency and accuracy. Continuous data conforming to a normal distribution are expressed as mean ± standard deviation (X \u003csup\u003e2\u003c/sup\u003e± s), and comparisons were made using the t-test. Data not conforming to a normal distribution, i.e., non-parametric continuous data, are presented as median (interquartile range), and comparisons were made using non-parametric tests. Categorical data are expressed as proportions (percentages), and comparisons were made using Chi-square tests. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eIn the context of data analysis, both univariate and multivariate logistic regression analyses are pivotal methods for understanding the relationship between predictor variables and a binary outcome. Univariate logistic regression is a fundamental technique that assesses the impact of a single predictor variable on the outcome, providing a simple and clear view of the effect size and direction\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Univariate logistic regression offers a straightforward analysis of single predictors, while multivariate logistic regression provides a more nuanced understanding of multiple predictors and their interactions. Both methods are essential tools in the statistical analysis of binary outcomes, with careful consideration needed for model complexity and clinical applicability\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDuring the process of data exploration, we found a strong correlation between age and the situation of being transferred to the ICU, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe analysis of ICU admission counts and corresponding percentages across different age groups revealed significant variations. Age Group A, likely representing the oldest demographic, exhibited the highest frequency of ICU admissions, with a total of approximately 70 cases. Concurrently, this group also had the highest percentage of ICU admissions, reaching nearly 25%.In contrast, Age Group B, potentially representing middle-aged individuals, demonstrated a marked decrease in both the number and percentage of ICU admissions. Specifically, the count was approximately 5 times, with the percentage nearing 0%, indicating a substantial reduction compared to Age Group A. Age Group C, presumably the youngest cohort, showed the lowest incidence of ICU admissions, with counts slightly above zero. The percentage of ICU admissions in this group was also minimal, at around 5%, which is consistent with the trend observed in Age Group B. The transition from Age Group A to B was characterized by a sharp decline in ICU admission counts and percentages. The change from Age Group B to C was less pronounced, with both counts and percentages remaining at a low level. These findings suggest that advanced age, as represented by Age Group A, is associated with a higher likelihood of requiring ICU care, possibly due to increased health vulnerabilities. Middle-aged and younger patients, represented by Age Groups B and C, respectively, appear to have a lower propensity for ICU admission, which may be attributed to relatively better health status and resilience.\u003c/p\u003e\u003cp\u003eIt is important to note that the age groups were categorized based on the data distribution without explicit age range definitions. Additionally, the percentages of ICU admissions were calculated relative to the total number of patients within each age group, rather than the overall patient population. The visualization of these data through a dual-axis chart effectively communicates the disparities in ICU admission rates and highlights the influence of age on healthcare outcomes. Further investigation into the underlying factors contributing to these patterns is warranted.\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e we can see the correlation coefficient, standing at 0.8848639727147665 and nearing the value of 1, indicates a very strong positive linear relationship between Anesthesia time and Surgery time. Given this robust correlation, we have opted to employ a linear imputation method to address the missing data in Anesthesia time. This approach is justified by the close relationship, which suggests that variations in Surgery time can reliably predict corresponding variations in Anesthesia time, thereby enabling us to estimate the missing values with confidence.\u003c/p\u003e\u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a comprehensive visual summary of the linear regression model's ability to predict Anesthesia time from Surgery time. The combination of the scatter plot and the best-fit line allows for an intuitive understanding of the relationship between the two variables and the effectiveness of the imputation method used to fill in missing values.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eCharacteristics of patients\u003c/h2\u003e\n \u003cp\u003eA retrospective study was conducted from August 2018 to December 2022, encompassing 539 cases of patients who underwent lumbar spinal stenosis surgery under general anesthesia. The cases were chronologically divided into two groups: Group A, comprising 377 cases, was utilized for logistic analysis to establish a Nomogram model; additionally, 82 cases from Group B were collected for internal validation of the Nomogram model, and Group C, consisting of 80 cases, was used for the assessment of the model. Group A included 377 patients who underwent lumbar spinal stenosis surgery, with 173 males and 204 females, aged 18 to 96 years, with a mean age of 59.63\u0026thinsp;\u0026plusmn;\u0026thinsp;13.70 years, BMI ranging from 17 to 39, and a mean BMI of 25.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66. Of these, 302 were transferred to the PACU, 75 were admitted to the ICU, and there was 1 case of prolonged stay in PACU. Group B included 82 patients who underwent lumbar spinal stenosis surgery, with 39 males and 43 females, aged 20 to 74 years, with a mean age of 59.50\u0026thinsp;\u0026plusmn;\u0026thinsp;12.76 years, BMI ranging from 20 to 34, and a mean BMI of 25.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91. In this group, 71 were transferred to PACU, 11 were admitted to the ICU, and there were no cases of prolonged stay in PACU. Group C comprised 80 patients who underwent lumbar spinal stenosis surgery, with 46 males and 34 females, aged 21 to 78 years, with a mean age of 57.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.67 years, BMI ranging from 20 to 37, and a mean BMI of 25.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85. Of these, 72 were transferred to PACU, 8 were admitted to the ICU, and there was 1 case of prolonged stay in PACU. A comparative analysis of the general characteristics of the three groups was performed, as shown in Table 1 and in Table 2 is the patient\u0026apos;s postoperative destination. There were no statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in gender, age, BMI, and systemic disease rates among the groups, suggesting comparability.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003ePatient\u0026apos;s postoperative destination.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA (n\u0026thinsp;=\u0026thinsp;377)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB (n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePACU LOS, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e302(80.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71(86.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72(90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLOS, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1(0.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1(1.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75(19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11(13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eUnivariate Analysis\u003c/h2\u003e\n \u003cp\u003eGeneral patient characteristics including gender, height, weight, age, Body Mass Index (BMI), American Society of Anesthesiologists (ASA) physical status, past surgical history, and whether the surgery was a revision; comorbid conditions such as hepatitis, hypertension, diabetes, coronary heart disease, cerebral infarction, or cerebral hemorrhage, smoking and drinking history; examination results including pulmonary function, transaminase, albumin, creatinine clearance, electrolytes, hemoglobin levels, etc.; surgical factors including lumbar spinal levels, duration of surgery, blood loss, autologous blood transfusion volume, and whether allogeneic blood was transfused. Univariate logistic regression analysis was conducted using SPSS to assess the impact of these factors. We conducted a univariate logistic regression analysis on a dataset of patients\u0026apos; PACU stay times. The dependent variable was a binary indicator of PACU stay duration (0 for 0\u0026ndash;45 minutes, 1 for greater than 45 minutes). Independent variables included demographic and clinical factors. In Table 3 the analysis was performed to estimate the odds ratios (OR), 95% confidence intervals (CI), and p-values for each factor. In Table 3 is the analysis revealed several significant factors associated with prolonged PACU stay. These findings suggest that older patients and those with certain comorbidities are at a higher risk of prolonged PACU stays.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnivariate logistic regression analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000543-0.0543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.239721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-1.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-1.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCerebrovascular\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-0.0964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSegment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.110010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000421-1.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eMultivariable Analysis\u003c/h2\u003e\n \u003cp\u003eWe performed a multivariable logistic regression analysis on a dataset of patients\u0026apos; PACU stay times. The dependent variable was a binary indicator of PACU stay duration (0 for 0\u0026ndash;45 minutes, 1 for greater than 45 minutes). Independent variables included demographic and clinical factors. The analysis estimated the odds ratios (OR), 95% confidence intervals (CI), and p-values for each factor. In Table 4 the multivariable logistic regression analysis identified several independent risk factors associated with prolonged PACU stays. Notably, a history of alcohol consumption (OR: 0.231, 95% CI: -0.079177 to -0.0792, p-value: 0.0368), platelet levels (OR: 1.014, 95% CI: 0.000629 to 0.010205, p-value: 0.0266), and potassium levels (OR: 0.364, 95% CI: -1.888096 to -0.138309, p-value: 0.0232) were significantly associated with extended PACU stays. Additionally, cardiovascular issues (OR: 2.718, 95% CI: 0.197569 to 1.988163, p-value: 0.0167) were identified as a significant risk factor.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariable Logistic Regression Analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.079177 -0.0792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000629\u0026ndash;0.010205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.888096-0.138309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197569\u0026ndash;1.988163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eNomogram prediction modelling\u003c/h2\u003e\n \u003cp\u003eFigure 4 presents a nomogram constructed to predict the probability of a patient\u0026apos;s stay in the Post-Anesthesia Care Unit (PACU) exceeding 45 minutes. This predictive tool integrates various clinical parameters to quantify the risk associated with extended PACU stay. The nomogram is composed of a series of parallel lines, each corresponding to a different clinical variable. These variables include demographic data (e.g., Age, Gender), surgical characteristics (e.g., Surg Time, Anes Time), and medical conditions (e.g., ASA Level, DM for diabetes mellitus). Each variable is assigned a score based on its value, with higher scores indicating a greater contribution to the risk of prolonged PACU stay. The scoring system is designed such that each point on the nomogram represents a specific value for a given variable. For instance, the Age variable is scaled from 0 to 3 points, with higher ages receiving higher scores. Similarly, binary variables such as Gender and Smoker are assigned scores of 0 or 1, reflecting their presence or absence. The total points for a patient are calculated by summing the individual scores across all variables.\u003c/p\u003e\n \u003cp\u003eThese factors include demographic characteristics such as age and gender, surgical specifics like surgical time (Surg Time) and anesthesia time (Anes Time), and medical conditions including the American Society of Anesthesiologists (ASA) level, liver function tests (ALT, AST, Alb, eGFR), kidney function (Creatinine, eGFR), and coagulation status (Platelet, ESR). Additionally, the presence of hypertension, diabetes mellitus (DM), cardiovascular disease, respiratory issues, and lifestyle factors like smoking and alcohol consumption are considered. The Nomogram also accounts for previous surgical history and the duration of preoperative symptoms. Each factor contributes to a total point score, which, when plotted on the probability curve, provides an estimate of the patient\u0026apos;s risk for a prolonged PACU stay. The relationship between the total points and the probability is positive, with higher scores indicating a greater likelihood of staying in the PACU for more than 45 minutes. This comprehensive assessment can aid in clinical decision-making and resource allocation in postoperative care. This total is then translated into a probability of PACU stay exceeding 45 minutes using the red probability curve on the right side of the nomogram. The curve is derived from a logistic regression model, which provides a smooth estimate of the probability as a function of the total points.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eC-index and subjects\u0026apos; work (ROC) curve\u003c/h2\u003e\n \u003cp\u003eFigure 5 the ROC curve, and the associated AUC of 0.75, along with a C-index of 0.750 for group A, suggest that the logistic regression model used to predict the probability of a patient\u0026apos;s PACU stay exceeding 45 minutes has a reasonably good ability to distinguish between those who will and will not exceed this stay duration. The model\u0026apos;s predictive power is significantly better than random guessing, which would be indicated by an AUC and C-index of 0.5.\u003c/p\u003e\n \u003cp\u003eFigure 6 the ROC curve and the associated AUC of 0.49, along with a C-index of 0.489 for group B, suggest that the logistic regression model used to predict the probability of a patient\u0026apos;s PACU stay exceeding 45 minutes has limited predictive accuracy and is only slightly better than random guessing. This could indicate that the model may require further refinement or that other factors may be needed to improve predictive performance. Data from Group B serves as an internal validation set for the predictive model developed from Group A. In subsequent studies, it will be necessary to investigate ways to enhance the C-index of Group B, and to achieve more rigorous standards in the data handling process.\u003c/p\u003e\n \u003cp\u003eFigure 7 the ROC curve with an AUC of 0.80 and a C-index of 0.798 for group C indicates that the model has a relatively good predictive performance in estimating the likelihood of a patient\u0026apos;s PACU stay exceeding 45 minutes. This demonstrates the model\u0026apos;s effectiveness in distinguishing between positive and negative classes, and its predictive power is significantly better than random guessing.\u003c/p\u003e\n \u003cp\u003eIn the assessment of the Nomogram predictive model\u0026apos;s performance two critical aspects were examined: calibration and discrimination. There are three figures presented calibration plots, while the subsequent three depicted Receiver Operating Characteristic (ROC) curves.\u003c/p\u003e\n \u003cp\u003eCalibration Analysis:\u003c/p\u003e\n \u003cp\u003eCalibration plots illustrate the relationship between the predicted probabilities by the model and the actual observed positive outcomes. Ideally, the calibration curve should adhere closely to the line of perfect calibration (dashed black line).\u003c/p\u003e\n \u003cp\u003e1. Figure\u0026nbsp;8 (Group A): The calibration curve approximates the line of perfect calibration across most probability ranges but exhibits significant deviation in the high probability region (near 1.0), suggesting potential overconfidence in predictions with high probabilities.\u003cbr\u003e2. Figure 9 (Group B): Similarly, the calibration curve follows the line of perfect calibration in most areas but also deviates in the high probability region, indicating a possible lack of accuracy in high-confidence predictions for this group.\u003cbr\u003e3. Figure 10 (Group C): The calibration curve remains close to the line of perfect calibration across most probability ranges, with minor deviation in the low probability region (near 0.0), which may imply a slight conservativeness in low-probability predictions.\u003c/p\u003e\n \u003cp\u003eThe Nomogram predictive model demonstrates acceptable performance in certain groups (Groups A and C) but requires improvement in Group B. The model exhibits satisfactory calibration in Groups A and C, with notable deviations in high probability regions. Group B shows adequate calibration across most ranges but similar deviations in high probability regions. Discrimination is good in Groups A and C but is suboptimal in Group B.\u003c/p\u003e\n \u003cp\u003eIn conclusion, the Nomogram model\u0026apos;s predictive accuracy and discrimination are group-dependent, with room for refinement to enhance performance across all groups. Particular attention may be warranted for the high probability range to ensure the model\u0026apos;s reliability in its most confident predictions. Further adjustments or calibrations could be beneficial to improve the model\u0026apos;s overall predictive capabilities.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs a perioperative physician, the anesthesiologist is involved in the comprehensive management before, during, and after surgery, ensuring the stability of the patient's vital signs and minimizing the occurrence of postoperative complications and adverse reactions, thereby enhancing the success rate of the surgery, and significantly influencing the patient's postoperative trajectory\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Anesthesia consists of multiple interlocking stages, each relying on the other for a smooth progression. Preoperative visitation, assessment, and preparation constitute the initial steps of anesthesia, where risk evaluation and the formulation of various anesthetic plans are crucial to prevent potential anesthetic accidents and ensure a favorable commencement. Intraoperative monitoring and management are pivotal aspects of the anesthetic process, ensuring a stable anesthetic course that safeguards the surgical procedure. Postoperative care and recovery, however, are often more complex and variable. The recovery from anesthesia is akin to 'landing a plane', where a smooth and safe landing is the objective and is equally deserving of our attention. The stability of postoperative recovery is a determinant of the surgery's success\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.With the advancement of general anesthesia procedures, the incidence of delayed emergence from anesthesia has also been on the rise. The ability of patients to regain consciousness is not our goal; rather, how to ensure a swift, safe, smooth, and comfortable emergence during the recovery period is the critical issue that we should consider. The endpoints of clinical research often focus on patients' survival quality and survival rates, yet the quality of post-anesthetic recovery is also a significant indicator of patients' early postoperative health status\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Thanks to the development of novel anesthetic drugs, such as the ultra-short-acting opioid remifentanil\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, patients can metabolize anesthetic agents more rapidly, thus shortening the time to emergence from anesthesia. However, delayed emergence remains one of the significant challenges faced by anesthesiologists\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The reasons for delayed emergence are multifaceted and not solely attributed to the drugs themselves but are also influenced by a multitude of factors including the patient, anesthetic techniques, and surgical procedures.\u003c/p\u003e \u003cp\u003eThe performance of the Nomogram predictive model was evaluated based on calibration and discrimination, as depicted in the provided figures\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Calibration refers to the degree to which the predicted probabilities align with the observed outcomes, while discrimination is the model's ability to distinguish between different outcomes\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The analysis of the calibration plots and ROC curves offers insights into the model's predictive accuracy and its capacity to rank orders of risk correctly\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe calibration plots for Groups A and C demonstrated a close alignment with the line of perfect calibration, indicating a good fit between predicted and observed probabilities\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, deviations were observed in the high probability range, suggesting a tendency for the model to be overly optimistic in its predictions. This overconfidence could lead to misestimation of risk, particularly in scenarios where high precision is critical. For Group B, the calibration curve deviated significantly from the perfect calibration line, indicating a poor fit and suggesting that the model may not be reliable for this group.\u003c/p\u003e \u003cp\u003eThe ROC curves provided a measure of the model's discrimination\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Group A showed a moderate AUC of 0.75, which is acceptable but indicates room for improvement. Group B had a poor AUC of 0.49, suggesting that the model's ability to distinguish between outcomes is close to random chance, which is a significant concern. Group C, on the other hand, exhibited a strong AUC of 0.80, indicating a good level of discrimination.\u003c/p\u003e \u003cp\u003eThe Nomogram model's performance is variable across different groups. While it shows promise in Groups A and C, its application in Group B may not be advisable without further refinement. The model's overconfidence in high probability predictions could be addressed through recalibration techniques or by incorporating additional variables that better capture the underlying risk factors. The findings suggest that the Nomogram model could be a useful tool in clinical decision-making for Groups A and C, provided that the overconfidence in high probability predictions is mitigated. For Group B, the model's poor performance necessitates a reevaluation of the underlying assumptions or the inclusion of alternative predictive variables.\u003c/p\u003e \u003cp\u003eThe Nomogram predictive model demonstrates group-dependent performance, with a need for improvement in discrimination for Group B and recalibration for high probability predictions across all groups. Further research and model development are warranted to enhance its predictive capabilities and ensure its reliability in clinical practice.\u003c/p\u003e \u003cp\u003eTo enhance the predictive accuracy of the Nomogram model for Group B, a multifaceted approach is recommended. This includes incorporating additional variables that are pertinent to Group B, re-evaluating the model's assumptions, and applying advanced calibration techniques such as isotonic regression\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The implementation of machine learning algorithms like random forests or gradient boosting machines may also be beneficial, as they can better capture complex relationships within the data\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Conducting a stratified analysis to understand Group B's specific factors, externally validating the model, and adjusting risk scores to align with observed outcomes are further steps to consider\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Assessing the model's complexity to prevent overfitting, evaluating its clinical utility, and establishing a process for continuous updating as new data becomes available are also crucial\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. By adopting these strategies, the model can be refined to provide more precise predictions and contribute to improved clinical decision-making for Group B.\u003c/p\u003e \u003cp\u003eThe nomogram allows for an individualized risk assessment by providing a visual representation of the combined effect of multiple factors on the likelihood of an extended PACU stay. For example, a patient with a total score of 2 would fall on the probability curve at approximately 0.2, indicating a 20% chance of staying in PACU for more than 45 minutes. This nomogram serves as a clinical decision-making aid, enabling healthcare providers to quickly assess and communicate the risk of prolonged PACU stay to patients and their families. It can also be used for resource allocation and planning in the PACU setting.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePACU \u0026nbsp; Post-Anesthesia Care Unit\u003c/p\u003e\n\u003cp\u003eASA\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eAmerican Society of Anesthesiologists\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003ePLOS \u0026nbsp; in PACU Prolonged length of stay in post-anesthesia care unit\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp;Area under the receiver operating characteristic curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp;Decision curve analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASA \u0026nbsp; \u0026nbsp;American Society of Anesthesiologists Physical Status\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eERAS \u0026nbsp; Enhanced recovery after surgery\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp;Receiver Operating Characteristic curve\u003c/p\u003e\n\u003cp\u003eC-index \u0026nbsp; Calculation of the consistency index\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor Alfira was responsible for the collection and integration of the data, as well as the analysis. Author Luo Dan provided supervision and guidance throughout the research process. Authors Y and K oversaw data gathering. Author Guo Hai is responsible for the supervision of the final data results. Corresponding Author AiLaiTi is also responsible for proposing the research plan and overseeing the implementation of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of Helsinki Declaration and was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval number:\u0026nbsp;K202502-13).\u0026nbsp;Due to the retrospective nature of the study and the use of de-identified patient data, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKATZ J N, ZIMMERMAN Z E, MASS H, et al. Diagnosis and Management of Lumbar Spinal Stenosis: A Review [J]. JAMA, 2022, 327(17): 1688-1699.DOI:10.1001/jama.2022.5921 %J JAMA\u003c/li\u003e\n\u003cli\u003eLURIE J, TOMKINS-LANE C. Management of lumbar spinal stenosis [J]. BMJ (Clinical research ed), 2016, 352: h6234.DOI:10.1136/bmj.h6234\u003c/li\u003e\n\u003cli\u003eWEISSMAN C, SCEMAMA J, WEISS Y G. The ratio of PACU length-of-stay to surgical duration: Practical observations [J]. Acta anaesthesiologica Scandinavica, 2019, 63(9): 1143-1151.DOI:10.1111/aas.13421\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 [J]. BMC Anesthesiology, 2023, 23(1): 404.DOI:10.1186/s12871-023-02365-w\u003c/li\u003e\n\u003cli\u003eHARTUNG T J, BR\u0026auml;HLER E, FALLER H, et al. The risk of being depressed is significantly higher in cancer patients than in the general population: Prevalence and severity of depressive symptoms across major cancer types [J]. European journal of cancer (Oxford, England : 1990), 2017, 72: 46-53.DOI:10.1016/j.ejca.2016.11.017\u003c/li\u003e\n\u003cli\u003eALEXOPOULOS E C. Introduction to multivariate regression analysis [J]. Hippokratia, 2010, 14(Suppl 1): 23-28\u003c/li\u003e\n\u003cli\u003eSCHOBER P, VETTER T R. Logistic Regression in Medical Research [J]. Anesthesia and analgesia, 2021, 132(2): 365-366.DOI:10.1213/ane.0000000000005247\u003c/li\u003e\n\u003cli\u003eFLEISHER L A. Quality Anesthesia: Medicine Measures, Patients Decide [J]. Anesthesiology, 2018, 129(6): 1063-1069.DOI:10.1097/aln.0000000000002455\u003c/li\u003e\n\u003cli\u003eMARTIN C J O-M U D. Perioperatives Management: vom OP in den Aufwachraum/auf die Station [J]. 2022, 2(01): 21-36\u003c/li\u003e\n\u003cli\u003eMYLES P, WEITKAMP B, JONES K, et al. Validity and reliability of a postoperative quality of recovery score: the QoR-40 [J]. 2000, 84(1): 11-15\u003c/li\u003e\n\u003cli\u003eFELDMAN P L J A. Insights into the chemical discovery of remifentanil [J]. 2020, 132(5): 1229-1234\u003c/li\u003e\n\u003cli\u003eMISAL U S, JOSHI S A, SHAIKH M M J A E, et al. Delayed recovery from anesthesia: A postgraduate educational review [J]. 2016, 10(2): 164-172\u003c/li\u003e\n\u003cli\u003eQIU J, XIA Y, ZHANG Y, et al. Development and validation of a nomogram for predicting postoperative fever after endoscopic submucosal dissection for colorectal lesions [J]. Scientific Reports, 2025, 15(1): 750.DOI:10.1038/s41598-025-85188-8\u003c/li\u003e\n\u003cli\u003ePENCINA M J, D\u0026rsquo;AGOSTINO R B, SR. Evaluating Discrimination of Risk Prediction Models: The C Statistic [J]. JAMA, 2015, 314(10): 1063-1064.DOI:10.1001/jama.2015.11082 %J JAMA\u003c/li\u003e\n\u003cli\u003eWALSH C G, SHARMAN K, HRIPCSAK G. 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Chemical Engineering Journal, 2024, 485: 149975.DOI:https://doi.org/10.1016/j.cej.2024.149975\u003c/li\u003e\n\u003cli\u003eLANG K, LIBERTY E, SHMAKOV K. Stratified sampling meets machine learning [Z]. Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. New York, NY, USA; JMLR.org. 2016: 2320\u0026ndash;2329.\u003c/li\u003e\n\u003cli\u003eEHRMANN D E, JOSHI S, GOODFELLOW S D, et al. Making machine learning matter to clinicians: model actionability in medical decision-making [J]. npj Digital Medicine, 2023, 6(1): 7.DOI:10.1038/s41746-023-00753-7\u003c/li\u003e\n\u003c/ol\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":"Prolonged stay in PACU, Lumbar spinal stenosis, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5974971/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5974971/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eLumbar spinal stenosis surgery is commonly performed to address conditions such as spinal canal narrowing and degenerative changes. The duration of a patient's stay in the Post-Anesthesia Care Unit (PACU) following surgery is influenced by a variety of factors including potential complications, anesthetic management, and the patient's overall health status. This study aims to analyze clinical data to identify the key factors that affect the length of stay in the PACU. By doing so, the study seeks to provide valuable insights that can lead to improvements in postoperative recovery and overall patient outcomes.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eWe collected data on 539 cases of patients undergoing lumbar spinal stenosis surgery under general anesthesia from August 2018 to December 2022. The cases were divided into three groups: Group A with 377 cases, Group B with 82 cases, and Group C with 80 cases. Univariate logistic regression analysis was conducted on Group A using SPSS software to identify factors significantly associated with postoperative retention in the PACU. Multivariate logistic regression was then applied to these selected factors to determine independent risk factors. The independent risk factors were used to construct a Nomogram predictive model using R software. Group B was utilized to externally validate the predictive model. Group C data was used for the evaluation of the predictive model. The model's consistency was assessed by calculating the C-index, constructing calibration plots, and generating the Receiver Operating Characteristic (ROC) curve to evaluate the model's predictive accuracy and discrimination ability.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression analysis was conducted to identify factors associated with prolonged stay in the Post-Anesthesia Care Unit (PACU), revealing age, Body Mass Index (BMI), coronary heart disease, surgery duration, and creatinine clearance rate as significant predictors. Subsequently, a multivariate logistic regression analysis was performed on these identified factors, yielding age, BMI, and muscle strength as independent risk factors for extended PACU stay. A Nomogram predictive model was constructed using the R programming language. The model's consistency was assessed across Groups A, B, and C by calculating the C-index and generating Receiver Operating Characteristic (ROC) curves, demonstrating good consistency across the three groups.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eThe Nomogram predictive model demonstrates acceptable performance in certain groups (Groups A and C) but requires improvement in Group B. The model exhibits satisfactory calibration in Groups A and C, with notable deviations in high probability regions. Group B shows adequate calibration across most ranges but similar deviations in high probability regions. Discrimination is good in Groups A and C but is suboptimal in Group B.\u003c/p\u003e\n\u003cp\u003eThe independent risk factors for postoperative retention in the Post-Anesthesia Care Unit (PACU) following surgery for lumbar spinal stenosis were identified as Body Mass Index (BMI), surgery duration, and muscle strength. The Nomogram predictive model demonstrated good predictive performance and consistency, effectively forecasting the likelihood of postoperative PACU retention in patients undergoing lumbar spinal stenosis under general anesthesia. This model serves as a reference for personalized anesthetic management.\u003c/p\u003e","manuscriptTitle":"Predicting Post-Anesthesia Care Unit (PACU) Length of Stay (LOS) Using Machine Learning for Patients Undergoing Lumbar Spinal Stenosis Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:06:55","doi":"10.21203/rs.3.rs-5974971/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":"0e936c47-d0cb-4dfd-ac9b-316f9d1ea09e","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-22T09:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:06:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5974971","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5974971","identity":"rs-5974971","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}