A Retrospective Analysis Investigating Whether Case Volume Experience of the Anesthesiologist Correlates with Intraoperative Efficiency for Joint Arthroplasty.

The objective of this retrospective study was to determine if there was an association between anesthesiology experience (e.g. historic case volume) and operating room (OR) efficiency times for lower extremity joint arthroplasty cases. The primary outcome was time from patient in the OR to anesthesia ready (i.e. after spinal or general anesthesia induction was complete). The secondary outcomes included time from anesthesia ready to surgical incision, and time from incision to closing completed. Mixed effects linear regression was performed, in which the random effect was the anesthesiology attending provider. There were 4,575 patients undergoing hip or knee arthroplasty included. There were 82 unique anesthesiology providers, in which the median [quartile] frequency of cases performed was 79 [45, 165]. On multivariable mixed effects linear regression - in which the primary independent variable (anesthesiologist case volume history for joint arthroplasty anesthesia) was log-transformed - the estimate for log-transformed case volume was - 0.91 (95% confidence interval [CI] -1.62, -0.20, P = 0.01). When modeling time from incision to closure complete, the estimate for log-transformed case volume was - 2.07 (95% -3.54, -0.06, P = 0.01). Thus, when comparing anesthesiologists with median case volume (79 cases) versus those with the lowest case volume (10 cases), the predicted difference in times added up to only approximately 6 min. If the purpose of faster anesthesia workflows was to open up more OR time to increase surgical volume in a given day, this study does not support the supposition that anesthesiologists with higher joint arthroplasty case volume would improve throughput.

Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.

The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.

Is Obesity Associated With Better Liver Transplant Outcomes? A Retrospective Study of Hospital Length of Stay and Mortality Following Liver Transplantation.

BACKGROUND: The rise in obesity in the United States, along with improvements in antiviral therapies, has led to an increase in the number of obese patients receiving liver transplants. Currently, obesity is a relative contraindication for liver transplant, although exact body mass index (BMI) limits continue to be debated. Studies conflict regarding outcomes in obese patients, while some argue that BMI should not be used as an exclusion criterion at all. Therefore, this retrospective study-utilizing a large national database-seeks to elucidate the association between recipient BMI and hospital length of stay and mortality following liver transplant. METHODS: A retrospective study was conducted using the United Network for Organ Sharing Standard Transplant Analysis and Research database. Fine-Gray competing risk regressions were used to explore the association between BMI and rate of discharge, which varies inversely with length of stay. In our model, subdistribution hazard ratio (SHR) represented the relative change in discharge rate compared to normal BMI, with in-hospital death was considered as a competing event for live discharge. Cox proportional hazard models were built to assess the association of BMI category on all-cause mortality after liver transplantation. Cluster-robust standard errors were used in all analyses to construct confidence intervals. RESULTS: Within the final sample (n = 47,038), overweight (≥25 and <30 kg/m2) patients comprised the largest BMI group (34.7%). The competing risk regression model showed an association for increased length of stay among underweight (SHR = 0.82, 95% confidence interval [CI], 0.77-0.89; P < .001) and class 3 obesity patients (SHR = 0.88, 95% CI, 0.83-0.94; P < .001), while overweight (SHR = 1.05, 95% CI, 1.03-1.08; P < .001) and class 1 obesity (SHR = 1.04, 95% CI, 1.01-1.07; P = .01) were associated with decreased length of stay. When the sample excluded patients with low pretransplant functional status, however, length of stay was not significantly shorter for overweight and obesity class 1 patients. Cox proportional hazard models demonstrated increased survival among overweight, class 1 and class 2 obesity patients and decreased survival among underweight patients. CONCLUSIONS: Our results provide evidence that overweight and obesity class 1 are associated with decreased length of stay and mortality following liver transplant, while underweight and obesity class 3 are associated with prolonged length of stay. Pretransplant functional status may contribute to outcomes for overweight and class 1 obese patients, which necessitates continued investigation of the isolated impact of BMI in those who have had a liver transplant.

Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center.

BACKGROUND: Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression. METHODS: Data were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm. We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm, 2 pm, 3 pm, or 4 pm. We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient. RESULTS: Among 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm, the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm, 2 pm, 3 pm, or 4 pm, respectively. CONCLUSIONS: We demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center could be appropriately booked.

Identification of Preanesthetic History Elements by a Natural Language Processing Engine.

BACKGROUND: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider. METHODS: For each patient, we collected all pertinent notes from the institution's electronic medical record that were available no later than 1 day before their preoperative anesthesia clinic appointment. Pertinent notes included free-text notes consisting of history and physical, consultation, outpatient, inpatient progress, and previous preanesthetic evaluation notes. The free-text notes were processed by a Named Entity Recognition pipeline, an NLP machine learning model trained to recognize and label spans of text that corresponded to medical concepts. These medical concepts were then mapped to a list of medical conditions that were of interest for a preanesthesia evaluation. For each condition, we calculated the percentage of time across all patients in which (1) the NLP pipeline and the anesthesiologist both captured the condition; (2) the NLP pipeline captured the condition but the anesthesiologist did not; and (3) the NLP pipeline did not capture the condition but the anesthesiologist did. RESULTS: A total of 93 patients were included in the NLP pipeline input. Free-text notes were extracted from the electronic medical record of these patients for a total of 9765 notes. The NLP pipeline and anesthesiologist agreed in 81.24% of instances on the presence or absence of a specific condition. The NLP pipeline identified information that was not noted by the anesthesiologist in 16.57% of instances and did not identify a condition that was noted by the anesthesiologist's review in 2.19% of instances. CONCLUSIONS: In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.

Obesity and unanticipated hospital admission following outpatient laparoscopic cholecystectomy.

INTRODUCTION: Compared to other common outpatient operations, laparoscopic cholecystectomy has higher rates of unanticipated hospital admission with reports ranging from 1.0 to 39.5%. Identification of simple preoperative risk factors for admission can aid appropriate patient selection. The aim of this study was to evaluate the association of obesity with need for hospital admission and day of surgery postoperative complications. METHODS: The ACS NSQIP database from 2007 to 2016 was used to evaluate patients ≥ 18 years old who had undergone outpatient laparoscopic cholecystectomy. The primary outcome was hospital admission, defined as hospital length of stay ≥ 24 h. The secondary endpoint was postoperative complications on day of surgery. A multivariable logistic regression was used to evaluate the association of body mass index (BMI) and the outcomes of interest. Odds ratio (OR) and their 95% confidence interval (CI) were reported. RESULTS: 192,750 patients underwent laparoscopic cholecystectomy in the outpatient setting. 38,945 (20.20%) required hospital admission. 89 (0.05%) had postoperative complications on the day of surgery. On multivariable logistic regression analysis, when compared to the baseline cohort of BMI ≥ 30 and < 40 kg/m2, patients with a BMI ≥ 50 kg/m2 had a 10% increased odds of hospital admission (OR 1.10, CI 1.02-1.19, p < 0.001). BMI ≥ 40 kg/m2 and < 50 kg/m2 was not associated with increased odds of hospital admission (OR 0.99, CI 0.95-1.03, p 0.725). There was no increased odds of postoperative complications for patients with higher BMI (OR 1.35, CI 0.32-3.89, p < 0.623). CONCLUSION: Patients with super obesity have a 10% increased odds of hospital admission following laparoscopic cholecystectomy. Obesity is not associated with increased odds of same-day postoperative complications. Ambulatory laparoscopic cholecystectomy for the morbidly obese is safe; however, those with BMI > 50 kg/m2 should be considered on a case-by-case basis.

Efficiency Metrics at an Academic Freestanding Ambulatory Surgery Center: Analysis of the Impact on Scheduled End-Times.

BACKGROUND: Understanding the impact of key metrics on operating room (OR) efficiency is important to optimize utilization and reduce costs, particularly in freestanding ambulatory surgery centers. The aim of this study was to assess the association between commonly used efficiency metrics and scheduled end-time accuracy. METHODS: Data from patients who underwent surgery from May 2018 to June 2019 at an academic freestanding ambulatory surgery center was extracted from the medical record. Unique operating room days (ORDs) were analyzed to determine (1) duration of first case delays, (2) turnover times (TOT), and (3) scheduled case duration accuracies. Spearman's correlation coefficients and mixed-effects multivariable linear regression were used to assess the association of each metric with scheduled end-time accuracy. RESULTS: There were 1378 cases performed over 300 unique ORDs. There were 86 (28.7%) ORDs with a first case delay, mean (standard deviation [SD]) 11.2 minutes (15.1 minutes), range of 2-101 minutes; the overall mean (SD) TOT was 28.1 minutes (19.9 minutes), range of 6-83 minutes; there were 640 (46.4%) TOT >20 minutes; the overall mean (SD) case duration accuracy was -6.6 minutes (30.3 minutes), range of -114 to 176; and there were 389 (28.2%) case duration accuracies ≥30 minutes. The mean (SD) scheduled end-time accuracy was 6.9 minutes (68.3 minutes), range of -173 to 229 minutes; 48 (15.9%) ORDs ended ≥1 hour before scheduled end-time and 56 (18.6%) ORDs ended ≥1 hour after scheduled end-time. The total case duration accuracy was strongly correlated with the scheduled end-time accuracy (r = 0.87, 95% confidence interval [CI], 0.84-0.89, P < .0001), while the total first case delay minutes (r = 0.12, 95% CI, 0.01-0.21, P = .04) and total turnover time (r = -0.16, 95% CI, 0.21-0.05, P = .005) were less relevant. Case duration accuracy had the highest association with the dependent variable (0.95 minutes changed in the difference between actual and schedule end time per minute increase in case duration accuracy, 95% CI, 0.90-0.99, P < .0001), compared to turnover time (estimate = 0.87, 95% CI, 0.75-0.99, P < .0001) and first case delay time (estimate = 0.83, 95% CI, 0.56-1.11, P < .0001). CONCLUSIONS: Standard efficiency metrics are similarly associated with scheduled end-time accuracy, and addressing problems in each is requisite to having an efficient ambulatory surgery center. Pursuing methods to narrow the gap between scheduled and actual case duration may result in a more productive enterprise.

National trends in nonoperating room anesthesia: procedures, facilities, and patient characteristics.

PURPOSE OF REVIEW: Nonoperating room anesthesia (NORA) continues to increase in popularity and scope. This article reviews current and new trends in NORA, trends in anesthesia management in nonoperating room settings, and the evolving debates surrounding these trends. RECENT FINDINGS: National data suggests that NORA cases will continue to rise relative to operating room (OR) anesthesia and there will continue to be a shift towards performing more interventional procedures outside of the OR. These trends have important implications for the safety of interventional procedures as they become increasingly more complex and patients continue to be older and more frail. In order for anesthesia providers and proceduralists to be prepared for this future, rigorous standards must be set for safe anesthetic care outside of the OR.Although the overall association between NORA and patient morbidity and mortality remains unclear, focused studies point toward trends specific to each non-OR procedure type. Given increasing patient and procedure complexity, anesthesiology teams may see a larger role in the interventional suite. However, the ideal setting and placement of anesthesia staff for interventional procedures remain controversial. Also, the impact of COVID-19 on the growth and utilization of non-OR anesthesia remains unclear, and it remains to be seen how the pandemic will influence the delivery of NORA procedures in postpandemic settings. SUMMARY: NORA is a rapidly growing field of anesthesia. Continuing discussions of complication rates and mortality in different subspecialty areas will determine the need for anesthesia care and quality improvement efforts in each setting. As new noninvasive procedures are developed, new data will continue to shape debates surrounding anesthesia care outside of the operating room.

A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery.

Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durations. In this study, we sought to use machine learning to develop an accurate predictive model for RAS case duration. We analyzed a random sample of robotic cases at our institution from January 2014 to June 2017. We compared the machine learning models to the baseline model, which is the scheduled case duration (determined by previous case duration averages and surgeon adjustments). Specifically, we used: 1) multivariable linear regression, 2) ridge regression, 3) lasso regression, 4) random forest, 5) boosted regression tree, and 6) neural network. We found that all machine learning models decreased the average root-mean-squared error (RMSE) as compared to the baseline model. The average RMSE was lowest with the boosted regression tree (80.2 min, 95% CI 74.0-86.4), which was significantly lower than the baseline model (100.4 min, 95% CI 90.5-110.3). Using boosted regression tree, we can increase the number of accurately booked cases from 148 to 219 (34.9% to 51.7%, p < 0.001). This study shows that using various machine learning approaches can improve the accuracy of RAS case length predictions, which will increase utilization of this limited resource. Further work is needed to operationalize these findings.

The Association of Preoperative Hematocrit and Transfusion with Mortality in Patients Undergoing Elective Non-cardiac Surgery.

BACKGROUND: Patients with anemia frequently undergo surgery, as it is unclear at what threshold clinicians should consider delaying surgery for preoperative anemia optimization. The primary objective of this study was to determine whether there is an association of varying degrees of anemia and transfusion with 30-day mortality. METHODS: This is a retrospective cohort study using the American College of Surgeons National Surgical Quality Improvement Program database from 2011 to 2013. Cohorts were analyzed based on preoperative hematocrit range-patients with: (1) no anemia, (2) hematocrit ≥33% and <36% in females or <39% in males, (3) hematocrit ≥30% and <33%, (4) hematocrit ≥27% and <30%, (5) hematocrit ≥24% and <27%, and (6) hematocrit ≥21% and less than 24%. Multivariable logistic regression was used to analyze the association of anemia and transfusion with 30-day in-hospital mortality. RESULTS: The odds for 30-day mortality increased incrementally as the hematocrit ranges decreased, in which preoperative hematocrit between 21 and 24% had the highest odds for this outcome (odds ratio [OR] 6.50, p < 0.0001) compared to the reference group (no anemia). The use of transfusion increased the odds of mortality even further (OR 5.57, p < 0.0001). Among patients that received an intra-/postoperative transfusion, preoperative anemia was not predictive of mortality. CONCLUSIONS: Healthcare providers making preoperative clinical decisions for patients undergoing elective surgery should consider the degree of preoperative anemia and likelihood of perioperative transfusion.

Postoperative mortality and morbidity following non-cardiac surgery in a healthy patient population.

PURPOSE: Perioperative mortality ranges from 0.4% to as high as nearly 12%. Currently, there are no large-scale studies looking specifically at the healthy surgical population alone. The primary objective of this study was to report 30-day mortality and morbidity in healthy patients and define any risk factors. METHODS: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) dataset, all patients assigned an American Society of Anesthesiologists physical status (ASA PS) classification score of 1 or 2 were included. Further patients were excluded if they had a comorbidity or underwent a procedure not likely to classify them as ASA PS 1 or 2. Multivariable logistic regression was performed to identify predictors of the outcomes, in which odds ratios (OR) and 95% confidence intervals (95% CI) were reported. RESULTS: There were 687,552 healthy patients included in the final analysis. Following surgery, 0.7, 7.0, and 0.7 per 1000 persons experienced 30-day mortality, sepsis, and stroke or myocardial infarction, respectively. Healthy patients greater than 80 years of age had the highest odds for mortality (OR 17.7, 95% CI 12.4-25.1, p < 0.001). Case duration was associated with increased mortality, especially in cases greater than or equal to 6 h (OR 3.0, 95% CI 2.0-4.5, p < 0.001). CONCLUSIONS: Thirty-day mortality and morbidity is, as expected, lower in the healthy surgical population. Age may be an indication to further risk stratify patients that are ASA PS 1 or 2 to better reflect perioperative risk.

A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries.

BACKGROUND: A predictive model that can identify patients who are at an increased risk for prolonged postanesthesia care unit (PACU) stay could help optimize resource utilization and case sequencing. Although previous studies identified some predictors, there is not a model that only utilizes various patients demographic and comorbidities, that are already known preoperatively, and that may affect PACU length of stay for outpatient procedures requiring the care of an anesthesiologist. METHODS: We collected data from 4151 patients at a single institution from 2014 to 2015. The data set was split into a training set (cases before 2015) and a test set (cases during 2015). Bootstrap samples were chosen (R = 1000 replicates) and a logistic regression model was built on the samples using a combined method of forward selection and backward elimination based on the Akaike Information Criterion. The trained model was applied to the test set. Model performance was evaluated with the area under the receiver operating characteristic (ROC) Curve (AUC) for discrimination and the Hosmer-Lemeshow (HL) test for goodness-of-fit. RESULTS: The final model had 5 predictor variables for prolonged PACU length of stay, which included the following: morbid obesity, hypertension, surgical specialty, primary anesthesia type, and scheduled case duration. The model had an AUC value of 0.754 (95% confidence interval 0.733-0.774) on the training set and 0.722 (95% confidence interval 0.698-0.747) on the test set, with no difference between the 2 ROC curves (P = .06). The model had good calibration for the data in both the training and test data set indicated by nonsignificant P values from the HL test (P = .211 and .719 for the training and test set, respectively). CONCLUSIONS: We developed a predictive model with excellent discrimination and goodness-of-fit that can help identify those at higher odds for extended PACU length of stay. This information may help optimize case-sequencing methodologies.

Association of amisulpride and recovery room length of stay among patients with postoperative nausea and vomiting following outpatient surgery.

STUDY OBJECTIVE: Postoperative nausea and vomiting (PONV) is a common sequela of surgery in patients undergoing general anesthesia. Amisulpride has shown promise in its ability to treat PONV. The objective of this study was to determine if amisulpride is associated with significant changes in PACU efficiency within a fast-paced ambulatory surgery center. METHODS: This was a retrospective cohort study of 816 patients at a single ambulatory surgery center who experienced PONV between 2018 and 2023. The two cohorts analyzed were patients who did or did not have amisulpride among their anti-emetic regimens in the PACU during two distinct time periods (before and after amisulpride was introduced). The primary outcome of the study was PACU length of stay. Both unmatched analysis and a linear multivariable mixed-effects model fit by restricted maximum likelihood (random effect being surgical procedure) were used to analyze the association between amisulpride and PACU length of stay. We performed segmented regression to account for cohorts occurring during two time periods. RESULTS: Unmatched univariate analysis revealed no significant difference in PACU length of stay (minutes) between the amisulpride and no amisulpride cohorts (115 min vs 119 min, respectively; P = 0.07). However, when addressing confounders by means of the mixed-effects multivariable segmented regression, the amisulpride cohort was associated with a statistically significant reduction in PACU length of stay by 26.1 min (P < 0.001). CONCLUSIONS: This study demonstrated that amisulpride was associated with a significant decrease in PACU length of stay among patients with PONV in a single outpatient surgery center. The downstream cost-savings and operational efficiency gained from this drug's implementation may serve as a useful lens through which this drug's widespread implementation may further be rationalized.

Development and benchmarking of machine learning models to classify patients suitable for outpatient lower extremity joint arthroplasty.

STUDY OBJECTIVE: Performing hip or knee arthroplasty as an outpatient surgery has been shown to be operationally and financially beneficial for selected patients. By applying machine learning models to predict patients suitable for outpatient arthroplasty, health care systems can better utilize resources efficiently. The goal of this study was to develop predictive models for identifying patients likely to be discharged same-day following hip or knee arthroplasty. DESIGN: Model performance was assessed with 10-fold stratified cross-validation, evaluated over baseline determined by the proportion of eligible outpatient arthroplasty over sample size. The models used for classification were logistic regression, support vector classifier, balanced random forest, balanced bagging XGBoost classifier, and balanced bagging LightGBM classifier. SETTING: The patient records were sampled from arthroplasty procedures at a single institution from October 2013 to November 2021. PATIENTS: The electronic intake records of 7322 knee and hip arthroplasty patients were sampled for the dataset. After data processing, 5523 records were kept for model training and validation. INTERVENTIONS: None. MEASUREMENTS: The primary measures for the models were the F1-score, area under the receiver operating characteristic curve (ROCAUC), and area under the precision-recall curve. To measure feature importance, the SHapley Additive exPlanations value (SHAP) were reported from the model with the highest F1-score. RESULTS: The best performing classifier (balanced random forest classifier) achieved an F1-score of 0.347: an improvement of 0.174 over baseline and 0.031 over logistic regression. The ROCAUC for this model was 0.734. Using SHAP, the top determinant features of the model included patient sex, surgical approach, surgery type, and body mass index. CONCLUSIONS: Machine learning models may utilize electronic health records to screen arthroplasty procedures for outpatient eligibility. Tree-based models demonstrated superior performance in this study.

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.

BACKGROUND: Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration. OBJECTIVE: The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. METHODS: We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance. RESULTS: A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. CONCLUSIONS: Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.

Surgical pit crew: initiative to optimise measurement and accountability for operating room turnover time.

BACKGROUND AND OBJECTIVES: Turnover time (TOT), defined as the time between surgical cases in the same operating room (OR), is often perceived to be lengthy without clear cause. With the aim of optimising and standardising OR turnover processes and decreasing TOT, we developed an innovative and staff-interactive TOT measurement method. METHODS: We divided TOT into task-based segments and created buttons on the electronic health record (EHR) default prelogin screen for appropriate staff workflows to collect more granular data. We created submeasures, including 'clean-up start', 'clean-up complete', 'set-up start' and 'room ready for patient', to calculate environmental services (EVS) response time, EVS cleaning time, room set-up response time, room set-up time and time to room accordingly. RESULTS: Since developing and implementing these workflows, measures have demonstrated excellent staff adoption. Median times of EVS response and cleaning have decreased significantly at our main hospital ORs and ambulatory surgery centre. CONCLUSION: OR delays are costly to hospital systems. TOT, in particular, has been recognised as a potential dissatisfier and cause of delay in the perioperative environment. Viewing TOT as one finite entity and not a series of necessary tasks by a variety of team members limits the possibility of critical assessment and improvement. By dividing the measurement of TOT into respective segments necessary to transition the room at the completion of one case to the onset of another, valuable insight was gained into the causes associated with turnover delays, which increased awareness and improved accountability of staff members to complete assigned tasks efficiently.

Body Mass Index Is Not an Independent Factor Associated With Recovery Room Length of Stay for Patients Undergoing Outpatient Surgery.

OBJECTIVES: Prolonged recovery time in the postanesthesia care unit (PACU) increases cost and administrative burden of outpatient surgical staff. The primary aim of this study was to determine whether body mass index (BMI) is associated with prolonged recovery in the PACU after outpatient surgery in a freestanding ambulatory surgery center. METHODS: We retrospectively studied 3 years of surgeries performed at a freestanding ambulatory surgery center. Mixed-effects logistic (for binary outcomes) and linear (for continuous outcomes) regressions were performed, in which the random effect was the surgical procedure. Prolonged PACU length of stay was modeled as a binary variable, that is, stay greater than the third quartile, and as a continuous variable, that is, actual duration of stay in minutes. We reported odds ratio and 95% confidence interval from the logistic regression and estimates with standard errors from the linear regression. RESULTS: Patients with obesity (BMI ≥ 30 kg/m 2 ) did not demonstrate increased odds for prolonged PACU length of stay (all P > 0.05). Furthermore, BMI-represented as a continuous variable-was not associated with actual PACU length of stay (estimate = 0.05, standard error = 0.06, P = 0.41). No association was found between obesity and PACU length of stay on a subgroup analysis where only patients with obstructive sleep apnea were analyzed. CONCLUSIONS: There was no association between BMI and PACU length of stay among patients who received outpatient surgery at a freestanding ambulatory surgery center.

Recommendations for anesthetic management for intraoperative neuromodulation cases.

This paper performs a review of current literature as well as uses our single-center experience to discuss pre-operative, intra-operative and, briefly, postoperative management for dorsal column stimulators (DCSs), dorsal root ganglion (DRG) stimulators, peripheral nerve stimulators (PNSs) and intrathecal pumps. Generally, pre-operative antibiotics are recommended with discontinuation within 24 h postoperatively. For dorsal column and DRG stimulation, monitored anesthesia care or general anesthesia with intra-operative neuromonitoring is recommended; for peripheral nerve stimulation and intrathecal pump implementation, monitored anesthesia care is preferred. There is little information on appropriate anesthetic management during these forms of neuromodulation. More research is necessary to articulate specific pre-operative, intra-operative and postoperative management guidelines and recommendations for dorsal column stimulator, DRG stimulation, PNS and intrathecal pump implantation.

Neuromodulation is a procedure wherein the nerves that are responsible for pain are stimulated, for example with electrical pulses, to reduce the pain signals originating from that nerve. The implantation of neuromodulation devices requires surgery. This paper reviews current literature and provides guidelines based on our single center experience to discuss anesthetic management of patients before surgery, during surgery and after the surgery. We review management for different forms of neuromodulation including dorsal column stimulators (DCSs), dorsal root ganglion (DRG) stimulators, peripheral nerve stimulators and intrathecal pumps. We searched various online databases to find papers that discussed anesthetic management around these surgeries. Generally, starting antibiotics before the surgery and then stopping the antibiotics within 24 h after the surgery is recommended. For dorsal column and DRG stimulation, monitored anesthesia care, where patients are awake but very relaxed, or general anesthesia with neuromonitoring during the operation (so that surgeons can check the function of the nerves in real time) is recommended. For peripheral nerve stimulation and intrathecal pump implementation, monitored anesthesia care is preferred. There is little information in the literature on appropriate anesthetic management during these forms of neuromodulation. More research is necessary to articulate specific management guidelines before surgery, during surgery and after surgery for DCSs, DRG stimulation, peripheral nerve stimulator and intrathecal pump implantation.