Dataset Information

Efficacy and Safety of a Patient Selection Tool for Predicted Discharge at an Ambulatory Surgical Center: A Pilot Study.

ABSTRACT:

Background

There is a paucity of validated risk stratification tools to assess which patients can safely and predictably undergo outpatient total hip (THA) or knee arthroplasty (TKA) in an ambulatory surgery center (ASC).

Methods

Our novel patient selection tool was prospectively applied to 190 consecutive primary THA and TKA performed by a single surgeon at a single ASC. We identified the proportion of patients discharged home the same day, those requiring a one-night stay, or those with failed discharge within 23 hours. A retrospective chart review was performed to determine if any demographic parameters were risk factors for an overnight stay.

Results

Overall, 190 (100%) patients selected for outpatient THA and TKA were discharged home within 23 hours. One hundred and four patients (55%) were discharged the same day of surgery, whereas 86 (45%) required overnight stay and were discharged on postoperative day 1. Female sex (odds ratio [OR]: 4.1, 95% confidence interval [CI]: 2.0-8.2, P < .001), THA (OR: 2.5, 95% CI: 1.1-5.5, P = .022), and heavier body mass index (OR: 1.0, 95% CI: 1.0-1.2, P = .022) were identified as independent risk factors for staying overnight in the ASC.

Conclusions

In this pilot study, we found that 100% of outpatient THA and TKA-eligible patients were able to be discharged home by postoperative day 1. Additionally, we found that this selection tool is safe and effective at predicting short-stay discharge in an ASC.

SUBMITTER: Forte SA

PROVIDER: S-EPMC11369445 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature

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Publications

Efficacy and Safety of a Patient Selection Tool for Predicted Discharge at an Ambulatory Surgical Center: A Pilot Study.

Forte Salvador A SA Bartlett Lucas L Osowa Temisan T Bondy Jed J Aprigliano Caroline C White Peter B PB Danoff Jonathan R JR

Arthroplasty today 20240812

<h4>Background</h4>There is a paucity of validated risk stratification tools to assess which patients can safely and predictably undergo outpatient total hip (THA) or knee arthroplasty (TKA) in an ambulatory surgery center (ASC).<h4>Methods</h4>Our novel patient selection tool was prospectively applied to 190 consecutive primary THA and TKA performed by a single surgeon at a single ASC. We identified the proportion of patients discharged home the same day, those requiring a one-night stay, or th ...[more]

PMID: 39228910

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Project description:BackgroundWe describe the implementation of enhanced recovery after surgery (ERAS) programs designed to minimize postoperative nausea and vomiting (PONV) and pain and reduce opioid use in patients undergoing selected procedures at an ambulatory cancer surgery center. Key components of the ERAS included preoperative patient education regarding the postoperative course, liberal preoperative hydration, standardized PONV prophylaxis, appropriate intraoperative fluid management, and multimodal analgesia at all stages.MethodsWe retrospectively reviewed data on patients who underwent mastectomy with or without immediate reconstruction, minimally invasive hysterectomy, thyroidectomy, or minimally invasive prostatectomy from the opening of our institution on January 2016 to December 2018. Data collected included use of total intravenous anesthesia (TIVA), rate of PONV rescue, time to first oral opioid, and total intraoperative and postoperative opioid consumption. Compliance with ERAS elements was determined for each service. Quality outcomes included time to first ambulation, postoperative length of stay (LOS), rate of reoperation, rate of transfer to acute care hospital, 30-day readmission, and urgent care visits ≤30 days.ResultsWe analyzed 6781 ambulatory surgery cases (2965 mastectomies, 1099 hysterectomies, 680 thyroidectomies, and 1976 prostatectomies). PONV rescue decreased most appreciably for mastectomy (28% decrease; 95% confidence interval [CI], -36 to -22). TIVA use increased for both mastectomies (28%; 95% CI, 20-40) and hysterectomies (58%; 95% CI, 46-76). Total intraoperative opioid administration decreased over time across all procedures. Time to first oral opioid decreased for all surgeries; decreases ranged from 0.96 hours (95% CI, 2.1-1.4) for thyroidectomies to 3.3 hours (95% CI, 4.5 to -1.7) for hysterectomies. Total postoperative opioid consumption did not change by a clinically meaningful degree for any surgery. Compliance with ERAS measures was generally high but varied among surgeries.ConclusionsThis quality improvement study demonstrates the feasibility of implementing ERAS at an ambulatory surgery center. However, the study did not include either a concurrent or preintervention control so that further studies are needed to assess whether there is an association between implementation of ERAS components and improvements in outcomes. Nevertheless, we provide benchmarking data on postoperative outcomes during the first 3 years of ERAS implementation. Our findings reflect progressive improvement achieved through continuous feedback and education of staff.

Project description:BackgroundDays 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.MethodsData 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.ResultsAmong 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.ConclusionsWe 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.

Dataset Information

Efficacy and Safety of a Patient Selection Tool for Predicted Discharge at an Ambulatory Surgical Center: A Pilot Study.

Background

Methods

Results

Conclusions

Publications

Efficacy and Safety of a Patient Selection Tool for Predicted Discharge at an Ambulatory Surgical Center: A Pilot Study.

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