Inpatient admission following anterior cruciate ligament reconstruction is associated with higher postoperative complications

Surgical Management of Breast Capsular Contracture-A Multi-institutional Data Analysis of Risk Factors for Early Complications

BACKGROUND

Capsular contracture (CC) is a common complication following implant-based breast surgery, often requiring surgical intervention. Yet, little is known about risk factors and outcomes following CC surgery.

METHODS

We reviewed the American College of Surgeons National Surgical Quality Improvement Program database (2008-2021) to identify female patients diagnosed with CC and treated surgically. Outcomes of interest included the incidence of surgical and medical complications at 30-days, reoperations, and readmissions. Confounder-adjusted multivariable analyses were performed to establish risk factors.

RESULTS

5,057 patients with CC were identified (mean age: 55 ± 12 years and mean body mass index [BMI]: 26 ± 6 kg/m2). While 2,841 (65%) women underwent capsulectomy, capsulotomy was performed in 742 patients (15%). Implant removal and replacement were recorded in 1,160 (23%) and 315 (6.2%) cases, respectively. 319 (6.3%) patients experienced postoperative complications, with 155 (3.1%) reoperations and 99 (2.0%) readmissions. While surgical adverse events were recorded in 139 (2.7%) cases, 86 (1.7%) medical complications occurred during the 30 day follow-up. In multivariate analyses, increased BMI (OR: 1.04; p = 0.009), preoperative diagnosis of hypertension (OR: 1.48; p = 0.004), and inpatient setting (OR: 4.15; p < 0.001) were identified as risk factors of complication occurrence.

CONCLUSION

Based on 14 years of multi-institutional data, we calculated a net 30 day complication rate of 6.3% after the surgical treatment of CC. We identified higher BMI, hypertension, and inpatient setting as independent risk factors of postoperative complications. Plastic surgeons may wish to integrate these findings into their perioperative workflows, thus optimizing patient counseling and determining candidates' eligibility for CC surgery.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Outpatient Deep Brain Stimulation Surgery Is a Safe Alternative to Inpatient Admission

BACKGROUND

Deep brain stimulation (DBS) is usually performed as an inpatient procedure. The COVID-19 pandemic effected a practice change at our institution with outpatient DBS performed because of limited inpatient and surgical resources. Although this alleviated use of hospital resources, the comparative safety of outpatient DBS surgery is unclear.

OBJECTIVE

To compare the safety and incidence of early postoperative complications in patients undergoing DBS procedures in the outpatient vs inpatient setting.

METHODS

We retrospectively reviewed all outpatient and inpatient DBS procedures performed by a single surgeon between January 2018 and November 2022. The main outcome measures used for comparison between the 2 groups were total complications, length of stay, rate of postoperative infection, postoperative hemorrhage rate, 30-day emergency department (ED) visits and readmissions, and IV antihypertensive requirement.

RESULTS

A total of 44 outpatient DBS surgeries were compared with 70 inpatient DBS surgeries. The outpatient DBS cohort had a shorter mean postoperative stay (4.19 vs 39.59 hours, P = .0015), lower total complication rate (2.3% vs 12.8%, P = .1457), and lower wound infection rate (0% vs 2.9%, P = .52) compared with the inpatient cohort, but the difference in complications was not statistically significant. In the 30-day follow-up period, ED visits were similar between the cohorts (6.8% vs 7.1%, P = .735), but no outpatient DBS patient required readmission, whereas all inpatient DBS patients visiting the ED were readmitted (P = .155).

CONCLUSION

Our study demonstrates that DBS can be safely performed on an outpatient basis with same-day hospital discharge and close continuous monitoring.

Editorial Commentary: Machine Learning Is Just a Statistical Technique, Not a Mystical Methodology or Peer Review Panacea

Orthopaedic and sports medicine research surrounding artificial intelligence (AI) has dramatically risen over the last 4 years. Meaningful application and methodologic rigor in the scientific literature are critical to ensure appropriate use of AI. Common but critical errors for those engaging in AI-related research include failure to 1) ensure the question is important and previously unknown or unanswered; 2) establish that AI is necessary to answer the question; and 3) recognize model performance is more commonly a reflection of the data than the AI itself. We must take care to ensure we are not repackaging and internally validating registry data. Instead, we should be critically appraising our data-not the AI-based statistical technique. Without appropriate guardrails surrounding the use of artificial intelligence in Orthopaedic research, there is a risk of repackaging registry data and low-quality research in a recursive peer-reviewed loop.

Increased Readmission Rates but No Difference in Complication Rates in Patients Undergoing Inpatient Versus Outpatient Hip Arthroscopy: A Large Matched-Cohort Insurance Database Analysis

Purpose

Methods

Results

Conclusions

Level of Evidence

Predicting surgical factors for unplanned overnight admission in ambulatory arthroscopic surgery of the knee: a prospective cohort in one hundred and eighty four patients

PURPOSE

Unplanned overnight admission (UOA) is an important indicator for quality of care with ambulatory knee arthroscopic surgery (AKAS). However, few studies have explored the factors related to the UOA and how to predict UOA after AKAS. This study aimed to evaluate the effectiveness of a standardized peri-operative protocol for the AKAS and identify whether a correlation exists between the peri-operative surgical factors and UOA in the patients undergoing AKAS. We hypothesized that more surgical invasiveness and prolong tourniquet time increase the risk of UOA after AKAS.

METHOD

A prospective cohort study was conducted between October 2017 and March 2021. All 184 patients operated on standard AKAS protocol. The UOA is defined as overnight hospitalization of a patient undergoing AKAS. Demographic and peri-operative data were recorded, and the procedure was categorized based on the surgical invasiveness based on less invasive (intra-articular soft tissue surgery) (n = 65) and more complex surgery (involving extra-articular soft tissue surgery or ligamentous reconstruction) (n = 119). The clinical risk factors for UOA were identified and analyzed with multivariate analysis.

RESULTS

The incidence of UOA in the more complex group (n = 7, 14.3%) was significantly higher than in the less invasive group (n = 3, 4.6%) (p = 0.049). The peri-operative factors significantly associated with UOA were age, more complex surgery, and longer tourniquet time (p < 0.10 all). However, the multivariate analysis revealed that longer tourniquet time was the only significant predictor for UOA (OR = 1.045, 95% CI = 1.022-1.067, p = 0.0001). The optimal cut-off points of tourniquet time for predicting UOA with the highest Youden index in the less invasive and more complex groups were 56 minutes and 107 minutes, respectively.

CONCLUSION

The UOA after AKAS is more common in more complex surgery compared to less invasive surgery. This study showed that unplanned admission significantly associated with many factors-as patient factors, surgical invasiveness, and tourniquet time. However, tourniquet time is the only independent predictor for UOA. Therefore, strict perioperative management protocol must be applied in AKAS, and all patients having these risk factors should be prepared for UOA.

Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction

PURPOSE

Overnight admission following anterior cruciate ligament reconstruction has implications on clinical outcomes as well as cost benefit, yet there are few validated risk calculators for reliable identification of appropriate candidates. The purpose of this study is to develop and validate a machine learning algorithm that can effectively identify patients requiring admission following elective anterior cruciate ligament (ACL) reconstruction.

METHODS

A retrospective review of a national surgical outcomes database was performed to identify patients who underwent elective ACL reconstruction from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), linear discriminant classifier (LDA), and adaptive boosting algorithms (AdaBoost), and an additional model was produced as a weighted ensemble of the four final algorithms.

RESULTS

Overall, of the 4,709 patients included, 531 patients (11.3%) required at least one overnight stay following ACL reconstruction. The factors determined most important for identification of candidates for inpatient admission were operative time, anesthesia type, age, gender, and BMI. Smoking history, history of COPD, and history of coagulopathy were identified as less important variables. The following factors supported overnight admission: operative time > 200 min, age < 35.8 or > 53.5 years, male gender, BMI < 25 or > 31.2 kg/m², positive smoking history, history of COPD and the presence of preoperative coagulopathy. The ensemble model achieved the best performance based on discrimination assessed via internal validation (AUC = 0.76), calibration, and decision curve analysis. The model was integrated into a web-based open-access application able to provide both predictions and explanations.

CONCLUSION

Modifiable risk factors identified by the model such as increased BMI, operative time, anesthesia type, and comorbidities can help clinicians optimize preoperative status to prevent costs associated with unnecessary admissions. If externally validated in independent populations, this algorithm could use these inputs to guide preoperative screening and risk stratification to identify patients requiring overnight admission for observation following ACL reconstruction.

LEVEL OF EVIDENCE

IV.