Predictors of extended length of stay after unicompartmental knee arthroplasty

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

INTRODUCTION

The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort.

METHODS

The ACS-NSQIP database was queried to acquire 267,966 TKA cases from 2013 to 2020. Four machine learning models-artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor were trained and tested on the dataset for the prediction of prolonged LOS (LOS exceeded the 75th of all values in the cohort). The model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility.

RESULTS

ANN delivered the best performance among the four models. ANN distinguished prolonged LOS in the study cohort with an AUC of 0.71 and accurately predicted the probability of prolonged LOS for individual patients (calibration slope: 0.82; calibration intercept: 0.03; Brier score: 0.089). All models demonstrated clinical utility by generating positive net benefits in decision curve analyses. Operation time, pre-operative transfusion, pre-operative laboratory tests (hematocrit, platelet count, and white blood cell count), and BMI were the strongest predictors of prolonged LOS.

CONCLUSION

ANN demonstrated modest discrimination capacity and excellent performance in calibration and clinical utility for the prediction of prolonged LOS following TKA. Clinical application of the machine learning models has the potential to improve care coordination and discharge planning for patients at high risk of extended hospitalization after surgery. Incorporating more relevant patient factors may further increase the models' prediction strength.

Length of stay and discharge dispositions following robotic arm-assisted total knee arthroplasty and unicompartmental knee arthroplasty versus conventional technique and predictors of delayed discharge

Aims

In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA).

Methods

This large-scale, single-institution study included patients of any age undergoing primary TKA (n = 1,375) or UKA (n = 337) for any cause between May 2019 and January 2023. Data extracted included patient demographics, LOS, need for post anaesthesia care unit (PACU) admission, anaesthesia type, readmission within 30 days, and discharge dispositions. Univariate and multivariate logistic regression models were also employed to identify factors and patient characteristics related to delayed discharge.

Results

The median LOS in the RO TKA group was 76 hours (interquartile range (IQR) 54 to 104) versus 82.5 (IQR 58 to 127) in the CO TKA group (p < 0.001) and 54 hours (IQR 34 to 77) in the RO UKA versus 58 (IQR 35 to 81) in the CO UKA (p = 0.031). Discharge dispositions were comparable between the two groups. A higher percentage of patients undergoing CO TKA required PACU admission (8% vs 5.2%; p = 0.040).

Conclusion

Our study showed that robotic arm assistance was associated with a shorter LOS in patients undergoing primary UKA and TKA, and no difference in the discharge destinations. Our results suggest that robotic arm assistance could be advantageous in partly addressing the upsurge of knee arthroplasty procedures and the concomitant healthcare burden; however, this needs to be corroborated by long-term cost-effectiveness analyses and data from randomized controlled studies.

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty

BACKGROUND

Existing machine learning models that predicted prolonged lengths of stay (LOS) following primary total hip arthroplasty (THA) were limited by the small training volume and exclusion of important patient factors. This study aimed to develop machine learning models using a national-scale data set and examine their performance in predicting prolonged LOS following THA.

METHODS

A total of 246,265 THAs were analyzed from a large database. Prolonged LOS was defined as exceeding the 75th percentile of all LOSs in the cohort. Candidate predictors of prolonged LOS were selected by recursive feature elimination and used to construct four machine learning models-artificial neural network, random forest, histogram-based gradient boosting, and k-nearest neighbor. The model performance was assessed by discrimination, calibration, and utility.

RESULTS

All models exhibited excellent performance in discrimination (area under the receiver operating characteristic curve [AUC] = 0.72 to 0.74) and calibration (slope: 0.83 to 1.18, intercept: -0.01 to 0.11, Brier score: 0.185 to 0.192) during both training and testing sessions. The artificial neural network was the best performer with an AUC of 0.73, calibration slope of 0.99, calibration intercept of -0.01, and Brier score of 0.185. All models showed great utility by producing higher net benefits than the default treatment strategies in the decision curve analyses. Age, laboratory tests, and surgical variables were the strongest predictors of prolonged LOS.

CONCLUSION

The excellent prediction performance of machine learning models demonstrated their capacity to identify patients prone to prolonged LOS. Many factors contributing to prolonged LOS can be optimized to minimize hospital stay for high-risk patients.

Metabolic equivalent of task scores avoid the ceiling effect observed with conventional patient-reported outcome scores following knee arthroplasty

The metabolic equivalent of task (MET) score examines patient performance in relation to energy expenditure before and after knee arthroplasty. This study assesses its use in a knee arthroplasty population in comparison with the widely used Oxford Knee Score (OKS) and EuroQol five-dimension index (EQ-5D), which are reported to be limited by ceiling effects. A total of 116 patients with OKS, EQ-5D, and MET scores before, and at least six months following, unilateral primary knee arthroplasty were identified from a database. Procedures were performed by a single surgeon between 2014 and 2019 consecutively. Scores were analyzed for normality, skewness, kurtosis, and the presence of ceiling/floor effects. Concurrent validity between the MET score, OKS, and EQ-5D was assessed using Spearman's rank. Postoperatively the OKS and EQ-5D demonstrated negative skews in distribution, with high kurtosis at six months and one year. The OKS demonstrated a ceiling effect at one year (15.7%) postoperatively. The EQ-5D demonstrated a ceiling effect at six months (30.2%) and one year (39.8%) postoperatively. The MET score did not demonstrate a skewed distribution or ceiling effect either at six months or one year postoperatively. Weak-moderate correlations were noted between the MET score and conventional scores at six months and one year postoperatively. In contrast to the OKS and EQ-5D, the MET score was normally distributed postoperatively with no ceiling effect. It is worth consideration as an arthroplasty outcome measure, particularly for patients with high expectations.

Rehabilitation approach in robot assisted total knee arthroplasty: an observational study

BACKGROUND

The purpose of this study is to evaluate the impact of total knee arthroplasty (TKA) with the aid of Navio Robot, comparing it with standard prosthetic surgery on the functional outcomes of patients after an intensive rehabilitation program.

METHOD

A case-control observational study was conducted on patients undergoing TKA for severe KOA. All patients underwent the same intensive hospital rehabilitation program of 14 daily sessions lasting 3 h. The following rating scales were administered: Numeric Rating Scale (NRS), Knee Society Score (KSS) and 12-Item Short Form Survey scale. Patient assessments were performed 1 week post-surgery (T0), 1 month post-surgery (T2), and 3 months post-surgery (T3). The primary outcomes were active knee extension and flexion and pain severity. The secondary outcomes were functional capacity and quality of life.

RESULTS

Using repeated measures ANOVA, we observed at T1 a statistically different difference for the treatment group compared to the control group about KSS (p < 0.05), pain (p < 0.05), and knee flexion (p < 0.05). No statistically significant difference between the two groups was observed for knee extension (p = 0.09) and the SF-12 scale (p = 0.52). At T2 instead, we observed a statistically significant difference for the treatment group compared to the control group as regards KSS (p < 0.05) and knee flexion (p < 0.05), while no statistically significant difference was observed for pain (p = 0.83), knee extension (p = 0.60), and the SF-12 scale (0.44).

CONCLUSIONS

Our study has demonstrated that robot-NAVIO assisted knee prosthesis surgery, associated with a specific intensive rehabilitation treatment, in the short and medium term, determines good pain control, better flexion recovery and a improvement of functional capacity.

Identification of risk factors associated with prolonged hospital stay following primary knee replacement surgery: a retrospective, longitudinal observational study

OBJECTIVES

To identify risk factors associated with prolonged length of hospital stay and staying in hospital longer than medically necessary following primary knee replacement surgery.

DESIGN

Retrospective, longitudinal observational study.

SETTING

Elective knee replacement surgeries between 2016 and 2019 were identified using routinely collected data from an NHS Trust in England.

PARTICIPANTS

There were 2295 knee replacement patients with complete data included in analysis. The mean age was 68 (SD 11) and 60% were female.

OUTCOME MEASURES

We assessed a binary length of stay outcome (>7 days), a continuous length of stay outcome (≤30 days) and a binary measure of whether patients remained in hospital when they were medically fit for discharge.

RESULTS

The mean length of stay was 5.0 days (SD 3.9), 15.4% of patients were in hospital for >7 days and 7.1% remained in hospital when they were medically fit for discharge. Longer length of stay was associated with older age (b=0.08, 95% CI 0.07 to 0.09), female sex (b=0.36, 95% CI 0.06 to 0.67), high deprivation (b=0.98, 95% CI 0.47 to 1.48) and more comorbidities (b=2.48, 95% CI 0.15 to 4.81). Remaining in hospital beyond being medically fit for discharge was associated with older age (OR=1.07, 95% CI 1.05 to 1.09), female sex (OR=1.71, 95% CI 1.19 to 2.47) and high deprivation (OR=2.27, 95% CI 1.27 to 4.06).

CONCLUSIONS

The regression models could be used to identify which patients are likely to occupy hospital beds for longer. This could be helpful in scheduling operations to aid hospital efficiency by planning these patients' operations for when the hospital is less busy.

A Validated Pre-operative Risk Prediction Tool for Extended Inpatient Length of Stay Following Primary Total Hip or Knee Arthroplasty

BACKGROUND

As value-based reimbursement models mature, understanding the potential trade-off between inpatient lengths of stay and complications or need for costly postacute care becomes more pressing. Understanding and predicting a patient's expected baseline length of stay may help providers understand how best to decide optimal discharge timing for high-risk total joint arthroplasty (TJA) patients.

METHODS

A retrospective review was conducted of 37,406 primary total hip (17,134, 46%) and knee (20,272, 54%) arthroplasties performed at two high-volume, geographically diverse, tertiary health systems during the study period. Patients were stratified by 3 binary outcomes for extended inpatient length of stay: 72 + hours (29%), 4 + days (11%), or 5 + days (5%). The predictive ability of over 50 sociodemographic/comorbidity variables was tested. Multivariable logistic regression models were created using institution #1 (derivation), with accuracy tested using the cohort from institution #2 (validation).

RESULTS

During the study period, patients underwent an extended length of stay with a decreasing frequency over time, with privately insured patients having a significantly shorter length of stay relative to those with Medicare (1.9 versus 2.3 days, P < .0001). Extended stay patients also had significantly higher 90-day readmission rates (P < .0001), even when excluding those discharged to postacute care (P < .01). Multivariable logistic regression models created from the training cohort demonstrated excellent accuracy (area under the curve (AUC): 0.755, 0.783, 0.810) and performed well under external validation (AUC: 0.719, 0.743, 0.763). Many important variables were common to all 3 models, including age, sex, American Society of Anesthesiologists (ASA) score, body mass index, marital status, bilateral case, insurance type, and 13 comorbidities.

DISCUSSION

An online, freely available, preoperative clinical decision tool accurately predicts risk of extended inpatient length of stay after TJA. Many risk factors are potentially modifiable, and these validated tools may help guide clinicians in preoperative patient counseling, medical optimization, and understanding optimal discharge timing.

Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

Background

Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization.

Objectives

This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data.

Methods

A total of 91,468 records of patient’s hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC). The study implemented Synthetic Minority Oversampling Technique (SMOTE) and tenfold cross-validation to determine the optimal features for predicting ELOHS while relying on multivariate Logistic Regression (LR) for computing the risk factors and the Relative Risk (RR) of ELOHS at a 95% confidence level.

Results

An estimated 11.54% of the patients have ELOHS, which increases with patient age as patients < 18 years, 18–40 years, 40–65 years and ≥ 65 years, respectively, have 2.57%, 4.33%, 8.1%, and 15.18% ELOHS rates. The RFECV-ETC algorithm predicted preadmission ELOHS to an accuracy of 89.3%. Age is a predominant risk factors of ELOHS with patients who are > 90 years—PAG (> 90) {RR: 1.85 (1.34–2.56), P:  < 0.001} having 6.23% and 23.3%, respectively, higher likelihood of ELOHS than patient 80–90 years old—PAG (80–90) {RR: 1.74 (1.34–2.38), P:  < 0.001} and those 70–80 years old—PAG (70–80) {RR: 1.5 (1.1–2.05), P: 0.011}. Those from admission category—ADC (US1) {RR: 3.64 (3.09–4.28, P:  < 0.001} are 14.8% and 70.5%, respectively, more prone to ELOHS compared to ADC (UC1) {RR: 3.17 (2.82–3.55), P:  < 0.001} and ADC (EMG) {RR: 2.11 (1.93–2.31), P:  < 0.001}. Patients from SES (low) {RR: 1.45 (1.24–1.71), P:  < 0.001)} are 13.3% and 45% more susceptible to those from SES (middle) and SES (high). Admission type (ADT) such as AS2, M2, NEWS, S2 and others {RR: 1.37–2.77 (1.25–6.19), P:  < 0.001} also have a high likelihood of contributing to ELOHS while the distance to hospital (DTH) {RR: 0.64–0.75 (0.56–0.82), P:  < 0.001}, Charlson Score (CCI) {RR: 0.31–0.68 (0.22–0.99), P:  < 0.001–0.043} and some VMO specialties {RR: 0.08–0.69 (0.03–0.98), P:  < 0.001–0.035} have limited influence on ELOHS.

Conclusions

Relying on the preadmission assessment of ELOHS helps identify those patients who are susceptible to exceeding their expected length of stay on admission, thus, making it possible to improve patients’ management and outcomes.

Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital

Background

Predicting patient’s Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures.

Objective

The objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator.

Methods

A total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient’s LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS.

Results

The root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient’s household income, and patient’s age.

Conclusion

This pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.

Unicompartmental Knee Arthroplasty in Octogenarians: A National Database Analysis Including Over 700 Octogenarians

Background

Material and methods

Results

Conclusion

Black and Hispanic Patients Do Not Stay Longer After Primary Total Knee Arthroplasty: Results From an Urban Center Serving a Predominantly Minority Cohort

INTRODUCTION

Previous reports identified minority race/ethnicity to be an independent risk factor for prolonged length of stay (LOS); however, these cohorts consisted of predominantly White patients. This study sought to evaluate minority status as an independent risk factor for prolonged LOS after primary total knee arthroplasty (TKA) in a predominantly Hispanic and Black cohort.

METHODS

This was a retrospective study using an institutional database of patients who underwent primary TKA between the years 2016 and 2019. Demographic and socioeconomic data, smoking, body mass index (BMI), medical comorbidities, discharge disposition, and 30-day readmission rates were collected. Patients were first categorized into racial/ethnic groups (Hispanic, Black, or White). An univariate analysis was performed comparing patient characteristics between racial/ethnic groups using the Wilcoxon rank sum, chi-squared, and Fisher exact tests. We then categorized patients into two groups-normal LOS (discharged on postoperative day 1 to 2) and prolonged LOS (discharged after postoperative day 2). An univariate analysis was again performed comparing patient characteristics between LOS groups using Wilcoxon rank sum, chi-squared, and Fisher exact tests. After identifying risk factors markedly associated with LOS, a multivariate logistic regression analysis was performed to identify independent risk factors for prolonged LOS.

RESULTS

A total of 3,093 patients were included-47.9% Hispanic and 38.3% Black. Mean LOS was 2.9 ± 1.6 days. An univariate analysis found race/ethnicity, age, low socioeconomic status (SES), discharge disposition, insurance type, weekday of surgery, BMI >40, smoking, increased American Society of Anesthesiologists (ASA)/Charlson Comorbidity Index (CCI) and several medical comorbidities to be associated with prolonged LOS (P < 0.05). A multivariate logistic regression analysis found Black and Hispanic patients were less likely to have prolonged LOS after adjusting for associated risk factors. White race/ethnicity, nonhome discharge, low SES, weekday of surgery, smoking, BMI >40, and increased ASA and CCI were identified as independent risk factors for prolonged LOS (P < 0.05). The overall 30-day readmission rate was 3.6%, with no notable difference between racial/ethnic and LOS groups (P = 0.98 and P = 0.78).

CONCLUSION

In contrast to previous reports, our study found that after adjusting for associated risk factors, minority patients do not have prolonged LOS after primary TKA in an urban, socioeconomically disadvantaged, predominantly minority patient cohort. White race/ethnicity, nonhome discharge, low SES, weekday of surgery, smoking, BMI >40, increased CCI, and ASA were all found to be independent risk factors for prolonged LOS. These findings highlight the need to further investigate the role of race/ethnicity on LOS after primary TKA using large-scale, randomized controlled trials with equally represented patient cohorts.

Unicompartmental Knee Arthroplasty in Octogenarians versus Younger Patients: A Comparison of 30-Day Outcomes

As the United States' octogenarian population (persons 80-89 years of age) continues to grow, understanding the risk profile of surgical procedures in elderly patients becomes increasingly important. The purpose of this study was to compare 30-day outcomes following unicompartmental knee arthroplasty (UKA) in octogenarians with those in younger patients. The American College of Surgeons National Surgical Quality Improvement Program database was queried. All patients, aged 60 to 89 years, who underwent UKA from 2005 to 2016 were included. Patients were stratified by age: 60 to 69 (Group 1), 70 to 79 (Group 2), and 80 to 89 years (Group 3). Multivariate regression models were estimated for the outcomes of hospital length of stay (LOS), nonhome discharge, morbidity, reoperation, and readmission within 30 days following UKA. A total of 5,352 patients met inclusion criteria. Group 1 status was associated with a 0.41-day shorter average adjusted LOS (99.5% confidence interval [CI]: 0.67-0.16 days shorter, p < 0.001) relative to Group 3. Group 2 status was not associated with a significantly shorter LOS compared with Group 3. Both Group 1 (odds ratio [OR] = 0.15, 99.5% CI: 0.10-0.23) and Group 2 (OR = 0.33, 99.5% CI: 0.22-0.49) demonstrated significantly lower adjusted odds of nonhome discharge following UKA compared with Group 3. There was no significant difference in adjusted odds of 30-day morbidity, readmission, or reoperation when comparing Group 3 patients with Group 1 or Group 2. While differences in LOS and nonhome discharge were seen, octogenarian status was not associated with increased adjusted odds of 30-day morbidity, readmission, or reoperation. Factors other than age may better predict postoperative complications following UKA.

On the generalizability of same-day partial knee replacement surgery-A non-selective interventional study evaluating efficacy, patient satisfaction, and safety in a public hospital setting

PURPOSE

Programs referred to as Fast-Track/Rapid Recovery/Enhanced Recovery After Surgery have proven both effective and safe in joint replacement surgery, to the degree where same-day discharge (SDD) has been attempted in carefully selected cases at specialized outpatient units. Therefore, the primary aim of this study was to evaluate a same-day surgery protocol regarding safety using the minor partial knee replacement (PKR) procedure by non-selectively recruiting patients at a public hospital for one consecutive year.

METHODS

33 unselected PKR cases were included in this open clinical trial. The inclusion/exclusion criteria were solely based on logistics, as all the procedures were medial PKRs, designated the first morning slots, and performed by one single-surgeon. Strict postoperative criteria based on vital parameters, urinary function, bleeding, and mobilization had to be met before discharge was considered. SDD rate, patient satisfaction, number of outpatient visits, adverse events and readmissions within 90 days were evaluated. A predetermined subgroup analysis was also conducted where patients <80 yrs. and with an American Society of Anesthesiologists (ASA) classification <III was compared with those aged ≥80 yrs. and/or ASA class ≥III.

RESULTS

29 of 33 (88%) successfully achieved SDD. In a univariate comparison, 100% of the patients <80 yrs. and ASA class <III achieved SDD, whereas a corresponding 43% applied for those aged ≥80 yrs. and/or ASA class ≥III (p = 0.001). A 93% overall satisfaction rate was reached. Only 8% extra outpatient visits were required, all occurring within the first 2 weeks (well in line with routine practice.) One plausible transient ischemic attack and one readmission caused by a penetrating trauma not affecting the knee were identified, both of which happened 10 weeks after surgery. No adverse events or readmissions occurred within the first 48 hours of surgery.

CONCLUSION

When following strict criteria for discharge, same-day partial knee replacement surgery may be both feasible and safe, even without preselection of patients.

Influence of body mass index and age on day-of-surgery discharge, prolonged admission, and 90-day readmission after fast-track unicompartmental knee arthroplasty

Background and purpose - The indications for unicompartmental knee arthroplasty (UKA) have become less restrictive and, today, high age and high BMI are not considered contraindications by many surgeons. While the influence of these patient characteristics on total knee arthroplasty is well documented, evidence on UKA is lacking. We investigated the effect of BMI and age on day of surgery (DOS) discharge, prolonged admission, and 90-day readmission following UKA surgery.Patients and methods - This retrospective cohort study included 3,897 UKA patients operated on between 2010 and 2018 in 8 fast-track arthroplasty centers. Patients were divided into 5 BMI groups and 5 age groups. Differences between groups in the occurrence of DOS discharge, prolonged admission > 2 days, and 90-day readmission was investigated using a chi-square test and mixed-effect models adjusted for patient characteristics using surgical center as a random effect.Results - Median LOS was 1 day. DOS discharge was achieved in 26% of patients with no statistically significant differences between BMI groups. DOS discharge was less likely in UKA patients aged > 70 years (age 71-80; odds ratio [OR] 0.7 [95% CI 0.6-0.9]). Prolonged admission was not affected by BMI or age in the adjusted analysis. 90-day readmission was more likely in patients with BMI > 35 (OR 1.9 [CI 1.1-3.1]) and patients aged 71-80 (OR 1.5 [CI 1.1-2.1]).Interpretation - Age > 70 years decreased the likelihood of DOS discharge after UKA. High BMI as well as advanced age increased the likelihood of 90-day readmission. This should be noted by surgeons operating on patients with high BMI and age.

Perioperative Systemic Dexamethasone Reduces Length of Stay in Total Joint Arthroplasty: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

The objective of this review is to examine the effect of perioperative systemic corticosteroids at varying doses and timings on early postoperative recovery outcomes following unilateral total knee and total hip arthroplasty. The primary outcome was length of stay (LOS).

METHODS

A systematic review and meta-analysis of randomized controlled trials was performed. MEDLINE, EMBASE, and Cochrane Library databases were searched from inception to June 1, 2020. Studies comparing the outcome of adult patients receiving a systemic steroid to patients who did not receive steroids were included.

RESULTS

Seventeen studies were included, incorporating 1957 patients. Perioperative corticosteroids reduced hospital LOS (mean difference [MD] = -0.39 days, 95% confidence interval [CI] -0.61 to -0.18). A subsequent dose of corticosteroid at 24 hours further reduced LOS (MD = -0.33, 95% CI -0.55 to -0.11). Corticosteroids resulted in reduced levels of pain on postoperative day (POD) 0 (MD = -1.99, 95% CI -3.30 to -0.69), POD1 (MD = -1.47, 95% CI -2.15 to -0.79), and POD2. Higher doses were more effective in reducing pain with activity on POD0 (P = .006) and 1 (P = .023). Steroids reduced the incidence of PONV on POD1 (log odds ratio [OR] = -1.05, 95% CI -1.26 to -0.84) and POD2, with greater effect at higher doses (P = .046). Corticosteroids did not increase the incidence of infection (P = 1.000), venous thromboembolism (P = 1.000), or gastrointestinal hemorrhage (P = 1.000) but were associated with an increase in blood glucose (MD = 5.30 mg/dL, 95% CI 2.69-7.90).

CONCLUSION

Perioperative corticosteroids are safe, facilitate earlier discharge, and improve patient recovery following unilateral total knee arthroplasty and total hip arthroplasty. Higher doses (15-20 mg of dexamethasone) are associated with further reductions in dynamic pain and PONV, and repeat dosing may further reduce LOS.

Obesity does not adversely impact the outcome of unicompartmental knee arthroplasty for osteoarthritis: a meta-analysis of 80,798 subjects

BACKGROUND

Patients with end-stage single compartment osteoarthritis benefit from the less invasive unicompartmental knee arthroplasty (UKA). With increasing financial restraints, some healthcare services have set specific BMI cut-offs when determining patient eligibility for knee arthroplasty due to perceived obesity-related complications. The aim of this systematic review is to determine the effect obesity has on outcomes following UKA, and thus elucidate whether obesity should be a contraindication for UKA.

METHODS

A PRISMA systematic review was conducted using five databases (MEDLINE, EMBASE, Cochrane, PubMed and Web of Science) to identify all clinical studies that examined the effect of obesity on outcomes following UKA. Quantitative meta-analysis was carried out using RevMan 5.3 software. Quality assessment was carried out using the Critical Appraisal Skills Programme (CASP) checklist.

RESULTS

Thirty studies, including a total of 80 798 patients were analysed. The mean follow- up duration was 5.42 years. Subgroup meta-analyses showed no statistically significant difference following UKA between patients cohorts with and without obesity in overall complication rates (95% CI, P = 0.52), infection rates (95% CI, P = 0.81), and revision surgeries (95% CI, P = 0.06). When further analysing complications, no differences were identified in minor (95% CI, P = 0.23) and major complications (95% CI, P = 0.68), or venous thromboembolism rates (95% CI, P = 0.06). When further analysing revision surgeries, no differences were identified for revisions specifically for infection (95% CI, P = 0.71) or aseptic loosening (95% CI, P = 0.75).

CONCLUSIONS

This meta-analysis shows that obesity does not result in poorer post-operative outcomes following UKA and should not be considered a contraindication for UKA. Future studies, including long-term follow-up RCTs and registry-level analyses, should examine factors associated with obesity and consider stratifying obesity to better delineate any potential differences in outcomes.