Risk Factors for Readmissions After Total Joint Replacement: A Meta-Analysis

Comparison of Anterior and Posterior Surgical Approaches in Total Hip Arthroplasty: Effect on Self-Reported and Functional Outcomes

Background/Objectives: Reported patient results after total hip arthroplasty (THA) have been described as a function of surgical approach. Such results have commonly been subjective. Though self-reported outcomes are of value and often utilized, inclusion of functional performance measures represents an objective measure to compare THA techniques. Methods: Patients that underwent primary THA surgery at our institution were grouped by surgical approach (Direct Anterior vs Posterior). Patient data were collected pre-operatively, as well as post-operatively at three and twelve months. Hip Dysfunction and Osteoarthritis Outcome Score (HOOS JR) was utilized, and function was assessed with the timed up and go test (TUGT), 4-m walk test (4MWT), and 30 s sit-to-stand (30STS) test. Unpaired T tests were used to compare mean results and differences between the groups. Results: Functional outcome scores were improved to a similar degree for both surgical approach groups at all the time points post-operatively. At 3 months, the TUGT was improved by 2.33 s for the posterior group, the 30STS was increased by 2.71 repetitions, and the 4MWT was increased by 1.23 s; the anterior group had 2.66 s, 2.49 repetition, and 1.18 s improvements in the three functional tests, respectively. At 12 months, the posterior group had improvements of 2.86 s, 3.99 repetition, and 1.19 s, while the anterior group had improvements of 3.15 s, 3.83 repetition, and 1.23 s, respectively. No clinical and statistical significant differences in surgical approach were noted in these measures. In contrast, the anterior group showed a statistically significant but not clinically significant improvement in self-reported HOOS JR scores compared to the posterior group at the 3-month post-operative mark (p = 0.045). Conclusions: This study suggests both anterior and posterior surgical approaches to total hip arthroplasty yield equivalent functional results at 3 months and one year post-operatively, while the anterior approach demonstrates more improved patient satisfaction than the posterior approach at the 3-month post-operative assessment.

Enhanced methods fulfilling early discharge criteria for total hip and knee arthroplasty patients

INTRODUCTION

Enhanced recovery strategies have resulted in significant reductions in length of hospitalization and postoperative morbidity in total hip (THA) and total knee (TKA) arthroplasties. The success and safety of the arthroplasties are characterized by the establishment of evidence-based criteria, which offer safe hospitalization and postoperative care.

Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model

BACKGROUND

Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility. This study aims to compare the predictive accuracy of the SRC with a novel artificial neural network (ANN) algorithm for 30-day readmission after primary TKA, using the same set of clinical variables from a large national database.

METHODS

Patients undergoing primary TKA between 2013 and 2020 were identified from the ACS-NSQIP database and randomly stratified into training and validation cohorts. The ANN was developed using data from the training cohort with fivefold cross-validation performed five times. ANN and SRC performance were subsequently evaluated in the distinct validation cohort, and predictive performance was compared on the basis of discrimination, calibration, accuracy, and clinical utility.

RESULTS

The overall cohort consisted of 365,394 patients (trainingN = 362,559; validationN = 2835), with 11,392 (3.1%) readmitted within 30 days. While the ANN demonstrated good discrimination and calibration (area under the curve (AUC)ANN = 0.72, slope = 1.32, intercept = -0.09) in the validation cohort, the SRC demonstrated poor discrimination (AUCSRC = 0.55) and underestimated readmission risk (slope = -0.21, intercept = 0.04). Although both models possessed similar accuracy (Brier score: ANN = 0.03; SRC = 0.02), only the ANN demonstrated a higher net benefit than intervening in all or no patients on the decision curve analysis. The strongest predictors of readmission were body mass index (> 33.5 kg/m2), age (> 69 years), and male sex.

CONCLUSIONS

This study demonstrates the superior predictive ability and potential clinical utility of the ANN over the conventional SRC when constrained to the same variables. By identifying the most important predictors of readmission following TKA, our findings may assist in the development of novel clinical decision support tools, potentially improving preoperative counseling and postoperative monitoring practices in at-risk patients.

Higher complication and readmission rates after total knee arthroplasty with discharge to inpatient facility vs. home: a propensity score matched analysis

INTRODUCTION

The purpose of this retrospective cohort study was to assess differences in complication rates, early readmission rates, and reasons for readmission following TKA based on discharge destination. Secondarily, we aimed to identify independent risk factors for developing any adverse event (AAE) in the 30-day postoperative period.

METHODS

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) was filtered using current procedural terminology (CPT) codes to identify patients undergoing TKA from 2015 to 2020. Patients were divided into three cohorts based on discharge destination: home, skilled nursing facility (SNF), or inpatient rehabilitation facility (IRF). Propensity score matching was used to account for confounding variables. Statistical analysis was conducted using one-way analysis of variance (ANOVA), Chi-square tests, and multivariable logistic regression.

RESULTS

352,824 patients were initially identified with 303,375 discharged home, 31,635 discharged to SNF, and 17,814 discharged to IRF. Following propensity score matching, there were 5,000 patients in each cohort. Regarding postoperative complications, the home cohort had significantly a lower readmission rate (p = 0.01) and rate of any adverse event (p < 0.001) when compared to the IRF and SNF cohorts. The IRF cohort had a significantly higher rate of AAE than the SNF cohort or the home cohort. On multivariable analysis, increasing age, increasing BMI, increasing length of hospital stay, male sex, American Society of Anesthesiologists (ASA) classification four, and history of COPD were all found to be independent risk factors for developing AAE.

CONCLUSIONS

This study demonstrates that patients who are discharged to a rehabilitation facility or SNF following TKA experienced significantly higher rates of readmission and postoperative complications than patients discharged home, even after controlling for baseline demographic differences and comorbidities. Given the high financial burden associated with these facilities, it is important for physicians to consider these potential impacts on outcomes when determining patient disposition following TKA.

Total Joint Arthroplasty Outcomes in Eligible Patients Versus Patients Who Failed to Meet at Least 1 Eligibility Criterion: A Single-Center Retrospective Analysis

BACKGROUND

This study looks to investigate how not meeting eligibility criteria affects postoperative outcomes following total joint arthroplasty surgery.

METHODS

A retrospective review was conducted of total joint arthroplasty patients at a single academic institution. Demographics, laboratory values, and complications were recorded. Continuous and categorical variables were compared using the Student's T-test and the Chi-Square test, respectively. Multivariable analysis was used to control for confounding variables.

RESULTS

Our study included 915 total hip and 1,579 total knee arthroplasty patients. For total hip and total knee arthroplasty, there were no significant differences in complications (P = .11 and .87), readmissions (P = .83 and .2), or revision surgeries (P = .3 and 1) when comparing those who met all criteria to those who did not. Total hip arthroplasty patients who did not meet two criteria had 16.1 higher odds (P = .02) of suffering a complication. There were no differences in complications (P = .34 and .41), readmissions (P = 1 and .55), or revision surgeries (P = 1 and .36) between ineligible patients treated by total joint arthroplasty surgeons and those who were not. Multivariable analysis demonstrated no eligibility factors were associated with outcomes for both total hip and knee arthroplasty.

CONCLUSIONS

There was no significant difference in outcomes between those who met all eligibility criteria and those who did not. Not meeting two criteria conferred significantly higher odds of suffering a complication for total hip arthroplasty patients. Total joint arthroplasty surgeons had similar outcomes to non-total joint surgeons, although their patient population was more complex.

LEVEL OF EVIDENCE

III.

An overview of reviews and synthesis across 440 studies examines the importance of hospital readmission predictors across various patient populations

OBJECTIVES

The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations.

STUDY DESIGN AND SETTING

An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations.

RESULTS

Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes.

CONCLUSION

Diagnoses and patients' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.

Machine Learning-Based Predictive Models for 90-Day Readmission of Total Joint Arthroplasty Using Comprehensive Electronic Health Records and Patient-Reported Outcome Measures

Background

The Centers for Medicare & Medicaid Services currently incentivizes hospitals to reduce postdischarge adverse events such as unplanned hospital readmissions for patients who underwent total joint arthroplasty (TJA). This study aimed to predict 90-day TJA readmissions from our comprehensive electronic health record data and routinely collected patient-reported outcome measures.

Methods

We retrospectively queried all TJA-related readmissions in our tertiary care center between 2016 and 2019. A total of 104-episode care characteristics and preoperative patient-reported outcome measures were used to develop several machine learning models for prediction performance evaluation and comparison. For interpretability, a logistic regression model was built to investigate the statistical significance, magnitudes, and directions of associations between risk factors and readmission.

Results

Given the significant imbalanced outcome (5.8% of patients were readmitted), our models robustly predicted the outcome, yielding areas under the receiver operating characteristic curves over 0.8, recalls over 0.5, and precisions over 0.5. In addition, the logistic regression model identified risk factors predicting readmission: diabetes, preadmission medication prescriptions (ie, nonsteroidal anti-inflammatory drug, corticosteroid, and narcotic), discharge to a skilled nursing facility, and postdischarge care behaviors within 90 days. Notably, low self-reported confidence to carry out social activities accurately predicted readmission.

Conclusions

A machine learning model can help identify patients who are at substantially increased risk of a readmission after TJA. This finding may allow for health-care providers to increase resources targeting these patients. In addition, a poor response to the "social activities" question may be a useful indicator that predicts a significant increased risk of readmission after TJA.

Readmission to a non-index hospital following total joint replacement

Aims

It is unclear whether mortality outcomes differ for patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) surgery who are readmitted to the index hospital where their surgery was performed, or to another hospital.

Methods

We analyzed linked hospital and death records for residents of New South Wales, Australia, aged ≥ 18 years who had an emergency readmission within 90 days following THA or TKA surgery between 2003 and 2022. Multivariable modelling was used to identify factors associated with non-index readmission and to evaluate associations of readmission destination (non-index vs index) with 90-day and one-year mortality.

Results

Of 394,248 joint arthroplasty patients (THA = 149,456; TKA = 244,792), 9.5% (n = 37,431) were readmitted within 90 days, and 53.7% of these were admitted to a non-index hospital. Non-index readmission was more prevalent among patients who underwent surgery in private hospitals (60%). Patients who were readmitted for non-orthopaedic conditions (62.8%), were more likely to return to a non-index hospital compared to those readmitted for orthopaedic complications (39.5%). Factors associated with non-index readmission included older age, higher socioeconomic status, private health insurance, and residence in a rural or remote area. Non-index readmission was significantly associated with 90-day (adjusted odds ratio (aOR) 1.69; 95% confidence interval (CI) 1.39 to 2.05) and one-year mortality (aOR 1.31; 95% CI 1.16 to 1.47). Associations between non-index readmission and mortality were similar for patients readmitted with orthopaedic and non-orthopaedic complications (90-day mortality aOR 1.61; 95% CI 0.98 to 2.64, and aOR 1.67; 95% CI 1.35 to 2.06, respectively).

Conclusion

Non-index readmission was associated with increased mortality, irrespective of whether the readmission was for orthopaedic complications or other conditions.

Pharmacological thromboprophylaxis as a risk factor for early periprosthetic joint infection following primary total joint arthroplasty

Venous thromboembolism (VTE) prophylaxis has been suggested for patients who underwent total join arthroplasty (TJA). However, the morbidity of surgical site complications (SSC) and periprosthetic joint infection (PJI) has not been well evaluated. We aimed to evaluate the impact of VTE prophylaxis on the risk of early postoperative SSC and PJI in a Taiwanese population. We retrospectively reviewed 7511 patients who underwent primary TJA performed by a single surgeon from 2010 through 2019. We evaluated the rates of SSC and PJI in the early postoperative period (30-day, 90-day) as well as 1-year reoperations. Multivariate regression analysis was used to identify possible risk factors associated with SSC and PJI, including age, sex, WHO classification of weight status, smoking, diabetes mellitus (DM), rheumatoid arthritis(RA), Charlson comorbidity index (CCI), history of VTE, presence of varicose veins, total knee or hip arthroplasty procedure, unilateral or bilateral procedure, or receiving VTE prophylaxis or blood transfusion. The overall 90-day rates of SSC and PJI were 1.1% (N = 80) and 0.2% (N = 16). VTE prophylaxis was a risk factor for 90-day readmission for SSC (aOR: 1.753, 95% CI 1.081-2.842), 90-day readmission for PJI (aOR: 3.267, 95% CI 1.026-10.402) and all 90-day PJI events (aOR: 3.222, 95% CI 1.200-8.656). Other risk factors included DM, underweight, obesity, bilateral TJA procedure, younger age, male sex and RA. Pharmacological thromboprophylaxis appears to be a modifiable risk factor for SSC and PJI in the early postoperative period. The increased infection risk should be carefully weighed in patients who received pharmacological VTE prophylaxis.