Readmission, Complication, and Disposition Calculators in Total Joint Arthroplasty: A Systemic Review

Trends in the Adoption of Outpatient Joint Arthroplasties and Patient Risk: A Retrospective Analysis of 2019 to 2021 Medicare Claims Data

INTRODUCTION

Total joint arthroplasties (TJAs) have recently been shifting toward outpatient arthroplasty. This study aims to explore recent trends in outpatient total joint arthroplasty (TJA) procedures and examine whether patients with a higher comorbidity burden are undergoing outpatient arthroplasty.

METHODS

Medicare fee-for-service claims were screened for patients who underwent total hip, knee, or shoulder arthroplasty procedures between January 2019 and December 2022. The procedure was considered to be outpatient if the patient was discharged on the same date of the procedure. The Hierarchical Condition Category Score (HCC) and the Charlson Comorbidity Index (CCI) scores were used to assess patient comorbidity burden. Patient adverse outcomes included all-cause hospital readmission, mortality, and postoperative complications. Logistic regression analyses were used to evaluate if higher HCC/CCI scores were associated with adverse patient outcomes.

RESULTS

A total of 69,520, 116,411, and 41,922 respective total knee, hip, and shoulder arthroplasties were identified, respectively. Despite earlier removal from the inpatient-only list, outpatient knee and hip surgical volume did not markedly increase until the pandemic started. By 2022Q4, 16%, 23%, and 36% of hip, knee, and shoulder arthroplasties were discharged on the same day of surgery, respectively. Both HCC and CCI risk scores in outpatients increased over time ( P < 0.001).

DISCUSSION

TJA procedures are shifting toward outpatient surgery over time, largely driven by the COVID-19 pandemic. TJA outpatients' HCC and CCI risk scores increased over this same period, and additional research to determine the effects of this should be pursued.

LEVEL OF EVIDENCE

Level III, therapeutic retrospective cohort study.

Effect of continuous nursing on rehabilitation of older patients with joint replacement after discharge

BACKGROUND

Joint replacement is a common treatment for older patients with high incidences of hip joint diseases. However, postoperative recovery is slow and complications are common, which reduces surgical effectiveness. Therefore, patients require long-term, high-quality, and effective nursing interventions to promote rehabilitation. Continuity of care has been used successfully in other diseases; however, little research has been conducted on older patients who have undergone hip replacement.

AIM

To explore the clinical effect of continuous nursing on rehabilitation after discharge of older individuals who have undergone joint replacement.

METHODS

A retrospective analysis was performed on the clinical data of 113 elderly patients. Patients receiving routine nursing were included in the convention group (n = 60), and those receiving continuous nursing, according to various methods, were included in the continuation group (n = 53). Harris score, short form 36 (SF-36) score, complication rate, and readmission rate were compared between the convention and continuation groups.

RESULTS

After discharge, Harris and SF-36 scores of the continuation group were higher than those of the convention group. The Harris and SF-36 scores of the two groups showed an increasing trend with time, and there was an interaction effect between group and time (Harris score: F intergroup effect = 376.500, F time effect = 20.090, F interaction effect = 4.824; SF-36 score: F intergroup effect = 236.200, F time effect = 16.710, F interaction effect = 5.584; all P < 0.05). Furthermore, the total complication and readmission rates in the continuation group were lower (P < 0.05).

CONCLUSION

Continuous nursing could significantly improve hip function and quality of life in older patients after joint replacement and reduce the incidence of complications and readmission rates.

Safety Considerations for Outpatient Arthroplasty

Since 2018, the number of total joint arthroplasties (TJAs) performed on an outpatient basis has dramatically increased. Both surgeon and anesthesiologist should be aware of the implications for the safety of outpatient TJAs and potential patient risk factors that could alter this safety profile. Although smaller studies suggest that the risk of negative outcomes is equivalent when comparing outpatient and inpatient arthroplasty, larger database analyses suggest that, even when matched for comorbidities, patients undergoing outpatient arthroplasty may be at increased risk of surgical or medical complications. Appropriate patient selection is critical for the success of any outpatient arthroplasty program. Potential exclusion criteria for outpatient TJA may include age greater than 75 years, bleeding disorder, history of deep vein thrombosis, uncontrolled diabetes mellitus, and hypoalbuminemia, among others. Patient optimization before surgery is also warranted. The potential risks of same-day versus next-day discharge have yet to be elicited in a large-scale manner.

Successful Transition to Same Calendar Day Discharge in Total Joint Arthroplasty at an Academic Center

Background

There has been a shift toward same-day discharge (SDD) in total joint arthroplasty (TJA) in recent years. Our clinical standard had been next-day discharge, but the COVID pandemic led to a hospital bed shortage, causing us to shift to SDD directly from the Post-Anesthesia Care Unit (PACU). The aim of our project was to investigate if the SDD protocol was successful and if it changed complications or 90-day readmission rates. Our secondary aim was to investigate if the protocol created disparities in patient selection.

Methods

A retrospective review compared the first 100 patients intended to discharge from PACU to the 100 patients prior to the SDD protocol undergoing elective primary TJA procedures at our academic institution from September 1, 2020, to March 23, 2021. The SDD protocol started on November 19, 2020.

Results

During this SDD period, 98% (98/100) of patients were successfully discharged from the PACU. The 90-day readmission rate changed from 0% to 2% (P = .4975), and the overall complication rate changed from 2% to 5% (P = .4448). Most complications were manipulation under anesthesia to improve range of motion. Manipulations under anesthesia changed from 1% to 4% (P = .3687).

Conclusions

The transition to same SDD in TJA at our academic institution was successfully implemented without markedly increasing complications, readmissions, or changing patient selection. The COVID-19 pandemic likely influenced the recovery of patients before and after the protocol. Future studies are needed to validate this data during the post-COVID era.

Predictive Modeling of Medical- and Orthopaedic-Related 90-Day Readmissions Following Primary Total Hip Arthroplasty

BACKGROUND

The rate of unplanned hospital readmissions following total hip arthroplasty (THA) varies from 3 to 10%, representing a major economic burden. However, it is unknown if specific factors are associated with different types of complications (ie, medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following THA.

METHODS

A prospective cohort of primary unilateral THAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 8,893 patients) using a validated institutional data collection system. Orthopaedic-related readmissions were specific complications affecting the prosthesis, joint, and surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between prespecified risk factors and 90-day readmissions, as well as medical and orthopaedic-related readmissions independently.

RESULTS

Overall, the rate of 90-day readmissions was 5.6%. Medical readmissions (4.2%) were found to be more prevalent than orthopaedic-related readmissions (1.4%). The area under the curve for the 90-day readmission model was 0.71 (95% confidence interval: 0.69 to 0.74). Factors significantly associated with medical-related readmissions were advanced age, Black race, education, Charlson Comorbidity Index, surgical approach, opioid overdose risk score, and nonhome discharge. In contrast, risk factors linked to orthopaedic-related readmissions encompassed body mass index, patient-reported outcome measure phenotype, nonosteoarthritis indication, opioid overdose risk, and nonhome discharge.

CONCLUSIONS

Of the overall 90-day readmissions following primary THA, 75% were due to medical-related complications. Our successful predictive model for complication-specific 90-day readmissions highlights how different risk factors may disproportionately influence medical versus orthopaedic-related readmissions, suggesting that patient-specific, tailored preventive measures could reduce postoperative readmissions in the current value-based health care setting.

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.

A Novel Patient Selection Tool Is Highly Efficacious at Identifying Candidates for Outpatient Surgery When Applied to a Nonselected Cohort of Patients in a Community Hospital

BACKGROUND

There is a paucity of validated selection tools to assess which patients can safely and predictably undergo same-day or 23-hour discharge in a community hospital. The purpose of this study was to assess the ability of our patient selection too to identify patients who are candidates for outpatient total joint arthroplasty (TJA) in a community hospital.

METHODS

A retrospective review of 223 consecutive (unselected) primary TJAs was performed. The patient selection tool was retrospectively applied to this cohort to determine eligibility for outpatient arthroplasty. Utilizing length of stay and discharge disposition, we identified the proportion of patients discharged home within 23 hours.

RESULTS

We found that 179 (80.1%) patients met eligibility criteria for short-stay TJA. Of the 223 patients in this study, 215 (96.4%) patients were discharged home; 17 (7.9%) were on the day of surgery, and 190 (88.3%) within 23 hours. Of the 179 eligible patients for short-stay discharge, 155 (86.6%) patients were discharged home within 23 hours. Overall, the sensitivity of the patient selection tool was 79%, the specificity was 92%, the positive predictive value was 87% and the negative predictive value was 96%.

CONCLUSION

In this study, we found that more than 80% of patients undergoing TJA in a community hospital are eligible for short-stay arthroplasty with this selection tool. We found that this selection tool is safe and effective at predicting short-stay discharge. Further studies are needed to better ascertain the direct effects of these specific demographic traits on their effects on short-stay protocols.

Risk factors for rehospitalization within 90 days in patients with total joint replacement: A meta-analysis

BACKGROUND

The risk factors influencing the readmission within 90 days following total joint replacement (TJR) are complex and heterogeneous, and few systematic reviews to date have focused on this issue.

METHODS

Web of Science, Embase, PubMed, and Chinese National Knowledge Infrastructure databases were searched from the inception dates to December 2022. Relevant, published studies were identified using the following keywords: risk factors, rehospitalization, total hip replacement, total knee replacement, total shoulder replacement, and total joint replacement. All relevant data were collected from the studies that meet the inclusion criteria. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS).

RESULTS

Of 68,336 patients who underwent TJR, 1,269,415 (5.4%) were readmitted within 90 days. High American Society of Anesthesiologists (ASA) class (OR, 1.502; 95%CI:1.405-1.605; P < .001), heart failure (OR,1.494; 95%CI: 1.235-1.754; P < .001), diabetes (OR, 1.246; 95%CI:1.128-1.377; P < .001), liver disease (OR, 1.339; 95%CI:1.237-1.450; P < .001), drinking (OR, 1.114; 95%CI:1.041-1.192; P = .002), depression (OR, 1.294; 95%CI:1.223-1.396; P < .001), urinary tract infection (OR, 5.879; 95%CI: 5.119-6.753; P < .001), and deep vein thrombosis (OR, 10.007; 95%CI: 8.787-11.396; P < .001) showed statistically positive correlation with increased 90-day readmissions after TJR, but high blood pressure, smoking, and pneumonia had no significant association with readmission risk.

CONCLUSION

The findings of this review and meta-analysis will aid clinicians as they seek to understand the risk factors for 90-day readmission following TJR. Clinicians should consider the identified key risk factors associated with unplanned readmissions and develop strategies to risk-stratify patients and provide dedicated interventions to reduce the rates of readmission and enhance the recovery process.

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes

BACKGROUND

Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making.

QUESTIONS/PURPOSES

(1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission?

METHODS

National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions.

RESULTS

For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes.

CONCLUSION

A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment.

LEVEL OF EVIDENCE

Level III, therapeutic study.

The Use of Risk Scores in Patient Preoperative Optimization for Total Joint Arthroplasty: A Survey of Orthopaedic Nurse Navigators

Preoperative optimization of patients seeking total joint arthroplasty is becoming more common, and risk scores, which provide an estimate for the risk of complications following procedures, are often used to assist with the preoperative decision-making process. The aim of this study was to characterize the use of risk scores at institutions that utilize nurse navigators in the preoperative optimization process. The survey included 207 nurse navigators identified via the National Association of Orthopaedic Nurses to better understand the use of risk scores in preoperative optimization and the different factors that are included in these risk scores. The study found that 48% of responding nurse navigators utilized risk scores in the preoperative optimization process. These risk scores often included patient comorbidities such as diabetes (85%) and body mass index (87%). Risk scores are commonly used by nurse navigators in preoperative optimization and involve a variety of comorbidities and patient-specific factors.

An orthopaedic intelligence application successfully integrates data from a smartphone-based care management platform and a robotic knee system using a commercial database

PURPOSE

To evaluate the feasibility of using a smartphone-based care management platform (sbCMP) and robotic-assisted total knee arthroplasty (raTKA) to collect data throughout the episode-of-care and assess if intra-operative measures of soft tissue laxity in raTKA were associated with post-operative outcomes.

METHODS

A secondary data analysis of 131 patients in a commercial database who underwent raTKA was performed. Pre-operative through six week post-operative step counts and KOOS JR scores were collected and cross-referenced with intra-operative laxity measures. A Kruskal-Wallis test or a Wilcoxon sign-rank was used to assess outcomes.

RESULTS

There were higher step counts at six weeks post-operatively in knees with increased laxity in both the lateral compartment in extension and medial compartment in flexion (p < 0.05). Knees balanced in flexion within < 0.5 mm had higher KOOS JR scores at six weeks post-operative (p = 0.034) compared to knees balanced within 0.5-1.5 mm.

CONCLUSION

A smartphone-based care management platform can be integrated with raTKA to passively collect data throughout the episode-of-care. Associations between intra-operative decisions regarding laxity and post-operative outcomes were identified. However, more robust analysis is needed to evaluate these associations and ensure clinical relevance to guide machine learning algorithms.

Development of a Novel Prospective Model to Predict Unplanned 90-Day Readmissions After Total Hip Arthroplasty

BACKGROUND

For hospitals participating in bundled payment programs, unplanned readmissions after surgery are often termed "bundle busters." The aim of this study was to develop the framework for a prospective model to predict 90-day unplanned readmissions after elective primary total hip arthroplasty (THA) at a macroscopic hospital-based level.

METHODS

A national, all-payer, inpatient claims and cost accounting database was used. A mixed-effect logistic regression model measuring the association of unplanned 90-day readmissions with a number of patient-level and hospital-level characteristics was constructed.

RESULTS

Using 427,809 unique inpatient THA encounters, 77 significant risk factors across 5 domains (ie, comorbidities, demographics, surgical history, active medications, and intraoperative factors) were identified. The highest frequency domain was comorbidities (64/100) with malignancies (odds ratio [OR] 2.26), disorders of the respiratory system (OR 1.75), epilepsy (OR 1.5), and psychotic disorders (OR 1.5), being the most predictive. Other notable risk factors identified by the model were the use of opioid analgesics (OR 7.3), Medicaid coverage (OR 1.8), antidepressants (OR 1.6), and blood-related medications (OR 1.6). The model produced an area under the curve of 0.715.

CONCLUSION

We developed a novel model to predict unplanned 90-day readmissions after elective primary THA. Fifteen percent of the risk factors are potentially modifiable such as use of tranexamic acid, spinal anesthesia, and opioid medications. Given the complexity of the factors involved, hospital systems with vested interest should consider incorporating some of the findings from this study in the form of electronic medical records predictive analytics tools to offer clinicians with real-time actionable data.

Safety Considerations for Outpatient Arthroplasty

Since 2018, the number of total joint arthroplasties (TJAs) performed on an outpatient basis has dramatically increased. Both surgeon and anesthesiologist should be aware of the implications for the safety of outpatient TJAs and potential patient risk factors that could alter this safety profile. Although smaller studies suggest that the risk of negative outcomes is equivalent when comparing outpatient and inpatient arthroplasty, larger database analyses suggest that, even when matched for comorbidities, patients undergoing outpatient arthroplasty may be at increased risk of surgical or medical complications. Appropriate patient selection is critical for the success of any outpatient arthroplasty program. Potential exclusion criteria for outpatient TJA may include age greater than 75 years, bleeding disorder, history of deep vein thrombosis, uncontrolled diabetes mellitus, and hypoalbuminemia, among others. Patient optimization before surgery is also warranted. The potential risks of same-day versus next-day discharge have yet to be elicited in a large-scale manner.

PLAN and AM-PAC "6-Clicks" Scores to Predict Discharge Disposition After Primary Total Hip and Knee Arthroplasty

BACKGROUND

Determination of the appropriate post-discharge disposition after total hip (THA) and knee (TKA) arthroplasty is a challenging multidisciplinary decision. Algorithms used to guide this decision have been administered both preoperatively and postoperatively. The purpose of this study was to simultaneously evaluate the predictive ability of 2 such tools-the preoperatively administered Predicting Location after Arthroplasty Nomogram (PLAN) and the postoperatively administered Activity Measure for Post-Acute Care (AM-PAC) "6-Clicks" basic mobility tools-in accurately determining discharge disposition after elective THA and TKA.

METHODS

The study included 11,672 patients who underwent THA (n = 4,923) or TKA (n = 6,749) at a single large hospital system from December 2016 through March 2020. PLAN and "6-Clicks" basic mobility scores were recorded for all patients. Regression models and receiver operator characteristic curves were constructed to evaluate the tools' prediction concordance with the actual discharge disposition (home compared with a facility).

RESULTS

PLAN scores had a concordance index of 0.723 for the THA cohort and 0.738 for the TKA cohort. The first "6-Clicks" mobility score (recorded within the first 48 hours postoperatively) had a concordance index of 0.813 for the THA cohort and 0.790 for the TKA cohort. When PLAN and first "6-Clicks" mobility scores were used together, a concordance index of 0.836 was observed for the THA cohort and 0.836 for the TKA cohort. When the PLAN and "6-Clicks" agreed on home discharge, higher rates of discharge to home (98.0% for THA and 97.7% for TKA) and lower readmission rates (5.1% for THA and 7.0% for TKA) were observed, compared with when the tools disagreed.

CONCLUSIONS

PLAN and "6-Clicks" basic mobility scores were good-to-excellent predictors of discharge disposition after primary total joint arthroplasty, suggesting that both preoperative and postoperative variables influence discharge disposition. We recommend that preoperative variables be collected and used to generate a tentative plan for discharge, and the final decision on discharge disposition be augmented by early postoperative evaluation.

CLINICAL RELEVANCE

The determination of post-discharge needs after THA and TKA remains a complex clinical decision. This study shows how simultaneously exploring the predictive ability of preoperative and postoperative assessment tools on discharge disposition after total joint arthroplasty may be a useful aid in a value-driven health-care model.

Macrophage Polarization and the Osteoimmunology of Periprosthetic Osteolysis

PURPOSE OF REVIEW

Joint replacement has revolutionized the treatment of end-stage arthritis. We highlight the key role of macrophages in the innate immune system in helping to ensure that the prosthesis-host interface remains biologically robust.

RECENT FINDINGS

Osteoimmunology is of great interest to researchers investigating the fundamental biological and material aspects of joint replacement. Constant communication between cells of the monocyte/macrophage/osteoclast lineage and the mesenchymal stem cell-osteoblast lineage determines whether a durable prosthesis-implant interface is obtained, or whether implant loosening occurs. Tissue and circulating monocytes/macrophages provide local surveillance of stimuli such as the presence of byproducts of wear and can quickly polarize to pro- and anti-inflammatory phenotypes to re-establish tissue homeostasis. When these mechanisms fail, periprosthetic osteolysis results in progressive bone loss and painful failure of mechanical fixation. Immune modulation of the periprosthetic microenvironment is a potential intervention to facilitate long-term durability of prosthetic interfaces.

Length of Stay After Joint Arthroplasty is Less Than Predicted Using Two Risk Calculators

BACKGROUND

Predicting the length of stay (LOS) after total joint arthroplasty (TJA) has become more important with their recent removal from inpatient-only designation. The American College of Surgeons (ACS) National Surgical Quality Improvement Program surgical risk calculator and the CMS' diagnosis-related group (DRG) calculator are two common LOS predictors. The aim of our study was to determine how our actual LOS compared with those predicted by both the ACS and DRG.

METHODS

99 consecutive TJA (49 hips and 50 knee procedures) were reviewed in Medicare-eligible patients from four fellowship-trained arthroplasty surgeons. Predicted LOS was calculated using the DRG and ACS risk calculators for each patient using demographics, medical histories, and comorbidities. LOS was compared between the predicted and the actual LOS for both total hip arthroplasty (THA) and total knee arthroplasty (TKA) using paired t-tests.

RESULTS

Actual LOS was shorter in the THA group vs the TKA group (1.29 days vs 1.46 days, P < .05). The actual LOS of patients at our institution was significantly shorter than both DRG and ACS predictions for both THA and TKA (P < .05). In both the THA and TKA patients, the actual LOS (1.29 and 1.46 day) was significantly shorter than the DRG-predicted LOS (2.15 and 2.15 days) which was significantly shorter than the ACS-predicted LOS (2.9 and 3.14 days).

CONCLUSION

We found the actual LOS was significantly shorter than that predicted by both the DRG and ACS risk calculators. Current risk calculators may not be accurate for contemporary fast-track protocols and newer tools should be developed.