Can We Predict Discharge Status After Total Joint Arthroplasty? A Calculator to Predict Home Discharge

A systematic literature review of predicting patient discharges using statistical methods and machine learning

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

High Risk of Readmission After THA Regardless of Functional Status in Patients Discharged to Skilled Nursing Facility

BACKGROUND

The postoperative period and subsequent discharge planning are critical in our continued efforts to decrease the risk of complications after THA. Patients discharged to skilled nursing facilities (SNFs) have consistently exhibited higher readmission rates compared with those discharged to home healthcare. This elevated risk has been attributed to several factors but whether readmission is associated with patient functional status is not known.

QUESTIONS/PURPOSES

After controlling for relevant confounding variables (functional status, age, gender, caregiver support available at home, diagnosis [osteoarthritis (OA) versus non-OA], Charlson comorbidity index [CCI], the Area Deprivation Index [ADI], and insurance), are the odds of 30- and 90-day hospital readmission greater among patients initially discharged to SNFs than among those treated with home healthcare after THA?

METHODS

This was a retrospective, comparative study of patients undergoing THA at any of 11 hospitals in a single, large, academic healthcare system between 2017 and 2022 who were discharged to an SNF or home healthcare. During this period, 13,262 patients were included. Patients discharged to SNFs were older (73 ± 11 years versus 65 ± 11 years; p < 0.001), less independent at hospital discharge (6-click score: 16 ± 3.2 versus 22 ± 2.3; p < 0.001), more were women (71% [1279 of 1796] versus 56% [6447 of 11,466]; p < 0.001), insured by Medicare (83% [1497 of 1796] versus 52% [5974 of 11,466]; p < 0.001), living in areas with greater deprivation (30% [533 of 1796] versus 19% [2229 of 11,466]; p < 0.001), and had less assistance available from at-home caregivers (29% [527 of 1796] versus 57% [6484 of 11,466]; p < 0.001). The primary outcomes assessed in this study were 30- and 90-day hospital readmissions. Although the system automatically flags readmissions occurring within 90 days at the various facilities in the overall healthcare system, readmissions occurring outside the system would not be captured. Therefore, we were not able to account for potential differential rates of readmission to external healthcare systems between the groups. However, given the large size and broad geographic coverage of the healthcare system analyzed, we expect the readmissions data captured to be representative of the study population. The focus on a single healthcare system also ensures consistency in readmission identification and reporting across subjects. We evaluated the association between discharge disposition (home healthcare versus SNF) and readmission. Covariates evaluated included age, gender, primary payer, primary diagnosis, CCI, ADI, the availability of at-home caregivers for the patient, and the Activity Measure for Post-Acute Care (AM-PAC) 6-clicks basic mobility score in the hospital. The adjusted relative risk (ARR) of readmission within 30 and 90 days of discharge to SNF (versus home healthcare) was estimated using modified Poisson regression models.

RESULTS

After adjusting for the 6-clicks mobility score, age, gender, ADI, OA versus non-OA, living environment, CCI, and insurance, patients discharged to an SNF were more likely to be readmitted within 30 and 90 days compared with home healthcare after THA (ARR 1.46 [95% CI 1.01 to 2.13]; p= 0.046 and ARR 1.57 [95% CI 1.23 to 2.01]; p < 0.001, respectively).

CONCLUSION

Patients discharged to SNFs after THA had a slightly higher likelihood of hospital readmission within 30 and 90 days compared with those discharged with home healthcare. This difference persisted even after adjusting for relevant factors like functional status, home support, and social determinants of health. These results indicate that for suitable patients, direct home discharge may be a safer and more cost-effective option than SNFs. Clinicians should carefully consider these risks and benefits when making postoperative discharge plans. Policymakers could consider incentives and reforms to improve care transitions and coordination across settings. Further research using robust methods is needed to clarify the reasons for higher SNF readmission rates. Detailed analysis of patient complexity, care processes, and causes of readmission in SNFs versus home health could identify areas for quality improvement. Prospective cohorts or randomized trials would allow stronger conclusions about cause-and-effect. Importantly, no patients should be unfairly "cherry-picked" or "lemon-dropped" based only on readmission risk scores. With proper support and care coordination, even complex patients can have good outcomes. The goal should be providing excellent rehabilitation for all, while continuously improving quality, safety, and value across settings.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Validation of a Discharge Risk Calculator for Rural Patients Following Total Joint Arthroplasty

BACKGROUND

As the volume of total joint arthroplasty in the US continues to grow, new challenges surrounding appropriate discharge surface. Arthroplasty literature has demonstrated discharge disposition to postacute care facilities carries major risks regarding the need for revision surgery, patient comorbidities, and financial burden. To quantify, categorize, and mitigate risks, a decision tool that uses preoperative patient variables has previously been published and validated using an urban patient population. The aim of our investigation was to validate the same predictive model using patients in a rural setting undergoing total knee arthroplasty (TKA) and total hip arthroplasty.

METHODS

All TKA and THA procedures that were performed between January 2012 and September 2022 at our institution were collected. A total of 9,477 cases (39.6% TKA, 60.4% THA) were included for the validation analysis. There were 9 preoperative variables that were extracted in an automated fashion from the electronic medical record. Included patients were then run through the predictive model, generating a risk score representing that patient's differential risk of discharge to a skilled nursing facility versus home. Overall accuracy, sensitivity and specificity were calculated after obtaining risk scores.

RESULTS

Score cutoff equally maximizing sensitivity and specificity was 0.23, and the proportion of correct classifications by the predictive tool in this study population was found to be 0.723, with an area under the curve of 0.788 - both higher than previously published accuracy levels. With the threshold of 0.23, sensitivity and specificity were found to be 0.720 and 0.723, respectively.

CONCLUSIONS

The risk calculator showed very good accuracy, sensitivity, and specificity in predicting discharge location for rural patients undergoing TKA and THA, with accuracy even higher than in urban populations. The model provides an easy-to-use interface, with automation representing a viable tool in helping with shared decision-making regarding postoperative discharge plans.

Aspirin is Not for Everyone: Discharge to Non-home Facilities After Total Hip and Knee Arthroplasty Increases Risk of Venous Thromboembolism

Background

Patients discharged to non-home facilities (NHD) after total hip arthroplasty (THA) and total knee (TKA) arthroplasty experience higher rates of adverse events and may require more aggressive venous thromboembolism (VTE) chemoprophylaxis. Our aim was to compare the rates of VTE in NHD patients and those discharged home (HD) after THA/TKA. Our secondary aim was to determine VTE rates within HD and NHD groups when stratified by chemoprophylactic regimen.

Methods

A retrospective cohort of primary THA and TKA patients were stratified into HD and NHD, then allocated into groups by chemoprophylactic regimen on discharge: aspirin alone (AA), more aggressive (MA) chemoprophylaxis, and other regimens (other). The primary outcome was VTE. Rates of VTE in HD and NHD patients, as well as AA and MA regimens, were analyzed using a generalized linear regression model.

Results

Six thousand three hundred seventy-nine patients were included with 1.03% experiencing VTE. HD had lower rates of VTE compared to NHD (0.83% vs 2.17%, P < .001). AA had similar rates of VTE compared to MA (0.99% vs 1.08%, P = .82). NHD patients had a lower VTE rate with MA vs AA prophylaxis (1.47% vs 3.83%, P = .016). HD patients treated with AA vs MA had no difference in VTE rates (0.76% vs 0.96%, P = .761).

Conclusions

NHD patients have higher rates of VTE than HD patients. However, NHD patients have significantly lower rates of VTE on MA chemoprophylaxis compared to those on AA. Providers should consider prescribing MA VTE chemoprophylaxis for NHD patients. Prospective, randomized studies are necessary to confirm these recommendations.

The Impact of Sarcopenia Risk on Postoperative Walking Independence in Older Adults Undergoing Total Joint Arthroplasty

BACKGROUND AND PURPOSE

Sarcopenia is known to be associated with poor outcomes after arthroplasty; however, no study has reported the relationship between sarcopenia and postoperative walking independence. This study aimed to determine the impact of sarcopenia risk screening using the SARC-CalF questionnaire and calf circumference on the time to walk independently after total hip or knee arthroplasty in older patients.

METHODS

We included 599 nonobese patients aged 65 years and older who underwent unilateral and primary total hip or knee arthroplasty. Preoperative sarcopenia risk was assessed using the SARC-CalF or calf circumference. The outcome of this study was the time to independent walking after surgery; it was calculated as the number of days from the date of surgery to the date when the patient was able to walk independently. The association between preoperative sarcopenia risk and time to independent walking after surgery was analyzed using Kaplan-Meier curves and Cox proportional hazards models.

RESULTS

Among the 599 patients undergoing total joint arthroplasty, 175 (29.2%) were determined to be at risk of sarcopenia using SARC-CalF and 193 (32.2%) using calf circumference. The Kaplan-Meier curve showed that sarcopenia risk assessed by SARC-CalF or calf circumference was associated with a prolonged time to independent walking in patients undergoing hip arthroplasty (log-rank test, P < .001 and P < .001, respectively). In patients undergoing hip arthroplasty, the Cox proportional hazards model showed that SARC-CalF score of 11 points and greater or a calf circumference less than the cutoff was a risk factor for delayed time to independent walking (hazard ratios: 0.55 and 0.57, P < .001 and P = .001, respectively). There was no association between preoperative sarcopenia risk and postoperative time to independent walking in patients who underwent knee arthroplasty.

CONCLUSIONS

Sarcopenia screening tools, such as SARC-CalF or calf circumference, should be useful for planning postoperative rehabilitation in older adults scheduled for hip arthroplasty. However, the accuracy of SARC-CalF or calf circumference measurement in patients scheduled for knee arthroplasty may be low.

Association of affected and non-affected side ability with postoperative outcomes in patients undergoing total hip arthroplasty

BACKGROUND

Although several reports have examined the association between preoperative function and postoperative outcomes in patients undergoing total hip arthroplasty (THA), it is unclear whether the ability of the affected or non-affected side particularly impacts on outcomes. We aimed to investigate the association between affected and non-affected side ability and walking independence.

METHODS

We prospectively enrolled 721 consecutive patients who underwent THA. Preoperatively, quadriceps isometric strength (QIS) and one-leg standing time (OLST) were measured. The endpoints were walking independence within 3, 5, 7, 10, and 14 days postoperatively. The associations between preoperative abilities and outcomes were examined using multivariate Cox hazard model, and the area under the curves (AUCs) for outcomes were compared.

RESULTS

We analysed 540 patients after excluding patients who met the exclusion criteria. Both affected and non-affected QIS predicted walking independence within 3 (p = 0.006 and 0.001, respectively), 5, 7, 10, and 14 (both p < 0.001) days postoperatively. For OLST, only the affected side did not predict walking independence within 3 days postoperatively (p = 0.154 and 0.012, respectively), and both sides did at days 5 (p = 0.019 and <0.001, respectively), 7, 10, and 14 (both p < 0.001). The AUCs of the non-affected side ability for walking independence were significantly greater than those of the affected side on postoperative days 3 (0.66 vs. 0.73; p = 0.021) and 5 (0.67 vs. 0.71; p = 0.040), with no significant difference after day 7.

CONCLUSIONS

Both sides abilities were associated with walking independence after THA, but non-affected side was found to be particularly crucial for early walking independence.

Internal and External Validation of the Generalizability of Machine Learning Algorithms in Predicting Non-home Discharge Disposition Following Primary Total Knee Joint Arthroplasty

BACKGROUND

Nonhome discharge disposition following primary total knee arthroplasty (TKA) is associated with a higher rate of complications and constitutes a socioeconomic burden on the health care system. While existing algorithms predicting nonhome discharge disposition varied in degrees of mathematical complexity and prediction power, their capacity to generalize predictions beyond the development dataset remains limited. Therefore, this study aimed to establish the machine learning model generalizability by performing internal and external validations using nation-scale and institutional cohorts, respectively.

METHODS

Four machine learning models were trained using the national cohort. Recursive feature elimination and hyper-parameter tuning were applied. Internal validation was achieved through five-fold cross-validation during model training. The trained models' performance was externally validated using the institutional cohort and assessed by discrimination, calibration, and clinical utility.

RESULTS

The national (424,354 patients) and institutional (10,196 patients) cohorts had non-home discharge rates of 19.4 and 36.4%, respectively. The areas under the receiver operating curve of the model predictions were 0.83 to 0.84 during internal validation and increased to 0.88 to 0.89 during external validation. Artificial neural network and histogram-based gradient boosting elicited the best performance with a mean area under the receiver operating curve of 0.89, calibration slope of 1.39, and Brier score of 0.14, which indicated that the two models were robust in distinguishing non-home discharge and well-calibrated with accurate predictions of the probabilities. The low inter-dataset similarity indicated reliable external validation. Length of stay, age, body mass index, and sex were the strongest predictors of discharge destination after primary TKA.

CONCLUSION

The machine learning models demonstrated excellent predictive performance during both internal and external validations, supporting their generalizability across different patient cohorts and potential applicability in the clinical workflow.

Validation and Generalizability of Machine Learning Models for the Prediction of Discharge Disposition Following Revision Total Knee Arthroplasty

BACKGROUND

Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation. This study aimed to establish ML model generalizability by externally validating its prediction for nonhome discharge following revision TKA using national and institutional databases.

METHODS

The national and institutional cohorts comprised 52,533 and 1,628 patients, respectively, with 20.6 and 19.4% nonhome discharge rates. Five ML models were trained and internally validated (five-fold cross-validation) on a large national dataset. Subsequently, external validation was performed on our institutional dataset. Model performance was assessed using discrimination, calibration, and clinical utility. Global predictor importance plots and local surrogate models were used for interpretation.

RESULTS

The strongest predictors of nonhome discharge were patient age, body mass index, and surgical indication. The area under the receiver operating characteristic curve increased from internal to external validation and ranged between 0.77 and 0.79. Artificial neural network was the best predictive model for identifying patients at risk for nonhome discharge (area under the receiver operating characteristic curve = 0.78), and also the most accurate (calibration slope = 0.93, intercept = 0.02, and Brier score = 0.12).

CONCLUSION

All five ML models demonstrated good-to-excellent discrimination, calibration, and clinical utility on external validation, with artificial neural network being the best model for predicting discharge disposition following revision TKA. Our findings establish the generalizability of ML models developed using data from a national database. The integration of these predictive models into clinical workflow may assist in optimizing discharge planning, bed management, and cost containment associated with revision TKA.

Comparative Analysis of the Ability of Machine Learning Models in Predicting In-hospital Postoperative Outcomes After Total Hip Arthroplasty

BACKGROUND

Machine learning (ML) methods have shown promise in a wide range of applications including the development of patient-specific predictive models before surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters after primary total hip arthroplasty.

METHODS

Data from the Nationwide Inpatient Sample were used to identify patients undergoing total hip arthroplasty from 2016 to 2017. Linear support vector machine (LSVM), random forest (RF), neural network (NN), and extreme gradient boost trees (XGBoost) predictive of mortality, length of stay, and discharge disposition were developed and validated using 15 predictive patient-specific and hospital-specific factors. Area under the curve of the receiver operating characteristic (AUCROC) curve and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed.

RESULTS

A total of 177,442 patients were included in this analysis. For mortality, the XGBoost, NN, and LSVM models all had excellent responsiveness during validation while RF had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.973 during validation. For the length of stay, the LSVM and NN models had fair responsiveness while the XGBoost and random forest models had poor responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.744 during validation. For the discharge disposition outcome, LSVM had good responsiveness while the XGBoost, NN, and RF models all had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.801.

DISCUSSION

The ML methods tested demonstrated a range of poor-to-excellent responsiveness and accuracy in the prediction of the assessed metrics, with LSVM being the best performer. Such models should be further developed, with eventual integration into clinical practice to inform patient discussions and management decision making, with the potential for integration into tiered bundled payment models.

Validation of a Predictive Tool for Discharge to Rehabilitation or a Skilled Nursing Facility After TJA

BACKGROUND

Cost excess in bundled payment models for total joint arthroplasty (TJA) is driven by discharge to rehabilitation or a skilled nursing facility (SNF). A recently published preoperative risk prediction tool showed very good internal accuracy in stratifying patients on the basis of likelihood of discharge to an SNF or rehabilitation. The purpose of the present study was to test the accuracy of this predictive tool through external validation with use of a large cohort from an outside institution.

METHODS

A total of 20,294 primary unilateral total hip (48%) and knee (52%) arthroplasty cases at a tertiary health system were extracted from the institutional electronic medical record. Discharge location and the 9 preoperative variables required by the predictive model were collected. All cases were run through the model to generate risk scores for those patients, which were compared with the actual discharge locations to evaluate the cutoff originally proposed in the derivation paper. The proportion of correct classifications at this threshold was evaluated, as well as the sensitivity, specificity, positive and negative predictive values, number needed to screen, and area under the receiver operating characteristic curve (AUC), in order to determine the predictive accuracy of the model.

RESULTS

A total of 3,147 (15.5%) of the patients who underwent primary, unilateral total hip or knee arthroplasty were discharged to rehabilitation or an SNF. Despite considerable differences between the present and original model derivation cohorts, predicted scores demonstrated very good accuracy (AUC, 0.734; 95% confidence interval, 0.725 to 0.744). The threshold simultaneously maximizing sensitivity and specificity was 0.1745 (sensitivity, 0.672; specificity, 0.679), essentially identical to the proposed cutoff of the original paper (0.178). The proportion of correct classifications was 0.679. Positive and negative predictive values (0.277 and 0.919, respectively) were substantially better than those of random selection based only on event prevalence (0.155 and 0.845), and the number needed to screen was 3.6 (random selection, 6.4).

CONCLUSIONS

A previously published online predictive tool for discharge to rehabilitation or an SNF performed well under external validation, demonstrating a positive predictive value 79% higher and number needed to screen 56% lower than simple random selection. This tool consists of exclusively preoperative parameters that are easily collected. Based on a successful external validation, this tool merits consideration for clinical implementation because of its value for patient counseling, preoperative optimization, and discharge planning.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

The number of stairs into home do not impact discharge disposition and patient reported outcomes after total joint arthroplasty

INTRODUCTION

The purpose of this study is to report on the association between the number of stairs to enter home and length of stay (LOS), discharge disposition, and patient reported outcome measures (PROMs) among patients who underwent primary total joint arthroplasty (TJA).

MATERIALS AND METHODS

We retrospectively reviewed patients who underwent primary total hip or knee arthroplasty between January 2016 and March 2020. Only patients with documentation of the number of stairs to enter their homes were included in the study. The two cohorts were separated into four groups: none, 1-10, 11-20, and > 20 stairs. Collected variables included demographic data, LOS, discharge disposition, and PROMs. Chi-square and ANOVA were utilized to determine significance.

RESULTS

Of the 1116 patients included, 510 underwent THA, and 606 underwent TKA. There was no statistical difference in LOS (THA: p = 0.308; TKA: p = 0.701) and discharge disposition (THA: p = 0.371; TKA: p = 0.484) in both cohorts regardless the number of stairs. There was no statistical difference in FJS-12 scores at 3 months (THA: p = 0.590; TKA: p = 0.206), 12 months (THA: p = 0.217; TKA: p = 0.845), and 21 months (THA: p = 0.782; TKA: p = 0.296) postoperatively for both cohorts. There was no statistical difference in HOOS, JR scores preoperatively (p = 0.278) and at 3 months postoperatively (p = 0.527) for the THA cohort, as well as KOOS, JR scores preoperatively and at 3 and 12 months postoperatively (p = 0.557; p = 0.522; p = 0.747) for the TKA cohort.

CONCLUSION

We found no statistical differences in LOS, discharge disposition, and PROMs in patients who underwent TJA, irrespective of the number of stairs negotiated to enter their home. These findings can aid surgeons to provide preoperative education and reassurance to patients who have concerns with their discharge planning due to the walk-up stairway at their residence.

Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

PURPOSE

Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty.

METHODS

A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis.

RESULTS

A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis.

CONCLUSION

This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients.

LEVEL OF EVIDENCE

IV.

Preoperative Emergency Department Visits Are Predictive of 90-Day Postoperative Emergency Department Visits and Discharge Disposition in Total Knee Arthroplasty Patients

The increasing number of patients undergoing total knee arthroplasty (TKA) has resulted in efforts to better understand patient utilization of healthcare services in the 90-day postoperative period. The primary purpose of this study was to examine whether emergency department (ED) visits in the year prior to elective TKA were predictive of postoperative ED visits in the 90-day global period following surgery. A retrospective chart review was performed for all patients undergoing TKA from June 1, 2011 to December 31, 2015 at a Veterans Affairs hospital. Total number of ED visits in the year prior to surgery and 90 days following surgery were tabulated. Binary and ordinal logistic regression analyses were utilized to determine if preoperative ED visits were predictive of postoperative ED visits. The significance level was set to α = 0.05. Overall, 611 eligible TKA procedures were performed. The logistic regression model for postoperative ED visits was significant (p < 0.001), with the number of preoperative ED visits (1 vs. 0: p < 0.001; 2 vs. 1: p = 0.012) and presence of diabetes (p = 0.007) both predicting the likelihood of a postoperative ED visit. Healthcare changes that are redefining the concept of quality of care to include the postoperative care episode, coupled with an increasingly aging population in need of TKA, will continue to challenge orthopaedic surgeons to provide safe, competent, and cost-effective care to patients. The results of this study demonstrate that a patient's propensity to visit the ED prior to TKA is predictive of a tendency to do so postoperatively and is of use to surgeons when evaluating and counselling patients who will be undergoing a TKA.

Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty

Background

Machine learning (ML) methods have shown promise in the development of patient-specific predictive models prior to surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters following primary total knee arthroplasty (TKA).

Methods

Data from the Nationwide Inpatient Sample was used to identify patients undergoing TKA during 2016-2017. Four distinct ML models predictive of mortality, length of stay (LOS), and discharge disposition were developed and validated using 15 predictive patient and hospital-specific factors. Area under the curve of the receiver operating characteristic curve (AUCROC) and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed.

Results

A total of 305,577 patients were included. For mortality, the XGBoost, neural network (NN), and LSVM models all had excellent responsiveness during validation, while random forest (RF) had fair responsiveness. For predicting LOS, all four models had poor responsiveness. For the discharge disposition outcome, the LSVM, NN, and XGBoost models had good responsiveness, while the RF model had poor responsiveness. LSVM and XGBoost had the highest responsiveness for predicting discharge disposition with an AUCROC of 0.747.

Discussion

The ML models tested demonstrated a range of poor to excellent responsiveness and accuracy in the prediction of the assessed metrics, with considerable variability noted in the predictive precision between the models. The continued development of ML models should be encouraged, with eventual integration into clinical practice in order to inform patient discussions, management decision making, and health policy.

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.

Pre-Operative Predictors for Discharge to Post-Acute Care Facilities After Total Knee Arthroplasty

BACKGROUND

Joint replacement surgery is in increasing demand and is the most common inpatient surgery for Medicare beneficiaries. The venue for post-operative rehabilitation, including early outpatient therapy after surgery, influences recovery and quality of life. As part of a comprehensive total joint program at Kaiser Permanente Colorado, we developed and validated a predictive model to anticipate and plan the disposition for rehabilitation of our patients after total knee arthroplasty (TKA).

METHODS

We analyzed data for TKA patients who completed a pre-operative Total Knee Risk Assessment in 2017 (the model development cohort) or during the first 6 months of 2018 (the model validation cohort). The Total Knee Risk Assessment, which is used to guide disposition for rehabilitation, included questions in mobility, social, and environment domains. Multivariable logistic regression was used to predict discharge to post-acute care facilities (PACFs) (ie, skilled nursing facilities or acute rehabilitation centers).

RESULTS

Data for a total of 1481 and 631 patients who underwent TKA were analyzed in the development and validation cohorts, respectively. Ninety-three patients (6.3%) in the development cohort and 22 patients (3.5%) in the validation cohort were discharged to PACFs. Eight risk factors for discharge to PACFs were included in the final multivariable model. Patients with a diagnosis of neurological disorder and with a mobility/balance issue had the greatest chance of discharge to PACFs.

CONCLUSION

This validated predictive model for discharge disposition following TKA may be used as a tool in shared decision-making and discharge planning for patients undergoing TKA.

Obesity and perioperative outcomes in older surgical patients undergoing elective spine and major arthroplasty surgery

STUDY OBJECTIVE

To determine whether obesity status is associated with perioperative complications, discharge outcomes and hospital length of stay in older surgical patients.

DESIGN

Secondary analysis of five independent study cohorts (N = 1262).

SETTING

An academic medical center between 2001 and 2017 in the United States.

PATIENTS

Patients aged 65 years or older who were scheduled to undergo elective spine, knee, or hip surgery with an expected hospital stay of at least 2 days.

MEASUREMENTS

Body mass index (BMI) was stratified as nonobese (BMI ≤ 30 kg/m2), obesity class 1 (30 kg/m2 ≤ BMI < 35 kg/m2) or obesity class 2-3 (BMI ≥ 35 kg/m2). Primary outcomes included predefined intraoperative and postoperative complications, hospital length of stay (LOS), and discharge location. Univariate and multivariate logistic regression was performed.

MAIN RESULTS

Obesity status was not associated with intraoperative adverse events. However, obesity class 2-3 significantly increased the risk for postoperative complications (IRR 1.43, 95% CI 1.03-1.95, P = 0.03), hospital LOS (IRR 1.13, 95% CI 1.02-1.25, P = 0.02) and non-home discharge destination (OR 1.95, 95% CI 1.35-2.81, P < 0.001) after accounting for patient related factors and surgery type.

CONCLUSIONS

Obesity class 2-3 status has prognostic value in predicting an increased incidence of postoperative complications, increased hospital LOS, and non-home discharge location. These results have important clinical implications for preoperative informed consent and provide areas to target for care improvement for the older obese individual.

Patient factors associated with referral to inpatient rehabilitation following knee or hip arthroplasty in a public sector cohort: A prognostic factor study

RATIONALE, AIMS AND OBJECTIVES

Inpatient rehabilitation following total knee or hip arthroplasty (TKA, THA) is resource intensive and expensive. Understanding who is referred is integral to the discourse concerning service and cost reform. This study aimed to determine patient prognostic factors associated with referral to inpatient rehabilitation following TKA or THA in a public sector setting. In this setting, surgeon or patient choice does not drive referral.

METHOD

Prognostic factor research based on secondary analysis of prospectively collected data. Consecutive people undergo elective, primary TKA, or THA at a high-volume public hospital. The outcome was referral to inpatient rehabilitation after acute care. Patient variables including sociodemographic, comorbidity, and complication details were used in multivariable logistic regression to determine the prognostic factors associated with referral.

RESULTS

Five hundred twenty people were included; 9.2% experienced the outcome. In the multivariable model, acute complications (OR 3.6, 95% CI 1.6-7.8), TKA surgery (OR 3.1, 95% CI 1.0-9.4), renal disease (OR 4.4, 95% CI 1.4-13.3), and higher body mass index (OR 1.1, 95% CI 1.0-1.2) were associated with referral; unilateral surgery (OR 0.1 (95% CI 0.01-0.2) and previous arthroplasty (OR 0.3 (95% CI 0.1-0.8) were protective. There were no significant associations found for sociodemographic factors (such as gender and residential status) in the multivariable model.

CONCLUSION

In the absence of choice, physical impairment and health factors are associated with referral to inpatient rehabilitation following TKA or THA.

National Trends in Post-Acute Care Costs Following Total Knee Arthroplasty From 2007 to 2016

BACKGROUND

Post-acute care continues to represent a target for cost savings with increasing popularity of value-based payment models in total knee arthroplasty (TKA). Rapid recovery and accelerated rehabilitation protocols have been successful in reducing costs at the institutional level, but national trends are less clear. This study aimed to determine if advancements in perioperative care led to a reduction in post-acute care costs and resource utilization following TKA.

METHODS

We reviewed a consecutive series of 79,843 primary TKA patients from the Humana claims dataset from 2007 to 2016. Post-acute care costs included any claims within 90 days of surgery for subacute or inpatient rehabilitation, home health, outpatient or emergency visits, prescription medications, physical therapy, and readmissions. Demographics, episode-of-care and post-acute care costs, readmissions, and discharge disposition were compared. Controlling for demographics and comorbidities, multivariate regression analyses were performed to compare trends in discharge disposition and post-acute care costs.

RESULTS

From 2007 to 2016, the average episode-of-care costs ($46,754 vs $31,856) and post-acute care costs per patient decreased ($20,224 vs $13,498). Rates of discharge to skilled nursing facilities (25.0% vs 22.5%) and inpatient rehabilitation also declined (12.4% vs 2.1%). Readmissions also decreased (8.1% vs 7.1%) saving an average of $324 per patient. When compared to 2007-2012, total costs declined most rapidly after 2013 primarily due to a $3516 (21%) decrease in post-acute spending.

CONCLUSION

There has been a substantial decline in post-acute care costs and resource utilization following TKA, with the largest decrease occurring following the introduction of Medicare bundled payment models in 2013.

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

BACKGROUND

Predictive tools are useful adjuncts in surgical planning. They help guide patient selection, candidacy for inpatient vs outpatient surgery, and discharge disposition as well as predict the probability of readmissions and complications after total joint arthroplasty (TJA). Surgeons may find it difficult due to significant variation among risk calculators to decide which tool is best suited for a specific patient for optimal decision-based care. Our aim is to perform a systematic review of the literature to determine the existing post-TJA readmission calculators and compare the specific elements that comprise their formula. Second, we intend to evaluate the pros and cons of each calculator.

METHODS

Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols protocol, we conducted a systematic search through 3 major databases for publications addressing TJA risk stratification tools for readmission, discharge disposition, and early complications. We excluded those manuscripts that were not comprehensive for hips and knees, did not list discharge, readmission or complication as the primary outcome, or were published outside the North America.

RESULTS

Ten publications met our criteria and were compared on their sourced data, variable types, and overall algorithm quality. Seven of these were generated with single institution data and 3 from large administrative datasets. Three tools determined readmission risk, 5 calculated discharge disposition, and 2 predicted early complications. Only 4 prediction tools were validated by external studies. Seven studies utilized preoperative data points in their risk equations while 3 utilized intraoperative or postsurgical data to delineate risk.

CONCLUSION

The extensive variation among TJA risk calculators underscores the need for tools with more individualized stratification capabilities and verification. The transition to outpatient and same-day discharge TJA may preclude or change the need for many of these calculators. Further studies are needed to develop more streamlined risk calculator tools that predict readmission and surgical complications.

The Effect of a Discharge Disposition Algorithm on Patient Outcomes and Satisfaction

Total joint arthroplasties are one of the most common procedures performed in the United States. As changes have occurred in the surgical techniques of these procedures, postoperative recovery time has decreased and patients have been able to safely transition to home rather than a post-acute care facility. The demand for total joint arthroplasty (TJA) is expected to grow 44% as the prevalence of lower extremity osteoarthritis continues to rise (Sher et al., 2017) because of an aging baby boomer population. In the next 20 years, it is expected that the demand for total hip arthroplasty will grow by 174% and demand for total knee arthroplasty will grow by as much as 670% (Napier et al., 2013). An area with high variability in the postoperative period is in postdischarge rehabilitation. Post-acute inpatient care can account for up to 36% of the bundled costs of a TJA. There is a lack of evidence that patients recover better or have decreased complications by transitioning to an inpatient rehabilitation setting compared with transitioning to home. The aims of this literature search were to (a) identify the safest discharge disposition for patients following TJA; (b) determine the rate of complications and readmissions among those discharged to skilled nursing facility, inpatient rehabilitation unit, and home; and (c) explore how specified care pathways affect patient expectations and outcomes. The Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, ProQuest, and Cochrane were searched using the following key terms: discharge disposition, total joint arthroplasty, joint replacement, hip arthroplasty, knee arthroplasty, care pathway, discharge outcomes and readmissions, discharge protocols, and discharge algorithms. Five key themes emerged. Patients with significant comorbidities may require longer length of stay in the hospital or potentially discharge to a facility, discharge to facility associated with high rate of complications, setting patient expectations increases likelihood of discharge home, discharge to inpatient facilities does not improve outcomes, and discharge to any post-acute care facility is more expensive than discharge to home. This review identified themes in postoperative care of TJA patients that can be utilized to create a discharge disposition algorithm using best practices to stratify patients into the appropriate discharge disposition while setting appropriate expectations for patients undergoing these procedures to ensure high levels of patient satisfaction following these procedures.

A Preoperative Risk Prediction Tool for Discharge to a Skilled Nursing or Rehabilitation Facility After Total Joint Arthroplasty

BACKGROUND

Discharge to rehabilitation or a skilled nursing facility (SNF) after total joint arthroplasty remains a primary driver of cost excess for bundled payments. An accurate preoperative risk prediction tool would help providers and health systems identify and modulate perioperative care for higher risk individuals and serve as a vital tool in preoperative clinic as part of shared decision-making regarding the risks/benefits of surgery.

METHODS

A total of 10,155 primary total knee (5,570, 55%) and hip (4,585, 45%) arthroplasties performed between June 2013 and January 2018 at a single institution were reviewed. The predictive ability of 45 variables for discharge location (SNF/rehab vs home) was tested, including preoperative sociodemographic factors, intraoperative metrics, postoperative labs, as well as 30 Elixhauser comorbidities. Parameters surviving selection were included in a multivariable logistic regression model, which was calibrated using 20,000 bootstrapped samples.

RESULTS

A total of 1786 (17.6%) cases were discharged to a SNF/rehab, and a multivariable logistic regression model demonstrated excellent predictive accuracy (area under the receiver operator characteristic curve: 0.824) despite requiring only 9 preoperative variables: age, partner status, the American Society of Anesthesiologists score, body mass index, gender, neurologic disease, electrolyte disorder, paralysis, and pulmonary circulation disorder. Notably, this model was independent of surgery (knee vs hip). Internal validation showed no loss of accuracy (area under the receiver operator characteristic curve: 0.8216, mean squared error: 0.0004) after bias correction for overfitting, and the model was incorporated into a readily available, online prediction tool for easy clinical use.

CONCLUSION

This convenient, interactive tool for estimating likelihood of discharge to a SNF/rehab achieves excellent accuracy using exclusively preoperative factors. These should form the basis for improved reimbursement legislation adjusting for patient risk, ensuring no disparities in access arise for vulnerable populations.

LEVEL OF EVIDENCE

III.

Preoperative malnutrition is a risk factor for delayed recovery of mobilization after total hip arthroplasty

INTRODUCTION

Many studies have examined nutritional status and deteriorated postoperative outcomes in patients undergoing total hip arthroplasty. However, few studies have focused on nutritional status and postoperative mobility.

OBJECTIVE

To investigate the impact of preoperative nutritional status on mobility after total hip arthroplasty.

DESIGN

Retrospective single-institution cohort study.

SETTING

Orthopedic inpatient rehabilitation center.

PARTICIPANTS

A total of 503 patients who underwent unilateral primary total hip arthroplasty from 2015 through 2019 were included.

METHODS

Data were collected on patient demographics, comorbidities, preoperative nutritional status, and quadriceps strength. Nutritional status was assessed using the Controlling Nutritional Status (CONUT) score.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

The primary outcome was postoperative mobility defined as the number of days from surgery to starting to walk independently.

RESULTS

Among 503 patients undergoing total hip arthroplasty, 18.9% were classified as malnourished. Patients with malnutrition had a one-day delay in achieving mobilization compared with patients with normal nutrition (6 vs. 5 days, P = .006). According to the Kaplan-Meier curves, patients with malnutrition had a significant delay in mobilization compared with those with normal nutrition (P < .001). All three Cox proportional hazards regression models showed that preoperative malnutrition was associated with a higher risk of delayed mobilization (hazard ratios 0.70-0.74).

CONCLUSIONS

Preoperative malnutrition as assessed by the CONUT is a significant risk factor for delayed recovery of mobilization after total hip arthroplasty.

Bleeding and thromboembolism risk of standard antithrombotic prophylaxis after hip or knee replacement within an enhanced recovery program

INTRODUCTION

Prevention of venous thromboembolism (VTE) generally consists of low molecular weight heparin (LMWH) or direct oral anticoagulants (DOACs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA). Enhanced recovery after surgery (ERAS) protocols may reduce the VTE risk after these procedures. The aim of this study was to compare the risk of VTE and the risk of bleeding complications in a non-selected population of primary THA and TKA cases done within an ERAS protocol.

HYPOTHESIS

The risk of postoperative VTE after primary THA and TKA is lower than the risk of bleeding complications within an ERAS protocol.

METHODS

This was a prospective observational study conducted on a national scale in France. All patients who underwent primary unilateral THA or TKA at one of 11 participating hospitals between October 2016 and October 2017 were enrolled and followed for 3 months. The occurrence of a VTE or major bleeding event was recorded. No patients were lost to follow-up at 3 months.

RESULTS

Of the 1110 THA cases, there were 5 VTE (0.4%) and 19 bleeding events (1.7%). Of the 893 TKA cases, there were 9 VTE (1.0%) and 14 bleeding events (1.7%). There was no significant difference in the VTE incidence and bleeding event incidence after THA and TKA. The overall incidence of bleeding complications (1.7%) was significantly greater than the overall incidence of VTE complications (0.7%) (p=0.005). This was the case after THA (p=0.004) but not after TKA.

DISCUSSION

The primary finding of this study is that bleeding complications are significantly more common than VTE complications after THA or TKA within an ERAS protocol. Based on these findings, the cost-benefit ratio of antithrombotic prophylaxis by LMWH or DOACs in this context should be reassessed.

LEVEL OF EVIDENCE

IV; Prospective cohort study without control group.

Fast track protocol for primary total hip arthroplasty in non-trauma cases reduces the length of hospital stay: Prospective French multicenter study

BACKGROUND

Fast-track (FT) procedures continue to evolve; however, their benefits are still controversial. This led us to conduct a prospective study of FT procedures for total hip arthroplasty (THA) on a national scale in France with historical control data. The aims were to (1) evaluate the effectiveness of FT procedures after THA on the length of hospital stay (LOS) in a multicenter analysis, (2) measure the immediate return to home, rehospitalization and reoperation rates.

HYPOTHESIS

FT procedures reduce the LOS after primary THA for non-traumatic indications relative to national historical data.

METHODS

A prospective observational study was done at 11 hospital facilities throughout France. Patients who underwent primary THA for a non-traumatic condition and FT procedures were followed for 3 months. The average LOS, discharge to home, unexpected readmissions, and reoperation rate were compared to 2016 figures from the French national database of 104,745 procedures on the same population.

RESULTS

The study included 1,110 patients, 499 men (45%) and 611 women (55%), with a mean age of 67.5±11.9 years. The average LOS was 3.3±2.9 days versus 7.5±5.3 days in the national database (p<0.001). Eight hundred eighty patients (79%) were discharged directly to home versus 72,577 (69%) in the national database (p<0.001). Forty-two patients (4%) were readmitted to the hospital within 90 days of the THA versus 11,092 (11%) in the national database (p<0.001). Eighteen patients (1.6%) were reoperated within 90 days of the THA procedure versus 2100 (2.0%) in the national database (p=0.72).

DISCUSSION

FT procedures help to significantly reduce the average LOS and rehospitalization rate after primary THA for non-traumatic conditions and significantly increased the percentage of patients being discharged directly to home relative to national historical data, without altering the risk of reoperation. FT procedures should become the standard of care after THA.

LEVEL OF EVIDENCE

III; prospective case-control study.

An Electronic Medical Record-Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

Background

Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm’s diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA.

Methods

This study was a retrospective cohort study from a single, tertiary referral center for primary TJA. We trained and validated an artificial neural network (ANN) based on 4368 distinct surgical encounters between 1/1/2013 and 6/28/2016. The ANN’s ability to identify discharge disposition was then tested on 1452 distinct surgical encounters between 1/3/17 and 11/30/17.

Results

The area under the curve and accuracy achieved during model validation were 0.973 and 91.7%, respectively, with 25% of patients being discharged to skilled nursing facilities (SNFs). Within our testing data set, 6.7% of patients went to SNFs. The performance in the testing set included an area under the curve of 0.804, accuracy of 61.3%, sensitivity of 28.9%, and specificity of 93.8%.

Conclusions

This is the first prediction tool using an electronic medical record–integrated ANN to predict discharge disposition after TJA based on locally generated data. Dramatically reduced numbers of patients discharged to SNFs due to implementation of a bundled payment model lead to poor recall in the testing model. This model serves as a proof of concept for developing an ML prediction tool using a relatively small data set and subsequent integration into the electronic medical record.

Postacute Care Utilization in Postsurgical Orthogeriatric Hip Fracture Care

INTRODUCTION

Among surgical patients, utilization of institutional-based postacute care (PAC) presents a notable financial burden and is associated with increased risk of complications and mortality rates when compared with discharge home. The purpose of this study was to identify predictors of postdischarge disposition to PAC in geriatric patients after surgical fixation of native hip fractures.

METHODS

We have done a query of the American College of Surgeons National Surgical Quality Improvement Program to identify geriatric (≥65 years) patients who sustained surgical femoral neck, intertrochanteric, and subtrochanteric hip fractures in 2016. Multivariate regression was used to compute risk factors for discharge to and prolonged stay (>30 days) in PAC.

RESULTS

Eight thousand one hundred thirty-three geriatric hip fracture patients with sufficient follow-up data were identified. Of these, 6,670 patients (82.0%) were initially discharged to PAC after their hip fracture episode of care, and 2,986 patients (36.7%) remained in PAC for >30 days. Age (odds ratio [OR] 1.06 [1.05 to 1.08], P < 0.001), partial (OR 2.41 [1.57 to 3.71], P < 0.001) or total dependence (OR 3.03 [1.92 to 4.46], P < 0.001) for activities of daily living, dementia (OR 1.62 [1.33 to 1.96], P < 0.001), diabetes (OR 1.46 [1.14 to 1.85], P = 0.002), hypertension (OR 1.32 [1.10 to 1.58], P = 0.002), and total hospital length of stay (OR 1.04 [1.01 to 1.08], P = 0.006) were independent risk factors for discharge to PAC. Age (OR 1.05 [1.04 to 1.06], P < 0.001), partial (OR 2.86 [1.93 to 3.79], P < 0.001) or total dependence (OR 3.12 [1.45 to 4.79], P < 0.001) for activities of daily living, American Society of Anesthesiologist's classification (OR 1.27 [1.13 to 1.43], P < 0.001), dementia (OR 1.49 [1.28 to 1.74], P < 0.001), and total hospital length of stay (OR 1.10 [1.08 to 1.13], P < 0.001) were independent risk factors for prolonged PAC stay >30 days.

DISCUSSION

Discharge to PAC is the norm among patients undergoing hip fracture surgery. Provider foreknowledge of risk factors may help improve hip fracture outcomes and decrease healthcare costs.

Prediction of Disposition Within 48 Hours of Hospital Admission Using Patient Mobility Scores

Delayed hospital discharges for patients needing rehabilitation in a postacute setting can exacerbate hospital-acquired mobility loss, prolong functional recovery, and increase costs. Systematic measurement of patient mobility by nurses early during hospitalization has the potential to help identify which patients are likely to be discharged to a postacute care facility versus home. To test the predictive ability of this approach, a machine learning classification tree method was applied retrospectively to a diverse sample of hospitalized patients (N = 761) using training and validation sets. Compared with patients discharged to home, patients discharged to a postacute facility were older (median, 64 vs 56 years old) and had lower mobility scores at hospital admission (median, 32 vs 41). The final decision tree accurately classified the discharge location for 73% (95% CI, 67%-78%) of patients. This study emphasizes the value of systematically measuring mobility in the hospital and provides a simple decision tree to facilitate early discharge planning.

Development and Validation of a Model for Predicting Rehabilitation Care Location Among Patients Discharged Home After Total Knee Arthroplasty

BACKGROUND

Demand for joint replacement is increasing, with many patients receiving postsurgical physical therapy (PT) in non-inpatient settings. Clinicians need a reliable tool to guide decisions about the appropriate PT setting for patients discharged home after surgery. We developed and validated a model to predict PT location for patients in our health system discharged home after total knee arthroplasty.

METHODS

We analyzed data for patients who completed a preoperative total knee risk assessment in 2017 (model development cohort) or during the first 6 months of 2018 (model validation cohort). The initial total knee risk assessment, to guide rehabilitation disposition, included 28 variables in mobility, social, and environment domains, and on patient demographics and comorbidities. Multivariable logistic regression was used to identify factors that best predict discharge to home health service (HHS) vs home with outpatient PT. Model performance was assessed by standard criteria.

RESULTS

The development cohort included 259 patients (19%) discharged to HHS and 1129 patients (81%) discharged to home with outpatient PT. The validation cohort included 609 patients, with 91 (15%) discharged to HHS. The final model included age, gender, motivation for outpatient PT, and reliable transportation. Patients without motivation for outpatient PT had the highest probability of discharge to HHS, followed by those without reliable transportation. Model performance was excellent in the development and validation cohort, with c-statistics of 0.91 and 0.86, respectively.

CONCLUSION

We developed and validated a predictive model for total knee arthroplasty PT discharge location. This model includes 4 variables with accurate prediction to guide patient-clinician preoperative decision making.

Fast-track procedures after primary total knee arthroplasty reduce hospital stay by unselected patients: a prospective national multi-centre study

PURPOSE

The aim of this study was to evaluate the impact of fast-track procedures (FTPs) on length of hospital stay after primary total knee arthroplasty (TKA) in a prospective, national, multicentric analysis. The innovative point was that no patient selection was used. The hypothesis was that FTPs reduce hospital stay after primary TKA for non-traumatic conditions compared with the national database.

METHODS

An observational prospective study was conducted in ten centres throughout France. A total of 839 patients included in FTPs were followed up for three months. The average LOS, direct return home rate, unscheduled re-admission rate, and re-intervention rate were compared with those in the national database (93,329 TKAs). Knee society and Oxford score were collected.

RESULTS

The mean LOS was 4.4 ± 3.3 days, while the national base LOS was 6.4 ± 3.1 days (p < 0.001). A total of 560 patients (66.7%) were able to return home, compared with 47,617 (49.6%) in the national database (p < 0.001). Thirty-five patients (4.2%) were re-admitted within 90 days of the intervention, compared with 10,399 (10.8%) in the national database (p < 0.001). Seventeen patients (2.0%) were re-operated upon within 90 days after the TKA, compared with 529 (0.5%) in the national database (p < 0.05).

CONCLUSION

The FTPs used by unselected patients allowed a significant decrease in the mean LOS and in the rate of re-admission and a significant increase of the rate of direct home return after primary TKA compared with the national database. The significant increase in the re-operation rate warrants further investigation. However, FTP should become the standard of care after this intervention.

Risk factors for manipulation under anaesthesia after total knee arthroplasty

Aims

Postoperative range of movement (ROM) is an important measure of successful and satisfying total knee arthroplasty (TKA). Reduced postoperative ROM may be evident in up to 20% of all TKAs and negatively affects satisfaction. To improve ROM, manipulation under anaesthesia (MUA) may be performed. Historically, a limited ROM preoperatively was used as the key harbinger of the postoperative ROM. However, comorbidities may also be useful in predicting postoperative stiffness. The goal was to assess preoperative comorbidities in patients undergoing TKA relative to incidence of postoperative MUA. The hope is to forecast those who may be at increased risk and determine if MUA is an effective form of treatment.

Methods

Prospectively collected data of TKAs performed at our institution’s two hospitals from August 2014 to August 2018 were evaluated for incidence of MUA. Comorbid conditions, risk factors, implant component design and fixation method (cemented vs cementless), and discharge disposition were analyzed. Overall, 3,556 TKAs met the inclusion criteria. Of those, 164 underwent MUA.

Results

Patients with increased age and body mass index (BMI) had decreased likelihood of MUA. For every one-year increase in age, the likelihood of MUA decreased by 4%. Similarly, for every one-unit increase in BMI the likelihood of MUA decreased by 6%. There were no differences in incidence of MUA between component type/design or fixation method. Current or former smokers were more likely to have no MUA. Surprisingly, patients discharged to home health service or skilled nursing facility were approximately 40% and 70% less likely than those discharged home with outpatient therapy to be in the MUA group. MUA was effective, with a mean increased ROM of 32.81° (SD 19.85°; -15° to 90°).

Conclusion

Younger, thinner patients had highest incidence of MUA. Effect of discharge disposition on rate of MUA was an important finding and may influence surgeons’ decisions. Interestingly, use of cement and component design (constraint) did not impact incidence of MUA. Level of Evidence II: Prospective cohort study. Cite this article: Bone Joint J 2020;102-B(6 Supple A):66–72.

Prospective evaluation and comparison of the predictive ability of different frailty scores to predict outcomes in geriatric trauma patients

BACKGROUND

Different frailty scores have been proposed to measure frailty. No study has compared their predictive ability to predict outcomes in trauma patients. The aim of our study was to compare the predictive ability of different frailty scores to predict complications, mortality, discharge disposition, and 30-day readmission in trauma patients.

METHODS

We performed a 2-year (2016-2017) prospective cohort analysis of all geriatric (age, >65 years) trauma patients. We calculated the following frailty scores on each patient; the Trauma-Specific Frailty Index (TSFI), the Modified Frailty Index (mFI) derived from the Canada Study of Health and Aging, the Rockwood Frailty Score (RFS), and the International Association of Nutrition and Aging 5-item a frailty scale (FS). Predictive models, using both unadjusted and adjusted logistic regressions, were created for each outcome. The unadjusted c-statistic was used to compare the predictive ability of each model.

RESULTS

A total of 341 patients were enrolled. Mean age was 76 ± 9 years, median Injury Severity Score was 13 [9-18], and median Glasgow Coma Scale score was 15 [12-15]. The unadjusted models indicated that both the TSFI and the RFS had comparable predictive value, as indicated by their unadjusted c-statistics, for mortality, in-hospital complications, skilled nursing facility disposition and 30-day readmission. Both TSFI and RFS models had unadjusted c-statistics indicating a relatively strong predictive ability for all outcomes. The unadjusted mFI and FS models did not have a strong predictive ability for predicting mortality and in-hospital complications. They also had a lower predictive ability for skilled nursing facility disposition and 30-day readmissions.

CONCLUSION

There are significant differences in the predictive ability of the four commonly used frailty scores. The TSFI and the RFS are better predictors of outcomes compared with the mFI and the FS. The TSFI is easy to calculate and might be used as a universal frailty score in geriatric trauma patients.

LEVEL OF EVIDENCE

Prognostic, level III.

Determinants of Cost Variation in Total Hip and Knee Arthroplasty: Implications for Alternative Payment Models

BACKGROUND

Alternative payment models have been proposed to deliver high-quality, cost-effective care. Under these models, payments may be shared between the hospital and the post-acute care services. Post-acute care services may account for one-third of the episode costs for total hip or knee arthroplasty (THA/TKA). Because hospitals or episode initiators bear notable financial risks in these payment models with minimal risk adjustment for complexity, it has been suggested these models may lead to prospective selection of healthier and younger patients. Studies evaluating the effect of patient demographics, medical complexity, and surgical characteristics on the cost of index hospitalization have been limited. We aimed to (1) quantify the impact of patient demographics, medical complexity, and surgical characteristics (type of anesthesia and operating time) on variation in direct cost of index hospitalization and (2) examine the association of these characteristics with discharge with home health services or to rehabilitation facility.

METHODS

Retrospective study of 3,542 patients admitted to our hospital for elective THA/TKA between 2012 and 2017. Multivariable generalized estimating equations were used for analysis.

RESULTS

Patient demographics and medical complexity accounted for 6.2% (THA) and 5.6% (TKA) of variation in direct cost of index hospitalization. Surgical characteristics accounted for 37.1% (THA) and 35.3% (TKA) of the cost variation. One thousand one hundred eighty-three (53.4%) patients were discharged with home health services, and 1,237 (29.4%) were discharged to rehabilitation facility. Patient demographics and higher medical complexity were markedly associated with discharge with home health services or to rehabilitation facility after THA/TKA.

DISCUSSION

Patient demographics and medical complexity had minimal impact on variation in direct cost of index hospitalization for elective THA/TKA compared with surgical characteristics but were markedly associated with discharge with home health services or to rehabilitation facility. Having additional risk adjustment in these payment models could mitigate concerns about access to care for higher risk, higher cost patients.

The Role of Social Support and Psychological Distress in Predicting Discharge: A Pilot Study for Hip and Knee Arthroplasty Patients

BACKGROUND

Bundled payment initiatives for joint replacement have prompted re-evaluation of the continuum of care with emphasis on anticipating disposition needs. The purpose of this study is to investigate the role of social support and psychological distress in patient optimization after lower joint replacement.

METHODS

Two hundred thirty-one patients undergoing elective joint replacement completed the Risk Assessment and Predictive Tool (RAPT) (social support assessment) and modified STarT Back Tool (mSBT) (assessment of pain-related psychological distress). Outcomes of interest were length of stay (LOS) and discharge location (home vs facility).

RESULTS

No significant differences in mSBT scores were observed across RAPT levels when comparing individuals by discharge location (P > .05). There was significant indirect effect (0.07; P < .001) between mSBT and LOS. Therefore, the mSBT does not predict discharge location as a standalone metric for this sample. Mediation analysis for LOS indicates that higher psychological distress was predictive of longer LOS. Higher psychological distress and lower social support are associated with longer LOS. Despite higher psychological distress scores, higher social support scores are associated with shorter LOS.

CONCLUSION

Analysis of this cohort suggests that pre-operative assessments of social and psychological constructs may provide preparatory information for patient discharge status. The RAPT is important for predicting LOS and discharge location. The mSBT may be important for predicting LOS for individuals with low to moderate social support.

Order of Total Hip or Total Knee Arthroplasty Does Not Affect Length of Stay or Discharge Disposition in Patients With Coexisting Hip and Knee Arthritis

Primary total joint arthroplasty (TJA) of the hip and knee are effective procedures for improving pain and function in patients with arthritis. This study examined whether order of surgery (TKA or THA first) affects length of stay (LOS) and discharge disposition among patients with coexisting knee and hip arthritis. A total joint arthroplasty database review was performed to collect all available data for arthroplasties performed at 2 campuses of a single institution between July 2013 and April 2017. Inclusion criteria were patients who underwent both primary THA and TKA within 18 months and were age 18 years or older. Patients were divided into 2 groups based on whether THA or TKA was performed first. For all procedures, the following data were collected: age, body mass index (BMI), time between cases, LOS, discharge disposition, and the number of 90-day adverse postoperative events. Adverse 90-day events included deep infection, fracture, hardware failure, urinary tract infection, other return to the operating room, emergency department visit, readmission, or death. A total of 211 patients underwent both THA and TKA within 18 months; 124 patients underwent THA first and 87 underwent TKA first. There was no difference in age or BMI between the 2 groups. There was a significantly longer time between the first and second arthroplasty in patients with TKA first by a mean of 2 months (P=.001). There was no difference in 90-day adverse postoperative events following THA whether done first or second (P=.371), and no difference in 90-day events following TKA whether done first or second (P=.524). There was no difference in discharge disposition (P=.833 and P=.395) or LOS (P=.695 and P=.473) between groups for the first or second procedure, respectively. In a patient with coexisting hip and knee arthritis, the current results do not support recommending THA or TKA first based on cost related to LOS and discharge disposition. [Orthopedics. 2019; 42(6):e528-e531.].

Factors Affecting Discharge Disposition After Primary Simultaneous Bilateral Joint Arthroplasty

BACKGROUND

Predicting discharge destination following total joint arthroplasty (TJA) is important, as discharge destination has major implications for post-operative costs, clinical outcomes, and readmissions. Few studies have looked at factors affecting discharge destination for patients following primary simultaneous bilateral total joint arthroplasty.

QUESTIONS/PURPOSES

The purpose of this study was to describe clinical and social factors that relate to a discharge to home versus rehabilitation facility (RF) for patients after primary simultaneous bilateral total hip arthroplasty (PSBTHA) or primary simultaneous bilateral total knee arthroplasty (PSBTKA).

METHODS

The inclusion criteria for this retrospective cohort study were all patients after PSBTHA or PSBTKA at a metropolitan orthopedic specialty hospital between February 1, 2016, and March 31, 2018. Exclusion criteria were revisions, differing weight-bearing status, bed-rest orders, and non-standard hip precautions. Social and clinical demographic data were collected. Multiple regression analysis was conducted to determine which factors related most to discharge plan.

RESULTS

Of 253 PSBTHA patients, 153 were discharged home and 100 to an RF. Regression analysis found a posterolateral approach to be the only significant factor associated with an RF discharge. Of 619 PSBTKA patients, 136 were discharged home and 483 to RF. Increased body mass index and older age increased the likelihood of discharge to an RF. Patients with an adductor-only nerve block were more likely to be sent to RF.

CONCLUSION

These findings shed light on the variables that contribute to discharge destination after PSBTHA and PSBTKA. Such information allows for safe pre-operative discharge planning and assists with discharge to the appropriate level of patient care. Future studies might investigate the effect of surgeon experience, surgical technique, and pre-operative discussion with a physical therapist on the discharge disposition of patients undergoing bilateral total joint arthroplasty.

The Effect of Length of Stay and Discharge Disposition on Hospital Consumer Assessment of Healthcare Providers and Systems Scores in Orthopaedic Patients

BACKGROUND

The study assesses whether Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores were influenced by hospital length of stay (LOS) and discharge disposition.

METHODS

HCAHPS scores from 5,682 orthopaedic patients were collected over a 4-year period. Statistical analyses were run to identify associations between Top-Box scores for each HCAHPS domain and LOS or discharge disposition (home versus rehabilitation facility).

RESULTS

Decreased LOS was associated with increased HCAHPS Top-Box scores for every Top-Box domain except for Discharge composite (P ≤ 0.001 to 0.011). Discharge to home was associated with increased HCAHPS scores for four Top-Box domains (P ≤ 0.001 to 0.009).

DISCUSSION

Shorter LOS and discharge to home after orthopaedic surgery are associated with better HCAHPS scores. Earlier discharge leads to an improved patient-reported experience and can increase reimbursements. Expedient, appropriate discharge of hospitalized orthopaedic patients should be a treatment goal after orthopaedic surgery.

Preoperative Patient Functional Status Is an Independent Predictor of Outcomes After Primary Total Hip Arthroplasty

This study was designed to determine whether preoperative functional status of patients with osteoarthritis predicts outcomes after primary total hip arthroplasty. The American College of Surgeons National Surgical Quality Improvement Program database was queried for records of patients who underwent primary total hip arthroplasty for a principal diagnosis of osteoarthritis from 2009 to 2013 (N=43,179). Patients were categorized as dependent or independent according to their preoperative functional status. The groups were compared regarding several potential confounders using Student's t and chi-square tests. Logistic and Poisson regression models (inclusion threshold of P<.1) were used to assess the associations of functional status with outcomes. The alpha level was set at 0.05. Compared with independent patients, dependent patients were likely to be older (mean, 70 vs 66 years, P<.01) and to have more preoperative comorbidities. After controlling for potential confounders, preoperative dependent functional status was predictive of major complications (odds ratio, 2.34; 95% confidence interval, 1.67-3.28), nonroutine discharge (odds ratio, 2.80; 95% confidence interval, 2.35-3.34), and longer hospital stay (incidence risk ratio, 1.19; 95% confidence interval, 1.12-1.27). Rates of unplanned reoperation were similar between groups on multivariate analysis. Compared with preoperative independent functional status, preoperative dependent functional status was independently associated with worse outcomes after primary total hip arthroplasty for osteoarthritis. [Orthopedics. 2019; 42(3):e326-e330.].

Race, Bundled Payment Policy, and Discharge Destination After TKA: The Experience of an Urban Academic Hospital

Background

Total knee arthroplasty (TKA) provides good clinical outcomes for the treatment of end-stage osteoarthritis; however, discharge destination after TKA has major implications on postoperative adverse outcomes and readmissions. With the initiation of Bundled Payments for Care Improvement (BPCI), it is unclear how racial disparities in discharge destination after TKA will be affected by the new bundled payment for TKA.

Methods

Bundled Payments for Care Improvement was implemented in July 01, 2014, at the University of Pennsylvania. We compared differences during early implementation (July 1, 2014, to, March 30, 2016) and during late policy implementation (April 1, 2016, to February 28, 2017) in patient characteristics (including race: African American [AA], white, and other race), discharge destination (skilled nursing facility [SNF], inpatient rehabilitation facility, home, home with home health, or other), and outcomes.

Results

We identified 2276 patients who underwent TKA (43.8% AA, 48.2% white, and 8.0% other race). African American patients were more likely to be discharged to SNF as opposed to home than white patients both during the early BPCI (AA: 53.0%, n = 320; white: 32.4%, n = 210, P < .05) and late BPCI implementation (AA: 44.4%, n = 169, white: 26.9%, n = 120, P < .05), though all races showed trends to decreasing SNF use during the late BPCI implementation.

Discussion

There were no significant differences in readmissions, length of stay, mortality, or intensive care unit days during early and late implementation of BPCI or when AA patients were compared to white patients.

Conclusion

We found no significant changes in racial variations in discharge destination and outcomes after elective TKA. Bundled Payments for Care Improvement has encouraged better preoperative preparation of patients and discharge planning; however, payment reforms alone might not be sufficient to address variation in post-op management following elective surgery.

Does Discharge Destination Matter after Total Knee Arthroplasty? A Single-Institution Korean Experience

Purpose

To compare extended care facility (ECF) and home as discharge destination after total knee arthroplasty (TKA) at a single high-volume tertiary center in South Korea.

Materials and Methods

We retrospectively analyzed 1,120 primary TKAs (614 patients) performed between January 2012 and December 2013. A telephonic survey was conducted to determine discharge destination. The data reviewed included demographic and surgical data, functional outcome at 2 years and complications within 3 months.

Results

ECF and home received 316 patients (51%) and 298 patients (49%), respectively. The ECF group had more bilateral TKA patients than the home group (272 vs. 234; p=0.014) and more patients with additional hospital stay (44 vs. 22; p=0.009). A higher tendency of complications was seen at home (n=8, 2.7%) than the ECF (n=2, 0.6%) (p=0.057). No significant differences were found in any functional outcome measure. Home patients had better patient satisfaction than ECF patients (81.9% vs. 54.3%; p<0.001).

Conclusions

Patients who returned home after discharge had similar functional outcome at 2 years after surgery and higher patient satisfaction than those in the ECF in spite of the higher tendency of complications. Patients need adequate counseling and education regarding advantages and limitations of the two discharge destinations.

Predicting patients requiring discharge to post-acute care facilities following primary total hip replacement: Does anesthesia type play a role?

STUDY OBJECTIVE

We sought to develop a predictive model for discharge to post-acute care facilities in patients undergoing unilateral total hip replacement (THR). Furthermore, we sought to determine if the use of neuraxial anesthesia was an important covariate for the predictive model.

DESIGN

Retrospective observational study.

SETTING

Preoperative care and operating room at a single institution.

PATIENTS

Patients (n = 960) who underwent an elective primary THR between 2014 and 2016.

INTERVENTIONS

No intervention was performed.

MEASUREMENTS

We collected variables that were known preoperatively including age, sex, body mass index (BMI), preoperative opioid use, functional status based on metabolic equivalents (METS), preoperative anemia, thrombocytopenia, osteoarthritis and contralateral osteoarthritis grade, anesthesia type, comorbidities and surgical approach. We then performed multivariable logistic regression to develop a predictive model.

MAIN RESULTS

Female sex, preoperative opioid use, older age, general anesthesia, anemia, hypertension, a psychiatric diagnosis, use of dialysis, metabolic equivalents <4 and obesity are all risk factors for a post-acute facility discharge. The use of general anesthesia compared to neuraxial anesthesia was associated with increased odds (odds ratio 1.98, 95% confidence interval 1.31-3.00, p = 0.001) for post-acute facility discharge. Model performance was assessed using ten-fold cross-validation - the average area under the receiver operating characteristic curve calculated was 0.794.

CONCLUSIONS

We developed a predictive model for post-acute care facility discharge following THR. The use of neuraxial anesthesia was associated with decreased odds for post-acute care facility discharge.

Discharge to Inpatient Rehab Does Not Result in Improved Functional Outcomes Following Primary Total Knee Arthroplasty

BACKGROUND

Monitored rehabilitation has long been considered an essential part of the recovery process in total knee arthroplasty (TKA). However, the optimal setting for rehabilitation remains uncertain. We sought to determine whether inpatient rehabilitation settings result in improved functional and patient-reported outcomes after primary TKA.

METHODS

All patients undergoing primary TKA from May 2007 to February 2011 were identified from our institutional total joint registry. Propensity score matching was then performed, resulting in a final cohort of 1213 matched pairs for discharge destination to either home or a rehabilitation facility (inpatient rehab or skilled nursing facility). Length of stay, need for manipulation, 6-month complications, and 2-year Western Ontario and McMaster Universities Osteoarthritis Index, Lower Extremity Activity Scale, 12-item Short Form Health Survey, and Hospital for Special Surgery knee expectations surveys were compared.

RESULTS

Patients discharged to a rehab facility were noted to have a shorter hospital length of stay (5.0 vs 5.4 days). Patients discharged to inpatient rehabilitation reported more fractures at 6 months postoperatively. However, no differences in manipulation rates, 2-year outcome scores, or changes in outcome scores were found between the 2 groups.

CONCLUSION

Inpatient rehabilitation settings did not result in lower complications at 6 months or improved functional or patient-reported outcomes at 2 years compared to discharge directly to home when patients are propensity matched for age, living situation, comorbidities, baseline functional status, and insurance status. This finding has important cost implications and calls into question whether the healthcare system should allow otherwise healthy patients to use inpatient rehabilitation services postoperatively after primary TKA.

Do illness rating systems predict discharge location, length of stay, and cost after total hip arthroplasty?

BACKGROUND

As procedure rates and expenditures for total hip arthroplasty (THA) rise, hospitals are developing models to predict discharge location, a major determinant of total cost. The predictive value of existing illness rating systems such as the American Society for Anesthesiologists (ASA) Physical Classification System, Severity of Illness (SOI) scoring system, or Mallampati (MP) rating scale on discharge location remains unclear. This study explored the predictive role of ASA, SOI, and MP scores on discharge location, lengths of stay, and total costs for THA patients.

METHODS

A retrospective analysis of patients undergoing elective primary or revision THA was conducted at a single institution. Multivariable regressions were utilized to assess the significant predictive factors for lengths of stay, total costs, and discharge to skilled nursing facilities (SNFs), rehabilitation centers, and home. Controls included demographic factors, insurance coverage, and the type of procedure.

RESULTS

ASA scores ≥3 are the only significant predictors of discharge to SNFs (odds ratio [OR] = 1.69, confidence interval [CI] = 1.04-2.74) and home (OR = 0.57, CI = 0.34-0.98). Medicaid coverage (OR = 2.61, CI = 1.37-4.96) and African-American race (OR = 2.60, CI = 1.59-4.25) were additional significant predictors of discharge to SNF. SOI scores are the only significant predictors of length of stay (β = 1.36 days, CI = 0.53-2.19) and total cost for an episode (β = $6,234, CI = $3577-$8891). MP scores possess limited predictive power over lengths of stay only.

CONCLUSIONS

These findings suggest that although ASA classifications predict discharge location and SOI scores predict length of stay and total costs, other factors beyond illness rating systems remain stronger predictors of discharge for THA patients.

Who Goes to Inpatient Rehabilitation or Skilled Nursing Facilities Unexpectedly Following Total Knee Arthroplasty?

BACKGROUND

Inpatient rehabilitation facilities (IRFs) and skilled nursing facilities (SNFs) represent a significant portion of post-operative expenses of bundled payments for total knee arthroplasty (TKA). Although many surgeons no longer routinely send patients to IRFs or SNFs, some patients are unable to be discharged directly home. This study identified patient factors for discharge to post-acute care facilities with an institutional protocol of discharging TKA patients home.

METHODS

A retrospective review of patients undergoing primary unilateral TKA at a single institution from 2012 to 2017 was performed. All surgeons discharged patients home as a routine protocol. An electronic query followed by manual review identified discharge disposition, demographic factors, co-morbidities, and other patient factors. In total, 2281 patients were identified, with 9.6% discharged to SNFs or IRFs and 90.4% discharged home. Univariate and multivariate analyses were conducted to create 2 predictive models for patient discharge: pre-operative visit and hospital course.

RESULTS

Among 43 variables studied, 6 were found to be significant pre-operative risk factors for a discharge disposition other than home. In descending order, age 75 or greater, female, non-Caucasian race, Medicare status, history of depression, and Charlson Comorbidity Index were predictors for patients going to IRFs. In addition, any in-hospital complications led to a higher likelihood of being discharged to IRFs and SNFs. Both models had excellent predictive assessments with area under curve values of 0.79 and 0.80 for pre-operative visit and hospital course.

CONCLUSION

This study identifies pre-operative and in-hospital factors that predispose patients to non-routine discharges, which allow surgeons to better predict patient post-operative disposition.

Using Illness Rating Systems to Predict Discharge Location Following Total Knee Arthroplasty

Purpose

Total knee arthroplasty (TKA) is increasing in frequency and cost. Optimization of discharge location may reduce total expenditure while maximizing patient outcomes. Although preoperative illness rating systems—including the American Society for Anesthesiologists Physical Classification System (ASA), severity of illness scoring system (SOI), and Mallampati rating scale (MP)—are associated with patient morbidity and mortality, their predictive value for discharge location, length of stay (LOS), and total costs remains unclear.

Materials and Methods

We conducted a retrospective analysis of 677 TKA patients (550 primary and 127 revision) treated at a single institution. The influence of ASA, SOI, and MP scores on discharge locations, LOS, and total costs was assessed using multivariable regression analyses.

Results

None of the systems were significant predictors of discharge location following TKA. SOI scores of major or higher (β=2.08 days, p<0.001) and minor (β=−0.25 days, p=0.009) significantly predicted LOS relative to moderate scores. Total costs were also significantly predicted by SOI scores of major or higher (β=$6,155, p=0.022) and minor (β=−$1,163, p=0.007).

Conclusions

SOI scores may be harnessed as a predictive tool for LOS and total costs following TKA, but other mechanisms are necessary to predict discharge location.

The Impact of Discharge Disposition on Episode-of-Care Reimbursement After Primary Total Hip Arthroplasty

BACKGROUND

Total joint arthroplasty (TJA) accounts for more Medicare expenditure than any other inpatient procedure. The Comprehensive Care for Joint Replacement model was introduced to decrease cost and improve quality in TJA. The largest portion of episode-of-care costs occurs after discharge. This study sought to quantify the cost variation of primary total hip arthroplasty (THA) according to discharge disposition.

METHODS

The Medicare and Humana claims databases were used to extract charges and reimbursements to compare day-of-surgery and 91-day postoperative costs simulating episode-of-care reimbursements. Of the patients who underwent primary THA, 257,120 were identified (204,912 from Medicare and 52,208 from Humana). Patients were stratified by discharge disposition: home with home health, skilled nursing facility, or inpatient rehabilitation facility.

RESULTS

There is a significant difference in the episode-of-care costs according to discharge disposition, with discharge to an inpatient rehabilitation facility the most costly and discharge to home the least costly.

CONCLUSION

Postdischarge costs represent a sizeable portion of the overall expense in THA, and optimizing patients to allow safe discharge to home may help reduce the cost of THA.

Modified Frailty Index Is an Effective Risk Assessment Tool in Primary Total Hip Arthroplasty

BACKGROUND

Frailty is described as decreased physiological reserve and typically increasing with age. Hospitals are being penalized for reoperations and readmissions, which can affect reimbursement. The purpose of this study was to determine if the modified frailty index (MFI) could be used as a risk assessment tool for preoperative counseling and to make an objective decision on whether to perform total hip arthroplasty (THA) on a frail patient.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database was queried by Current Procedural Terminology code for primary THA (27130) from 2005 to 2014. MFI was calculated using 11 variables extracted from the medical record. Bivariate analysis was performed for outcomes and complications, and the multiple logistic regression model was used to compare MFI with other predictors of readmission, any complication, and reoperation.

RESULTS

A total of 51,582 patients underwent primary THA during the study period. MFI was a significant and stronger predictor than the American Society of Anesthesiologists class and age for readmission (odds ratio [OR], 14.72; 95% confidence interval [CI], 6.95-31.18; P < .001), any complication (OR, 3.63; 95% CI, 1.64-8.05; P = .002), and reoperation (OR, 8.78; 95% CI, 3.67-20.98; P < .001). As MFI increased, adverse discharge, any complication, readmission, reoperation, and mortality significantly increased (P < .001). Rates of systemic complications and length of stay significantly increased with increasing MFI.

CONCLUSION

MFI is a simple and effective risk assessment tool to preoperatively counsel and make an objective decision on whether to perform THA on a frail patient.