A Novel Risk Calculator Predicts 90-Day Readmission Following Total Joint Arthroplasty

A preoperative risk assessment tool for predicting adverse outcomes among total shoulder arthroplasty patients

BACKGROUND

With the increased utilization of Total Shoulder Arthroplasty (TSA) in the outpatient setting, understanding the risk factors associated with complications and hospital readmissions becomes a more significant consideration. Prior developed assessment metrics in the literature either consisted of hard-to-implement tools or relied on postoperative data to guide decision-making. This study aimed to develop a preoperative risk assessment tool to help predict the risk of hospital readmission and other postoperative adverse outcomes.

METHODS

We retrospectively evaluated the 2019-2022(Q2) Medicare fee-for-service inpatient and outpatient claims data to identify primary anatomic or reserve TSAs and to predict postoperative adverse outcomes within 90 days postdischarge, including all-cause hospital readmissions, postoperative complications, emergency room visits, and mortality. We screened 108 candidate predictors, including demographics, social determinants of health, TSA indications, prior 12-month hospital, and skilled nursing home admissions, comorbidities measured by hierarchical conditional categories, and prior orthopedic device-related complications. We used two approaches to reduce the number of predictors based on 80% of the data: 1) the Least Absolute Shrinkage and Selection Operator logistic regression and 2) the machine-learning-based cross-validation approach, with the resulting predictor sets being assessed in the remaining 20% of the data. A scoring system was created based on the final regression models' coefficients, and score cutoff points were determined for low, medium, and high-risk patients.

RESULTS

A total of 208,634 TSA cases were included. There was a 6.8% hospital readmission rate with 11.2% of cases having at least one postoperative adverse outcome. Fifteen covariates were identified for predicting hospital readmission with the area under the curve of 0.70, and 16 were selected to predict any adverse postoperative outcome (area under the curve = 0.75). The Least Absolute Shrinkage and Selection Operator and machine learning approaches had similar performance. Advanced age and a history of fracture due to orthopedic devices are among the top predictors of hospital readmissions and other adverse outcomes. The score range for hospital readmission and an adverse postoperative outcome was 0 to 48 and 0 to 79, respectively. The cutoff points for the low, medium, and high-risk categories are 0-9, 10-14, ≥15 for hospital readmissions, and 0-11, 12-16, ≥17 for the composite outcome.

CONCLUSION

Based on Medicare fee-for-service claims data, this study presents a preoperative risk stratification tool to assess hospital readmission or adverse surgical outcomes following TSA. Further investigation is warranted to validate these tools in a variety of diverse demographic settings and improve their predictive performance.

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

BACKGROUND

As the demand for total knee arthroplasty (TKA) escalates, 90-day readmissions have emerged as a pressing clinical and economic concern for the current value-based health care system. Consequently, health care providers have focused on estimating the risk levels of readmitted patients; however, it is unknown if specific factors are associated with different types of complications (ie, medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following TKA.

METHODS

A prospective cohort of primary unilateral TKAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 10,521 patients). Unplanned readmissions were reviewed individually to determine their primary cause, either medical or orthopaedic-related. Orthopaedic-related readmissions were specific complications affecting the joint, prosthesis, or surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between prespecified risk factors and 90-day readmissions, as well as medical and orthopaedic-related readmissions independently.

RESULTS

Overall, the rate of 90-day readmissions was 6.7% (n = 704). Over 82% of these readmissions were due to medical-related causes (n = 580), with the remaining 18% being orthopaedic-related (n = 124) readmissions. The area under the curve for the 90-day readmission model was 0.68 (95% confidence interval: 0.67 to 0.70). Sex, smoking, length of stay, and discharge disposition were associated with orthopaedic readmission, while age, sex, race, the Charlson Comorbidity Index, insurance, surgery day, opioid overdose risk score, length of stay, and discharge disposition were associated with medical-related 90-day readmissions.

CONCLUSIONS

Medical-related readmissions after TKA are more prevalent than orthopaedic-related readmissions. Through successfully constructing and validating multiple 90-day readmission predictive models, we highlight the distinct risk profiles for medical and orthopaedic-related readmissions. This emphasizes the necessity for nuanced, patient-specific risk stratification and preventive measures.

Safety of perioperative intravenous different doses of dexamethasone in primary total joint arthroplasty: a retrospective large-scale cohort study

PURPOSE

Perioperative intravenous different doses of dexamethasone (DEX) can realize effective clinical outcomes in total joint arthroplasty (TJA). However, the effect of different DEX doses on readmission rates and postoperative complications remains unclear.

METHODS

We retrospectively analyzed patients who underwent primary TJA between December 2012 and October 2020. Patients were categorized into three groups based on the total perioperative dose of DEX: control group (DEX = 0 mg), low-dose group (DEX < 15 mg), and high-dose group (DEX ≥ 15 mg). Primary outcomes included 30-day and 90-day readmission rates. Secondary outcomes included the rates of periprosthetic joint infection (PJI) and wound complications, with treatment outcomes for these complications were also evaluated. Multivariable analysis was used to identify risk factors for readmission.

RESULTS

A total of 14,557 procedures were included, with 6,686 in the control group, 4,325 in the low-dose group, and 3,546 in the high-dose group. No significant differences were observed among the groups for 30-day (p = 0.645) or 90-day readmission rates (p = 0.539). Additionally, there were no significant differences in rates of PJI (p = 0.401) or wound complications (p = 0.079). Treatment for PJI and wound complications was successful across all groups. Risk factors for 30-day readmission included age > 80 years (OR: 2.585, 95% CI: 1.123-5.954, p = 0.026) and undergoing total hip arthroplasty (THA) (OR: 1.692, 95% CI: 1.137-2.518, p = 0.009). For 90-day readmission, age 71-80 years (OR: 2.199, 95% CI: 1.349-3.583, p = 0.002), age > 80 years (OR: 3.897, 95% CI: 1.966-7.727, p < 0.001), and THA (OR: 1.622, 95% CI: 1.179-2.230, p = 0.003) were significant risk factors. However, neither low-dose nor high-dose DEX was associated with increased 30-day or 90-day readmission rates.

CONCLUSIONS

Perioperative intravenous DEX may be not associated with the readmission, PJI, and wound complications in patients undergoing primary TJA.

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.

The Removal of Total Knee Arthroplasty From the Inpatient-Only List has Improved Patient Optimization

INTRODUCTION

On January 1, 2018, the Centers for Medicare and Medicaid Services removed total knee arthroplasty (TKA) from the inpatient-only (IPO) list, expanding outpatient TKA (oTKA) to include patients with insurance coverage through their programs. These regulatory changes reinforced the need for preoperative optimization to ensure a safe and timely discharge after surgery. This study compared modifiable preoperative optimization metrics in patients who underwent oTKA pre-IPO and post-IPO removal. The authors hypothesized that patients post-IPO removal would demonstrate improvement in the selected categories.

METHODS

Outpatient TKA in a national database was identified and stratified by surgical year (2015 to 2017 versus 2018 to 2020). Preoperative optimization thresholds were established for the following modifiable risk factors: albumin, hematocrit, sodium, smoking, and body mass index. The percentage of patients who did not meet thresholds pre-IPO and post-IPO removal were compared.

RESULTS

In total, 2,074 patients underwent oTKA from 2015 to 2017 compared with 46,480 from 2018 to 2020. Patients undergoing oTKA after IPO removal were significantly older (67.0 versus 64.4 years; P < 0.01). A lower percentage of patients in the post-IPO cohort fell outside the threshold for all modifiable risk factors. Results were significant for preoperative sodium (10.7% versus 8.8%; P < 0.01), body mass index (12.4% versus 11.0% P = 0.05), and smoking history (9.9% versus 6.6%; P < 0.01).

CONCLUSION

Outpatient TKA has increased considerably post-IPO removal. As this regulatory change has allowed older patients with increased comorbidities to undergo oTKA, the need for appropriate preoperative optimization has increased. The current data set demonstrates that surgeons have improved preoperative optimization efforts for select modifiable risk factors.

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

Background

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

Methods

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

Results

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

Conclusions

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

Concentrated Economic Disadvantage Predicts Resource Utilization After Total Knee Arthroplasty

BACKGROUND

The Index of Concentration at the Extremes (ICE), a measure of geographic socioeconomic polarization, predicts several health outcomes but has not been evaluated concerning total knee arthroplasty (TKA). This study evaluates ICE as a predictor of post-TKA resource utilization.

METHODS

Using the Healthcare Cost and Utilization Project's New York State database from 2016 to 2017, we retrospectively evaluated 57,426 patients ≥50 years undergoing primary TKA. The ICE values for extreme concentrations of income and race were calculated using United States Census Bureau data with the formula ICEi = (Pi-Di)/Ti where Pi, Di, and Ti are the number of households in the most privileged extreme, disadvantaged extreme, and total population in zip code i, respectively. Extremes of privilege and disadvantage were defined as ≥$150,000 versus <$25,000 for income and non-Hispanic White versus non-Hispanic Black for race. Association of ICE values, demographics, and comorbidities with 90-day readmission and 90-day emergency department (ED) visits was examined using multivariable analysis.

RESULTS

Overall 90-day readmission and ED visit rates were 12.8% and 9.4%, respectively. On multivariable analysis, the lowest ICEIncome quintile (concentrated poverty) predicted 90-day readmission (odds ratio 1.17, 95% confidence interval 1.05 to 1.30, P = .005) and 90-day ED visit (odds ratio 1.22, 95% confidence interval 1.08 to 1.38, P = .001). The ICERace was not predictive of either outcome.

CONCLUSION

Patients in communities with the lowest ICEIncome values use more inpatient and ED resources after primary TKA. Incorporating ICEIncome into risk-adjusted payment models may help align incentives for equitable care.

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

BACKGROUND

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

QUESTIONS/PURPOSES

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

METHODS

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

RESULTS

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

CONCLUSION

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

LEVEL OF EVIDENCE

Level III, therapeutic study.

The removal of total hip arthroplasty from the inpatient-only list has improved patient selection and expanded optimization efforts

INTRODUCTION

On January 1, 2020, the Centers for Medicare and Medicaid Services removed total hip arthroplasty (THA) from the Inpatient-Only (IPO) list. This study evaluated patient demographics and comorbidities, pre-operative optimization efforts, and 30-day outcomes of patients undergoing outpatient THA pre- and post- IPO-removal. The authors hypothesized that patients undergoing THA post-IPO removal would have improved optimization of modifiable risk factors and equivalent 30-day outcomes.

METHODS

There were 17,063 outpatient THA in a national database stratified by surgery performed pre- (2015 to 2019: 5,239 patients) and post-IPO (2020: 11,824 patients) removal. Demographics, comorbidities, and 30-day outcomes were compared with univariable and multivariable analyses. Pre-operative optimization thresholds were established for the following modifiable risk factors: albumin, creatinine, hematocrit, smoking history, and body mass index. The percentage of patients who fell outside the thresholds in each cohort were compared.

RESULTS

Patients undergoing outpatient THA post-IPO removal were significantly older; mean age 65 years (range, 18 to 92) vs 62 (range, 18 to 90) years (P<0.01), with a higher percentage of American Society of Anesthesiologists scores 3 and 4 (P<0.01). There was no difference in 30-day readmissions (P=0.57) or reoperations (P=1.00). A significantly lower percentage of patients fell outside the established threshold for albumin (P<0.01) post-IPO removal, and trended towards lower percentages for hematocrit and smoking status.

CONCLUSION

The removal of THA from the IPO list expanded patient selection for outpatient arthroplasty. Pre-operative optimization is critical to minimize post-operative complications, and the current study demonstrates that 30-day outcomes have not worsened post-IPO removal.

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

DISCUSSION

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

Machine Learning for the Orthopaedic Surgeon: Uses and Limitations

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Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research.

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There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction.

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Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.

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 utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty

BACKGROUND

A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

METHODS

A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis.

RESULTS

Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis.

CONCLUSION

This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.

Incidence, patterns and risk factors for readmission following knee arthroplasty in China: A national retrospective cohort study

BACKGROUND

Limited data exist on readmission following knee arthroplasty (KA) in countries without well-established referral or extended care systems. This study aimed to investigate the incidence, patterns and risk factors for readmission following KA in China.

MATERIAL AND METHODS

In this national retrospective cohort study, we reviewed 167,265 primary KAs registered in the Hospital Quality Monitoring System in China between 2013 and 2018. Readmissions after KA within 30 and 90 days were evaluated. The causes for readmission were identified and classified as surgical or medical. The potential risk factors of readmission were assessed using multivariable logistic regression.

RESULTS

4017 (2.4%) patients readmitted within 30 days, and 7258 (4.3%) patients readmitted within 90 days. The readmission rate exhibited a downward trend during the period from 2013 to 2018 (2.7%-2.3% for 30-day readmission; 4.5%-4.2% for 90-day readmission). Surgical causes contributed to 54.3% readmissions within 30 days and 47.3% readmissions within 90 days. Wound infection/complication, joint pain, and thromboembolism were the most frequently reported reasons for surgical readmission. Older age, male sex, single marital status, non-osteoarthritis indication, a high comorbidity index, non-provincial hospitals, low hospital volume, and longer length of stay were associated with an increased risk of readmission. The geographic regions of hospitals contributed greatly to the variety of readmissions.

CONCLUSION

The readmission rate following KA decreased from 2013 to 2018. Surgery-related causes, especially wound infection/complication and pain, accounted for a large proportion. Both patient and hospital factors were associated with readmissions. Improved primary care and targeted measures are needed to help further prevent readmissions and optimize resource utilization.

Tranexamic Acid Was Not Associated with Increased Complications in High-Risk Patients with Intertrochanteric Fracture

BACKGROUND

For elective total joint arthroplasty, tranexamic acid (TXA) is considered safe and efficacious. However, evidence of TXA's safety in high-risk patients undergoing nonelective surgery for hip fracture is sparse. This study aimed to assess whether TXA administration to high-risk patients with an intertrochanteric (IT) hip fracture increased the risk of thromboembolic complications or mortality.

METHODS

All patients treated surgically for IT hip fracture between 2015 and 2019 across 4 hospitals of a single hospital system were considered. High- versus low-risk patients and those receiving TXA versus no TXA treatment were identified. Propensity scores adjusted for risk differences between patient groups with TXA and no TXA administration were calculated for (1) high-risk patients (n = 141) and (2) the entire population (n = 316). Postoperative mortality, deep venous thrombosis (DVT), pulmonary embolism (PE), myocardial infarction (MI), and stroke within 90 days of surgery were evaluated.

RESULTS

No association between TXA administration and increased risk of mortality or complications in either group was identified. Specifically, out of 282 matched high-risk patients, no differences in mortality (odds ratio [OR], 0.97 [95% confidence interval (CI), 0.90, 1.05]), DVT (OR, 0.97 [95% CI, 0.93, 1.00]), PE (OR 1.00 [95% CI, 0.95, 1.05]), MI (OR, 1.04 [95% CI, 0.98, 1.10]), or stroke (OR, 1.00 [95% CI, 0.95, 1.05]) were identified.

CONCLUSIONS

In our review of propensity-matched high-risk patients undergoing surgical repair for IT fracture, we found that TXA administration compared with no TXA administration was not associated with an increased risk of mortality, DVT, PE, MI, or stroke within 90 days of surgery.

LEVEL OF EVIDENCE

Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

A validated preoperative risk prediction tool for discharge to skilled nursing or rehabilitation facility following anatomic or reverse shoulder arthroplasty

BACKGROUND

As bundled payment models continue to spread, understanding the primary drivers of cost excess helps providers avoid penalties and ensure equal health care access. Recent work has shown discharge to rehabilitation and skilled nursing facilities (SNFs) to be a primary cost driver in total joint arthroplasty, and an accurate preoperative risk calculator for shoulder arthroplasty would not only help counsel patients in clinic during shared decision-making conversations but also identify high-risk individuals who may benefit from preoperative optimization and discharge planning.

METHODS

Anatomic and reverse total shoulder arthroplasty cohorts from 2 geographically diverse, high-volume centers were reviewed, including 1773 cases from institution 1 (56% anatomic) and 3637 from institution 2 (50% anatomic). The predictive ability of a variety of candidate variables for discharge to SNF/rehabilitation was tested, including case type, sociodemographic factors, and the 30 Elixhauser comorbidities. Variables surviving parameter selection were incorporated into a multivariable logistic regression model built from institution 1's cohort, with accuracy then validated using institution 2's cohort.

RESULTS

A total of 485 (9%) shoulder arthroplasties overall were discharged to post-acute care (anatomic: 6%, reverse: 14%, P < .0001), and these patients had significantly higher rates of unplanned 90-day readmission (5% vs. 3%, P = .0492). Cases performed for preoperative fracture were more likely to require post-acute care (13% vs. 3%, P < .0001), whereas revision cases were not (10% vs. 10%, P = .8015). A multivariable logistic regression model derived from the institution 1 cohort demonstrated excellent preliminary accuracy (area under the receiver operating characteristic curve [AUC]: 0.87), requiring only 11 preoperative variables (in order of importance): age, marital status, fracture, neurologic disease, paralysis, American Society of Anesthesiologists physical status, gender, electrolyte disorder, chronic pulmonary disease, diabetes, and coagulation deficiency. This model performed exceptionally well during external validation using the institution 2 cohort (AUC: 0.84), and to facilitate convenient use was incorporated into a freely available, online prediction tool. A model built using the combined cohort demonstrated even higher accuracy (AUC: 0.89).

CONCLUSIONS

This validated preoperative clinical decision tool reaches excellent predictive accuracy for discharge to SNF/rehabilitation following shoulder arthroplasty, providing a vital tool for both patient counseling and preoperative discharge planning. Further, model parameters should form the basis for reimbursement legislation adjusting for patient comorbidities, ensuring no disparities in access arise for at-risk populations.

A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative

BACKGROUND

Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA.

METHODS

Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation.

RESULTS

A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively.

CONCLUSION

Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration.

KEY POINTS

• Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies. • Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.

The American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator is not reliable in predicting complications and length of stay after primary total hip arthroplasty at an institution implementing clinical pathways

INTRODUCTION

The authors aimed to: (1) determine how length of stay (LOS) and complication rates changed over the past 10 years, in comparison to values estimated by the ACS-NSQIP surgical risk calculator, at a single private institution open to external surgeons; and (2) determine preoperative patient factors associated with complications.

METHODS

We retrospectively assessed 1018 consecutive patients who underwent primary elective THA over 10 years. We excluded 87 with tumours and 52 with incomplete records. Clinical data of the remaining 879 were used to determine real LOS and rate of 9 adverse events over time, as well as to estimate these values using the risk calculator. Its predictive reliability was represented on receiver operating characteristic curves. Multivariable analyses were performed to determine associations of complications with age, sex, ASA score, diabetes, hypertension, heart disease, smoking and BMI.

RESULTS

Over the 10-year period, real LOS and real complication rates decreased considerably, while LOS and complication rates estimated by the surgical risk calculator had little or no change. The difference between real and estimated LOS decreased over time. The overall estimated and real rates of any complication were respectively 3.3% and 2.8%. The risk calculator had fair reliability for predicting any complications (AUC 0.72). Overall estimated LOS was shorter than the real LOS in 764 (86.9%) patients. Multivariable analysis revealed risks of any complication to be greater in patients aged ⩾75 (OR = 4.36, p = 0.002), and with hypertension (OR = 3.13, p = 0.016).

CONCLUSIONS

Since the implementation of clinical pathways at our institution, real LOS and complication rates decreased considerably, while LOS and complication rates estimated by the surgical risk calculator had little or no change. The difference between real and estimated LOS decreased over time, which could lead some clinicians to reconsider their discharge criteria, knowing that advanced age and hypertension increased risks of encountering complications.

Characteristics and risk factors for 90-day readmission following shoulder arthroplasty

BACKGROUND

Anatomic total shoulder arthroplasty (TSA) and reverse TSA are the standard of care for end-stage shoulder arthritis. Advancements in implant design, perioperative management, and patient selection have allowed shorter inpatient admissions. Unplanned readmissions remain a significant complication. Identification of risk factors for readmission is prudent as physicians and payers prepare for the adoption of bundled care reimbursement models. The purpose of this study was to identify characteristics and risk factors associated with readmission following shoulder arthroplasty using a large, bi-institutional cohort.

METHODS

A total of 2805 anatomic TSAs and 2605 reverse TSAs drawn from 2 geographically diverse, tertiary health systems were examined for unplanned inpatient readmissions within 90 days following the index operation (primary outcome). Forty preoperative patient sociodemographic and comorbidity factors were tested for their significance using both univariable and multivariable logistic regression models, and backward stepwise elimination selected for the most important associations for 90-day readmission. Readmissions were characterized as either medical or surgical, and subgroup analysis was performed. A short length of stay (discharge by postoperative day 1) and discharge to a rehabilitation or skilled nursing facility were also examined as secondary outcomes. Parameters associated with increased readmission risk were included in a predictive model.

RESULTS

Within 90 days of surgery, 175 patients (3.2%) experienced an unanticipated readmission, with no significant difference between institutions (P = .447). There were more readmissions for surgical complications than for medical complications (62.9% vs. 37.1%, P < .001). Patients discharged to a rehabilitation or skilled nursing facility were significantly more likely to be readmitted (13.1% vs. 8.8%, P = .049), but a short inpatient length of stay was not associated with an increased rate of 90-day readmission (42.9% vs. 41.3%, P = .684). Parameter selection based on predictive ability resulted in a multivariable logistic regression model composed of 16 preoperative patient factors, including reverse TSA, revision surgery, right-sided surgery, and various comorbidities. The area under the receiver operator characteristic curve for this multivariable logistic regression model was 0.716.

CONCLUSION

Risk factors for unplanned 90-day readmission following shoulder arthroplasty include reverse shoulder arthroplasty, surgery for revision and fracture, and right-sided surgery. Additionally, there are several modifiable and nonmodifiable risk factors that can be used to ascertain a patient's readmission probability. A shorter inpatient stay is not associated with an increased risk of readmission, whereas discharge to post-acute care facilities does impose a greater risk of readmission. As scrutiny around health care cost increases, identifying and addressing risk factors for readmission following shoulder arthroplasty will become increasingly important.

Short stay after shoulder arthroplasty does not increase 90-day readmissions in Medicare patients compared with privately insured patients

BACKGROUND

As of January 1, 2021, total shoulder arthroplasty was removed from the Medicare inpatient-only list, reflecting a growing belief in the potential merits of same-day discharge regardless of insurance type. It is yet unknown whether Medicare populations, which frequently have more severe comorbidity burdens, would experience higher complication rates relative to privately insured patients, who are often younger with fewer comorbidities. Given the limited number of true outpatient cohorts available to study, discharge at least by postoperative day 1 may serve as a useful proxy for true same-day discharge, and we hypothesized that these Medicare patients would have increased 90-day readmission rates compared with their privately insured counterparts.

METHODS

Data on 4723 total shoulder arthroplasties (anatomic in 2459 and reverse in 2264) from 2 large, geographically diverse health systems in patients having either Medicare or private insurance were collected. The unplanned 90-day readmission rate was the primary outcome, and patients were stratified into those who were discharged at least by postoperative day 1 (short inpatient stay) and those who were not. Patients with private insurance (n = 1845) were directly compared with those with Medicare (n = 2878), whereas cohorts of workers' compensation (n = 198) and Medicaid (n = 58) patients were analyzed separately. Forty preoperative variables were examined to compare overall health burden, with the χ² and Wilcoxon rank sum tests used to test for statistical significance.

RESULTS

Medicare patients undergoing short-stay shoulder arthroplasty were not significantly more likely than those with private insurance to experience an unplanned 90-day readmission (3.6% vs. 2.5%, P = .14). This similarity existed despite a substantially worse comorbidity burden in the Medicare population (P < .05 for 26 of 40 factors). Furthermore, a short inpatient stay did not result in an increased 90-day readmission rate in either Medicare patients (3.6% vs. 3.4%, P = .77) or their privately insured counterparts (2.5% vs. 2.4%, P = .92). Notably, when the analysis was restricted to a single insurance type, readmission rates were significantly higher for reverse shoulder arthroplasty compared with total shoulder arthroplasty (P < .001 for both), but when the analysis was restricted to a single procedure (anatomic or reverse), readmission rates were similar between Medicare and privately insured patients, whether undergoing a short or extended length of stay.

CONCLUSIONS

Despite a substantially more severe comorbidity profile, Medicare patients undergoing short-stay shoulder arthroplasty did not experience a significantly higher rate of unplanned 90-day readmission relative to privately insured patients. A higher incidence of reverse shoulder arthroplasty in Medicare patients does increase their overall readmission rate, but a similar increase also appears in privately-insured patients undergoing a reverse indicating that Medicare populations may be similarly appropriate for accelerated-care pathways.

Rapid preoperative predicting tools for 1-year mortality and walking ability of Asian elderly femoral neck fracture patients who planned for hip arthroplasty

BACKGROUND

Femoral neck fractures in elderly patients typically warrant operative treatment and are related to high risks of mortality and morbidity. As early hip arthroplasties for elderly femoral neck fractures are widely accepted, rapid predicting models that allowed quantitative and individualized prognosis assessments are strongly needed as references for orthopedic surgeons during preoperative conversations.

METHODS

Data of patients aged ≥ 65 years old who underwent primary unilateral hemiarthroplasty or total hip arthroplasty due to femoral neck fracture between January 1st, 2012 and June 30th, 2019 in our center were collected. Candidate variables included demographic data, comorbidities, and routine preoperative screening tests. The main outcomes included 1-year mortality and free walking rate after hip arthroplasty. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. One thousand bootstraps were used for internal validation. Those models were further tested in the validation group for external validation.

RESULTS

The final analysis was performed on 702 patients after exclusion and follow-up. All-cause 1-year mortality of the entire data set was 23.4%, while the free walking rate was 57.3%. Preoperative walking ability showed the biggest impact on predicting 1-year mortality and walking ability. Static nomograms were created from the final multivariable models, which allowed simplified graphical computations for the risks of 1-year mortality and walking ability in a certain patient. The bias-corrected C index of those nomograms for predicting 1-year mortality in the derivation group and the validation group were 0.789 and 0.768, while they were 0.807 and 0.759 for predicting postoperative walking ability. The AUC of the mortality and walking ability predicting models were 0.791 and 0.818, respectively.

CONCLUSIONS

Our models enabled rapid preoperative 1-year mortality and walking ability predictions in Asian elderly femoral neck fracture patients who planned for hip arthroplasty, with adequate predictive discrimination and calibration. Those rapid assessment models could help surgeons in making more reasonable clinical decisions and subsequently reducing the risk of potential medical dispute via quantitative and individualized prognosis assessments.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Resilience and Depression Influence Clinical Outcomes Following Primary Total Joint Arthroplasty

BACKGROUND

Resilience and depression may impact clinical outcomes following primary total joint arthroplasty (TJA). This study aimed to quantify baseline resilience and depression prevalence in patients undergoing primary TJA and evaluate their influence on patient-reported clinical outcomes.

METHODS

We prospectively enrolled 98 patients undergoing primary TJA. Exclusion criteria included patients under 18 years of age, undergoing surgery for fracture, or who underwent additional surgery during the study period. Patients completed the Brief Resilience Scale to measure resilience, Patient Health Questionnaire-9 to measure depression, and Patient-Reported Outcomes Measurement Information System-10 to measure global physical and mental health preoperatively and 1 year postoperatively.

RESULTS

Preoperatively, 22% and 15% of patients demonstrated major and mild depression, respectively. High resilience was identified in 34% of patients, normal resilience in 55%, and low resilience in 11%. Preoperative depression correlated with lower resilience, global physical health, and global mental health scores preoperatively as well as at 1 year after surgery (P < .001). Higher levels of preoperative resilience correlated with higher global physical and mental health scores preoperatively and at 1 year postoperatively (P < .001).

CONCLUSION

Depression symptoms are common among patients undergoing primary TJA and are associated with worse patient-reported outcomes. Patients with higher levels of resilience have higher global physical and mental health scores before and after TJA. Psychological traits and depression impact clinical outcomes following TJA.

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.

From Winners to Losers: The Methodology of Bundled Payments for Care Improvement Advanced Disincentivizes Participation in Bundled Payment Programs

BACKGROUND

The Bundled Payments for Care Improvement (BPCI) initiative improved quality and reduced costs following total hip (THA) and knee arthroplasty (TKA). In October 2018, the BPCI-Advanced program was implemented. The purpose of this study is to compare the quality metrics and performance between our institution's participation in the BPCI program with the BPCI-Advanced initiative.

METHODS

We reviewed a consecutive series of Medicare primary THA and TKA patients. Demographics, medical comorbidities, discharge disposition, readmission, and complication rates were compared between BPCI and BPCI-Advanced groups. Medicare claims data were used to compare episode-of-care costs, target price, and margin per patient between the cohorts.

RESULTS

Compared to BPCI patients (n = 9222), BPCI-Advanced patients (n = 2430) had lower rates of readmission (5.8% vs 3.8%, P = .001) and higher rate of discharge to home (72% vs 78%, P < .001) with similar rates of complications (4% vs 4%, P = .216). Medical comorbidities were similar between groups. BPCI-Advanced patients had higher episode-of-care costs ($22,044 vs $18,440, P < .001) and a higher mean target price ($21,154 vs $20,277, P < .001). BPCI-Advanced patients had a reduced per-patient margin compared to BPCI ($890 loss vs $1459 gain, P < .001), resulting in a $2,138,670 loss in the first three-quarters of program participation.

CONCLUSION

Despite marked improvements in quality metrics, our institution suffered a substantial loss through BPCI-Advanced secondary to methodological changes within the program, such as the exclusion of outpatient TKAs, facility-specific target pricing, and the elimination of different risk tracks for institutions. Medicare should consider adjustments to this program to keep surgeons participating in alternative payment models.

Artificial Intelligence for the Orthopaedic Surgeon: An Overview of Potential Benefits, Limitations, and Clinical Applications

Artificial intelligence (AI), along with its subset technology machine learning, has transformed numerous industries through newfound efficiencies and supportive decision-making. These technologies have similarly begun to find application within United States healthcare, particularly orthopaedics. Although these modalities have the potential to similarly transform health care, there exist limitations that must also be recognized and understood. Unfortunately, most clinicians do not have an understanding of the fundamentals of AI and therefore may have challenges in contextualizing its impact in modern healthcare. The purpose of this review was to provide an overview of the key concepts of AI and machine learning with the orthopaedic surgeon in mind. The review further highlights the potential benefits and limitations of AI, along with an overview of its applications, in orthopaedics.

Outcomes and Risk Factors Associated With 2-Stage Reimplantation Requiring an Interim Spacer Exchange for Periprosthetic Joint Infection

BACKGROUND

Patients undergoing a 2-stage revision for periprosthetic joint infection (PJI) often require a repeat spacer in the interim due to persistent infection. This study aims to report outcomes for patients with repeat spacer exchange and to identify risk factors associated with interim spacer exchange in 2-stage revision arthroplasty.

METHODS

A total of 256 consecutive 2-stage revisions for chronic infection of total hip arthroplasty and total knee arthroplasty with reimplantation and minimum 2-year follow-up were investigated. An interim spacer exchange was performed in 49 patients (exchange cohort), and these patients were propensity score matched to 196 patients (nonexchange cohort). Multivariate analysis was performed to analyze risk factors for failure of interim spacer exchange.

RESULTS

Patients in the propensity score-matched exchange cohort demonstrated a significantly increased reinfection risk compared to patients without interim spacer exchange (24% vs 15%, P = .03). Patients in the propensity score-matched exchange cohort showed significantly lower postoperative scores for 3 patient-reported outcome measures (PROMs): hip disability and osteoarthritis outcome score physical function (46.0 vs 54.9, P = .01); knee disability and osteoarthritis outcome score physical function (43.1 vs 51.7, P < .01); and patient-reported outcomes measurement information system physical function short form (41.6 vs 47.0, P = .03). Multivariate analysis demonstrated Charles Comorbidity Index (odds ratio, 1.56; P = .01) and the presence of Enterococcus species (odds ratio, 1.43; P = .03) as independent risk factors associated with 2-stage reimplantation requiring an interim spacer exchange for periprosthetic joint infection.

CONCLUSION

This study demonstrates that patients with spacer exchange had a significantly higher risk of reinfection at 2 years of follow-up. Additionally, patients with spacer exchange demonstrated lower postoperative PROM scores and diminished improvement in multiple PROM scores after reimplantation, indicating that an interim spacer exchange in 2-stage revision is associated with worse patient outcomes.

Development of a Preoperative Risk Calculator for Reinfection Following Revision Surgery for Periprosthetic Joint Infection

BACKGROUND

A recent systematic review demonstrated that reinfection rates following eradication of hip and knee periprosthetic joint infection (PJI) may be as high as 29%. This study aimed to develop a preoperative risk calculator for assessing patient's individual risk associated with reinfection following treatment of PJI in total joint arthroplasty (TJA).

METHODS

A total of 1081 consecutive patients who underwent revision TJA for PJI were evaluated. In total, 293 patients were diagnosed with TJA reinfection. A total of 56 risk factors, including patient characteristics and surgical variables, were evaluated with multivariate regression analysis. Analysis of the area under the receiver operating characteristics curve was performed to evaluate the strength of the predictive model.

RESULTS

Of the 56 risk factors studied, 19 were found to have a significant effect as risk factor for TJA reinfection. The strongest predictors for TJA reinfection included previous PJI treatment techniques such as irrigation and debridement, the number of previous surgical interventions, medical comorbidities such as obesity, drug abuse, depression and smoking, as well as microbiology including the presence of Enterococcus species. The combined area under the receiver operating characteristics curve of the risk calculator for periprosthetic hip and knee joint reinfection was 0.75.

CONCLUSIONS

The study findings demonstrate that surgical factors, including previous PJI surgical treatment techniques as well as the number of previous surgeries, alongside microbiology including the presence of Enterococcus species have the strongest effect on the risk for periprosthetic THA and TKA joint reinfection, suggesting the limited applicability of the existing risk calculators for the development of PJI following primary TJA in predicting the risk of periprosthetic joint reinfection.

A History of Past Prostate Cancer Still Carries Risk After Total Knee Arthroplasty

Prostate cancer (PCa) is one of the most prevalent diseases in the North American elderly population. Moreover, many patients undergo prostate resection without further treatment and are often considered cured. As such, it is expected that many undergo total knee arthroplasty (TKA) for osteoarthritis while having a history of PCa. Nonetheless, limited research is available on this topic, and without it, surgeons may not be aware of increased complication rates. Therefore, the purpose of this study was to evaluate whether patients at a national level with a history of PCa are at increased risk for complications after TKA. A retrospective case-control, comorbidity matched paired analysis was performed. Patients were identified based on International Classification of Diseases, Ninth Revision codes and matched 1:1 ratio to age, smoker status, chronic kidney disease, diabetes, chronic lung disease, smoking status, and obesity. Patients with active disease were excluded. The 90-day outcomes of TKA were compared through univariate regressions (odds ratios [ORs] and 95% confidence intervals). A total of 2,381,706 TKA patients were identified, and after matching, each comprised 113,365 patients with the same prevalence of the matched comorbidities and demographic characteristics. A significant increase in thromboembolic events that was clinically relevant was found in pulmonary embolisms (PEs) (1.44 vs. 0.4%, OR: 3.04, p < 0.001), Moreover, an increased rate of deep vein thromboses was also seen but was found to be not clinically significant (2.55 vs. 2.85%, OR: 1.19). Although length of stay and other complications were similar, average reimbursements were higher for those with a history of PCa. In conclusion, a history of prior PCa carries significant risk as these patients continue to develop increased PE rates during the 90-day postoperative period which appears to lead to greater economic expenditure. Surgeons and payers should include this comorbidity in risk and patient-specific payment models.

Are We at the Bottom? BPCI Programs Now Disincentivize Providers Who Maintain Quality Despite Caring for Increasingly Complex Patients

BACKGROUND

The Bundled Payments for Care Improvement (BPCI) initiative has been successful at reducing Medicare costs after total joint arthroplasty (TJA). Target pricing is based on each institution's historical performance and is periodically reset. The purpose of this study was to examine the performance of our BPCI program accounting for patient complexity, quality, and resource utilization.

METHODS

We reviewed a consecutive series of 9195 Medicare patients undergoing primary TJA from 2015 to 2018. Demographics, comorbidities, and readmissions by year were compared. We then examined 90-day episode-of-care costs, changes in target price, and financial margins during the duration of the BPCI program using Medicare claims data.

RESULTS

Patients undergoing TJA in 2018 had a higher prevalence of diabetes and cardiac disease (all P < .001) as compared with those in 2015. From 2015 to 2018, there was a decrease in the rate of discharge to rehabilitation facilities (23% vs 14%, P < .001) and length of stay (2.1 vs 1.7 days, P < .001) with no difference in readmissions (6% vs 6%, P = .945). There was a reduction in postacute care costs ($6076 vs $4,890, P < .001) and 90-day episode-of-care costs ($19,954 vs $18,449, P < .001). However, the target price also decreased ($22,280 vs $18,971, P < .001), and the per-patient margin diminished ($2683 vs $522, P < .001).

CONCLUSION

Surgeons have maintained quality of care at a reduced cost despite increasing patient complexity. The target price adjustments resulted in declining margins during the course of our BPCI experience. Policy makers should consider changes to target price methodology to encourage participation in these successful cost-saving programs.

A Case Complexity Modifier Is Warranted for Primary Total Knee Arthroplasty

BACKGROUND

The Center for Medicare and Medicaid Services is faced with a challenge of decreasing the cost of care for total knee arthroplasty (TKA) but must make efforts to prevent patient selection bias in the process. Currently, no appropriate modifier codes exist for primary TKA based on case complexity. We sought to determine differences in perioperative parameters for patients with complex primary TKA with the hypothesis that they would require increased cost of care, prolonged care times, and have worse postoperative outcome metrics.

METHODS

We performed a single-center retrospective review from 2015 to 2018 of all primary TKAs. Patient demographics, medial proximal tibial angle (mPTA), lateral distal femoral angle (lDFA), flexion contracture, cost of care, and early postoperative outcomes were collected. Complex patients were defined as those requiring stems or augments, and multivariable logistic regression analysis and propensity score matching were performed to evaluate perioperative outcomes.

RESULTS

About 1043 primary TKAs were studied, and 84 patients (8.3%) were deemed complex. For this cohort, surgery duration was greater (P < .001), cost of care higher (P < .001), and patients had a greater likelihood for 90-day hospital return. Deviation of mPTA and lDFA was significantly greater preoperatively before and after propensity score matching. Cut point analysis demonstrated that preoperative mPTA <83^o or >91^o, lDFA <84^o or >90^o, flexion contracture >10^o, and body mass index >35.7 were associated with complex procedures.

CONCLUSION

Complex primary TKA may be identifiable preoperatively and those cases associated with prolonged operative time, excess hospital cost of care, and increased 90-day hospital returns. This should be considered in future reimbursement models to prevent patient selection bias, and a complexity modifier is warranted.

Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review

Web-based personalized predictive tools in orthopedic surgery are becoming more widely available. Despite rising numbers of these tools, many orthopedic surgeons may not know what tools are available, how these tools were developed, and how they can be utilized. The aim of this scoping review is to compile and synthesize the profile of existing web-based orthopedic tools. We conducted two separate PubMed searches-one a broad search and the second a more targeted one involving high impact journals-with the aim of comprehensively identifying all existing tools. These articles were then screened for functional tool URLs, methods regarding the tool's creation, and general inputs and outputs required for the tool to function. We identified 57 articles, which yielded 31 unique web-based tools. These tools involved various orthopedic conditions (e.g., fractures, osteoarthritis, musculoskeletal neoplasias); interventions (e.g., fracture fixation, total joint arthroplasty); outcomes (e.g., mortality, clinical outcomes). This scoping review highlights the availability and utility of a vast array of web-based personalized predictive tools for orthopedic surgeons. Increased awareness and access to these tools may allow for better decision support, surgical planning, post-operative expectation management, and improved shared decision-making.

Perioperative patient-specific factors-based nomograms predict short-term periprosthetic bone loss after total hip arthroplasty

BACKGROUND

Although medical intervention of periprosthetic bone loss in the immediate postoperative period was recommended, not all the patients experienced periprosthetic bone loss after total hip arthroplasty (THA). Prediction tools that enrolled all potential risk factors to calculate an individualized prediction of postoperative periprosthetic bone loss were strongly needed for clinical decision-making.

METHODS

Data of the patients who underwent primary unilateral cementless THA between April 2015 and October 2017 in our center were retrospectively collected. Candidate variables included demographic data and bone mineral density (BMD) in spine, hip, and periprosthetic regions that measured 1 week after THA. Outcomes of interest included the risk of postoperative periprosthetic bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year. Nomograms were presented based on multiple logistic regressions via R language. One thousand Bootstraps were used for internal validation.

RESULTS

Five hundred sixty-three patients met the inclusion criteria were enrolled, and the final analysis was performed in 427 patients (195 male and 232 female) after the exclusion. The mean BMD of Gruen zone 1, 7, and total were decreased by 4.1%, 6.4%, and 1.7% at the 1st year after THA, respectively. 61.1% of the patients (261/427) experienced bone loss in Gruen zone 1 at the 1st postoperative year, while there were 58.1% (248/427) in Gruen zone 7 and 63.0% (269/427) in Gruen zone total. Bias-corrected C-index for risk of postoperative bone loss in Gruen zone 1, 7, and total zones in the 1st postoperative year were 0.700, 0.785, and 0.696, respectively. The most highly influential factors for the postoperative periprosthetic bone loss were primary diagnosis and BMD in the corresponding Gruen zones at the baseline.

CONCLUSIONS

To the best of our knowledge, our study represented the first time to use the nomograms in estimating the risk of postoperative periprosthetic bone loss with adequate predictive discrimination and calibration. Those predictive models would help surgeons to identify high-risk patients who may benefit from anti-bone-resorptive treatment in the early postoperative period effectively. It is also beneficial for patients, as they can choose the treatment options based on a reasonable expectation following surgery.

The Removal of Total Hip and Total Knee Arthroplasty From the Inpatient-Only List Increases the Administrative Burden of Surgeons and Continues to Cause Confusion

BACKGROUND

Several studies have shown that the removal of total knee arthroplasty (TKA) from the Centers for Medicare and Medicaid Services (CMS) inpatient-only (IPO) list has caused confusion among surgeons, hospitals, and patients. The purpose of this study is to determine whether similar confusion was present after CMS recently removed total hip arthroplasty (THA) from the IPO list.

METHODS

We surveyed the American Association of Hip and Knee Surgeons membership via an online web-based questionnaire in February 2020. The 12-question form asked about practice type and the impact that having both THA and TKA removed from the IPO list has had on each surgeon's practice. Responses were tabulated and descriptive statistics of each question reported.

RESULTS

Of the 2847 American Association of Hip and Knee Surgeons members surveyed, 419 responded (14.7% response rate). Three hundred forty-one surgeons (81%) stated that changes to IPO status have increased their practice's administrative burden. Fifty-four percent of surgeons reported that they have needed to obtain preauthorization or appeal a denial of preauthorization for an inpatient total joint arthroplasty at least monthly, while 257 surgeons (61%) have had patients contact their office regarding an unexpected copayment. Despite the commitment of CMS to waiving certain audits for 2 years, 43 respondents (10%) stated they had undergone an audit regarding a patient's inpatient status.

CONCLUSION

The removal of THA and TKA from the IPO list continues to be an administrative burden for arthroplasty surgeons and a source of confusion among patients. CMS should provide additional guidance to address surgeons' concerns about preauthorization for inpatient stays, unexpected patient copayments, and CMS audits.

Traditional Risk Factors and Logistic Regression Failed to Reliably Predict a "Bundle Buster" After Total Joint Arthroplasty

BACKGROUND

The purpose of this study was to determine if we could identify patient factors that were predictive of Medicare and privately insured patients being "high-cost."

METHODS

Ninety-day episode-of-care insurance company payments along with collected demographics, comorbidities, and readmissions were reviewed for a consecutive series of primary total joint arthroplasty patients from 2015 to 2016 at our institution. High-cost patients were identified by determining those patients above the cutoff, where the cost data became demonstrably nonparametric and both univariate analysis and logistical regressions were performed to identify risk factors that lead to increased costs. Receiver operator curves were created to determine the predictive nature of these risk factors.

RESULTS

Univariate analysis showed that high-cost privately insured patients were significantly older, more likely to be readmitted and less likely to be discharged to home (P < .001) whereas high-cost Medicare total knee/total hip arthroplasty patients were more likely to have many of the comorbidities analyzed. Logistical regression did not find any predictive factors for privately insured patients and found that diabetes (OR 1.47 and 1.75, respectively), congestive heart failure (OR 1.94 and 3.46, respectively), cerebrovascular event (OR 2.20 and 2.20, respectively) and rheumatic disease (OR 1.78 and 1.78, respectively) were all predictive of being a high-cost Medicare patient.

CONCLUSION

Traditional risk factors for postoperative complications are not reliably associated with increased patient costs after total hip and total knee arthroplasty. Furthermore, the risk factors associated with increased costs vary greatly between privately insured and Medicare-insured patients. Further investigation is necessary to identify cost drivers in this patient subset to preventive higher costs.

Complications and Costs Are Not Increased After Total Hip Arthroplasty in Patients With a History of Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is a largely prevalent disease in the United States. Moreover, it is unclear whether the thromboembolic burden of disease remains present after the cancer has been treated and whether such state impacts the short-term outcomes of orthopedic procedures. Therefore, the purpose of this study is to assess 90-day postoperative complications and costs after total hip arthroplasty (THA) for osteoarthritis in patients with a history of PCa.

METHODS

Two groups of patients who underwent THA for osteoarthritis in the Medicare Standard Analytical Files were identified through the PearlDiver server. Both groups were matched based on age, diabetes, smoking status, chronic kidney disease, alcohol abuse, chronic liver disease, and obesity in order to create a case-control study comparison. The 90-day complication rates after THA were compared using univariate regressions (odds ratio). We hypothesized that patients with a history of PCa would develop increased rates of thromboembolic complications based on a prolonged procoagulative state.

RESULTS

After matching, each group was comprised of 62,571 patients. Our findings identified greater 90-day pneumonia rates for those without a history of PCa (3.26% vs 2.68%; odds ratio, 0.82). All other complications including thromboembolic diseases were clinically comparable in both groups during the 90-day postoperative period. The charges and reimbursements for the 90-day period were also comparable.

CONCLUSION

In our large case-control study of 125,142 patients, we found that patients with a history of PCa do not have increased risk of short-term complications after THA and that the mean 90-day reimbursements were similar for both groups at $14,153 for PCa patients and $14,033 for those without (P = .114).

Visualising statistical models using dynamic nomograms

Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.