Can an arthroplasty risk score predict bundled care events after total joint arthroplasty?

The Impact of Hospital Exposures Prior to Total Knee Arthroplasty on Postoperative Outcomes

Background

Total knee arthroplasty (TKA) procedures are expected to grow exponentially in the upcoming years, highlighting the importance of identifying preoperative risk factors that predispose patients to poor outcomes. The present study sought to determine if preoperative healthcare events (PHEs) influenced outcomes following TKA.

Methods

This was a retrospective review of all patients who underwent TKA at a single institution from June 2011 to April 2022. Patients who had a PHE within 90 days of surgery, defined as an emergency department visit or hospital admission, were compared to patients with no history of PHE. Patients who underwent revision, nonelective, and/or bilateral TKA were excluded. Chi-squared analysis and independent sample t-tests were used to determine significant differences between demographic variables. All significant covariates were included in binary logistic regressions used to predict discharge disposition, 90-day readmission, and 1-year revision.

Results

Of the 10,869 patients who underwent TKA, 265 had ≥1 PHE. Patients who had a PHE were significantly more likely to require facility discharge (odds ratio [OR]: 1.662; P = .001) than patients who did not have a PHE. Any PHE predisposed patients to significantly higher 90-day readmission rates (OR: 2.173; P = .002). Patients with ≥2 PHEs were at a significantly higher risk of 1-year revision (OR: 5.870; P = .004) compared to patients without a PHE.

Conclusions

Our results demonstrate that PHEs put patients at significantly greater risk of facility discharge, 90-day readmission, and 1-year revision. Moving forward, consideration of elective surgery scheduling in the context of a recent PHE may lead to improved postoperative outcomes.

Level III Evidence

Retrospective Cohort Study.

The Impact of Pre-Operative Healthcare Utilization on Complications, Readmissions, and Post-Operative Healthcare Utilization Following Total Joint Arthroplasty

BACKGROUND

Identifying risk factors for adverse outcomes and increased costs following total joint arthroplasty (TJA) is needed to ensure quality. The interaction between pre-operative healthcare utilization (pre-HU) and outcomes following TJA has not been fully characterized.

METHODS

This is a retrospective cohort study of patients undergoing elective, primary total hip arthroplasty (THA, N = 1785) or total knee arthroplasty (TKA, N = 2159) between 2015 and 2019 at a single institution. Pre-HU and post-operative healthcare utilization (post-HU) included non-elective healthcare utilization in the 90 days prior to and following TJA, respectively (emergency department, urgent care, observation admission, inpatient admission). Multivariate regression models including age, gender, American Society of Anesthesiologists, Medicaid status, and body mass index were fit for 30-day readmission, Centers for Medicare and Medicaid services (CMS)-defined complications, length of stay, and post-HU.

RESULTS

The 30-day readmission rate was 3.2% and 3.4% and the CMS-defined complication rate was 3.8% and 2.9% for THA and TKA, respectively. Multivariate regression showed that for THA, presence of any pre-HU was associated with increased risk of 30-day readmission (odds ratio [OR] 2.85, 95% confidence interval [CI] 1.48-5.50, P = .002), CMS complications (OR 2.42, 95% CI 1.27-4.59, P = .007), and post-HU (OR 3.65, 95% CI 2.54-5.26, P < .001). For TKA, ≥2 pre-HU events were associated with increased risk of 30-day readmission (OR 3.52, 95% CI 1.17-10.61, P = .026) and post-HU (OR 2.64, 95% CI 1.29-5.40, P = .008). There were positive correlations for THA (any pre-HU) and TKA (≥2 pre-HU) with length of stay and number of post-HU events.

CONCLUSION

Patients who utilize non-elective healthcare in the 90 days prior to TJA are at increased risk of readmission, complications, and unplanned post-HU.

LEVEL OF EVIDENCE

Level III.

The Hospital Frailty Risk Score is Not an Accurate Predictor of Treatment Costs for Total Joint Replacement Patients in a Medicare Bundled Payment Population

BACKGROUND

Medically complex patients require more resources and experience higher costs within total joint arthroplasty (TJA) bundled payment models. While risk adjustment would be beneficial for such patients, no tool currently exists which can reliably identify these patients preoperatively. The purpose of this study is to determine if the Hospital Frailty Risk Score (HFRS) is a valid predictor of high-TJA treatment costs.

METHODS

Retrospective analysis was performed on patients who underwent primary TJA between 2015 and 2020 from a single large orthopedic practice. ICD-10 codes from an institutional database were used to calculate HFRS. Cost data including inpatient, postacute, and episode of care (EOC) costs were collected. Charlson comorbidity index, demographics, readmissions, and complications were analyzed.

RESULTS

4936 patients had a calculable HFRS and those with intermediate and high scores experienced more frequent readmissions/complications after TJA, as well as higher EOC costs. However, HFRS did not reliably predict EOC costs, yielding a sensitivity of 49% and specificity of 66%. Multivariate analysis revealed that both patient age and sex are superior individual cost predictors when compared with HFRS. Secondary analyses indicated that HFRS more effectively predicts TJA complications and readmissions but is still nonideal for clinical applications.

CONCLUSION

HFRS has poor sensitivity as a predictor of high-EOC costs for TJA patients but has adequate specificity for predicting postoperative readmissions and complications. Further research is needed to develop a scale that can appropriately predict orthopedic cost outcomes.

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.

Implementation and results of an enhanced recovery (fast-track) program in total knee replacement patients at a French university hospital

INTRODUCTION

In total knee replacement (TKR) surgeries, "fast-track" or enhanced recovery after surgery (ERAS) programs are being developed, but their impact on care pathway quality and safety has not been fully explored in the French literature. The present study aimed to compare results in TKR between fast-track and conventional pathways, addressing the following questions: (1) Are 90-day rates of complications, readmission and surgical revision higher with fast-track? (2) Is mean length of stay (LoS) shorter with fast-track? (3) Are postoperative pain and clinical results improved by fast-track? And, (4) are patients and care staff satisfied with these new programs?

HYPOTHESIS

Implementing fast-track for TKR in a university hospital center is beneficial for the patient and does not impair the quality and safety of care.

PATIENTS AND METHOD

A case-control study was performed using a retrospective analysis of prospectively collected data. A fast-track program was implemented for TKR by modifying the care pathway. This involved instituting a therapeutic education consultation, optimizing blood sparing, modifying surgical practices, and hastening early mobilization thus actively involving patients in their own management. Between January 2017 and January 2019, 216 patients with a mean age of 69.23±7.80years and mean BMI of 30.15±4.79kg/m² were included in the fast-track group, with 335 matched patients included in the conventional group.

RESULTS

At 90days, there were no significant inter-group differences in rates of infection (fast-track=1.39%, conventional=0.90%; p=0.34), readmission (fast-track=3.24%, conventional=3.58%; p=0.49), or surgical revision (fast-track=2.78%, conventional=2.69%; p=0.298). The visual analog scale (VAS) pain rating was 1.56±1.36 in the fast-track group versus 5±2.41 in the conventional group; p<0.001. LoS was 3.17±1.59days in fast-track versus 7.25±1.85days in the conventional group; p<0.001. Ninety-five percent of patients and 96% of care staff were satisfied with the fast-track program.

DISCUSSION

Fast-track implementation ensured quality and safety of care; it did not increase the rate of complications in primary TKR. Mean length of stay was drastically reduced. Both patients and care staff were very satisfied with these new procedures.

LEVEL OF EVIDENCE

III; case-control study.

How to Develop a Fair Revision Arthroplasty Bundle? Using Perioperative Complications and Readmissions to Investigate

BACKGROUND

The next frontier for value-based health care in total joint arthroplasty is revision surgery. Although the disparity in health care utilization between revision procedures compared with primary total hip and total knee arthroplasty (THA/TKA) procedures is recognized, no agreement regarding the risk adjustment necessary to make revision bundles fair to both payors and providers exists. The purpose of this study is to use the risk of perioperative complications and readmissions of patients undergoing revision THA/TKA to establish the foundations of a fair revision arthroplasty bundle.

METHODS

We retrospectively evaluated a consecutive series of 484 aseptic THA/TKA revisions performed at our institution over a 12-month period and compared complications, length of stay, reoperations, and 90-day readmissions to a group of 802 consecutive patients undergoing primary THA/TKA.

RESULTS

169 (34.9%) patients experienced major complications after revision THA/TKA compared with 176 (21.9%) patients undergoing primary THA/TKA (P < .001), (OR 1.91 CI 1.49-2.45, P < .001). Patients undergoing revision TKA were 3.64 times more likely to require hospitalization greater than 3 days (OR 2.59-5.12, CI 95%, P < .001), whereas patients undergoing revision THA were 4.46 times more likely to require hospitalization greater than 3 days (OR 2.89-6.87, CI 95%, P < .001). Revision patients were 3X more likely to have a 90-day readmission and 4X more likely to have a reoperation.

CONCLUSION

For a revision bundle to be fair and widely adopted, either significant financial incentive must be instituted or the latitude given to exclude outliers from the final reconciliation. This must be adjusted to not disincentivize institutions from providing care for failed hip and knee arthroplasties.

Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients

BACKGROUND

In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows.

METHODS

We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using bootstrapping with 2000 samples.

RESULTS

Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index ≥2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/).

CONCLUSIONS

A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.

Concomitant Malnutrition and Frailty Are Uncommon, but Significant Risk Factors for Mortality and Complication Following Primary Total Knee Arthroplasty

BACKGROUND

Total knee arthroplasty (TKA) demand continues to rise, but we are also gaining greater insight into patient risk factors for postoperative complications and excess resource utilization. There has been growing interest in frailty and malnutrition as risk factors, although they are often mistakenly used interchangeably. We aimed at identifying the incidence of their coexistence, and the magnitude of risk they confer to TKA patients.

METHODS

We queried the American College of Surgeons-National Surgery Quality Improvement Program database to identify 4 patient cohorts: healthy/normal serum albumin, healthy/hypoalbuminemic patients, normoalbuminemic/medically frail patients (defined by modified frailty index), and hypoalbuminemic/frail patients. We performed both univariate and multivariate analyses to quantify the risk conferred by each condition in isolation, and in coexistence.

RESULTS

Of 179,702 elective TKA cases from 2006 to 2018, 18.6% of patients were frail only, 3.0% were hypoalbuminemic -only, and just 1.2% were both frail and hypoalbuminemic. The raw rate of any complication was highest in frail/hypoalbuminemic patients (8.7%), 5.2% in hypoalbuminemic patients, 4.8% in frail patients, and just 3.4% in healthy patients (P < .001); the multivariate model revealed odds ratio of a complication in frail/hypoalbuminemic group of 2.40 (95% confidence interval = 1.27-1.63; P < .001). Mortality within 30 days was highest in the frail/hypoalbuminemic cohort (1.0%), and just 0.1% in healthy patients, and the multivariate model noted an odds ratio of 9.43 for these patients (95% confidence interval = 5.92-14.93; P < .001). The odds of all studied complications were highest in the frail/hypoalbuminemic group.

CONCLUSION

Frailty and hypoalbuminemia represent distinct conditions and are independent risk factors for a complication after TKA. Their coexistence imparts a synergistic association with the risk of post-TKA complications.

Effect of Posthospital Syndrome on Discharge Disposition and Healthcare Utilization After Primary Total Joint Arthroplasty

BACKGROUND

The aim of this study is to evaluate the impact of posthospital syndrome (PHS), a physiologically deconditioned state experienced by patients after hospitalizations, on postoperative healthcare utilization and discharge disposition following total hip (THA) and knee (TKA) arthroplasty.

METHODS

Insurance claims from the Truven MarketScan Databases were used to perform this cross-sectional study of patients who underwent unilateral, primary THA or TKA between January 2010 and December 2016. PHS, defined as a hospitalization within 90 days before surgery, and non-PHS cohorts were compared. Multivariable logistic regression analyses were used to identify risk of postoperative discharge to an extended care facility (ECF), hospital readmissions, and emergency department visits within 90 days.

RESULTS

This study included 115,465 THA and 190,398 TKA patients who underwent elective surgery for osteoarthritis. PHS was identified in 1.9% and 1.6% of cohorts, respectively, and was more common in patients with higher comorbidities. The PHS cohort had higher crude rates of discharge to ECF (THA 38.8% and TKA 33.8%) and readmissions (21.8% and 18%). Adjusted odds ratios showed that PHS increased risk of disposition to ECF (THA 1.9 and TKA 1.4), readmission (2.8 and 2.0), and emergency department encounters (1.6 and 1.4). Among PHS patients, acute hospitalizations within 30 days of surgery and those lasting greater than 5 days had the highest risk of postoperative healthcare utilization.

CONCLUSION

In this study of commercially insured patients, those with an acute hospitalization within 90 days before elective total joint arthroplasty were nearly twice as likely to be discharged to an ECF and twice as likely to be readmitted in the global postoperative period.

The Risk Assessment and Prediction Tool (RAPT) for Discharge Planning in a Posterior Lumbar Fusion Population

BACKGROUND

As the use of bundled care payment models has become widespread in neurosurgery, there is a distinct need for improved preoperative predictive tools to identify patients who will not benefit from prolonged hospitalization, thus facilitating earlier discharge to rehabilitation or nursing facilities.

OBJECTIVE

To validate the use of Risk Assessment and Prediction Tool (RAPT) in patients undergoing posterior lumbar fusion for predicting discharge disposition.

METHODS

Patients undergoing elective posterior lumbar fusion from June 2016 to February 2017 were prospectively enrolled. RAPT scores and discharge outcomes were recorded for patients aged 50 yr or more (n = 432). Logistic regression analysis was used to assess the ability of RAPT score to predict discharge disposition. Multivariate regression was performed in a backwards stepwise logistic fashion to create a binomial model.

RESULTS

Escalating RAPT score predicts disposition to home (P &lt; .0001). Every unit increase in RAPT score increases the chance of home disposition by 55.8% and 38.6% than rehab and skilled nursing facility, respectively. Further, RAPT score was significant in predicting length of stay (P = .0239), total surgical cost (P = .0007), and 30-d readmission (P &lt; .0001). Amongst RAPT score subcomponents, walk, gait, and postoperative care availability were all predictive of disposition location (P &lt; .0001) for both models. In a generalized multiple logistic regression model, the 3 top predictive factors for disposition were the RAPT score, length of stay, and age (P &lt; .0001, P &lt; .0001 and P = .0001, respectively).

CONCLUSION

Preoperative RAPT score is a highly predictive tool in lumbar fusion patients for discharge disposition.

The Effect of Malnutrition on Postoperative Complications Following Surgery for Distal Radius Fractures

PURPOSE

Malnutrition is known to negatively affect outcomes after arthroplasty, hip fracture, and spine surgery. Although distal radius fracture surgery may be considered in a similar patient cohort, the effect of malnutrition in this scenario is unknown. We hypothesized that admission serum albumin level, as a marker for malnutrition, would correlate with the rate of postoperative complications following surgery for distal radius fracture.

METHODS

We performed a retrospective cohort study of the American College of Surgeons National Surgery Quality Improvement database. Patients undergoing open reduction and internal fixation of a distal radius fracture were identified using Current Procedural Terminology codes. We excluded patients who were septic at presentation, were multiply injured, or had open fractures. We collected patient demographics, length of stay, 30-day complications, reoperation, and readmission rates. We performed multivariable linear regression analysis controlling for age, sex, body mass index, operative time, discharge destination, and modified Frailty Index score.

RESULTS

We identified 1,989 patients (mean age, 56 years; range, 18-90 years) with available albumin levels, and 14.7% had hypoalbuminemia (albumin, < 3.5 g/dL). Multivariable regression revealed that malnourished patients had higher rates of postoperative complications (6.5% vs 1.3%; odds ratio [OR] 4.88; 95% confidence interval [95% CI], 2.47-9.66). Specifically, these patients had increased rates of Clavien-Dindo IV (life-threatening) complications (2.4% vs 0%), readmission (7.2% vs 2%; OR, 3.37; 95% CI, 1.88-6.03), and mortality (1.7% vs 0.1%; OR, 9.23; 95% CI, 1.55-54.87). Malnourished patients had significantly longer length of stay (3.55 vs 0.73 days). Albumin concentration was inversely associated with risk of death (OR, 0.12; 95% CI, 0.03-0.52).

CONCLUSIONS

Malnutrition, indicated by albumin less than 3.5 g/dL, is a powerful predictor of uncommon, but important, postoperative complications, including mortality, following surgery for distal radius fracture. Evaluation of preoperative albumin level may, therefore, help surgeons provide individualized counseling and more accurately stratify the risk of patients.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic II.

Modified frailty index is an effective risk-stratification tool for patients undergoing total shoulder arthroplasty

BACKGROUND

Frailty, as quantified by the modified frailty index (mFI), has emerged as a promising method to identify patients at high risk of complications after surgery. Several studies have shown that frailty, as opposed to age, is more predictive of adverse surgical outcomes. We hypothesized that a 5-item mFI could be used to identify patients at elevated risk of complications after total shoulder arthroplasty (TSA).

METHODS

We identified patients aged 50 years or older who underwent TSA in the American College of Surgeons National Surgical Quality Improvement Program database. Pearson χ² analysis and linear regression were used to determine the association of the mFI score with 30-day postoperative complications, reoperation, readmission, length of stay (LOS), adverse hospital discharge, and mortality rate.

RESULTS

The study included 9861 patients with a mean age of 70 years. As the mFI score increased from 0 to 2 or greater, the following rates increased: postoperative complications from 4.2% to 9.4%, readmission from 1.6% to 4.4%, adverse hospital discharge from 6.3% to 19.6%, and LOS from 1.88 days to 2.43 days (P < .001). Multivariate analysis showed that patients with an mFI score of 2 or greater were over twice as likely to sustain a postoperative complication (odds ratio [OR], 2.4; 95% confidence interval [CI], 1.86-3.10), readmission (OR, 2.80; 95% CI, 1.88-4.17), reoperation (OR, 1.82; 95% CI, 1.02-3.25), and adverse hospital discharge (OR, 3.14; 95% CI, 2.51-3.92). These effects were all significantly higher compared with age.

CONCLUSION

Frailty is associated with increased rates of 30-day postoperative complications, readmission, reoperation, adverse hospital discharge, and hospital LOS after TSA. Use of a simple frailty evaluation may help inform decision making and risk assessment when considering TSA.

Evaluation of the Risk Assessment and Prediction Tool for Postoperative Disposition Needs After Cervical Spine Surgery

BACKGROUND

Bundled care payment models are becoming more prevalent in neurosurgery. Such systems place the cost of postsurgical facilities in the hands of the discharging health system. Opportunity exists to leverage prediction tools for discharge disposition by identifying patients who will not benefit from prolonged hospitalization and facilitating discharge to post-acute care facilities.

OBJECTIVE

To validate the use of the Risk Assessment and Predictive Tool (RAPT) along with other clinical variables to predict discharge disposition in a cervical spine surgery population.

METHODS

Patients undergoing cervical spine surgery at our institution from June 2016 to February 2017 and over 50 yr old had demographic, surgical, and RAPT variables collected. Multivariable regression analyzed each variable's ability to predict discharge disposition. Backward selection was used to create a binomial model to predict discharge disposition.

RESULTS

A total of 263 patients were included in the study. Lower RAPT score, RAPT walk subcomponent, older age, and a posterior approach predicted discharge to a post-acute care facility compared to home. Lower RAPT also predicted an increased risk of readmission. RAPT score combined with age increased the predictive capability of discharge disposition to home vs skilled nursing facility or acute rehabilitation compared to RAPT alone (P < .001).

CONCLUSION

RAPT score combined with age is a useful tool in the cervical spine surgery population to predict postdischarge needs. This tool may be used to start early discharge planning in patients who are predicted to require post-acute care facilities. Such strategies may reduce postoperative utilization of inpatient resources.

A Weighted Index of Elixhauser Comorbidities for Predicting 90-day Readmission After Total Joint Arthroplasty

BACKGROUND

Evolving reimbursement models increasingly compel hospitals to assume costs for 90-day readmission after total joint arthroplasty. Although risk assessment tools exist, none currently reach the predictive performance required to accurately identify high-risk patients and modulate perioperative care accordingly. Although unlikely to perform adequately alone, the Elixhauser index is a set of 31 variables that may lend value in a broader model predicting 90-day readmission.

METHODS

Elixhauser comorbidities were examined in 10,022 primary unilateral total joint replacements, of which 4535 were hip replacements and 5487 were knee replacements, all performed between June 2013 and January 2018 at a single tertiary referral center. Data were extracted from electronic medical records using structured query language. After randomizing to derivation (80%) and validation (20%) subgroups, predictive models for 90-day readmission were generated and transformed into a system of weights based on each parameter's relative performance.

RESULTS

We observed 497 90-day readmissions (5.0%) during the study period, which demonstrated independent associations with 14 of the 31 Elixhauser comorbidity groups. A score created from the sum of each patient's weighted comorbidities did not lose substantial predictive discrimination (area under the curve: 0.653) compared to a comprehensive multivariable model containing all 31 unweighted Elixhauser parameters (area under the curve: 0.665). Readmission risk ranged from 3% for patients with a score of 0 to 27% for those with a score of 8 or higher.

CONCLUSIONS

The Elixhauser comorbidity score already meets or exceeds the predictive discrimination of available risk calculators. Although insufficient by itself, this score represents a valuable summary of patient comorbidities and merits inclusion in any broader model predicting 90-day readmission risk after total joint arthroplasty.

LEVEL OF EVIDENCE

III.

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

BACKGROUND

A reliable prediction tool for 90-day adverse events not only would provide patients with valuable estimates of their individual risk perioperatively, but would also give health-care systems a method to enable them to anticipate and potentially mitigate postoperative complications. Predictive accuracy, however, has been challenging to achieve. We hypothesized that a broad range of patient and procedure characteristics could adequately predict 90-day readmission after total joint arthroplasty (TJA).

METHODS

The electronic medical records on 10,155 primary unilateral total hip (4,585, 45%) and knee (5,570, 55%) arthroplasties performed at a single institution from June 2013 to January 2018 were retrospectively reviewed. In addition to 90-day readmission status, >50 candidate predictor variables were extracted from these records with use of structured query language (SQL). These variables included a wide variety of preoperative demographic/social factors, intraoperative metrics, postoperative laboratory results, and the 30 standardized Elixhauser comorbidity variables. The patient cohort was randomly divided into derivation (80%) and validation (20%) cohorts, and backward stepwise elimination identified important factors for subsequent inclusion in a multivariable logistic regression model.

RESULTS

Overall, subsequent 90-day readmission was recorded for 503 cases (5.0%), and parameter selection identified 17 variables for inclusion in a multivariable logistic regression model on the basis of their predictive ability. These included 5 preoperative parameters (American Society of Anesthesiologists [ASA] score, age, operatively treated joint, insurance type, and smoking status), duration of surgery, 2 postoperative laboratory results (hemoglobin and blood-urea-nitrogen [BUN] level), and 9 Elixhauser comorbidities. The regression model demonstrated adequate predictive discrimination for 90-day readmission after TJA (area under the curve [AUC]: 0.7047) and was incorporated into static and dynamic nomograms for interactive visualization of patient risk in a clinical or administrative setting.

CONCLUSIONS

A novel risk calculator incorporating a broad range of patient factors adequately predicts the likelihood of 90-day readmission following TJA. Identifying at-risk patients will allow providers to anticipate adverse outcomes and modulate postoperative care accordingly prior to discharge.

LEVEL OF EVIDENCE

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