Length of Stay Increases 90-day Readmission Rates in Patients Undergoing Primary Total Joint Arthroplasty

Medical Causes Account for 75% of Readmissions After Primary Total Hip Arthroplasty: Differences in Episodes of Care

BACKGROUND

Recent reports have suggested that readmissions due to medical or orthopaedic surgical causes after total hip arthroplasty (THA) differ regarding risk factors and cost. Further work is needed to elucidate explanations for cost differences to develop targeted initiatives for improved quality of care and health care utilization surrounding THA. This study aimed to determine differences in episodes of care (EOC) between patients readmitted within 90 days of THA for medical and orthopaedic causes.

METHODS

The study included all patients who underwent elective, unilateral, primary THA at a tertiary medical center from 2016 to 2020 and were subsequently readmitted within 90 days. Readmissions were classified as related to medical or orthopaedic surgical causes. Demographic and clinical information related to the EOC for the readmission hospital stay was collected.

RESULTS

The 90-day readmission rate after THA was 5.6% (502 of 8,893 patients), with 75.1% (377 of 502) related to medical causes and 24.9% (125 of 502) related to orthopaedic causes. The EOC between the two groups differed in several ways. Patients readmitted for medical causes more frequently required intensive care unit admissions (12.0 versus 4.9%, P = 0.024), while a larger proportion of patients who had orthopaedic-related readmissions required blood product transfusions (36.3 versus 12.0%, P < 0.001), minimally invasive procedures (34.4 versus 18.9%, P < 0.001), and surgical interventions (79.2 versus 7.2%, P < 0.001).

CONCLUSIONS

Understanding differences in readmission EOC related to medical and orthopaedic causes after THA can help optimize health care allocation strategies and inform targeted quality improvement initiatives. As the demand for THA grows and reimbursement declines, insights into the predominance of medical readmissions and key differences in EOC are crucial for enhancing the quality and cost-effectiveness of THA care delivery models.

Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database

INTRODUCTION

Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository.

MATERIALS AND METHODS

We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility.

RESULTS

The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age.

CONCLUSIONS

Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.

Readmission rate and early complications in patients undergoing total knee arthroplasty: A retrospective study

BACKGROUND

Total knee arthroplasty (TKA) can improve pain, quality of life, and functional outcomes. Although uncommon, postoperative complications are extremely consequential and thus must be carefully tracked and communicated to patients to assist their decision-making before surgery. Identification of the risk factors for complications and readmissions after TKA, taking into account common causes, temporal trends, and risk variables that can be changed or left unmodified, will benefit this process.

AIM

To assess readmission rates, early complications and their causes after TKA at 30 days and 90 days post-surgery.

METHODS

This was a prospective and retrospective study of 633 patients who underwent TKA at our hospital between January 1, 2017, and February 28, 2022. Of the 633 patients, 28 were not contactable, leaving 609 who met the inclusion criteria. Both inpatient and outpatient hospital records were retrieved, and observations were noted in the data collection forms.

RESULTS

Following TKA, the 30-day and 90-day readmission rates were determined to be 1.1% (n = 7) and 1.8% (n = 11), respectively. The unplanned visit rate at 30 days following TKA was 2.6% (n = 16) and at 90 days was 4.6% (n = 28). At 90 days, the unplanned readmission rate was 1.4% (n = 9). Reasons for readmissions included medical (27.2%, n = 3) and surgical (72.7%, n = 8). Unplanned readmissions and visits within 90 days of follow-up did not substantially differ by age group (P = 0.922), body mass index (BMI) (P = 0.633), unilateral vs bilateral TKA (P = 0.696), or patient comorbidity status (30-day P = 0.171 and 90-day P = 0.813). Reoperation rates after TKA were 0.66% (n = 4) at 30 days and 1.15% (n = 8) at 90 days. The average length of stay was 6.53 days.

CONCLUSION

In this study, there was a low readmission rate following TKA. There was no significant correlation between readmission rate and patient factors such as age, BMI, and co-morbidity status.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5 days), body mass index (BMI) (> 33.21 kg/m2), and operation time (> 93 min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept =  - 0.11; RF, slope = 0.93, intercept =  - 0.12; HGB, slope = 0.90, intercept =  - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Are social determinants of health associated with an increased length of hospitalization after revision total hip and knee arthroplasty? A comparison study of social deprivation indices

INTRODUCTION

Length of stay (LOS) has been extensively assessed as a marker for healthcare utilization, functional outcomes, and cost of care for patients undergoing arthroplasty. The notable patient-to-patient variation in LOS following revision hip and knee total joint arthroplasty (TJA) suggests a potential opportunity to reduce preventable discharge delays. Previous studies investigated the impact of social determinants of health (SDoH) on orthopaedic conditions and outcomes using deprivation indices with inconsistent findings. The aim of the study is to compare the association of three publicly available national indices of social deprivation with prolonged LOS in revision TJA patients.

MATERIALS AND METHODS

1,047 consecutive patients who underwent a revision TJA were included in this retrospective study. Patient demographics, comorbidities, and behavioral characteristics were extracted. Area deprivation index (ADI), social deprivation index (SDI), and social vulnerability index (SVI) were recorded for each patient, following which univariate and multivariate logistic regression analyses were performed to determine the relationship between deprivation measures and prolonged LOS (greater than five days postoperatively).

RESULTS

193 patients had a prolonged LOS following surgery. Categorical ADI was significantly associated with prolonged LOS following surgery (OR = 2.14; 95% CI = 1.30-3.54; p = 0.003). No association with LOS was found using SDI and SVI. When accounting for other covariates, only ASA scores (ORrange=3.43-3.45; p < 0.001) and age (ORrange=1.00-1.03; prange=0.025-0.049) were independently associated with prolonged LOS.

CONCLUSION

The varying relationship observed between the length of stay and socioeconomic markers in this study indicates that the selection of a deprivation index could significantly impact the outcomes when investigating the association between socioeconomic deprivation and clinical outcomes. These results suggest that ADI is a potential metric of social determinants of health that is applicable both clinically and in future policies related to hospital stays including bundled payment plan following revision TJA.

Do preoperative PROMIS scores independently predict 90-day readmission following primary total knee arthroplasty?

INTRODUCTION

The rising demand for total knee arthroplasty (TKA) is expected to increase the total number of TKA-related readmissions, presenting significant public health and economic burden. With the increasing use of Patient-Reported Outcomes Measurement Information System (PROMIS) scores to inform clinical decision-making, this study aimed to investigate whether preoperative PROMIS scores are predictive of 90-day readmissions following primary TKA.

MATERIALS AND METHODS

We retrospectively reviewed a consecutive series of 10,196 patients with preoperative PROMIS scores who underwent primary TKA. Two comparison groups, readmissions (n = 79; 3.6%) and non-readmissions (n = 2091; 96.4%) were established. Univariate and multivariate logistic regression analyses were then performed with readmission as the outcome variable to determine whether preoperative PROMIS scores could predict 90-day readmission.

RESULTS

The study cohort consisted of 2170 patients overall. Non-white patients (OR = 3.53, 95% CI [1.16, 10.71], p = 0.026) and patients with cardiovascular or cerebrovascular disease (CVD) (OR = 1.66, 95% CI [1.01, 2.71], p = 0.042) were found to have significantly higher odds of 90-day readmission after TKA. Preoperative PROMIS-PF10a (p = 0.25), PROMIS-GPH (p = 0.38), and PROMIS-GMH (p = 0.07) scores were not significantly associated with 90-day readmission.

CONCLUSION

This study demonstrates that preoperative PROMIS scores may not be used to predict 90-day readmission following primary TKA. Non-white patients and patients with CVD are 3.53 and 1.66 times more likely to be readmitted, highlighting existing racial disparities and medical comorbidities contributing to readmission in patients undergoing TKA.

Preoperative Prediction and Risk Factor Identification of Hospital Length of Stay for Total Joint Arthroplasty Patients Using Machine Learning

Background

The aim of this study was to improve understanding of hospital length of stay (LOS) in patients undergoing total joint arthroplasty (TJA) in a high-efficiency, hospital-based pathway.

Methods

We retrospectively reviewed 1401 consecutive primary and revision TJA patients across 67 patient and preoperative care characteristics from 2016 to 2019 from the institutional electronic health records. A machine learning approach, testing multiple models, was used to assess predictors of LOS.

Results

The median LOS was 1 day; outpatients accounted for 16.5%, 1-day inpatient stays for 38.0%, 2-day stays for 26.4%, and 3-days or more for 19.1%. Patients characteristically fell into 1 of 3 broad categories that contained relatively similar characteristics: outpatient (0-day LOS), short stay (1- to 2-day LOS), and prolonged stay (3 days or greater). The random forest models suggested that a lower Risk Assessment and Prediction Tool score, unplanned admission or hospital transfer, and a medical history of cardiovascular disease were associated with an increased LOS. Documented narcotic use for surgery preparation prior to hospitalization and preoperative corticosteroid use were factors independently associated with a decreased LOS.

Conclusions

After TJA, most patients have either an outpatient or short-stay hospital episode. Patients who stay 2 days do not differ substantially from patients who stay 1 day, while there is a distinct group that requires prolonged admission. Our machine learning models support a better understanding of the patient factors associated with different hospital LOS categories for TJA, demonstrating the potential for improved health policy decisions and risk stratification for centers caring for complex patients.

How to Create an Orthopaedic Arthroplasty Administrative Database Project: A Step-by-Step Guide Part I: Study Design

BACKGROUND

Use of clinical and administrative databases in orthopaedic surgery research has grown substantially in recent years. It is estimated that approximately 10% of all published lower extremity arthroplasty research have been database studies. The aim of this review is to serve as a guide on how to (1) design, (2) execute, and (3) publish an orthopaedic administrative database arthroplasty project.

METHODS

In part I, we discuss how to develop a research question and choose a database (when databases should/should not be used), detailing advantages/disadvantages of those most commonly used. To date, the most commonly published databases in orthopaedic research have been the National Inpatient Sample, Medicare, National Surgical Quality Improvement Program, and those provided by PearlDiver. General advantages of most database studies include accessibility, affordability compared to prospective research studies, ease of use, large sample sizes, and the ability to identify trends and aggregate outcomes of multiple health care systems/providers.

RESULTS

Disadvantages of most databases include their retrospective observational nature, limitations of procedural/billing coding, relatively short follow-up, limited ability to control for confounding variables, and lack of functional/patient-reported outcomes.

CONCLUSION

Although this study is not all-encompassing, we hope it will serve as a starting point for those interested in conducting and critically reviewing lower extremity arthroplasty database studies.