Readmissions after THA can negatively impact patients, increase healthcare utilization, and subsequently increase costs. However, there is little to demonstrate whether readmission after THA is associated with poorer results in outcomes that matter to patients, such as pain relief, restoration of function, or activities of daily living. Moreover, the impact of medical-related readmissions compared with orthopaedic-related readmissions on postoperative outcomes is currently unknown.

Are patients readmitted within 90 days of an index THA less likely to achieve a minimum clinically important difference (MCID) or patient acceptable symptom state (PASS) threshold in postoperative patient-reported outcome measures (PROMs) compared with those who are not readmitted? (2) Is the cause of readmission within 90 days (medical or orthopaedic) associated with a lower likelihood of achieving either MCID or PASS thresholds 1 year after THA? (3) How does 90-day readmission compare with other patient-specific factors associated with a lower likelihood of attaining either MCID or PASS? (4) Are patients readmitted within 90 days more likely to undergo further surgery?

Between January 2016 and December 2020, we treated 11,026 patients with primary THA at a large tertiary academic center. Patients undergoing nonelective (n = 551) and bilateral THAs (n = 1582) were excluded, leaving 8893 for final analysis here. Of these, 2476 patients were lost to follow-up and thus not included in the 1-year PROMs analysis, but they remained included in the study overall. Data for each patient were extracted from a validated longitudinally maintained institutional database. The mean ± SD age of patients was 65 ± 11.5 years, 57% were women, and 86% self-reported as White. Readmissions were individually reviewed and grouped into medical-related or orthopaedic-related causes based on the primary cause. Multivariable logistic regression models were used to investigate the association of 90-day readmission with 1-year PROMs. Overall, 6% (502 of 8893) were readmitted within 90 days, with 75% (377 of 502) being medically related and 25% (125 of 502) being orthopaedically related readmissions. PROM scores were obtained preoperatively and at 1 year after each of the THAs and included Hip Disability and Osteoarthritis Outcome Score (HOOS) pain, physical function, and joint replacement scores, as well as the Veterans Rand 12-Item Health Survey mental component summary score. Each was scored from 0 to 100, with higher scores representing better patient-perceived outcomes. Anchor-based MCID (HOOS pain 33, HOOS physical function 34, and HOOS joint replacement 18) and PASS thresholds (HOOS pain 80.6, HOOS physical function 83.6, and HOOS joint replacement 76.8) were utilized.

Patients readmitted within 90 days of an index THA were less likely to achieve the MCID in HOOS joint replacement (OR 1.5 [95% confidence interval (CI) 1.1 to 2.06]; p = 0.01). Similarly, compared with patients who were not readmitted, readmitted patients were less likely to achieve PASS thresholds in HOOS pain (OR 1.37 [95% CI 1.07 to 1.77]; p = 0.01) and HOOS joint replacement (OR 1.4 [95% CI 1.05 to 1.92]; p = 0.02). Patients readmitted for orthopaedic causes compared with those not readmitted were less likely to achieve the MCID in HOOS joint replacement (OR 2.59 [95% CI 1.56 to 4.31]; p < 0.001) and the PASS threshold in HOOS pain (OR 1.79 [95% 1.12 to 2.86]; p = 0.02) and HOOS joint replacement (1.76 [95% CI 1.06 to 2.93]; p = 0.03) at 1 year. However, patients readmitted for medical causes, compared with patients who were not readmitted, were not less likely to achieve MCID or PASS thresholds in any of the HOOS subscales. The 90-day readmission (Akaike information criterion [AIC] increase 14.1) was the sixth most important variable influencing the likelihood of not achieving the MCID in HOOS joint replacement, following PROM phenotype (AIC 265), opioid use (Narx score), smoking, insurance, and race. Similarly, 90-day readmission ranked as the seventh and sixth most important variable for not achieving PASS thresholds in HOOS pain (AIC increase 7.5) and HOOS joint replacement (AIC increase 8.3), respectively. Patients readmitted within 90 days were more likely to undergo further surgery compared with those who were not readmitted (20.3% versus 0.04%; p < 0.001).

Although medical-related readmissions are more frequent after THA, patients readmitted for orthopaedic causes are less likely to perceive meaningful improvements in pain relief and hip function at 1 year. Surgeons can reassure patients readmitted for medical reasons that their long-term symptom relief and functional recovery remain achievable. These findings highlight the importance of specifically identifying patients at higher risk of orthopaedic-related readmissions and implementing personalized preoperative optimization strategies tailored to their specific risk factors to reduce the chance of complications that may lead to readmission. Future studies should focus on identifying optimal management strategies for readmitted patients to ensure that they continue to perceive substantial benefits in their hip symptoms and function after surgery.

Level III, therapeutic study.

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.

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

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.

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.

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.

Although there have been several studies describing risk factors for complications after outpatient total hip arthroplasty (THA), data describing the timing of such complications is lacking.

Patients who underwent outpatient or inpatient primary THA were identified in the 2012-2019 National Surgical Quality Improvement Program database. For 9 different 30-day complications, the median postoperative day of diagnosis was determined. Multivariable regressions were used to compare the risk of each complication between outpatient vs inpatient groups. Multivariable Cox proportional hazards modeling was used to evaluate the differences in the timing of each adverse event between the groups.

After outpatient THA, the median day of diagnosis for readmission was 12.5 (interquartile range 5-22), surgical site infection 15 (2-21), urinary tract infection 13.5 (6-19.5), deep vein thrombosis 13 (8-21), myocardial infarction 4.5 (1-7), pulmonary embolism 15 (8-25), sepsis 16 (9-26), stroke 2 (0-7), and pneumonia 6.5 (3-10). On multivariable regressions, outpatients had a lower relative risk (RR) of readmission (RR = 0.73), surgical site infection (RR = 0.72), and pneumonia (RR = 0.1), all P < .05. On multivariable cox proportional hazards modeling, there were no statistically significant differences in the timing of each complication between outpatient vs inpatient procedures (P > .05).

The timing of complications after outpatient THA was similar to inpatient procedures. Consideration should be given to lowering thresholds for diagnostic testing after outpatient THA for each complication during the at-risk time periods identified here. Although extremely rare, this is especially important for catastrophic adverse events, which tend to occur early after discharge.

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.

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.

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.

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 primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications.

This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information.

The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility.

In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement.

Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

A higher volume of primary total hip arthroplasty (THA) is starting to be performed as an outpatient procedure. However, data on appropriate patient selection for this surgical protocol is scarce.

Patients who underwent primary THA were identified in the 2012-2018 National Surgical Quality Improvement Program database. Outpatient procedure was defined as having a hospital length of stay of 0 days. The primary outcome was a readmission within the 30-day postoperative period. Risk factors for and effect of overnight hospital stay on 30-day readmission after outpatient THA were identified through multivariable models. Reasons for and timing of readmission were also identified.

A total of 5245 outpatient THA patients and 44,171 patients who stayed 1 night were identified. The incidence of 30-day readmission after outpatient THA was 1.60% (95% confidence interval [CI] 1.26-1.94). Risk factors for 30-day readmission after outpatient THA include the following: older age relative to 18-60 years old (most notably 71-75 years old, relative risk [RR] = 2.3, 95% CI = 1.15-4.62; 76-80 years old, RR = 6.6, 95% CI = 3.55-12.43; and >80 years old, RR = 5.6, 95% CI = 2.43-12.89, P < .001) and bleeding disorders (RR = 4.5, 95% CI = 1.45-14.31, P = .010). For patients who had some of these risk factors, their risk of medically related 30-day readmission was reduced if they had stayed 1 night at the hospital (P < .05). The majority of readmissions were surgically related (62%), including wound complications (27%) and periprosthetic fractures (25%).

The rate of 30-day readmission after outpatient THA was low. Patients who are at high risk for 30-day readmission after outpatient THA include those with older age and bleeding disorders. Some of these patients may benefit from an inpatient hospital stay.

To quantify the rate of readmission from inpatient rehabilitation facilities (IRFs) to acute care hospitals (ACHs) during the first 30 days of rehabilitation stay. To measure variation in 30-day readmission rate across IRFs, and the extent that patient and facility characteristics contribute to this variation.

Retrospective analysis of an administrative database.

Adult IRF discharges from 944 US IRFs captured in the Uniform Data System for Medical Rehabilitation database between October 1, 2015 and December 31, 2017.

Multilevel logistic regression was used to calculate adjusted rates of readmission within 30 days of IRF admission and examine variation in IRF readmission rates, using patient and facility-level variables as predictors.

There were a total of 104,303 ACH readmissions out of a total of 1,102,785 IRFs discharges. The range of 30-day readmission rates to ACHs was 0.0%‒28.9% (mean = 8.7%, standard deviation = 4.4%). The adjusted readmission rate variation narrowed to 2.8%‒17.5% (mean = 8.7%, standard deviation = 1.8%). Twelve patient-level and 3 facility-level factors were significantly associated with 30-day readmission from IRF to ACH. A total of 82.4% of the variance in 30-day readmission rate was attributable to the model predictors.

Fifteen patient and facility factors were significantly associated with 30-day readmission from IRF to ACH and explained the majority of readmission variance. Most of these factors are nonmodifiable from the IRF perspective. These findings highlight that adjusting for these factors is important when comparing readmission rates between IRFs.

The 30-day all-cause readmission rate is a widely used metric of hospital performance. However, there is lack of clarity as to whether 30 days is an appropriate time frame following surgical procedures. Our aim is to determine whether a 90-day time window is superior to a 30-day time window in capturing surgically relevant readmissions after total hip arthroplasty (THA) and total knee arthroplasty (TKA).

We analyzed readmissions following all primary THAs and TKAs recorded in the English National Health Service Hospital Episode Statistics database from 2008 to 2018. We compared temporal patterns of 30- and 90-day readmission rates for the following types of readmission: all-cause, surgical, return to theater, and those related to specific surgical complications.

A total of 1.47 million procedures were recorded. After THA and TKA, over three-quarters of 90-day surgical readmissions took place within the first 30 days (78.5% and 75.7%, respectively). All-cause and surgical readmissions both peaked at day 4 and followed a similar temporal course thereafter. The ratio of surgical to medical readmissions was greater for THA than for TKA, with THA dislocation both being one of the most common surgical complications and clustering early after discharge, with 73.5% of 90-day dislocations occurring within the first 30 days.

The 30-day all-cause readmission rate is a good reflection of surgically relevant readmissions that take place in the first 90 days after THA and TKA.

A higher volume of primary total knee arthroplasty (TKA) is starting to be performed in the outpatient setting. However, data on appropriate patient selection in the current literature are scarce.

Patients who underwent primary TKA were identified in the 2012-2017 National Surgical Quality Improvement Program database. Outpatient procedure was defined as having a hospital length of stay of 0 days. The primary outcome was a readmission within the 30-day postoperative period. Reasons for and timing of readmission were identified. Risk factors for and effect of overnight hospital stay on 30-day readmission were evaluated.

A total of 3015 outpatient TKA patients were identified. The incidence of 30-day readmission was 2.59% (95% confidence interval [CI] 2.02-3.15). The majority of readmissions were nonsurgical site related (64%), which included thromboembolic and gastrointestinal complications. Risk factors for 30-day readmission include dependent functional status prior to surgery (relative risk [RR] 6.4, 95% CI 1.91-21.67, P = .003), hypertension (RR 2.5, 95% CI 1.47-4.25, P = .001), chronic obstructive pulmonary disease (RR 2.4, 95% CI 1.01-5.62, P = .047), and operative time ≥91 minutes (≥70th percentile) (RR 1.9, 95% CI 1.17-2.98, P = .008). For patients who had some of these risk factors, their rate of 30-day readmission was significantly reduced if they had stayed at least 1 night at the hospital.

Overall, the rate of 30-day readmission after outpatient TKA was low. Patients who are at high risk for 30-day readmission after outpatient TKA include those with dependent functional status, hypertension, chronic obstructive pulmonary disease, and prolonged operative time. These patients had reduced readmissions after overnight admission and seem to benefit from an inpatient hospital stay.