The Validity of All-Cause 30-Day Readmission Rate as a Hospital Performance Metric After Primary Total Hip and Knee Arthroplasty: A Systematic Review

Frequency and Timing of Postoperative Complications After Outpatient Total Hip Arthroplasty

Background

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.

Methods

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.

Results

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).

Conclusions

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.

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 Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

What Are the Reasons and Risk Factors for 30-Day Readmission After Outpatient Total Hip Arthroplasty?

BACKGROUND

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.

METHODS

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.

RESULTS

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%).

CONCLUSION

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.

Variation in 30-Day Readmission Rates from Inpatient Rehabilitation Facilities to Acute Care Hospitals

OBJECTIVES

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.

DESIGN

Retrospective analysis of an administrative database.

SETTING AND PARTICIPANTS

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS AND IMPLICATIONS

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.

Timing of Readmissions After Elective Total Hip and Knee Arthroplasty: Does a 30-Day All-Cause Rate Capture Surgically Relevant Readmissions?

BACKGROUND

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).

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Reasons and Risk Factors for 30-Day Readmission After Outpatient Total Knee Arthroplasty: A Review of 3015 Cases

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.