Is the Centers for Medicare and Medicaid Services Hierarchical Condition Category Risk Adjustment Model Satisfactory for Quantifying Risk After Spine Surgery?

How Do Bundled Payment Initiatives Account for Differences in Patient Risk Profiles? A Systematic Review

OBJECTIVES

Bundled payments (BPs) are increasingly being adopted to enable the delivery of high-value care. For BPs to reach their goals, accounting for differences in patient risk profiles (PRPs) predictive of spending is crucial. However, insight is lacking into how this is done in practice. This study aims to fill this gap.

METHODS

We conducted a systematic review of literature published until February 2024, focusing on BP initiatives in the Organization for Economic Cooperation and Development countries. We collected data on initiatives' general characteristics, details on the (stated reasons for) approaches used to account for PRP, and suggested improvements. Patterns within and across initiatives were analyzed using extraction tables and thematic analysis.

RESULTS

We included 95 documents about 17 initiatives covering various conditions and procedures. Across these initiatives, patient exclusion (n = 14) and risk adjustment (n = 12) of bundle prices were the most applied methods, whereas risk stratification was less common (n = 3). Most authors stated mitigating perverse incentives as the primary reason for PRP accounting. Commonly used risk factors included comorbidities and sociodemographic and condition/procedure-specific characteristics. Our findings show that, despite increasingly sophisticated approaches over time, key areas for improvement included better alignment with value and equity goals, and enhanced data availability for more comprehensive corrections for relevant risk factors.

CONCLUSIONS

BP initiatives use various approaches to account for PRP differences. Despite a trend toward more sophisticated approaches, most remain basic with room for improvement. To enable cross-initiative comparisons and learning, it is important that stakeholders involved in BPs be transparent about the (reasons for) design choices made.

Low Household Income Increases Hospital Length of Stay and Decreases Home Discharge Rates in Lumbar Fusion

STUDY DESIGN

Retrospective Matched Cohort Study.

OBJECTIVES

Low median household income (MHI) has been correlated with worsened surgical outcomes, but few studies have rigorously controlled for demographic and medical factors at the patient level. This study isolates the relationship between MHI and surgical outcomes in a lumbar fusion cohort using coarsened exact matching.

METHODS

Patients undergoing single-level, posterior lumbar fusion at a single institution were consecutively enrolled and retrospectively analyzed (n = 4263). Zip code was cross-referenced to census data to derive MHI. Univariate regression correlated MHI to outcomes. Patients with low MHI were matched to those with high MHI based on demographic and medical factors. Outcomes evaluated included complications, length of stay, discharge disposition, 30- and 90 day readmissions, emergency department (ED) visits, reoperations, and mortality.

RESULTS

By univariate analysis, MHI was significantly associated with 30- and 90 day readmission, ED visits, reoperation, and non-home discharge, but not mortality. After exact matching (n = 270), low-income patients had higher odds of non-home discharge (OR = 2.5, P = .016) and higher length of stay (mean 100.2 vs 92.6, P = .02). There were no differences in surgical complications, ED visits, readmissions, or reoperations between matched groups.

CONCLUSIONS

Low MHI was significantly associated with adverse short-term outcomes from lumbar fusion. A matched analysis controlling for confounding variables uncovered longer lengths of stay and higher rates of discharge to post-acute care (vs home) in lower MHI patients. Socioeconomic disparities affect health beyond access to care, worsen surgical outcomes, and impose costs on healthcare systems. Targeted interventions must be implemented to mitigate these disparities.

Trends in the Adoption of Outpatient Joint Arthroplasties and Patient Risk: A Retrospective Analysis of 2019 to 2021 Medicare Claims Data

INTRODUCTION

Total joint arthroplasties (TJAs) have recently been shifting toward outpatient arthroplasty. This study aims to explore recent trends in outpatient total joint arthroplasty (TJA) procedures and examine whether patients with a higher comorbidity burden are undergoing outpatient arthroplasty.

METHODS

Medicare fee-for-service claims were screened for patients who underwent total hip, knee, or shoulder arthroplasty procedures between January 2019 and December 2022. The procedure was considered to be outpatient if the patient was discharged on the same date of the procedure. The Hierarchical Condition Category Score (HCC) and the Charlson Comorbidity Index (CCI) scores were used to assess patient comorbidity burden. Patient adverse outcomes included all-cause hospital readmission, mortality, and postoperative complications. Logistic regression analyses were used to evaluate if higher HCC/CCI scores were associated with adverse patient outcomes.

RESULTS

A total of 69,520, 116,411, and 41,922 respective total knee, hip, and shoulder arthroplasties were identified, respectively. Despite earlier removal from the inpatient-only list, outpatient knee and hip surgical volume did not markedly increase until the pandemic started. By 2022Q4, 16%, 23%, and 36% of hip, knee, and shoulder arthroplasties were discharged on the same day of surgery, respectively. Both HCC and CCI risk scores in outpatients increased over time ( P < 0.001).

DISCUSSION

TJA procedures are shifting toward outpatient surgery over time, largely driven by the COVID-19 pandemic. TJA outpatients' HCC and CCI risk scores increased over this same period, and additional research to determine the effects of this should be pursued.

LEVEL OF EVIDENCE

Level III, therapeutic retrospective cohort study.

Integration of Chronological Age Does Not Improve the Performance of a Mixed-Effect Model Using Comorbidity Burden and Frailty to Predict 90-Day Readmission After Surgery for Degenerative Scoliosis

OBJECTIVE

We evaluated the contributions of chronological age, comorbidity burden, and/or frailty in predicting 90-day readmission in patients undergoing degenerative scoliosis surgery.

METHODS

Patients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, and/or ECI. Area under the curve (AUC) was compared using DeLong's test.

RESULTS

A total of 8104 patients were identified. Readmission rate was 9.8%, with infection representing the most common cause (3.5%). Our first model utilized chronological age, ECI, and/or frailty as primary predictors. The combination of ECI + frailty + age performed best, but the inclusion of chronological age did not significantly improve performance compared to ECI + frailty alone (AUC 0.603 vs. 0.599, P = 0.290). A second model using only chronological age and frailty as primary predictors performed better, however the inclusion of chronological age worsened performance when compared to frailty alone (AUC 0.747 vs. 0.743, P = 0.043).

CONCLUSIONS

These data support frailty as a predictor of 90-day readmission within a nationally representative sample. Frailty alone performed better than combinations of ECI and age. Interestingly, the integration of chronological age did not dramatically improve the model's performance. Limitations include the use of a national registry and a single frailty index. This provides impetus to explore biological age, rather than chronological age, as a potential tool for surgical risk assessment.

Developing Mixed-Effects Models to Compare the Predictive Ability of Various Comorbidity Indices in a Contemporary Cohort of Patients Undergoing Lumbar Fusion

BACKGROUND AND OBJECTIVE

As incidence of operative spinal pathology continues to grow, so do the rates of lumbar spinal fusion procedures. Comorbidity indices can be used preoperatively to predict potential complications. However, there is a paucity of research defining the optimal comorbidity indices in patients undergoing spinal fusion surgery. We aimed to use modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof.

METHODS

Patients who underwent spinal fusion were queried using data from the Nationwide Readmissions Database for the years 2016 through 2019. Using comorbidity indices as predictor variables, receiver operating characteristic curves were developed for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 30-day readmission.

RESULTS

A total of 750 183 patients were included. Nonroutine discharges occurred in 161 077 (21.5%) patients. The adjusted all-payer cost for the procedure was $37 616.97 ± $27 408.86 (top quartile: $45 409.20), and the length of stay was 4.1 ± 4.4 days (top quartile: 8.1 days). By comparing receiver operating characteristics of various models, it was found that models using Frailty + Elixhauser Comorbidity Index (ECI) as the primary predictor performed better than other models with statistically significant P -values on post hoc testing. However, for prediction of mortality, the model using Frailty + ECI was not better than the model using ECI alone ( P = .23), and for prediction of all-payer cost, the ECI model outperformed the models using frailty alone ( P < .0001) and the model using Frailty + ECI ( P < .0001).

CONCLUSION

This investigation is the first to use big data and modeling strategies to delineate the relative predictive utility of the ECI and Johns Hopkins Adjusted Clinical Groups comorbidity indices for the prognostication of patients undergoing lumbar fusion surgery. With the knowledge gained from our models, spine surgeons, payers, and hospitals may be able to identify vulnerable patients more effectively within their practice who may require a higher degree of resource utilization.