Including socioeconomic status reduces readmission penalties to safety-net hospitals.

OBJECTIVE: Medicare's Hospital Readmissions Reduction Program (HRRP) financially penalizes "excessive" postoperative readmissions. Concerned with creating a double standard for institutions treating a high percentage of economically vulnerable patients, Medicare elected to exclude socioeconomic status (SES) from its risk-adjustment model. However, recent evidence suggests that safety-net hospitals (SNHs) caring for many low-SES patients are disproportionately penalized under the HRRP. We sought to simulate the impact of including SES-sensitive models on HRRP penalties for hospitals performing lower extremity revascularization (LER). METHODS: This is a retrospective, cross-sectional analysis of national data on Medicare patients undergoing open or endovascular LER procedures between 2007 and 2009. We used hierarchical logistic regression to generate hospital risk-standardized 30-day readmission rates under Medicare's current model (adjusting for age, sex, comorbidities, and procedure type) compared with models that also adjust for SES. We estimated the likelihood of a penalty and penalty size for SNHs compared with non-SNHs under the current Medicare model and these SES-sensitive models. RESULTS: Our study population comprised 1708 hospitals performing 284,724 LER operations with an overall unadjusted readmission rate of 14.4% (standard deviation: 5.3%). Compared with the Centers for Medicare and Medicaid Services model, adjusting for SES would not change the proportion of SNHs penalized for excess readmissions (55.1% vs 53.4%, P = .101) but would reduce penalty amounts for 38% of SNHs compared with only 17% of non-SNHs, P < .001. CONCLUSIONS: For LER, changing national Medicare policy to including SES in readmission risk-adjustment models would reduce penalty amounts to SNHs, especially for those that are also teaching institutions. Making further strides toward reducing the national disparity between SNHs and non-SHNs on readmissions, performance measures require strategies beyond simply altering the risk-adjustment model to include SES.

Indication for Lower Extremity Revascularization and Hospital Profiling of Readmissions.

BACKGROUND: Surgical readmissions are common, costly, and the focus of national quality improvement efforts. Given the relatively high readmission rates among vascular patients, pay-for-performance initiatives such as Medicare's Hospital Readmissions Reduction Program (HRRP) have targeted vascular surgery for increased scrutiny in the near future. Yet, the extent to which institutional case mix influences hospital profiling remains unexplored. We sought to evaluate whether higher readmission rates in vascular surgery are a reflection of worse performance or of treating sicker patients. METHODS: This retrospective observational cohort study of the national Medicare population includes 479,047 beneficiaries undergoing lower extremity revascularization (LER) in 1,701 hospitals from 2005 to 2009. We employed hierarchical logistic regression to mimic Center for Medicare and Medicaid Services methodology accounting for age, gender, preexisting comorbidities, and differences in hospital operative volume. We estimated 30-day risk-standardized readmission rates (RSRR) for each hospital when including (1) all LER patients; (2) claudicants; or (3) high-risk patients (rest pain, ulceration, or tissue loss). We stratified hospitals into quintiles based on overall RSRR for all LERs and examined differences in RSRR for claudicants and high-risk patients between and within quintiles. Next, we evaluated differences in case mix (the proportion of claudicants and high-risk patients treated) across quintiles. Finally, we simulated differences in the receipt of penalties before and after adjusting for hospital case mix. RESULTS: Readmission rates varied widely by indication: 7.3% (claudicants) vs. 19.5% (high risk). Even after adjusting for patient demographics, length of stay, and discharge destination, high-risk patients were significantly more likely to be readmitted (odds ratio 1.76, 95% confidence interval 1.71-1.81). The Best hospitals (top quintile) under the HRRP treated a much lower proportion of high-risk patients compared with the Worst hospitals (bottom quintile) (20% vs. 56%, P < 0.001). In the absence of case-mix adjustment, we observed a stepwise increase in the proportion of hospitals penalized as the proportion of high-risk patients treated increased (35-60%, P < 0.001). However, after case-mix adjustment, there were no differences between quintiles in the proportion of hospitalized penalized (50-46%, P = 0.30). CONCLUSION: Our findings suggest that the differences in readmission rates following LER are largely driven by hospital case mix rather than true differences in quality.

Time-to-readmission and Mortality After High-risk Surgery.

OBJECTIVES: To determine if mortality varies by time-to-readmission (TTR). BACKGROUND: Although readmissions reduction is a national health care priority, little progress has been made toward understanding why only some readmissions lead to adverse outcomes. METHODS: In this retrospective cross-sectional cohort analysis, we used 2005-2009 Medicare data on beneficiaries undergoing colectomy, lung resection, or coronary artery bypass grafting (n = 1,033,255) to created 5 TTR groups: no 30-day readmission (n = 897,510), less than 6 days (n = 44,361), 6 to 10 days (n = 31,018), 11 to 15 days (n = 20,797), 16 to 20 days (n = 15,483), or more than 21 days (n = 24,086). Our analyses evaluated TTR groups for differences in risk-adjusted mortality (30, 60, and 90 days) and complications during the index admission. RESULTS: Increasing TTR was associated with a stepwise decline in mortality. For example, 90-day mortality rates in patients readmitted between 1 and 5 days, 6 and 10 days, and 11 and 15 days were 12.6%, 11.4%, and 10.4%, respectively (P < 0.001). Compared to nonreadmitted patients, the adjusted odds ratios (and 95% confidence intervals) were 4.88 (4.72-5.05), 4.20 (4.03-4.37), and 3.81 (3.63-3.99), respectively. Similar patterns were observed for 30- and 60-day mortality. There were no sizable differences in complication rates for patients readmitted within 5 days versus after 21 days (24.8% vs 26.2%, P < 0.001). CONCLUSIONS: Surgical readmissions within 10 days of discharge are disproportionately common and associated with increased mortality independent of index complications. These findings suggest 10-day readmissions should be specially targeted by quality improvement efforts.

Validating administratively derived frailty scores for use in Veterans Health Administration emergency departments.

OBJECTIVES: Frailty is a clinical syndrome characterized by decreased physiologic reserve that diminishes the ability to respond to stressors such as acute illness. Veterans Health Administration (VA) emergency departments (ED) are the primary venue of care for Veterans with acute illness and represent key sites for frailty recognition. As questionnaire-based frailty instruments can be cumbersome to implement in the ED, we examined two administratively derived frailty scores for use among VA ED patients. METHODS: This national retrospective cohort study included all VA ED visits (2017-2020). We evaluated two administratively derived scores: the Care Assessment Needs (CAN) score and the VA Frailty Index (VA-FI). We categorized all ED visits across four frailty groups and examined associations with outcomes of 30-day and 90-day hospitalization and 30-day, 90-day, and 1-year mortality. We used logistic regression to assess the model performance of the CAN score and the VA-FI. RESULTS: The cohort included 9,213,571 ED visits. With the CAN score, 28.7% of the cohort were classified as severely frail; by VA-FI, 13.2% were severely frail. All outcome rates increased with progressive frailty (p-values for all comparisons < 0.001). For example, for 1-year mortality based on the CAN score frailty was determined as: robust, 1.4%; prefrail, 3.4%; moderately frail, 7.0%; and severely frail, 20.2%. Similarly, for 90-day hospitalization based on VA-FI, frailty was determined as prefrail, 8.3%; mildly frail, 15.3%; moderately frail, 29.5%; and severely frail, 55.4%. The c-statistics for CAN score models were higher than for VA-FI models across all outcomes (e.g., 1-year mortality, 0.721 vs. 0.659). CONCLUSIONS: Frailty was common among VA ED patients. Increased frailty, whether measured by CAN score or VA-FI, was strongly associated with hospitalization and mortality and both can be used in the ED to identify Veterans at high risk for adverse outcomes. Having an effective automatic score in VA EDs to identify frail Veterans may allow for better targeting of scarce resources.