Temporal trends and patient characteristics associated with 30-day hospital readmission rates after a first acute myocardial infarction

Background: It is unknown if financial pressures to reduce hospital readmission rates following passage of the Affordable Care Act (ACA) have had the unintended effect of increasing mortality rates after hospitalization. We therefore examined correlations between paired changes in hospital 30-day readmission rates and 30-day mortality rates among Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008 to 2014. Methods: We used linear regression to calculate monthly changes in hospitals’ 30-day risk-adjusted readmission rates (RARRs) and 30-day risk-adjusted mortality rates (RAMRs) after discharge for HF, AMI, and pneumonia from 2008 to 2014. Adjustment was made for patient age, sex, comorbidities, hospital length of stay, and season. We then examined the correlation of hospitals’ paired monthly changes in 30-day RARRs and monthly changes in 30-day RAMRs after discharge. Results: From 2008 to 2014, we identified 2,962,554, 1,229,939, and 2,544,530 hospitalizations for HF, AMI, and pneumonia at 5,016, 4,772, and 5,057 hospitals, respectively. Hospital 30-day RARRs declined for all three conditions from 2008 to 2014; the monthly change in RARRs was -0.053 (95% CI -0.055, -0.051) for HF, -0.044 (95% CI -0.047, -0.041) for AMI, and -0.033 (95% CI -0.035, -0.031) for pneumonia. In contrast, the monthly change in hospital 30-day RAMRs after discharge varied by admitting condition and was 0.008 (95% CI 0.007, 0.010) for HF, -0.003 (95% CI -0.006, -0.001) for AMI, and 0.001 (95% CI -0.001, 0.003) for pneumonia. The correlation between monthly changes in hospitals’ 30-day RARRs and 30-day RAMRs after discharge was 0.060 for HF (p<0.001), 0.059 for AMI (p=0.003), and 0.106 for pneumonia (p<0.001). Representative data showing the poor correlation in hospitals’ paired monthly changes in 30-day RARRs and 30-day RAMRs for AMI is shown in the Figure. Conclusion: Changes in hospital readmission rates for HF, AMI, and pneumonia were poorly correlated with changes in mortality rates after hospitalization between 2008 and 2014. These findings suggest that financial incentives to improve hospitals’ readmission performance have not increased mortality after hospitalization.

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Abstract 169: Targeting Patients with Multiple Readmissions in the Year after Non-ST-Elevation Myocardial Infarction Could Lower Hospital Readmission Rates

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.6.suppl_1.a169 ◽

2013 ◽

Vol 6 (suppl_1) ◽

Author(s):

Gwen Bernacki ◽

Karen Alexander ◽

Matthew Roe ◽

Shuang Li ◽

Laine Thomas ◽

...

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Transitional Care ◽

Multivariable Analysis ◽

Patient Characteristics ◽

High Risk Group ◽

Billing Data ◽

Readmission Rates ◽

Bundled Payment ◽

Hospital Readmission Rates

Background: Bundled payment policies have focused on 30-day readmission rates after AMI, yet these are likely to lengthen over time. Identifying patients with multiple readmissions in the year after AMI could help focus transitional care efforts on these high risk patients. Methods: Data from the CRUSADE registry linked to Medicare billing data was used to examine longitudinal outcomes of 32,776 NSTEMI patients ≥ 65 years between 2003 and 2006 with 12-month follow-up. Defining frequent readmissions as ≥3 hospitalizations in 12 months, we compared characteristics of patients frequently readmitted vs. not. The association between frequent readmissions and patient characteristics was examined using multivariable logistic regression. Results: Readmission within 12 months after NSTEMI occurred: once (N=8,830, 26.9%); twice (N=4334, 13.2%); 3 times (N=2,319, 7.1%); ≥4 times (N=2470, 7.5%). Those with multiple (≥3) readmissions (14.6%) were older with recent prior hospitalization and greater prevalence of comorbidities. In multivariable analysis, these factors increased discrimination of patients with frequent readmissions, (c-statistic=0.714). Conclusions: Comorbidities and recent prior hospitalization can predict patients with frequent readmissions. Better understanding of the influence of these clinical factors in this high-risk group presents an opportunity to lower hospital readmission rates.

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