Author + information
- Received February 19, 2019
- Revision received March 21, 2019
- Accepted March 31, 2019
- Published online June 17, 2019.
- Devraj Sukul, MD, MSa,b,∗ (, )@d_sukul@umichCVC@BMC2PCI,
- Milan Seth, MSa,
- Geoffrey D. Barnes, MD, MSa,b,
- James M. Dupree, MD, MPHb,c,d,
- John D. Syrjamaki, MPHc,d,
- Simon R. Dixon, MBChBe,
- Ryan D. Madder, MDf,
- Daniel Lee, MDg and
- Hitinder S. Gurm, MDa,b,h
- aDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MichiganDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- bInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MichiganInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
- cMichigan Value Collaborative, University of Michigan, Ann Arbor, MichiganMichigan Value Collaborative, University of Michigan, Ann Arbor, Michigan
- dDow Division of Health Services Research, Department of Urology, University of Michigan, Ann Arbor, MichiganDow Division of Health Services Research, Department of Urology, University of Michigan, Ann Arbor, Michigan
- eDepartment of Cardiovascular Medicine, Beaumont Hospital, Royal Oak, MichiganDepartment of Cardiovascular Medicine, Beaumont Hospital, Royal Oak, Michigan
- fDivision of Cardiology, Spectrum Health, Grand Rapids, MichiganDivision of Cardiology, Spectrum Health, Grand Rapids, Michigan
- gDivision of Cardiology, McLaren Health Care, Bay City, MichiganDivision of Cardiology, McLaren Health Care, Bay City, Michigan
- hDivision of Cardiology, Department of Internal Medicine, VA Ann Arbor Healthcare System, Ann Arbor, MichiganDivision of Cardiology, Department of Internal Medicine, VA Ann Arbor Healthcare System, Ann Arbor, Michigan
- ↵∗Address for correspondence:
Dr. Devraj Sukul, University of Michigan Health System, 1500 East Medical Center Drive, Ann Arbor, Michigan 48109-5853.
Among patients with coronary artery disease, participation in outpatient cardiac rehabilitation (CR) is associated with improved quality of life and reduced rates of readmission and cardiovascular mortality (1). For these reasons, CR is recommended in international guidelines (2,3). Despite its proven benefit, the use of CR remains poor, with historical enrollment rates between 20% and 40% (4–6).
There are many reasons for this gap in CR utilization, including low rates of referral. Therefore, over the past decade, there has been increased emphasis on improving rates of CR referral. However, there is a dearth of research examining the link between CR referral and utilization, and the patient factors associated with downstream utilization. Identification of such factors may help inform health care policies and hospital initiatives focused on improving CR use, not just referral.
In this context, we used a multicenter registry of percutaneous coronary interventions (PCIs) performed in the state of Michigan combined with administrative claims to identify demographic, procedural, geographic, and socioeconomic factors associated with CR use after PCI.
We linked 2 data sources for this research. The first was the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) clinical PCI registry. BMC2 is a prospective, multicenter, statewide registry of all patients who undergo PCI at all nonfederal hospitals in Michigan. The second data source was a claims-based registry developed by the Michigan Value Collaborative (MVC). This claims-based registry includes 90-day price-standardized episodes of care from Medicare fee-for-service (FFS) and Blue Cross Blue Shield of Michigan preferred provider organization (BCBSM PPO) administrative claims (7). All clinically related claims within 90 days after discharge from the index hospitalization or procedure were included in the episode.
We linked BMC2 PCI records to MVC’s 90-day episodes of care where PCI occurred through indirect matching of the index PCI procedure using multiple variables including hospital and operator National Provider Identifier numbers; admission, discharge, and procedure dates for the index hospitalization; and patient gender and date of birth.
Using the linked dataset, we evaluated consecutive patients who were discharged home after inpatient or outpatient PCI at 47 PCI-capable hospitals in Michigan between January 2012 and October 2016. Referral to CR before PCI discharge was obtained from the clinical registry (8). We excluded patients who were deemed ineligible for CR referral as defined by the National Cardiovascular Data Registry CathPCI data dictionary v4.4 (8). We also excluded patients with a missing residential ZIP code.
CR use within the 90 days following discharge was obtained from administrative claims, based on the following coding: Current Procedural Terminology codes (93797 and 93798), Healthcare Common Procedure Coding System codes (G0422 and G0423), and revenue center code 943.
Among patients referred for CR, we assessed the association between CR attendance and patient factors including demographics, clinical characteristics, comorbidities, insurance status, and travel distances (Figure 1). Travel distances from the site of PCI to the nearest CR facility (>2 miles vs. ≤2 miles), and distance from the centroid of the patient’s ZIP code to the nearest CR facility were included in the models. Geographic distances were determined using the Google Maps application program interface through the R (version 3.2.1) package ggmap (R Foundation for Statistical Computing, Vienna, Austria). A list of CR facilities was obtained from our institution’s CR scheduling center.
Finally, using the patient’s residential ZIP code, we assessed whether publicly available measures of ZIP–code-level socioeconomic status were individually associated with CR attendance after adjusting for patient characteristics. Socioeconomic measures included the Area Deprivation Index (9) and ZIP–code-level educational and poverty status obtained from the American Community Survey. Of note, patient ZIP codes were mapped to ZIP code tabulation areas, the geographic unit used by the American Community Survey. The Area Deprivation Index is a composite measure of neighborhood disadvantage, with a higher number indicating greater neighborhood disadvantage (9).
We developed logistic regression models using robust standard errors accounting for clustering at hospitals to evaluate the association between the previously described covariates and the use of CR among referred patients. The association between covariates and CR use was expressed as odds ratio and 95% confidence interval (CI) adjusted for all other patient factors included in the model. All analyses were performed using R.
Of 42,334 PCI episodes between January 2012 and October 2016, 30,075 (73.1%) were discharged alive to their home with a referral for CR. Of these, 26,168 (87.0%) had a valid residential ZIP code available and formed the study cohort. A total of 8,246 (31.5%) patients attended at least 1 CR session within 90 days after discharge.
Patients were more likely to attend CR with increasingly acute presentations for PCI such as ST-segment elevation myocardial infarction (STEMI) and non–ST-segment elevation myocardial infarction (NSTEMI) (Figure 1). The presence of comorbidities was generally associated with decreased odds of attending CR. Compared with patients insured by BCBSM PPO insurance, those with Medicare FFS insurance were less likely to attend CR after a referral was made (adjusted odds ratio [aOR]: 0.81; 95% CI: 0.72 to 0.90; p < 0.001) (Figure 1). Among patients with Medicare FFS, patients with both Medicare FFS and Medicaid insurance were significantly less likely to attend CR after a referral was made than those with only Medicare FFS insurance (aOR: 0.44; 95% CI: 0.38 to 0.51; p < 0.001).
The distance from a patient’s ZIP code centroid to the nearest CR was not significantly associated with increased odds of attending CR (aOR: 1.00; 95% CI: 0.98 to 1.02; p = 0.926). Patients who underwent PCI at sites where the nearest CR facility was >2 miles away from the PCI site were significantly less likely to attend CR compared with patients who underwent PCI at sites where the nearest CR facility was ≤2 miles away (aOR: 0.27; 95% CI: 0.14 to 0.55; p < 0.001). Patients living in ZIP codes with higher levels of educational attainment were significantly more likely to attend CR (Figure 1). Patients living in ZIP codes with a higher proportion of families below 125% of the federal poverty level or a higher Area Deprivation Index were associated with a trend toward lower odds of attending CR (Figure 1).
Existing health care policies have succeeded in increasing CR referral rates following PCI. However, this success has not translated into high CR utilization, which remains below 1 in 3 patients following PCI. Additionally, CR utilization was more likely among patients with private insurance and who had their PCI at a health care site with a closely located (and possibly affiliated) CR facility. Finally, multiple socioeconomic factors were associated with CR attendance. Taken together, these findings suggest that simply improving rates of referrals may only partially improve downstream CR utilization.
Only one-third of patients who received a referral for CR after PCI attended at least 1 session—an estimate that is consistent with prior research (5). This gap highlights the need for novel quality improvement initiatives focused on CR use even after prompt in-hospital referral. Similar to prior research, we found that patient insurance status and geographic socioeconomic factors were significantly associated with CR attendance (5). For instance, patients insured by BCBSM PPO, a private insurer, were more likely to attend CR compared with patients insured by Medicare FFS. We speculate that the reason for these differences is multifactorial. For instance, BCBSM PPO plans may have different out-of-pocket costs for patients. If a patient attends a typical 36-session CR program, even a $30 copay per session adds up to >$1,000. Furthermore, attending CR may interfere with employment, placing further financial stress on patients. Indeed, some area-level socioeconomic factors are strongly associated with CR attendance.
Insurers, including Medicare, have considered various ways of incentivizing CR use. In 2016, Medicare announced the Cardiac Rehabilitation Incentive Payment Model, where Medicare would pay hospitals for each session of CR that patients attended after treatment for acute myocardial infarction or coronary artery bypass graft surgery. This program was designed to incentivize hospitals to invest in initiatives aimed at improving CR use. However, this proposed incentive program was cancelled in December 2017 (10). Novel payment models, such as bundled payments, may also incentivize CR use given that CR has been associated with decreased hospitalizations, which are an important driver of payment variation in PCI bundles of care (1).
Our findings should be considered in the context of some important limitations. First, our findings were limited to a single state with a long-standing quality improvement program, thus limiting the generalizability of our findings to other states. Second, we were only able to evaluate CR use in patients insured by Medicare FFS or BCBSM PPO. However, these represent 2 large insurers in the state of Michigan with diverse patient populations. Third, we only had access to area-level, rather than patient-level, socioeconomic factors and geographic distances, which may have different associations with CR utilization and may mask the extent of patient-level variability, potentially subjecting these analyses to the ecological fallacy and misclassification bias.
These findings are instructive to hospitals, physicians, and other policymakers seeking to improve CR use after PCI. From a policy perspective, novel insurance designs that incentivize both CR referral and CR use may be beneficial given the association between insurance type and CR use. Moreover, interventions aimed at reducing socioeconomic disparities, such as financially incentivizing patients to attend CR, may prove beneficial. Further research is needed to understand the mechanisms by which socioeconomic and geographic factors influence CR attendance, so that policies are more precisely designed to improve CR attendance.
Support for the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) and the Michigan Value Collaborative is provided by Blue Cross Blue Shield of Michigan as part of the Blue Cross Blue Shield of Michigan Value Partnerships program; however, the opinions, beliefs and viewpoints expressed by the authors do not necessarily reflect those of Blue Cross Blue Shield of Michigan or any of its employees. Dr. Sukul was supported by a National Institutes of Health T32 postdoctoral research training grant (T32-HL007853); and has received grant support from Blue Cross Blue Shield of Michigan. Dr. Barnes has received grant support from Blue Cross Blue Shield of Michigan, Pfizer/Bristol-Myers Squibb, and the National Institutes of Health; and consulting fees from Pfizer/Bristol-Myers Squibb, Janssen, and Portola. Dr. Dupree has received grant support from Blue Cross Blue Shield of Michigan for his roles with the Michigan Value Collaborative and the Michigan Urological Surgery Improvement Collaborative. Mr. Syrjamaki has received salary support from Blue Cross Blue Shield of Michigan for his role with the Michigan Value Collaborative. Dr. Gurm has received research funding from the National Institutes of Health; and has served as a consultant for Osprey Medical. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received February 19, 2019.
- Revision received March 21, 2019.
- Accepted March 31, 2019.
- 2019 American College of Cardiology Foundation
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