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In-Hospital Risk Standardized Mortality for Percutaneous Coronary Intervention (excluding cardiogenic shock and cardiac arrest)

CBE ID
0133
Endorsement Status
1.0 New or Maintenance
Previous Endorsement Cycle
Is Under Review
No
Next Maintenance Cycle
Spring 2029
1.6 Measure Description

This measure estimates a hospital-level risk standardized mortality rate (RSMR) in adult patients without cardiogenic shock or cardiac arrest undergoing PCI. The outcome is defined as in-hospital mortality following a PCI procedure performed during the episode of care. Mortality is defined as death for any cause during the episode of care.

Measure Specs
General Information
1.7 Measure Type
1.7 Composite Measure
No
1.3 Electronic Clinical Quality Measure (eCQM)
1.8 Level of Analysis
1.9 Care Setting
1.10 Measure Rationale

Measuring patient mortality following a percutaneous coronary intervention (PCI) provides valuable information on a facility’s performance that can be used for benchmarking against the national aggregate and other facilities with similar volumes. These data can assist those facilities with high mortality rates in engaging in quality improvement activities and can be used for clinical decision-making and inform patients as they make decisions regarding their health and healthcare. 

 

The American College of Cardiology developed and iterated on several versions of a mortality model over the years and received endorsement for a measure using one that accounted for patients undergoing high-risk PCI in 2013. That measure was in use within the National Cardiovascular Data Registry (NCDR) CathPCI Registry since 2013[HB1] . Due to ongoing concerns that the most recent model did not adequately account for patients at extreme risk or facilities with lower volumes and therefore hospitals with larger numbers of these individuals would perform more poorly on mortality, ACC integrated additional variables (e.g., frailty, cardiovascular instability) into the CathPCI registry and subsequently developed a new hierarchical mortality model (Castro-Dominguez, 2021; Boyden, 2015; McCabe, 2016; Hannan, 2017). This model uses these new variables, accounts for case mix and hospital volume, and improves on our ability to predict the risk of mortality across different cohorts. 

 

This hierarchical model includes variables that evaluate the patient’s level of consciousness following cardiac arrest and refractory cardiogenic shock, which may classify her or him as extreme risk. The updates to this measure now focus on those patients who do not fall within this risk category and therefore minimizes the risk of penalizing clinicians and facilities who are willing to provide care for these individuals who are more likely to have poorer outcomes unrelated to the care that they subsequently receive at the facility.

 

As a result of this updated model, facilities have access to data that better classify a patient’s mortality risk and allows in-depth analyses of the causes behind variations in mortality during or post PCI leading to the identification of best practices. In addition, detailed case reviews can identify clinicians with poorer performance for whom additional training or reduced caseloads could be considered. Active dissemination of those best practices and support to enable their adoption will improve outcomes and reduce variations in clinical practice. Improvements in the quality of care resulting from the evaluation of the risk for mortality, before and after implementing quality improvement interventions, can enable facilities to quantify their improved outcomes with respect to peri-procedural mortality and a reduction in cost associated with these events. Additionally, by putting the responsibility for improved quality in the hands of physicians and other healthcare providers, this updated risk standardized mortality measure engages the medical community around the common goal of better healthcare value.

 

References: 

Boyden TF, Joynt KE, McCoy L, Neely ML, Cavender MA, Dixon S, Masoudi FA, Peterson E, Rao SV, Gurm HS. Collaborative quality improvement vs public reporting for percutaneous coronary intervention: A comparison of percutaneous coronary intervention in New York vs Michigan. Am Heart J. 2015 Dec;170(6):1227-33. doi: 10.1016/j.ahj.2015.09.006. Epub 2015 Sep 16. PMID: 26678645; PMCID: PMC6948714.

 

Castro-Dominguez YS, Wang Y, Minges KE, et al. Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention. J Am Coll Cardiol. 2021 Jul 20;78(3):216-229. doi: 10.1016/j.jacc.2021.04.067. Epub 2021 May 3. PMID: 33957239.

 

Hannan EL, Zhong Y, Cozzens K, Gesten F, Friedrich M, Berger PB, Jacobs AK, Walford G, Ling FS, Venditti FJ, King SB 3rd. The Impact of Excluding Shock Patients on Hospital and Physician Risk-Adjusted Mortality Rates for Percutaneous Coronary Interventions: The Implications for Public Reporting. JACC Cardiovasc Interv. 2017 Feb 13;10(3):224-231. doi: 10.1016/j.jcin.2016.10.040. PMID: 28183462.

 

McCabe JM, Waldo SW, Kennedy KF, Yeh RW. Treatment and Outcomes of Acute Myocardial Infarction Complicated by Shock After Public Reporting Policy Changes in New York. JAMA Cardiol. 2016 Sep 1;1(6):648-54. doi: 10.1001/jamacardio.2016.1806. PMID: 27463734.

 

 

1.20 Types of Data Sources
1.25 Data Source Details

National Cardiovascular Data Registryâ (NCDR) CathPCI Registry