There is significant and important variation in the rate of cardiac arrest in hospitalized patients that is not explained by case mix. We also identified hospital factors that were independently predictive of IHCA event rates. These findings have meaningful policy implications—as IHCA rates are easily measurable and case-mix adjustment is straightforward, IHCA event rates can potentially be used for tracking hospital performance.
Prior studies have identified variability and potential causes for variability in out-of-hospital cardiac arrest incidence, but less is known about variability and the causes of variability in IHCA incidence.(23
) As cardiac arrest in hospitalized patients is both a function of a patients’ underlying illness severity and a hospitals’ process of care for treating acutely and chronically ill patients, valid comparisons of IHCA event rates must account for risk adjustment of both patient and hospital level factors. This has been evident in prior work of coronary artery bypass graft procedural outcomes illustrating that if risk adjustment is inadequate, outcomes will appear worse in hospitals which provide care for patients with more severe illnesses.(25
) We included in our model several measures of patient and hospital case-mix previously studied and readily available in the American Hospital Association survey. Using these variables, we demonstrated that case-mix adjustment of IHCA rates is feasible-and non-GWTG-R hospitals can calculate (Appendix A
) their own case-mix adjusted event rate and understand how they compare with other hospitals.
Beyond case-mix, we sought to identify other useful factors that may explain variability in hospital event rates. While the causal pathway for IHCA is complex and multi-factorial, it is important for individual facilities to know how their adjusted IHCA rates compare to other facilities, particularly peer institutions. This knowledge can be a key motivator for quality improvement; Berwick et al refers to this as “move your dot”—an approach for hospitals looking to compare their organizational performance with in-hospital mortality across hospitals and then improve this rate relative to peer institutions.(26
) Clearly, hospitals cannot change their fixed characteristics (size, demographics etc) and it is unlikely that these factors inherently represent the direct mechanisms impacting event rate (i.e. the physical size of the hospital is not protective against IHCA). It is likely however, that these hospital measures provide clues to potentially modifiable factors that impact rates of arrest in hospitalized patients (e.g., large hospitals may be more likely to have rapid response teams).
Other hospital factors predictive of event rate in our model were worrisome and raise equity concerns (e.g., race). The high “excess” IHCA rates at hospitals with large numbers of black patients are troubling, as they provide further evidence that these hospitals may be providing lower quality inpatient care.(27
) Prior work shows that black patients with cardiovascular disease are more likely to receive care at hospitals with fewer evidence-based therapies, longer delays in reperfusion treatment, and overall worse risk-adjusted outcomes from cardiac surgery.(28
) Others have shown that black patients with in-hospital cardiac arrest had longer times to defibrillation than other patients.(18
) Clearly a solution to reducing IHCA event rate would not involve altering the demographics of a hospitals’ patient population but rather examining overall process and quality measures in place at hospitals with high rates of arrest in hospitalized patients. Additional work is needed to determine how to best reduce unexpected cardiac emergencies for all hospitalized patients regardless of race.
Our findings related to hospital volume (i.e., smaller hospitals had a higher IHCA event rate and larger hospitals had a lower IHCA event rate) could reflect differences in hospital resources relative to size. Large hospitals may have more resources in place to recognize early indications of clinical deterioration, more advanced technology, and greater overall intensity of services. Although a higher nurse-to-bed ratio was not associated with lower IHCA event rate, there may be other markers of staffing volume (e.g. housestaff coverage, in-house overnight attending coverage, critical care nurse coverage) that we did not measure which may contribute to the observed lower IHCA event rate at large hospitals.
Our study was limited in that our estimates of event rate were based on registry data, and while extensive checks were in place to insure a high likelihood of capturing most events at an institution, we cannot verify that all IHCA resuscitations were identified. Our denominator (hospital bed days) may not be recorded accurately at all facilities and may not account for all the potential person-time that could contribute to a hospital’s population at risk of cardiac arrest, including patients seen in clinics, hospital visitors, etc. Nevertheless, bed-days are a commonly measured indicator of hospital size that likely reflects the general dimensions of the population at risk.(12
We were also limited in our findings and generalizations about event rates at small hospitals (<100 beds) as these are underrepresented in GWTG-R. It is unlikely however that these event rates can be estimated from other existing administrative datasets. Notably, our results also did not change when we excluded small hospitals from the analysis.
It is possible that hospitals participating in GWTG-R may have been motivated to join the registry because they have lower or higher rates of arrest than non-GWTG-R hospitals. This may be important for contextualizing our reported event rates, which therefore might be higher or lower than the general population of US hospitals.
Finally, we identified differences in event rate according to broad hospital categories, but we were unable to fully explain the mechanism that accounts for differences in case-mix adjusted IHCA. Specifically, there may be differences in end-of-life policies, use of rapid response teams, quality improvement initiatives, or other organizational or demographic factors (e.g. insurance status)(31
) that could further explain the variability in event rate across facilities that were not included in our model.(32
) In addition, validated quality measures for other cardiovascular diseases (e.g. AMI, heart failure) were not included in our analysis but may correlate with IHCA rates as better surrogates for quality and require further study.