To increase access to PCI in our model of a large urban, suburban and rural region, an EMS strategy of transporting all patients to existing PCI-capable hospitals was more effective and less costly than 13 hospital-based strategies of new construction and staffing. While hospital strategies were cost-effective under a variety of conditions, the EMS strategy dominated in all of the scenarios we tested and in multivariate sensitivity analyses. Our results strongly suggest that construction and staffing of new PCI hospitals may not be warranted if an EMS strategy is both available and feasible. Demonstration programs have shown that EMS detection and diversion of patients with STEMI for delayed PCI are both safe and effective.33,34
Our results suggest that, in EMS systems where STEMI detection and diversion are feasible, such a strategy is more effective and less costly than hospital-based regionalization alternatives. This finding persisted even when the estimated new cost of an EMS strategy was multiplied by a factor of nearly 20, or when its expected benefits were decreased by 55% or more.
Expansion of access to timely PCI is widely considered to be critical for improving outcomes after STEMI. To accomplish this goal, a range of regionalization approaches have been reviewed or evaluated in the research literature.35-39
In order to understand the potential of STEMI regionalization strategies in their full context, however, it is critical that the benefits, risks and costs of all hospital- and EMS strategies be compared in head-to-head match-ups. While the preferred method to compare such strategies might be a randomized effectiveness trial,40
such an approach would not likely be feasible given the large numbers needed to measure rare outcomes after heart attack, as well as the ethical problem of randomizing patients to receive FT when timely PCI is known to be superior.
In this context, the use of mathematical modeling to compare predicted outcomes from PCI expansion strategies is a promising approach. The model we employed combined empirical data from clinical, health systems and geographic sources with clinical predictive instruments to perform head-to-head comparisons of regionalization strategies. Our model for estimating outcomes was sensitive to the number of new PCI treatments resulting from an expansion strategy, and therefore to the regional population's baseline rate of access to PCI. In our model of Dallas County, we estimated a baseline access rate of 30.4%. Two aspects of our model explain why our baseline rate was 50 percentage points lower than a recent national estimate indicating that 80% of the population lives within a one-hour drive of a PCI-capable hospital.4
First, patients in our base case were transported to the closest hospital even if PCI was available within a one-hour drive. Second, the 80% estimate assumes that hospitals with a PCI lab operate the lab 24 hours per day, 7 days per week. Of the 16 hospitals in our model of a large county, 14 had a PCI lab but only two operated the lab full-time. In the base case, we operated the part-time PCI labs from Monday through Friday, 7 am to 5 pm. Two classic papers on the circadian and weekly patterns of heart attack onset estimate that approximately 39% begin during these weekday hours.41,42
We used these estimates to stochastically estimate STEMI onset day and time. In our model, therefore, approximately 61% of patients with STEMI onset in locations served by a part-time lab received immediate FT in the local hospital or delayed PCI after transport to a more distant full-time lab. We believe that our method of accounting for the part-time operation of PCI labs is reflective of actual operations in a region that has not yet introduced regionalization measures. Assuming full-time operation at all hospitals would have led to a significant overestimate of the true baseline access rate.
Nevertheless, in regions with a higher baseline rate of access to primary PCI, we would expect that an EMS strategy would fare better and the hospital strategies would fare worse than in our model. In a hospital strategy, the high fixed costs of construction can be defrayed only by increasing the number of patients with newly created access to PCI. In an EMS strategy, new costs are substantially lower and vary with the number of new transports that are needed. This relatively low variable cost is the primary advantage of an EMS strategy. A second advantage was explored in our previous work: the opportunity to select for transport to existing PCI hospitals only those patients who are predicted to benefit most from PCI. We did not exploit this opportunity in the present study; we transported every patient with suspected STEMI directly to a PCI-capable hospital regardless of predicted benefit. The EMS strategy dominated hospital strategies on the basis of its low variable costs and its potential to reach every patient with STEMI, but we believe an even stronger case could be made for a strategy that involves selective transport of only those patients who are individually predicted to benefit from delayed PCI.
Public policy remains unsettled on the optimal strategy to increase access to PCI. In some states, Certificate of Need laws are used to control the widespread diffusion of high cost and volume-sensitive procedures such as PCI. In 2008, these laws existed in 36 States, but only 23 had provisions for cardiac catheterization services review.43
From 2001 through 2006, American Hospital Association (AHA) data show a steady increase of 50-125 new hospitals with PCI capability in the US each year, in both urban and rural areas.44,45
There is substantial contradictory activity in the public arena that is aimed both at curtailing and at sustaining the diffusion of PCI labs. We believe our approach to comparing alternative strategies can help clarify the impact of such decisions.
For several reasons, Dallas County represents an ideal place to test our model. First, Dallas has a diversity of urban, suburban and rural areas. The majority of Census tracts in Dallas County are designated as urban (comprising 69.7% of the county's dry land area), but a substantial portion of the county is suburban and rural. Second, there is significant variation in PCI capability at hospitals inside the county. Our model showed that just 30.4% of the county's population lived closest to a PCI-capable hospital, leaving substantial room for growth in the availability of PCI. Third, Dallas is bordered to the north, east and south by sparsely populated areas and to the west by Dallas-Fort Worth Airport, creating natural and man-made barriers to EMS transport outside the County's borders. These factors allowed us to test the EMS strategy inside a diverse yet nearly closed emergency system.
While Dallas offered an excellent choice for the first test of our model, large and less densely populated regions are of great interest for further testing. In rural areas where access to PCI is lowest, the need for further study is especially urgent. Empirical evidence suggests that new hospital PCI capability results in modest new access to PCI.37
To answer the question about what works best in urban, suburban and rural counties, head-to-head comparisons of all available strategies are needed. Our triage and allocation model can help planners and policy-makers decide on the approach that best fits the specific features of a county or region.
Our main finding, that an EMS strategy is more effective and less costly than any hospital strategy, was based on the estimated societal impact of alternative regional planning strategies in the care of patients with STEMI. The implications for individual hospitals are less clear. However, if it were recast to take in the hospital perspective, our model could help to inform the business case for regional planning. This would lend a great deal of clarity to discussion about the implications of our main finding.
In some circumstances, we recognize that a hospital strategy may be warranted even when it is dominated by an EMS strategy. First, resource constraints may preclude EMS strategies from being considered. Ambulance staff must be able to identify patients with STEMI accurately, the vehicles must be equipped with electrocardiograms, and EMS-hospital handoff should be organized to pre-notify receiving hospitals. Second, hospital expansion may be of particular importance in some suburban and rural settings, where the risks of exceptionally lengthy drive times to PCI hospitals can be prohibitive. Third, hospital strategies may be acceptable or desirable if the geographic distribution of PCI hospitals is inequitable and hospital expansion could lead to outcome improvements for a presently underserved population.
Our study has limitations. First, there are limitations inherent to simulations, insofar that they incorporate empirical data from multiple sources and resort to assumptions where empirical data are not available. Our simulation was no different in this regard. However, we conducted a wide range of multivariate sensitivity analyses and the results were robust to all potential changes. Perhaps the strongest assumptions we made concerned the costs of EMS transport and hospital lab construction, which were estimated from a study of new construction and staffing at U.S. hospitals from the mid-1990s. We chose this model because it allowed us to compare a range of hospital costs in discrete categories and thus to compare 13 alternative hospital strategies with each other and with the EMS strategy. We updated the cost model using the most reliable index for inflation of medical care and construction costs, the National Income Products Account (NIPA) GDP deflator. In a sensitivity analysis, our main finding was robust to changes in baseline costs by a factor of nearly 20 across the board. A second important assumption included adherence to the tested strategies. We assumed that 100% of patients use 9-1-1, an assumption that would lead to overestimates of benefit in the EMS strategy in locales where hospital walk-ins occur at a high rate. In a post hoc sensitivity analysis, our findings were robust until 55% or more of patients arrive to the hospital by means other than EMS. A third important set of assumptions included the utility weights for quality adjustment. In sensitivity analyses, we used high and low estimates from a search of the Cost Effectiveness Analysis Registry to estimate the upper and lower bounds for each utility measure in our model. Quality adjustment had minor effects on the ordering of preferred hospital strategies, but did not change the main result showing that the EMS strategy was both more effective and less costly than all hospital strategies.
A second limitation was that we conducted the study in a single county. We selected Dallas County for its size, diversity, and composition of urban, suburban and rural districts, but the primary advantage of this setting was its self-contained emergency system. Further research is planned in a broadly representative sample of U.S. counties.
In summary, while expansion of hospital PCI capability can be cost-effective for improving quality-adjusted survival after STEMI, a strategy of EMS transport to existing PCI-capable hospitals was dominant in a regional hospital system with 30% baseline access to PCI. Further inquiry is needed into the relationship of regional health system characteristics and optimal strategies for increasing access to PCI, and we have begun a five-year research project funded by the Agency for Healthcare Research and Quality (AHRQ) to explore these relationships. Our results suggest that regional planners should consider EMS strategies for increasing access to PCI before adopting strategies involving new construction or increased staffing of PCI hospitals.