- Email: [email protected]
Regionalization of Congenital Heart Surgery in the United States
CONGENITAL Original Submission
Regionalization of Congenital Heart Surgery in the United States D2X XKarl F. Welke, D3X XMD, MS,* D4X XSara K. Pasquali, D5X XMD, MHS,†,‡ D6X XPaul Lin, D7X XMS,‡ D8X XCarl L. Backer, D9X XMD,§ D10X XDavid M. Overman, D1X XMD,¶ D12X XJennifer C. Romano, D13X XMD, MS,ǁ and D14X XTara Karamlou, D15X XMD, MSc** The objective of this study is to simulate regionalization of congenital heart surgery (CHS) in the United States and assess the impact of such a system on travel distance and mortality. Patients ≤18 years of age who underwent CHS were identified in 2012 State Inpatient Databases. Operations were stratified by the Risk Adjustment for Congenital Heart Surgery, version 1 (RACHS-1) method, with high risk defined as RACHS-1 levels 46. Regionalization was simulated by progressive closure of hospitals, beginning with the lowest volume hospital. Patients were moved to the next closest hospital. Analyses were conducted (1) maintaining original hospital mortality rates and (2) estimating mortality rates based on predicted surgical volumes after absorbing moved patients. One hundred fifty-three hospitals from 36 states performed 1 or more operation (19,064 operations). With regionalization wherein, all hospitals performed >310 operations, 37 hospitals remained, from 12.5% to 17.4% fewer deaths occurred (83116/666), and median patient travel distance increased from 38.5 to 69.6 miles (P < 0.01). When only high-risk operations were regionalized, 3.95.9% fewer deaths occurred (2639/666), and the overall mortality rate did not change significantly. Regionalization of CHS in the United States to higher volume centers may reduce mortality with minimal increase in patient travel distance. Much of the mortality reduction may be missed if solely high-risk patients are regionalized. Semin Thoracic Surg &&:&&–&& © 2019 Elsevier Inc. All rights reserved. Keywords: Congenital heart surgery, Risk adjustment, Zone improvement plan, CHSD
Hospital locations and referral patterns: minimum volume 311 congenital heart operations. Central Message Regionalization of congenital heart surgery in the United States to higher volume centers may reduce mortality with minimal increase in patient travel distance. Perspective Statement Prior studies have established a relationship between higher operative volumes and improved outcomes after congenital heart surgery. We applied this knowledge by simulating regionalization of congenital heart surgery in the United States and found that regionalization to higher volume centers may reduce mortality with minimal increase in patient travel distance.
Abbreviations: CHS, congenital heart surgery; RACHS-1, Risk Adjustment for Congenital Heart Surgery, version 1; STS-CHSD, Society of Thoracic Surgeons Congenital Heart Surgery Database; US, United States; ZIP, zone improvement plan *Division of Pediatric Cardiac Surgery, Levine Children’s Hospital/Atrium Health, Charlotte, North Carolina y Division of Pediatric Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children’s Hospital, Ann Arbor, Michigan z Institute for Healthcare Policy and Innovation, University of Michigan Medical School, Ann Arbor, Michigan x Division of Cardiovascular-Thoracic Surgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois { Division of Cardiovascular Surgery, The Children’s Heart Clinic, Children’s Hospitals and Clinics of Minnesota, Minneapolis, Minnesota ǁ Department of Cardiac Surgery, University of Michigan C. S. Mott Children’s Hospital, Ann Arbor, Michigan **Division of Pediatric Cardiac Surgery and the Heart and Vascular Institute, The Cleveland Clinic, Cleveland, Ohio Funding: This research was supported by a generous grant from the Children’s Heart Foundation (Dr Welke, principal investigator). Dr. Pasquali receives support from the Janette Ferrantino Professorship. Conflicts of Interest: The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships to disclose. Address reprint requests to Karl F. Welke, MD, MS, Sanger Heart and Vascular Institute, 1001 Blythe Blvd., Suite 200D, Charlotte, NC 28203. E-mail: [email protected]
1043-0679/$see front matter © 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1053/j.semtcvs.2019.09.005
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CONGENITAL REGIONALIZATION OF CHS INTRODUCTION Regionalization of health care services is seen as a potential route to improve quality and outcomes. Yet, the expected gain of such change can be difficult to quantify. The care of children undergoing congenital heart surgery (CHS) may serve as a model of both the limitations of the current system and the potential benefits of regionalization. The relative infrequency of CHS, complex nature of CHS care, associated mortality risk, and high resource utilization required for optimization, all suggest regionalization to specialized centers may be advisable.1,2 A study of regionalization of CHS may also stimulate investigation of regionalization in other high complexity areas of medicine. Prior studies have established a relationship between higher operative volumes and improved outcomes after CHS.36 However, practical application of this knowledge has not occurred in the United States (US). Limited simulation data from California support the concept that regionalization to higher volume centers may optimize outcomes with minimal additional travel burden.1 In Sweden, the consolidation of CHS from 4 hospitals to 2 was temporally associated with a decrease in the national mortality rate from 9.5% to 1.9%.7 A system that regionalizes CHS across the US and its potential impact on patients has not been described. The purpose of this investigation is to simulate the regionalization of CHS in the US and assess the impact of such a system on in-hospital mortality and patient travel distance. We create an algorithm to systematically close smaller hospitals and move the affected patients to larger hospitals. We then describe the geographic distribution of the remaining hospitals and calculate the theoretical change in both mortality and travel distance. PATIENTS AND METHODS Data Source and Study Population The study population consisted of patients aged 18 years or less who underwent CHS and were discharged in calendar year 2012 in the US. We obtained 29 State Inpatient Databases for calendar year 2012 from the Health Care Cost Utilization Project.8 Data were requested directly from all remaining states and obtained from 10. As California and Mississippi 2012 data were not available, 2011 data were acquired. Administrative data were used for this study to capture the largest number of hospitals performing CHS and to allow for the use of zone improvement plan (ZIP) codes.9 Small volume hospitals and adult hospitals performing CHS may not report to The Society of Thoracic Surgeons’ Congenital Heart Surgery Database (STS-CHSD) but may be identified using administrative data. Hospitals reporting to the STS-CHSD under umbrella structures can be individually characterized using administrative data. A congenital cardiac operation was defined as one classified in the Risk Adjustment for Congenital Heart Surgery, Version 1 (RACHS-1) system.10 RACHS-1 is the only case
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ascertainment and risk adjustment methodology to identify and classify congenital cardiac operations using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes in administrative data. RACHS-1 groups CHS operations with similar expected short-term mortality rates into 6 risk categories. Category 1 has the lowest risk of death and category 6 the highest. Congenital cardiac operations not included in RACHS-1 were assigned a code of RACHS-1 category 0. Patient origin (home address) was approximated using ZIP code. The Federal Information Processing Standard was utilized for patients with an invalid or missing ZIP code. Travel distance was calculated in miles based on driving distance using Google Maps (Google, Mountain View, CA). Regionalization Simulation We simulated hospital closure in a one-by-one fashion. After sorting the hospitals by their total CHS operative volumes (RACHS-1 categories 06), we simulated closing the lowest volume hospital and relocating each affected patient to the closest remaining hospital based on the patient’s home ZIP code. The next remaining lowest volume hospital was then closed, and the redistribution of patients repeated. Travel distance and mortality were calculated after each simulated closure and transfer. The Google Maps algorithm used to determine travel distance accounted for multiple routes to reach the destination. In each case, Google Maps selected the quickest route to the destination. Additional checks were performed to ensure that the route selected was based on land travel. The simulation continued until only 2 hospitals remained in the US. Regionalization Simulation—High-Risk Operations We conducted a second simulation in which only patients who underwent an operation within RACHS-1 categories 46 were moved. Patients who underwent an operation with a RACHS-1 score of 03 did not change hospitals. To avoid patients being moved into a hospital with a low operative volume, all patients from hospitals that performed ≤20 operations were moved to the next nearest hospital.3 Hospitals were then listed by total congenital cardiac operative volume and simulation rounds begun. Patients were required to move to a hospital that had at least 1 RACHS-1 category 46 operation performed prior to the start of the simulation. Simulation rounds then proceeded as described for the first simulation. Since the mortality rate for the RACHS-1 category 0 (5.2%, 184/3516) fell in between the mortality rates for RACHS-1 categories 3 (3.6%, 204/5716) and 4 (6.7%, 120/1798), we conducted a second version of the high-risk simulation. In this scenario, patients who underwent an operation with a RACHS1 score of 0 or 46 were moved while those with a RACHS-1 score of 13 did not change hospitals. First Mortality Rate Analysis For the first mortality rate analysis, hospitals maintained their original mortality rate, despite changes in volumes, throughout the simulation.
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CONGENITAL REGIONALIZATION OF CHS We calculated potential lives saved after each simulation using the following formula: Potential Lives Saved ¼ ððDiedHosp þ DiedMoved Þ RACHSHosp þ RACHSMoved MRQuantile Þ
DiedHosp = Number of RACHS-1 deaths at the receiving hospital (not including moved patients) DiedMoved = Number of RACHS-1 deaths in patients moved to the receiving hospital RACHSHosp = Number of RACHS-1 operations performed at the receiving hospital (not including moved patients) RACHSMoved = Number of RACHS-1 operations moved to the receiving hospital MRQuantile = Mortality rate of the of the receiving hospital
Second Mortality Rate Analysis Given that there is an inverse relationship between hospital volume and mortality after CHS, we conducted a second analysis in which a stratified mortality rate was built to simulate the decrease in mortality that would be anticipated to accompany an increase in surgical volume.1,36 Using the baseline data, hospitals were divided into 5 groups. Hospitals with less than 150 operations were placed in one group. Hospitals with 150 or more operations were divided into volume quartiles to ensure similar numbers of hospitals in each quartile. Mortality rates were calculated for each group, using the dataset created for this investigation, by dividing the number of deaths by the number of operations. Hospital operative volume groups, the numbers of hospitals in the groups at baseline, and the mortality rates assigned to the groups for the simulation involving all RACHS-1 operations (categories 06) were as follows (number of hospitals, mortality rate): <150 operations (111 hospitals, 4.5%), 150199 operations (11 hospitals, 3.6%), 200242 operations (12 hospitals, 3.4%), 243310 operations (12 hospitals, 3.0%), and >310 operations (12 hospitals, 2.8%). For the simulation including only high-risk operations (RACHS-1 categories 46), the hospital operative volumes (number of hospitals, mortality rate) were grouped as follows: <150 operations (64 hospitals, 10.1%), 150199 operations (11 hospitals, 7.7%), 200242 operations (12 hospitals, 5.8%), 243310 operations (12 hospitals, 7.9%), and >310 operations (12 hospitals, 6.1%). These mortality rates were consistent with previous reports.3,4 Hospitals that gained volume due to redistribution of operations after regionalization were assigned mortality rates consistent with their new volumes rather than retaining their original mortality rates. Hospital volumes were recalculated after each simulation round and mortality rates assigned. We then applied the methodology
described in the preceding sections to calculate potential lives saved with the only change being: MRQuantile = Mortality rate of the of the volume group of the receiving hospital including moved operations Statistical Methods Relevant patient and hospital data were summarized with regards to the RACHS-1 operation categories. Mean and median were calculated for distance. Counts and percentages were used for categorical variables. Paired t test was used to assess the mean difference of the baseline distance and distance after each simulation round. Signed rank test was used to assess the median difference of the baseline distance and distance after each simulation round. Binomial test was used to assess mortality rate significance. Binomial proportion was used to derive the exact confidence interval of mortality rate and the range of lives potentially saved. Two-tailed P values were used for all analyses, with P value <0.05 as significant. The study was approved by the Peoria Institutional Review Board at the University of Illinois College of Medicine at Peoria, the University of Michigan Institutional Review Board, and additional individual state institutional review boards depending on individual state requirements. Analyses were conducted with SAS software, v9.4 (SAS Institute, Cary, NC). RESULTS Study Population Characteristics We identified 19,880 discharges after a congenital cardiac operation during the study period from 330 hospitals. After exclusion of hospitals performing only isolated patent ductus arteriosus ligations (n = 177) our sample included 19,064 discharges from 153 hospitals in 36 states. This cohort was used for the mortality analyses. The exclusion of 3177 additional patient discharges for which the home ZIP code was unknown resulted in a cohort of 15,887 patient discharges. This cohort was used for the distance calculations (Fig. 1). Regionalization Simulation—All Operations Prior to simulation, the overall mortality rate was 3.5% (666/ 19,064). In the initial simulation rounds of the first mortality analysis, there was little change in the overall mortality rate. However, as more patients were moved, there was a decrease in mortality such that by the time the smallest remaining hospital had an overall volume of 150 congenital cardiac operations, the number of deaths had decreased 5.7% to 628, resulting in 38 potential lives saved (Fig. 2). At this point, the mortality rate dropped to 3.3% (95% confidence interval [CI]: 3.03.6%, P = 0.13 compared to baseline) and 62 hospitals remained (Fig. 3). When the minimum hospital volume reached D16X X31D1 17X X operations, the number of deaths had decreased by 12.5% to 583, resulting in 83 potential lives saved (Fig. 2). The overall mortality rate decreased to 3.1% (95% CI:
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Figure 1. Map of the US depicting the locations of hospitals where congenital cardiac operations occurred in 2012. Hospitals are grouped by surgical volume (colored crosses). Black triangles represent patient origins for patientsD1X Xand lines link them to the hospital where they had surgery. Note that triangles and lines do not represent individual patients, but rather all patients within a ZIP code. Patient location data (ZIP codes) not available for California. RACHS-1 category 06 patients included.
2.83.3%, P < 0.01 compared to baseline) and 37 hospitals remained (Fig. 4). The results of the second mortality analysis were consistent with the first. There was little change in the overall mortality rate in the early simulation rounds. When the smallest remaining hospital had an overall volume of 150 congenital cardiac operations, the number of deaths had decreased 8.0% to 610, resulting in 56 potential lives saved. At this point, the mortality rate dropped to 3.2% (95% CI: 3.03.5%, P = 0.03 compared to baseline; Fig. 5). When the minimum hospital volume reached D18X X31D19X X1 operations, the number of deaths had decreased by 17.4% to 550, resulting in 116 potential lives saved. The overall mortality rate decreased to 2.9% (95% CI: 2.73.1%, P < 0.01 compared to baseline; Fig. 5). Of the 15 hospitals that did less than 150 operations at baseline and ended up with more than 150 operations by the time in the simulation at which the smallest remaining hospital had a volume of 150 operations, none had an assigned mortality rate that was statistically different from their baseline rate. Of the 25 hospitals that D20X Xdid less than 311 D21X Xoperations at baseline and ended up with 311 or moreD2X X D23X Xoperations after our simulation, 8 had an assigned mortality rate that was 0.5% or greater above baseline (of which 2 were statistically different) while 7
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had an assigned mortality rate that was 0.5% or more below baseline (of which 1 was statistically different). To achieve a minimum hospital volume of 150 operations, 3384 patients were moved, and to achieve a minimum hospital volume of 311 operations, 7019 patients were moved (Fig. 2). The movement of patients not only eliminated smaller volume hospitals but also created a larger number of high-volume hospitals. At baseline, 55 hospitals (36%) performed less than 50 operations, 51 (33%) performed between 50 and 149 operations, 35 (23%) performed 150310 operations, and 12 (8%) performed >310 operations. At the point in the simulation where all hospitals were at or above 150 operations, 91 hospitals (59%) were closed. Of the remaining 62 hospitals, 44 (71%) performed 150310 operations and 18 (29%) performed >310 operations. Regionalization Simulation—High-Risk Operations The analysis of high-risk operations simulated regionalization of the 2183 patients who underwent high-risk operations (RACHS-1 categories 46), while unclassified (RACHS-1 category 0) patients and patients undergoing low-risk operations (RACHS-1 categories 13) remained at their original hospitals. At baseline, 111 hospitals performed at least 1 high-risk
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Figure 2. Mortality rates and numbers of patients moved from their original hospitals as the regionalization simulation progressed. Hospitals retained their actual mortality rates throughout the simulation. RACHS-1 category 06 patients included.
operation, and the mortality rate for this group of patients was 7.5% (164/2183). When patients were redistributed such that all high-risk patients underwent surgery at hospitals that performed >310 total congenital cardiac operations, 21 hospitals (19%) remained that performed high-risk operations. At this point, the mortality rate for high-risk patients dropped from 7.5% to 6.3% (95% CI: 5.37.4%, P = 0.04). The number of deaths among high-risk patients decreased by 15.9% to 138, resulting in 26 potential lives saved. However, with low-risk patients remaining at their original hospitals, the overall number of deaths decreased by only 3.9%, and the overall mortality rate decreased from 3.5% to 3.4% (95% CI: 3.13.6%, P = 0.31). The alternative version of the analysis of high-risk operations simulated regionalization of the 5699 patients who underwent high-risk (RACHS-1 categories 46) or unclassified (RACHS-1 category 0) operations, while patients undergoing low-risk operations (RACHS-1 categories 13) remained at their original hospitals. At baseline, 136 hospitals performed at least 1 high-risk/unclassified operation, and the mortality rate for this group of patients was 6.1% (348/5699). When patients were redistributed such that all high-risk/unclassified patients underwent surgery at hospitals that performed >310 total
congenital cardiac operations, 24 hospitals (17.6%) remained that performed high-risk/unclassified operations. At this point, the mortality rate for these patients dropped from 6.1% to 5.4% (95% CI: 4.86.0%, P = 0.03). The number of deaths among these patients decreased by 11.2% to 309, resulting in potential 39 lives saved. However, with low-risk patients remaining at their original hospitals, the overall number of deaths decreased by only 5.9%, and the overall mortality rate decreased from 3.5% to 3.3% (95% CI: 3.13.6%, P = 0.12). Impact of Regionalization on Travel Distance As regionalization was simulated, travel distance increased modestly. At baseline, patients traveled a median distance of 38.5 miles from their home ZIP code to the hospital where they underwent surgery (Fig. 6). At the point at which all hospitals performed 150 or more operations, median travel distance increased to 50.4 miles, P < 0.01 compared to baseline. Median travel distance for patients who underwent surgery at a hospital other than their original hospital increased to 82.3 miles, P < 0.01 compared to baseline. When all operations were done at hospitals with volumes >310 operations, the overall median travel distance for the cohort increased to 69.6 miles, P < 0.01 compared to baseline. Median travel distance
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Figure 3. Map of the US depicting the locations of hospitals where congenital cardiac operations would occur if hospitals were closed until all remaining hospitals had surgical volumes of 150 operations or more. Black triangles represent patient origins for patients who changed hospitals and lines link them to the hospital where they would undergo surgery. Note that triangles and lines do not represent individual patients, but rather all patients within a ZIP code. RACHS-1 category 06 patients included.
for patients who traveled to new hospitals increased to 123.0 miles, P < 0.01 compared to baseline. At baseline, patients who underwent high-risk operations traveled a median distance of 39.8 miles. At the point at which all hospitals performed >310 overall operations, the overall median travel distance for all high-risk patients increased to 131.0 miles, P < 0.01 compared to baseline. Median travel distance for patients who underwent high-risk surgery at a hospital other than their original hospital increased to 226.5 miles, P < 0.01 compared to baseline. DISCUSSION We simulated regionalization of CHS in the US and assessed the impact a regionalized system would have on patient travel distance and mortality. Our data suggest that the number of hospitals performing a high volume of CHS would increase and mortality after surgery would decrease. These data also demonstrate that only a modest increase in patient travel distance was needed to gain the potential advantages of higher volume hospitals and reduced mortality. While reduction in mortality was seen when regionalizing all patients undergoing CHS as well asD24X X only high-risk patients, the greatest number of
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lives saved was in patients undergoing lower risk operations. Regionalization of high-risk patients alone resulted in fewer lives saved and substantially increased travel burden. The results of regionalization attempts in other specialties in the US have been mixed. Outcomes for bariatric surgery did not improve in response to the designation of centers of excellence (COE) by the Centers for Medicare and Medicaid Services.11 However, as an unintended consequence, the proportion of nonwhite Medicare patients receiving bariatric surgery declined.12 Non-COE hospitals remained in existence and had outcomes similar to COE hospitals. Of note, the volume outcome relationships demonstrated for bariatric surgery are not as strong as for CHS.13 The institution of evidencebased hospital referral (EBHR) by the Leapfrog Group had a negligible impact on statewide outcomes for pancreatic or esophageal resection or abdominal aortic aneurysm repair in Washington.14 Many patients continued to have surgery at non-EBHR hospitals even though, in general, hospitals meeting EBHR volume standards had lower adverse event rates. As small or low performing hospitals remained in both these examples, complete regionalization was not achieved. Pediatric surgery has become increasingly regionalized in the US in part
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Figure 4. Map of the US depicting the locations of hospitals where congenital cardiac operations would occur if hospitals were closed until all remaining hospitals had surgical volumes of more than 310 operations. Black triangles represent patient origins for patients who changed hospitals and lines link them to the hospital where they would undergo surgery. Note that triangles and lines do not represent individual patients, but rather all patients within a ZIP code. RACHS-1 category 06 patients included.
due to favorable outcomes seen in designated pediatric trauma centers and higher volume neonatal intensive care units.1518 Compared to many types of medical therapy, CHS is an infrequent event. We identified 19,064 CHS operations performed in 36 states. With extrapolation of this number to the entire US, the volume of CHS operations performed annually is similar to other infrequent operations such as solid organ transplant (33,610 transplants in 2016).19 Similar to organ transplant, CHS is resource intensive and requires complex systems and specialized expertise for success. Organ transplant is centralized to specialized centers to improve outcomes and reduce cost. The additional travel burden imposed on patients is thought to be balanced by better outcomes. The combination of low national volume and high resource intensity may make CHS a more favorable choice for regionalization than other specialties. The relationship between volume and mortality after CHS is such that as case complexity increases, so does the performance gap between smaller and larger programs.4 However, we found that regionalization of high-risk patients alone would achieve only a fraction of the total potential lives saved. This was a result of several factors. First, of the potential lives saved in our
study, a minority were high-risk patients. Second, most highrisk patients were already undergoing surgery at larger centers so were not moved with regionalization. Third, a far larger number of patients underwent low-risk operations. Only 11% of operations were high risk (2183/19,064) while 89% were low risk (16,881/19,064). As a result, despite a higher risk of mortality (7.5% vs 3.0%), the absolute number of low-risk patients who died was far greater. Therefore, the greatest impact of regionalization would not be achieved by focusing on high-risk patients alone but rather all patient risk categories. The relationship between volume and mortality after CHS is also such that while on average larger programs have lower mortality rates than smaller programs, there is variability within both groups. The consistency between our analysis in which each hospital’s original mortality rate was maintained and our analysis that assigned volume-based mortality rates to hospitals addresses this relationship. If all CHS hospitals across the US were included in a regionalization effort, higher average volumes would result, and overall mortality would be lower. In addition, the potential benefits of regionalization may be greater than demonstrated in this study which focused on
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Figure 5. Mortality rates and numbers of patients moved from their original hospitals as the regionalization simulation progressed. Hospitals started with their actual mortality rates and were assigned new mortality rates based on their volumes at each stage of the simulation. The assigned mortality rates were the means of the overall cohort for each volume category: <150 (4.5%), 150199 (3.6%), 200242 (3.4%), 243310 (3.0%), >310 (2.8%). RACHS-1 category 06 patients included.
mortality. Hospital surgical volume has also been shown to be inversely associated with complication and failure to rescue rates.2022 In addition, lower hospital surgical volumes are associated with higher costs.23,24 Staffing models, expertise, and physical resources that are cost prohibitive when treating a low volume of patients become more affordable when caring for a high volume. Our choice to assign new mortality rates to hospitals that gained patients during the simulation was based in part on this rationale. The distribution of CHS hospitals in the US is characterized by frequent geographic clustering. The majority of CHS hospitals (66%) are within 25 miles of another CHS hospital.8 Because of this phenomenon, regionalization of CHS was associated with a modest impact on patient travel burden. The impact of distance is difficult to assess. Mortality risk and the incidence of adverse events after discharge may not be adversely impacted by distance.25 Travel burden may impact families with fewer resources, both financially and socially, differently than those with a wealth of resources. In addition, families in rural areas may be more accustomed to longer travel distances for a variety of services than urban residents.
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However, any negative impact of regionalization is balanced by the survival benefit a patient may realize by undergoing surgery at a higher volume hospital. While regionalization would create larger hospitals, none of the hospitals in our study increased to a volume greater than that of the largest, current CHS hospital. Although there may be a volume above which decline in mortality plateaus, there is no evidence to support a volume above which quality is negatively affected.4 Limitations The limitations of this investigation primarily relate to the data source. We obtained data from 39 states, which included 90.3% of the US population. Of the missing 11 states, the District of Columbia, and Puerto Rico, 7 do not have a known hospital with a dedicated CHS program. It is unlikely that the structure of CHS care in the remaining 6 locales differs significantly from the captured 39. As a result, the findings of our study should be representative of the US Administrative data is the only source of patient ZIP codes, which were required to simulate regionalization. The use of administrative data necessitates reliance on ICD-9-CM diagnosis and procedure
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Figure 6. Change in mean and median travel distance as the regionalization simulation progressed. RACHS-1 category 06 patients included.
codes. Some operations may be miscoded or not accurately captured due to the lack of an appropriate code. RACHS-1 captures approximately 85% of CHS operations, including all common operations, and represents the universe of CHS operations.26 Previously, we found that analyses of hospital-level volume-mortality relationship in administrative and clinical datasets provided similar results.3,4 We regionalized based on volume rather than mortality. An alternative approach would be to regionalize patients to lower mortality rather than higher volume hospitals. However, the instability of mortality rates, especially for small-volume hospitals, would complicate this approach.27 A comprehensive understanding of the potential advantages and disadvantages of a regionalized system of CHS would need to also consider the potential negative impact of removing CHS from hospitals. CHS programs generate revenue that supports underfunded programs in children’s hospitals and provide expertise to care for critically ill children without cardiac disease as well. The impetus to regionalize CHS could come from a variety of sources; however, significant hurdles exist. Increased transparency through public reporting of surgical volumes and outcomes would allow patients, their parents, and referring
physicians to select high performing hospitals. To date, transparency has been hampered by limited scope, incomplete reporting, and difficulty in interpretation of data. Hospitals could take the initiative and adhere to a volume pledge by which they only offer CHS if a minimum volume standard is met.28 However, the current remuneration system and local pride are barriers to shuttering programs. The Center for Medicare and Medicaid Services (CMS) and private insurers could alter reimbursements to favor hospitals meeting selected criteria. Yet, the administration of Medicaid at the state level limits the potential for national policy initiatives. Legislative efforts could mandate regionalization by requiring hospitals to meet volume or other criteria. While there is opposition to a single-payor health care system in the US, nationalization of pediatric care may be more palatable since children’s health care accounts for less than 10% of total health care expenditures and 50% of pediatric inpatient costs are already covered by Medicaid.29,30 CONCLUSION Regionalization of CHS in the US could result in more highvolume hospitals and significantly reduced mortality at the cost of only a modest increase in patient travel burden. Much of the mortality reduction may be missed if solely high-risk
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CONGENITAL REGIONALIZATION OF CHS patients were regionalized. In addition to improving the care for patients with congenital heart disease, regionalization of CHS could serve as a model for the coordination of other low volume, high complexity specialties.
REFERENCES 1. Chang RK, Klitzner TS: Can regionalization decrease the number of deaths for children who undergo cardiac surgery? A theoretical analysis. Pediatrics 109:173–181, 2002 2. Pasqauli SK, Dimick JB, Ohye RG: Time for a more unified approach to pediatric heath care policy? The case for congenital heart care. JAMA 314:1689–1690, 2015 3. Welke KF, Diggs BS, Karamlou T, et al: The relationship between hospital surgical case volumes and mortality rates in pediatric cardiac surgery: A national sample, 1988-2005. Ann Thorac Surg 86:889–896, 2008 4. Welke KF, O’Brien SM, Peterson ED, et al: The complex relationship between pediatric cardiac surgical case volumes and mortality rates in a national clinical database. J Thorac Cardiovasc Surg 137:1133– 1140, 2009 5. Pasquali SK, Jacobs JP, He X, et al: The complex relationship between center volume and outcome among patients undergoing the Norwood procedure. Ann Thorac Surg 93:1556–1562, 2012 6. Karamlou T, Jacobs ML, Pasquali SK, et al: Surgeon and center volume influence on outcomes after arterial switch operation: Analysis of the Society of Thoracic Surgeons Congenital Heart Surgery Database. Ann Thorac Surg 98:904–911, 2014 7. Lundstrom NR, Bergen H, Bjorkhem G, et al: Centralization of pediatric heart surgery in Sweden. Pediatr Cardiol 21:353–357, 2000 8. Healthcare Cost and Utilization Project: State Inpatient Databases (SID). Rockville, MD, Agency for Healthcare Research and Quality; 2018 https:// www.hcup-us.ahrq.gov/sidoverview.jsp 9. Welke KF, Pasquali SK, Lin P, et al: Hospital distribution and patient travel patterns for congenital heart surgery in the United States. Ann Thorac Surg 107:574–581, 2019 10. Jenkins KJ, Gauvreau K, Newburger JW, et al: Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg 123:110–118, 2002 11. Dimick JB, Nicholas LH, Ryan AM, et al: Bariatric surgery complications before vs after implementation of a national policy restricting coverage to centers of excellence. JAMA 309:792–799, 2013 12. Nicholas LH, Dimick JB: Bariatric surgery in minority patients before and after implementation of a centers of excellence program. JAMA 310:1399– 1400, 2013 13. Varban OA, Reames BN, Finks JF, et al: Hospital volume and outcomes for laparoscopic gastric bypass and adjustable gastric banding in the modern era. Surg Obes Relat Dis 11:343–349, 2015
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14. Massarweh NN, Flum DR, Symons RG, et al: A critical evaluation of the impact of leapfrog’s evidence-based hospital referral. J Am Coll Surg 212:150–159, 2011 15. Salazar JH, Goldstein SD, Yang J, et al: Regionalization of pediatric surgery: Trends already underway. Ann Surg 263:1062–1066, 2016 16. Wang N, Saynina O, Vogel L, et al: The effect of trauma center care on pediatric injury mortality in California, 1999 to 2011. J Traum Acute Care Surg 75:704–716, 2013 17. Phibbs CS, Baker LC, Caughey AB, et al: Level and volume of neonatal intensive care and mortality in very-low-birth weight infants. N Eng J Med 356:2165–2175, 2007 18. Kastenberg ZJ, Lee HC, Profit J, et al: Effect of deregionalized care on mortality in very low-birth-weight infants with necrotizing entercolitis. JAMA Pediatr 169:26–32, 2015 19. United Network for Organ Sharing 2016 Annual Report. Richmond. VA: United Network for Organ Sharing. (https://unos.org/about/annualreport/2016-annual-report/) 20. Gonzalez AA, Dimick JB, Birkmeyer JD, et al: Understanding the volumeoutcome effect in cardiovascular surgery: The role of failure to rescue. JAMA Surg 149:119–123, 2014 21. Pasquali SK, Li JS, Burstein DS, et al: Association of center volume with mortality and complications in pediatric heart surgery. Pediatrics 129: e370–e376, 2012 22. Kansy A, Ebels T, Scheiber C, et al: Association of center volume with outcomes: Analysis of verified data of European association for cardio-thoracic surgery congenital database. Ann Thorac Surg 98:2159–2164, 2014 23. Pasquali SK, Jacobs JP, Bove EL, et al: Quality-cost relationship in congenital heart surgery. Ann Thorac Surg 100:1416–1421, 2015 24. Chan T, Kim J, Minich L, et al: Surgical volume, hospital quality, and hospitalization cost in congenital heart surgery in the United States. Pediatr Cardiol 36:205–213, 2015 25. Pinto NM, Lasa J, Dominguez TE, et al: Regionalization in neonatal congenital heart surgery: The impact of distance on outcome after discharge. Pediatr Cardiol 33:229–238, 2012 26. Al-Radi OO, Harrell FE Jr., Caldarone CA, et al: Case complexity scores in congenital heart surgery: A comparative study of the Aristotle basic score and the risk-adjustment in congenital heart surgery (RACHS-1) system. J Thorac Cardiovasc Surg 133:865–875, 2007 27. Welke KF, Karamlou T, Ungerlieder RM, et al: Mortality rate is not a valid indicator of quality differences between pediatric cardiac surgery programs. Ann Thorac Surg 89:139–144, 2010 28. Urbanch DR: Pledging to eliminate low-volume surgery. N Engl J Med 373, 2015. :1388-90 29. Bui AL, Dieleman JL, Hamavid H, et al: Spending on children’s personal health care in the United States, 1009-2013. JAMA Pediatr 171:181–189, 2017 30. Witt WP, Weiss AJ, Elixhauser A: Overview of Hospital Stays for Children in the United States, 2012: Statistical Brief #187. Rockville, MD, Agency for Healthcare Research and Quality; 2014
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Regionalization of Congenital Heart Surgery in the United States D2X XKarl F. Welke, D3X XMD, MS,* D4X XSara K. Pasquali, D5X XMD, MHS,†,‡ D6X XPaul Lin, D7X XMS,‡ D8X XCarl L. Backer, D9X XMD,§ D10X XDavid M. Overman, D1X XMD,¶ D12X XJennifer C. Romano, D13X XMD, MS,ǁ and D14X XTara Karamlou, D15X XMD, MSc** The objective of this study is to simulate regionalization of congenital heart surgery (CHS) in the United States and assess the impact of such a system on travel distance and mortality. Patients ≤18 years of age who underwent CHS were identified in 2012 State Inpatient Databases. Operations were stratified by the Risk Adjustment for Congenital Heart Surgery, version 1 (RACHS-1) method, with high risk defined as RACHS-1 levels 46. Regionalization was simulated by progressive closure of hospitals, beginning with the lowest volume hospital. Patients were moved to the next closest hospital. Analyses were conducted (1) maintaining original hospital mortality rates and (2) estimating mortality rates based on predicted surgical volumes after absorbing moved patients. One hundred fifty-three hospitals from 36 states performed 1 or more operation (19,064 operations). With regionalization wherein, all hospitals performed >310 operations, 37 hospitals remained, from 12.5% to 17.4% fewer deaths occurred (83116/666), and median patient travel distance increased from 38.5 to 69.6 miles (P < 0.01). When only high-risk operations were regionalized, 3.95.9% fewer deaths occurred (2639/666), and the overall mortality rate did not change significantly. Regionalization of CHS in the United States to higher volume centers may reduce mortality with minimal increase in patient travel distance. Much of the mortality reduction may be missed if solely high-risk patients are regionalized. Semin Thoracic Surg &&:&&–&& © 2019 Elsevier Inc. All rights reserved. Keywords: Congenital heart surgery, Risk adjustment, Zone improvement plan, CHSD
Hospital locations and referral patterns: minimum volume 311 congenital heart operations. Central Message Regionalization of congenital heart surgery in the United States to higher volume centers may reduce mortality with minimal increase in patient travel distance. Perspective Statement Prior studies have established a relationship between higher operative volumes and improved outcomes after congenital heart surgery. We applied this knowledge by simulating regionalization of congenital heart surgery in the United States and found that regionalization to higher volume centers may reduce mortality with minimal increase in patient travel distance.
Abbreviations: CHS, congenital heart surgery; RACHS-1, Risk Adjustment for Congenital Heart Surgery, version 1; STS-CHSD, Society of Thoracic Surgeons Congenital Heart Surgery Database; US, United States; ZIP, zone improvement plan *Division of Pediatric Cardiac Surgery, Levine Children’s Hospital/Atrium Health, Charlotte, North Carolina y Division of Pediatric Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan C.S. Mott Children’s Hospital, Ann Arbor, Michigan z Institute for Healthcare Policy and Innovation, University of Michigan Medical School, Ann Arbor, Michigan x Division of Cardiovascular-Thoracic Surgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois { Division of Cardiovascular Surgery, The Children’s Heart Clinic, Children’s Hospitals and Clinics of Minnesota, Minneapolis, Minnesota ǁ Department of Cardiac Surgery, University of Michigan C. S. Mott Children’s Hospital, Ann Arbor, Michigan **Division of Pediatric Cardiac Surgery and the Heart and Vascular Institute, The Cleveland Clinic, Cleveland, Ohio Funding: This research was supported by a generous grant from the Children’s Heart Foundation (Dr Welke, principal investigator). Dr. Pasquali receives support from the Janette Ferrantino Professorship. Conflicts of Interest: The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships to disclose. Address reprint requests to Karl F. Welke, MD, MS, Sanger Heart and Vascular Institute, 1001 Blythe Blvd., Suite 200D, Charlotte, NC 28203. E-mail: [email protected]
1043-0679/$see front matter © 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1053/j.semtcvs.2019.09.005
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CONGENITAL REGIONALIZATION OF CHS INTRODUCTION Regionalization of health care services is seen as a potential route to improve quality and outcomes. Yet, the expected gain of such change can be difficult to quantify. The care of children undergoing congenital heart surgery (CHS) may serve as a model of both the limitations of the current system and the potential benefits of regionalization. The relative infrequency of CHS, complex nature of CHS care, associated mortality risk, and high resource utilization required for optimization, all suggest regionalization to specialized centers may be advisable.1,2 A study of regionalization of CHS may also stimulate investigation of regionalization in other high complexity areas of medicine. Prior studies have established a relationship between higher operative volumes and improved outcomes after CHS.36 However, practical application of this knowledge has not occurred in the United States (US). Limited simulation data from California support the concept that regionalization to higher volume centers may optimize outcomes with minimal additional travel burden.1 In Sweden, the consolidation of CHS from 4 hospitals to 2 was temporally associated with a decrease in the national mortality rate from 9.5% to 1.9%.7 A system that regionalizes CHS across the US and its potential impact on patients has not been described. The purpose of this investigation is to simulate the regionalization of CHS in the US and assess the impact of such a system on in-hospital mortality and patient travel distance. We create an algorithm to systematically close smaller hospitals and move the affected patients to larger hospitals. We then describe the geographic distribution of the remaining hospitals and calculate the theoretical change in both mortality and travel distance. PATIENTS AND METHODS Data Source and Study Population The study population consisted of patients aged 18 years or less who underwent CHS and were discharged in calendar year 2012 in the US. We obtained 29 State Inpatient Databases for calendar year 2012 from the Health Care Cost Utilization Project.8 Data were requested directly from all remaining states and obtained from 10. As California and Mississippi 2012 data were not available, 2011 data were acquired. Administrative data were used for this study to capture the largest number of hospitals performing CHS and to allow for the use of zone improvement plan (ZIP) codes.9 Small volume hospitals and adult hospitals performing CHS may not report to The Society of Thoracic Surgeons’ Congenital Heart Surgery Database (STS-CHSD) but may be identified using administrative data. Hospitals reporting to the STS-CHSD under umbrella structures can be individually characterized using administrative data. A congenital cardiac operation was defined as one classified in the Risk Adjustment for Congenital Heart Surgery, Version 1 (RACHS-1) system.10 RACHS-1 is the only case
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ascertainment and risk adjustment methodology to identify and classify congenital cardiac operations using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes in administrative data. RACHS-1 groups CHS operations with similar expected short-term mortality rates into 6 risk categories. Category 1 has the lowest risk of death and category 6 the highest. Congenital cardiac operations not included in RACHS-1 were assigned a code of RACHS-1 category 0. Patient origin (home address) was approximated using ZIP code. The Federal Information Processing Standard was utilized for patients with an invalid or missing ZIP code. Travel distance was calculated in miles based on driving distance using Google Maps (Google, Mountain View, CA). Regionalization Simulation We simulated hospital closure in a one-by-one fashion. After sorting the hospitals by their total CHS operative volumes (RACHS-1 categories 06), we simulated closing the lowest volume hospital and relocating each affected patient to the closest remaining hospital based on the patient’s home ZIP code. The next remaining lowest volume hospital was then closed, and the redistribution of patients repeated. Travel distance and mortality were calculated after each simulated closure and transfer. The Google Maps algorithm used to determine travel distance accounted for multiple routes to reach the destination. In each case, Google Maps selected the quickest route to the destination. Additional checks were performed to ensure that the route selected was based on land travel. The simulation continued until only 2 hospitals remained in the US. Regionalization Simulation—High-Risk Operations We conducted a second simulation in which only patients who underwent an operation within RACHS-1 categories 46 were moved. Patients who underwent an operation with a RACHS-1 score of 03 did not change hospitals. To avoid patients being moved into a hospital with a low operative volume, all patients from hospitals that performed ≤20 operations were moved to the next nearest hospital.3 Hospitals were then listed by total congenital cardiac operative volume and simulation rounds begun. Patients were required to move to a hospital that had at least 1 RACHS-1 category 46 operation performed prior to the start of the simulation. Simulation rounds then proceeded as described for the first simulation. Since the mortality rate for the RACHS-1 category 0 (5.2%, 184/3516) fell in between the mortality rates for RACHS-1 categories 3 (3.6%, 204/5716) and 4 (6.7%, 120/1798), we conducted a second version of the high-risk simulation. In this scenario, patients who underwent an operation with a RACHS1 score of 0 or 46 were moved while those with a RACHS-1 score of 13 did not change hospitals. First Mortality Rate Analysis For the first mortality rate analysis, hospitals maintained their original mortality rate, despite changes in volumes, throughout the simulation.
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CONGENITAL REGIONALIZATION OF CHS We calculated potential lives saved after each simulation using the following formula: Potential Lives Saved ¼ ððDiedHosp þ DiedMoved Þ RACHSHosp þ RACHSMoved MRQuantile Þ
DiedHosp = Number of RACHS-1 deaths at the receiving hospital (not including moved patients) DiedMoved = Number of RACHS-1 deaths in patients moved to the receiving hospital RACHSHosp = Number of RACHS-1 operations performed at the receiving hospital (not including moved patients) RACHSMoved = Number of RACHS-1 operations moved to the receiving hospital MRQuantile = Mortality rate of the of the receiving hospital
Second Mortality Rate Analysis Given that there is an inverse relationship between hospital volume and mortality after CHS, we conducted a second analysis in which a stratified mortality rate was built to simulate the decrease in mortality that would be anticipated to accompany an increase in surgical volume.1,36 Using the baseline data, hospitals were divided into 5 groups. Hospitals with less than 150 operations were placed in one group. Hospitals with 150 or more operations were divided into volume quartiles to ensure similar numbers of hospitals in each quartile. Mortality rates were calculated for each group, using the dataset created for this investigation, by dividing the number of deaths by the number of operations. Hospital operative volume groups, the numbers of hospitals in the groups at baseline, and the mortality rates assigned to the groups for the simulation involving all RACHS-1 operations (categories 06) were as follows (number of hospitals, mortality rate): <150 operations (111 hospitals, 4.5%), 150199 operations (11 hospitals, 3.6%), 200242 operations (12 hospitals, 3.4%), 243310 operations (12 hospitals, 3.0%), and >310 operations (12 hospitals, 2.8%). For the simulation including only high-risk operations (RACHS-1 categories 46), the hospital operative volumes (number of hospitals, mortality rate) were grouped as follows: <150 operations (64 hospitals, 10.1%), 150199 operations (11 hospitals, 7.7%), 200242 operations (12 hospitals, 5.8%), 243310 operations (12 hospitals, 7.9%), and >310 operations (12 hospitals, 6.1%). These mortality rates were consistent with previous reports.3,4 Hospitals that gained volume due to redistribution of operations after regionalization were assigned mortality rates consistent with their new volumes rather than retaining their original mortality rates. Hospital volumes were recalculated after each simulation round and mortality rates assigned. We then applied the methodology
described in the preceding sections to calculate potential lives saved with the only change being: MRQuantile = Mortality rate of the of the volume group of the receiving hospital including moved operations Statistical Methods Relevant patient and hospital data were summarized with regards to the RACHS-1 operation categories. Mean and median were calculated for distance. Counts and percentages were used for categorical variables. Paired t test was used to assess the mean difference of the baseline distance and distance after each simulation round. Signed rank test was used to assess the median difference of the baseline distance and distance after each simulation round. Binomial test was used to assess mortality rate significance. Binomial proportion was used to derive the exact confidence interval of mortality rate and the range of lives potentially saved. Two-tailed P values were used for all analyses, with P value <0.05 as significant. The study was approved by the Peoria Institutional Review Board at the University of Illinois College of Medicine at Peoria, the University of Michigan Institutional Review Board, and additional individual state institutional review boards depending on individual state requirements. Analyses were conducted with SAS software, v9.4 (SAS Institute, Cary, NC). RESULTS Study Population Characteristics We identified 19,880 discharges after a congenital cardiac operation during the study period from 330 hospitals. After exclusion of hospitals performing only isolated patent ductus arteriosus ligations (n = 177) our sample included 19,064 discharges from 153 hospitals in 36 states. This cohort was used for the mortality analyses. The exclusion of 3177 additional patient discharges for which the home ZIP code was unknown resulted in a cohort of 15,887 patient discharges. This cohort was used for the distance calculations (Fig. 1). Regionalization Simulation—All Operations Prior to simulation, the overall mortality rate was 3.5% (666/ 19,064). In the initial simulation rounds of the first mortality analysis, there was little change in the overall mortality rate. However, as more patients were moved, there was a decrease in mortality such that by the time the smallest remaining hospital had an overall volume of 150 congenital cardiac operations, the number of deaths had decreased 5.7% to 628, resulting in 38 potential lives saved (Fig. 2). At this point, the mortality rate dropped to 3.3% (95% confidence interval [CI]: 3.03.6%, P = 0.13 compared to baseline) and 62 hospitals remained (Fig. 3). When the minimum hospital volume reached D16X X31D1 17X X operations, the number of deaths had decreased by 12.5% to 583, resulting in 83 potential lives saved (Fig. 2). The overall mortality rate decreased to 3.1% (95% CI:
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Figure 1. Map of the US depicting the locations of hospitals where congenital cardiac operations occurred in 2012. Hospitals are grouped by surgical volume (colored crosses). Black triangles represent patient origins for patientsD1X Xand lines link them to the hospital where they had surgery. Note that triangles and lines do not represent individual patients, but rather all patients within a ZIP code. Patient location data (ZIP codes) not available for California. RACHS-1 category 06 patients included.
2.83.3%, P < 0.01 compared to baseline) and 37 hospitals remained (Fig. 4). The results of the second mortality analysis were consistent with the first. There was little change in the overall mortality rate in the early simulation rounds. When the smallest remaining hospital had an overall volume of 150 congenital cardiac operations, the number of deaths had decreased 8.0% to 610, resulting in 56 potential lives saved. At this point, the mortality rate dropped to 3.2% (95% CI: 3.03.5%, P = 0.03 compared to baseline; Fig. 5). When the minimum hospital volume reached D18X X31D19X X1 operations, the number of deaths had decreased by 17.4% to 550, resulting in 116 potential lives saved. The overall mortality rate decreased to 2.9% (95% CI: 2.73.1%, P < 0.01 compared to baseline; Fig. 5). Of the 15 hospitals that did less than 150 operations at baseline and ended up with more than 150 operations by the time in the simulation at which the smallest remaining hospital had a volume of 150 operations, none had an assigned mortality rate that was statistically different from their baseline rate. Of the 25 hospitals that D20X Xdid less than 311 D21X Xoperations at baseline and ended up with 311 or moreD2X X D23X Xoperations after our simulation, 8 had an assigned mortality rate that was 0.5% or greater above baseline (of which 2 were statistically different) while 7
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had an assigned mortality rate that was 0.5% or more below baseline (of which 1 was statistically different). To achieve a minimum hospital volume of 150 operations, 3384 patients were moved, and to achieve a minimum hospital volume of 311 operations, 7019 patients were moved (Fig. 2). The movement of patients not only eliminated smaller volume hospitals but also created a larger number of high-volume hospitals. At baseline, 55 hospitals (36%) performed less than 50 operations, 51 (33%) performed between 50 and 149 operations, 35 (23%) performed 150310 operations, and 12 (8%) performed >310 operations. At the point in the simulation where all hospitals were at or above 150 operations, 91 hospitals (59%) were closed. Of the remaining 62 hospitals, 44 (71%) performed 150310 operations and 18 (29%) performed >310 operations. Regionalization Simulation—High-Risk Operations The analysis of high-risk operations simulated regionalization of the 2183 patients who underwent high-risk operations (RACHS-1 categories 46), while unclassified (RACHS-1 category 0) patients and patients undergoing low-risk operations (RACHS-1 categories 13) remained at their original hospitals. At baseline, 111 hospitals performed at least 1 high-risk
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Figure 2. Mortality rates and numbers of patients moved from their original hospitals as the regionalization simulation progressed. Hospitals retained their actual mortality rates throughout the simulation. RACHS-1 category 06 patients included.
operation, and the mortality rate for this group of patients was 7.5% (164/2183). When patients were redistributed such that all high-risk patients underwent surgery at hospitals that performed >310 total congenital cardiac operations, 21 hospitals (19%) remained that performed high-risk operations. At this point, the mortality rate for high-risk patients dropped from 7.5% to 6.3% (95% CI: 5.37.4%, P = 0.04). The number of deaths among high-risk patients decreased by 15.9% to 138, resulting in 26 potential lives saved. However, with low-risk patients remaining at their original hospitals, the overall number of deaths decreased by only 3.9%, and the overall mortality rate decreased from 3.5% to 3.4% (95% CI: 3.13.6%, P = 0.31). The alternative version of the analysis of high-risk operations simulated regionalization of the 5699 patients who underwent high-risk (RACHS-1 categories 46) or unclassified (RACHS-1 category 0) operations, while patients undergoing low-risk operations (RACHS-1 categories 13) remained at their original hospitals. At baseline, 136 hospitals performed at least 1 high-risk/unclassified operation, and the mortality rate for this group of patients was 6.1% (348/5699). When patients were redistributed such that all high-risk/unclassified patients underwent surgery at hospitals that performed >310 total
congenital cardiac operations, 24 hospitals (17.6%) remained that performed high-risk/unclassified operations. At this point, the mortality rate for these patients dropped from 6.1% to 5.4% (95% CI: 4.86.0%, P = 0.03). The number of deaths among these patients decreased by 11.2% to 309, resulting in potential 39 lives saved. However, with low-risk patients remaining at their original hospitals, the overall number of deaths decreased by only 5.9%, and the overall mortality rate decreased from 3.5% to 3.3% (95% CI: 3.13.6%, P = 0.12). Impact of Regionalization on Travel Distance As regionalization was simulated, travel distance increased modestly. At baseline, patients traveled a median distance of 38.5 miles from their home ZIP code to the hospital where they underwent surgery (Fig. 6). At the point at which all hospitals performed 150 or more operations, median travel distance increased to 50.4 miles, P < 0.01 compared to baseline. Median travel distance for patients who underwent surgery at a hospital other than their original hospital increased to 82.3 miles, P < 0.01 compared to baseline. When all operations were done at hospitals with volumes >310 operations, the overall median travel distance for the cohort increased to 69.6 miles, P < 0.01 compared to baseline. Median travel distance
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Figure 3. Map of the US depicting the locations of hospitals where congenital cardiac operations would occur if hospitals were closed until all remaining hospitals had surgical volumes of 150 operations or more. Black triangles represent patient origins for patients who changed hospitals and lines link them to the hospital where they would undergo surgery. Note that triangles and lines do not represent individual patients, but rather all patients within a ZIP code. RACHS-1 category 06 patients included.
for patients who traveled to new hospitals increased to 123.0 miles, P < 0.01 compared to baseline. At baseline, patients who underwent high-risk operations traveled a median distance of 39.8 miles. At the point at which all hospitals performed >310 overall operations, the overall median travel distance for all high-risk patients increased to 131.0 miles, P < 0.01 compared to baseline. Median travel distance for patients who underwent high-risk surgery at a hospital other than their original hospital increased to 226.5 miles, P < 0.01 compared to baseline. DISCUSSION We simulated regionalization of CHS in the US and assessed the impact a regionalized system would have on patient travel distance and mortality. Our data suggest that the number of hospitals performing a high volume of CHS would increase and mortality after surgery would decrease. These data also demonstrate that only a modest increase in patient travel distance was needed to gain the potential advantages of higher volume hospitals and reduced mortality. While reduction in mortality was seen when regionalizing all patients undergoing CHS as well asD24X X only high-risk patients, the greatest number of
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lives saved was in patients undergoing lower risk operations. Regionalization of high-risk patients alone resulted in fewer lives saved and substantially increased travel burden. The results of regionalization attempts in other specialties in the US have been mixed. Outcomes for bariatric surgery did not improve in response to the designation of centers of excellence (COE) by the Centers for Medicare and Medicaid Services.11 However, as an unintended consequence, the proportion of nonwhite Medicare patients receiving bariatric surgery declined.12 Non-COE hospitals remained in existence and had outcomes similar to COE hospitals. Of note, the volume outcome relationships demonstrated for bariatric surgery are not as strong as for CHS.13 The institution of evidencebased hospital referral (EBHR) by the Leapfrog Group had a negligible impact on statewide outcomes for pancreatic or esophageal resection or abdominal aortic aneurysm repair in Washington.14 Many patients continued to have surgery at non-EBHR hospitals even though, in general, hospitals meeting EBHR volume standards had lower adverse event rates. As small or low performing hospitals remained in both these examples, complete regionalization was not achieved. Pediatric surgery has become increasingly regionalized in the US in part
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Figure 4. Map of the US depicting the locations of hospitals where congenital cardiac operations would occur if hospitals were closed until all remaining hospitals had surgical volumes of more than 310 operations. Black triangles represent patient origins for patients who changed hospitals and lines link them to the hospital where they would undergo surgery. Note that triangles and lines do not represent individual patients, but rather all patients within a ZIP code. RACHS-1 category 06 patients included.
due to favorable outcomes seen in designated pediatric trauma centers and higher volume neonatal intensive care units.1518 Compared to many types of medical therapy, CHS is an infrequent event. We identified 19,064 CHS operations performed in 36 states. With extrapolation of this number to the entire US, the volume of CHS operations performed annually is similar to other infrequent operations such as solid organ transplant (33,610 transplants in 2016).19 Similar to organ transplant, CHS is resource intensive and requires complex systems and specialized expertise for success. Organ transplant is centralized to specialized centers to improve outcomes and reduce cost. The additional travel burden imposed on patients is thought to be balanced by better outcomes. The combination of low national volume and high resource intensity may make CHS a more favorable choice for regionalization than other specialties. The relationship between volume and mortality after CHS is such that as case complexity increases, so does the performance gap between smaller and larger programs.4 However, we found that regionalization of high-risk patients alone would achieve only a fraction of the total potential lives saved. This was a result of several factors. First, of the potential lives saved in our
study, a minority were high-risk patients. Second, most highrisk patients were already undergoing surgery at larger centers so were not moved with regionalization. Third, a far larger number of patients underwent low-risk operations. Only 11% of operations were high risk (2183/19,064) while 89% were low risk (16,881/19,064). As a result, despite a higher risk of mortality (7.5% vs 3.0%), the absolute number of low-risk patients who died was far greater. Therefore, the greatest impact of regionalization would not be achieved by focusing on high-risk patients alone but rather all patient risk categories. The relationship between volume and mortality after CHS is also such that while on average larger programs have lower mortality rates than smaller programs, there is variability within both groups. The consistency between our analysis in which each hospital’s original mortality rate was maintained and our analysis that assigned volume-based mortality rates to hospitals addresses this relationship. If all CHS hospitals across the US were included in a regionalization effort, higher average volumes would result, and overall mortality would be lower. In addition, the potential benefits of regionalization may be greater than demonstrated in this study which focused on
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Figure 5. Mortality rates and numbers of patients moved from their original hospitals as the regionalization simulation progressed. Hospitals started with their actual mortality rates and were assigned new mortality rates based on their volumes at each stage of the simulation. The assigned mortality rates were the means of the overall cohort for each volume category: <150 (4.5%), 150199 (3.6%), 200242 (3.4%), 243310 (3.0%), >310 (2.8%). RACHS-1 category 06 patients included.
mortality. Hospital surgical volume has also been shown to be inversely associated with complication and failure to rescue rates.2022 In addition, lower hospital surgical volumes are associated with higher costs.23,24 Staffing models, expertise, and physical resources that are cost prohibitive when treating a low volume of patients become more affordable when caring for a high volume. Our choice to assign new mortality rates to hospitals that gained patients during the simulation was based in part on this rationale. The distribution of CHS hospitals in the US is characterized by frequent geographic clustering. The majority of CHS hospitals (66%) are within 25 miles of another CHS hospital.8 Because of this phenomenon, regionalization of CHS was associated with a modest impact on patient travel burden. The impact of distance is difficult to assess. Mortality risk and the incidence of adverse events after discharge may not be adversely impacted by distance.25 Travel burden may impact families with fewer resources, both financially and socially, differently than those with a wealth of resources. In addition, families in rural areas may be more accustomed to longer travel distances for a variety of services than urban residents.
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However, any negative impact of regionalization is balanced by the survival benefit a patient may realize by undergoing surgery at a higher volume hospital. While regionalization would create larger hospitals, none of the hospitals in our study increased to a volume greater than that of the largest, current CHS hospital. Although there may be a volume above which decline in mortality plateaus, there is no evidence to support a volume above which quality is negatively affected.4 Limitations The limitations of this investigation primarily relate to the data source. We obtained data from 39 states, which included 90.3% of the US population. Of the missing 11 states, the District of Columbia, and Puerto Rico, 7 do not have a known hospital with a dedicated CHS program. It is unlikely that the structure of CHS care in the remaining 6 locales differs significantly from the captured 39. As a result, the findings of our study should be representative of the US Administrative data is the only source of patient ZIP codes, which were required to simulate regionalization. The use of administrative data necessitates reliance on ICD-9-CM diagnosis and procedure
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Figure 6. Change in mean and median travel distance as the regionalization simulation progressed. RACHS-1 category 06 patients included.
codes. Some operations may be miscoded or not accurately captured due to the lack of an appropriate code. RACHS-1 captures approximately 85% of CHS operations, including all common operations, and represents the universe of CHS operations.26 Previously, we found that analyses of hospital-level volume-mortality relationship in administrative and clinical datasets provided similar results.3,4 We regionalized based on volume rather than mortality. An alternative approach would be to regionalize patients to lower mortality rather than higher volume hospitals. However, the instability of mortality rates, especially for small-volume hospitals, would complicate this approach.27 A comprehensive understanding of the potential advantages and disadvantages of a regionalized system of CHS would need to also consider the potential negative impact of removing CHS from hospitals. CHS programs generate revenue that supports underfunded programs in children’s hospitals and provide expertise to care for critically ill children without cardiac disease as well. The impetus to regionalize CHS could come from a variety of sources; however, significant hurdles exist. Increased transparency through public reporting of surgical volumes and outcomes would allow patients, their parents, and referring
physicians to select high performing hospitals. To date, transparency has been hampered by limited scope, incomplete reporting, and difficulty in interpretation of data. Hospitals could take the initiative and adhere to a volume pledge by which they only offer CHS if a minimum volume standard is met.28 However, the current remuneration system and local pride are barriers to shuttering programs. The Center for Medicare and Medicaid Services (CMS) and private insurers could alter reimbursements to favor hospitals meeting selected criteria. Yet, the administration of Medicaid at the state level limits the potential for national policy initiatives. Legislative efforts could mandate regionalization by requiring hospitals to meet volume or other criteria. While there is opposition to a single-payor health care system in the US, nationalization of pediatric care may be more palatable since children’s health care accounts for less than 10% of total health care expenditures and 50% of pediatric inpatient costs are already covered by Medicaid.29,30 CONCLUSION Regionalization of CHS in the US could result in more highvolume hospitals and significantly reduced mortality at the cost of only a modest increase in patient travel burden. Much of the mortality reduction may be missed if solely high-risk
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CONGENITAL REGIONALIZATION OF CHS patients were regionalized. In addition to improving the care for patients with congenital heart disease, regionalization of CHS could serve as a model for the coordination of other low volume, high complexity specialties.
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