Abstract
Background
Despite significant changes in the past decade for children undergoing heart-transplantation, including the evolution of mechanical circulatory support and increasing patient complexity, costs and resource utilization have not been reassessed. We sought to utilize a novel linkage of clinical-registry and administrative data to examine changes in hospitalization costs over time in this population.
Methods
We identified all pediatric heart transplant recipients in a unique linked PHIS/SRTR dataset (2002–2016). Hospital costs were estimated from charges using cost-to-charge ratios, inflated to 2016 dollars. Severity-adjusted costs were calculated using generalized linear mixed-effects models. Costs were compared across 3 eras (Era-1:2002–2006; Era-2:2007–2011; and Era-3:2012–2016).
Results
A total of 2896 pediatric heart transplant recipients were included; Era-1:649 (22.4%), Era-2:1028 (35.5%), and Era-3:1219 (42.1%). ECMO support at transplant decreased over time, concurrent with an increase in VAD-supported patients. Between Era-1 and Era-2 there was an increase in pretransplant hospitalization costs ($343,692 vs. $435,554; p<0.001). However, between Era-2 and Era-3 there was a decline in total ($906,454 vs. $767,221; p<0.001), pretransplant ($435,554 vs. $353,364; p<0.001), and posttransplant ($586,133 vs. $508,719; p=0.002) hospitalization costs.
Conclusions
Concurrent with the increase in utilization of VAD support, there has been an increase in pretransplant costs associated with pediatric heart transplantation. However, in the most recent era, costs have declined. These findings suggest the evolution of more cost-effective management strategies, which may be related to shifts in the approach to pediatric mechanical circulatory support.
Introduction
Heart transplant (HT) has become a widely accepted treatment for end stage heart failure in both children and adults1,2. However, it is resource-intensive with reported median hospital charges of $137,679 in adults and mean hospital charges of >$450,000 in children3,4. At least one prior report from patients transplanted between 1997 and 2006 suggests that hospital costs associated with pediatric HT have increased over time3; however, there have been no publications describing resource utilization in this population from a more contemporary cohort. Additionally, many changes have occurred over time that may impact resource utilization including shifts in the use of mechanical circulatory support and increasing patient complexity.
Ventricular assist device (VAD) use in children has increased significantly over the past 10 years5, likely impacting the costs associated with HT. Mahle et al reported mean hospital costs of $758,199 for pediatric patients bridged to HT with a VAD between 2002 and 20076. Waitlist survival has also improved over time7, likely resulting in increased waitlist duration and associated costs. In addition, recent changes in pediatric heart allocation that prioritize donor hearts to the most critically ill candidates, including those with congenital heart disease and those who require VAD support, are likely to further impact waitlist durations and contribute to increases in the costs associated with pediatric HT8.
This project aimed to describe the changes in hospitalization costs associated with pediatric HT over time. We hypothesized that improvements in waitlist survival and increases in the frequency of VAD utilization would result in increases in cost over time.
Materials and Methods
This study utilized a unique database linkage between the Scientific Registry of Transplant Recipients (SRTR, Minneapolis Medical Research Foundation, Minneapolis, MN) and the Pediatric Health Information System (PHIS, Children’s Hospital Association, Lenexa, KS) administrative database, and has been previously described9. The SRTR data system includes data on all donors, waitlisted candidates, and transplant recipients in the U.S., submitted by the members of the Organ Procurement and Transplantation Network (OPTN). The Health Resources and Services Administration, U.S. Department of Health and Human Services provides oversight to the activities of the OPTN and SRTR contractors. SRTR data are derived from multiple sources including the OPTN, transplant programs, organ procurement organizations, histocompatibility laboratories, the Centers for Medicare and Medicaid Services, and the National Technical Information Service’s Death Master File. The SRTR database includes data from every organ transplant and waitlist addition within the U.S. since October 19879. The PHIS database is an administrative database that collects clinical and resource utilization data for hospital encounters from 49 tertiary care children’s hospitals. This includes data from inpatient hospitalizations, observation, ambulatory surgery, and emergency department visits. This database accumulates encounter–level diagnosis and procedural ICD-9 and ICD-10 codes, payer information, along with daily encounter-level hospital charge data9.
All pediatric (age < 21 years) patients (2002 – 2016) with available hospital charge information in the linked database were included. Hospital charges were converted to costs using hospital-specific and year-specific cost-to-charge ratios. All costs were adjusted for inflation to 2016 U.S. dollars using the medical component of the Consumer Price Index. Costs were assessed for the entire transplant hospitalization, the pretransplant hospitalization period (day of admission to the day prior to HT), and for the post-HT period (day prior to HT to the day of discharge). Adjusted patient costs were calculated with generalized linear mixed effects models using an exponential distribution, and included a random hospital intercept. Variables included in the total and posttransplant cost models were selected a priori and included era, patient age, diagnosis (cardiomyopathy, congenital heart disease, or retransplant), race, the need for ECMO support pre or posttransplant, VAD support, ventilator support, inotropic support, total length of stay, rejection prior to hospital discharge, and the need for dialysis posttransplant. The pretransplant cost model included the same variables but excluded those that were dependent on posttransplant events (posttransplant ECMO, total length of stay, rejection prior to hospital discharge, and the need for dialysis posttransplant). Adjusted costs were expressed as the least squares mean with 95% confidence intervals obtained from the generalized linear mixed models.
Included patients were divided into 3 eras: Era-1 (2002 – 2006), Era-2 (2007 – 2011), and Era-3 (2012 – 2016). Patient demographics were compared across eras using standard summary statistics and either the chi squared or Kruskal-Wallis test, as appropriate. Hospitalization costs were compared across eras using generalized linear mixed effects models. Hospitalization costs were subdivided into categories including pharmacy, laboratory, imaging, supply, clinical, and other costs and the analysis was repeated. Other costs are comprised primarily of room (including operating room) and nursing costs. To assess the impact of VAD support on the changes in hospitalization costs over time, a separate generalized linear mixed effects model was generated to assess the changes in VAD costs across eras, adjusting for the same variables included in the original models. All statistical analyses were performed in SAS version 9.4 (SAS Institute; Cary, NC) or STATA version 13 (StataCorp LLC; College Station, TX) with p<0.05 considered statistically significant. This project was approved by the Vanderbilt University IRB, PHIS, and SRTR.
Results
A total of 2896 patients were included in the study with 649 (22.4%) transplanted during Era-1, 1028 (35.5%) transplanted during Era-2, and 1219 (42.1%) transplanted during Era-3. Patient characteristics by era are shown in Table 1. Over time there has been a trend towards higher waitlist urgency status with 85.1% of patients listed UNOS status 1A at the time of HT in the 2 most recent eras compared to 74.4% of patients listed UNOS status 1A at HT in Era-1 (p<0.001). Fewer patients have been supported with ECMO at HT over time (9.1% vs. 5.4% vs. 3.6% across eras 1, 2, and 3 respectively, p<0.001) and VAD use has increased over the same time period (7.1% vs. 17.1% vs. 22.2%, p<0.001). There has also been an increase in the use of inhaled nitric oxide following HT and a trend towards fewer patients requiring ventilator support at HT. Between Era-1 and Era-2 there was a significant increase in the total (41 days vs. 52 days, p<0.001) and pre-HT (16 days vs. 24 days, p<0.001) length of stay but a decrease in the number of days mechanical ventilation was used after HT (mean 16.9 days vs. 9.7 days, p<0.001). There has also been a decrease in the incidence of acute rejection prior to hospital discharge (18.4% vs. 13.5%, p=0.02) and an increased incidence of chylothorax (1.8% vs. 6.2%, p<0.001) between Era-1 and Era-2. There was no difference across eras based on patient age, diagnosis, gender, blood type, need for pre-HT inotropic support, ICU length of stay, the incidence of stroke, and the need for post-HT dialysis or cardiac reoperation.
Table 1.
Population differences by era
| Total N=2896 |
Era 1 (2002 – 2006) N=649 (22.4%) |
Era 2 (2007 – 2011) N=1028 (35.5%) |
Era 3 (2012 – 2016) N=1219 (42.1%) |
p–valuea | |||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| Age | |||||||||
| <1 year | 896 | (30.9%) | 216 | (33.3%) | 328 | (31.9%) | 352 | (28.9%) | 0.458 |
| 1–5 years | 688 | (23.8%) | 152 | (23.4%) | 239 | (23.2%) | 297 | (24.4%) | |
| 6–10 years | 407 | (14.1%) | 94 | (14.5%) | 131 | (12.7%) | 182 | (14.9%) | |
| 11–17 years | 829 | (28.6%) | 174 | (26.8%) | 302 | (29.4%) | 353 | (29.%) | |
| 18–21 years | 76 | (2.6%) | 13 | (2.%) | 28 | (2.7%) | 35 | (2.9%) | |
| Diagnosis | |||||||||
| Cardiomyopathy | 1330 | (46.5%) | 284 | (44.4%) | 496 | (48.6%) | 550 | (45.7%) | 0.2 |
| Congenital Heart Disease | 1373 | (48.%) | 313 | (48.9%) | 466 | (45.7%) | 594 | (49.4%) | |
| Retransplant | 160 | (5.6%) | 43 | (6.7%) | 58 | (5.7%) | 59 | (4.9%) | |
| Race | |||||||||
| Caucasian | 1701 | (58.7%) | 381 | (58.7%) | 622 | (60.5%) | 698 | (57.3%) | 0.013 |
| African-American | 535 | (18.5%) | 143 | (22.%) | 184 | (17.9%) | 208 | (17.1%) | |
| Hispanic | 493 | (17.%) | 94 | (14.5%) | 162 | (15.8%) | 237 | (19.4%) | |
| Other | 167 | (5.8%) | 31 | (4.8%) | 60 | (5.8%) | 76 | (6.2%) | |
| Male Gender | 1585 | (54.7%) | 346 | (53.3%) | 556 | (54.1%) | 683 | (56.%) | 0.465 |
| Blood Type | |||||||||
| O | 1310 | (45.2%) | 286 | (44.1%) | 469 | (45.6%) | 555 | (45.5%) | 0.619 |
| A | 1087 | (37.5%) | 260 | (40.1%) | 379 | (36.9%) | 448 | (36.8%) | |
| B | 376 | (13.%) | 74 | (11.4%) | 133 | (12.9%) | 169 | (13.9%) | |
| AB | 123 | (4.2%) | 29 | (4.5%) | 47 | (4.6%) | 47 | (3.9%) | |
| Status at Transplant | |||||||||
| 1A | 2395 | (82.7%) | 483 | (74.4%) | 875 | (85.1%) | 1037 | (85.1%) | <0.001 |
| 1B | 320 | (11.%) | 80 | (12.3%) | 98 | (9.5%) | 142 | (11.6%) | |
| 2 | 181 | (6.3%) | 86 | (13.3%) | 55 | (5.4%) | 40 | (3.3%) | |
| ECMO at Transplant | 158 | (5.5%) | 59 | (9.1%) | 55 | (5.4%) | 44 | (3.6%) | <0.001 |
| VAD at Transplant | 493 | (17.%) | 46 | (7.1%) | 176 | (17.1%) | 271 | (22.2%) | <0.001 |
| Ventilator at Transplant | 493 | (17.%) | 129 | (19.9%) | 177 | (17.2%) | 187 | (15.3%) | 0.045 |
| Inotropes at Transplant | 1438 | (49.7%) | 297 | (45.8%) | 513 | (49.9%) | 628 | (51.5%) | 0.059 |
| Post-transplant iNO | 1452 | (50.2%) | 217 | (33.4%) | 494 | (48.1%) | 741 | (61.1%) | <0.001 |
| Total Length of Stay (Days) | 50 | (20–98) | 41 | (18–77) | 52 | (23–97) | 54 | (20–111) | <0.001 |
| Pre-transplant Length of Stay (Days) | 23 | (1–62) | 16 | (1–41) | 24 | (1–63) | 26 | (1–77) | <0.001 |
| Post-transplant Length of Stay (Days) | 18 | (12–32) | 18 | (10–31) | 18 | (12–33) | 19 | (12–33) | 0.016 |
| Post-Transplant ICU Days | 9 | (4–20) | 8 | (4–18) | 9 | (5–21) | 9 | (5–21) | 0.103 |
| Post-Transplant Days on Ventilator | 2 | (1–8) | 3 | (1–11.75) | 2 | (1–8) | 2 | (1–7) | <0.001 |
| Post-Transplant Complications | |||||||||
| Dialysis | 161 | (5.6%) | 48 | (7.4%) | 49 | (4.8%) | 64 | (5.3%) | 0.063 |
| Rejection Prior to Discharge | 360 | (13.6%) | 74 | (18.4%) | 139 | (13.5%) | 147 | (12.1%) | 0.006 |
| Stroke | 99 | (3.4%) | 18 | (2.8%) | 37 | (3.6%) | 44 | (3.6%) | 0.585 |
| Chylothorax | 147 | (5.1%) | 12 | (1.8%) | 64 | (6.2%) | 71 | (5.8%) | <0.001 |
| Cardiac Reoperation | 188 | (8.3%) | 50 | (7.8%) | 83 | (9.%) | 55 | (7.9%) | 0.63 |
p-values from the chi square test for categorical and Kruskal Wallis test for continuous variables
Adjusted total, pre-, and post-HT costs by era are shown in Table 2. Between Era-1 and Era-2 there was no significant change in total or posttransplant hospitalization costs. Over the same time period, there was a significant increase in pretransplant hospitalization costs ($343,692 vs. $435,554; p<0.001). In the most recent era, there was a decline in total ($906,454 vs. $767,221; p<0.001), pretransplant ($435,554 vs. $353,364; p<0.001), and posttransplant ($586,133 vs. $508,719; p=0.002) hospitalization costs.
Table 2.
Unadjusted and adjusted hospitalization costs based on era
| Era 1 (2002 – 2006) N=649 (22.4%) |
Era 2 (2007 – 2011) N=1028 (35.5%) |
Era 3 (2012 – 2016) N=1219 (42.1%) |
p-valuea | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
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| Overall | Era 1 vs. Era 2 | Era 2 vs. Era 3 | |||||||
|
|
|||||||||
| Unadjusted costs | |||||||||
| Total cost | $680,831 | ($587,849– $788,521) | $730,861 | ($636,746– $838,885) | $670,273 | $587,527– $764,673) | 0.12 | 0.170 | 0.048 |
| Pre-transplant costs | $182,311 | ($154,509– $215,117) | $280,286 | ($239,387– $328,173) | $279,555 | ($240,160– $325,412) | <0.001 | < 0.001 | 0.953 |
| Post-transplant costs | $476,947 | ($407,388– $558,383) | $429,596 | ($370,058– $498,711) | $385,713 | ($334,151– $445,231) | <0.001 | 0.043 | 0.014 |
| Adjusted costsb | |||||||||
| Total cost | $913,390 | ($604,342– $1,380,480) | $906,454 | ($599,850– $1,369,776) | $767,221 | ($508,211– $1,158,237) | <0.001 | 0.899 | <0.001 |
| Pre-transplant costs | $343,692 | ($270,794– $436,214) | $435,554 | ($344,417– $550,805) | $353,364 | ($281,608– $443,404) | <0.001 | <0.001 | < 0.001 |
| Post-transplant costs | $586,298 | ($385,560– $891,550) | $586,133 | ($385,650– $890,837) | $508,719 | ($334,998– $772,526) | 0.003 | 0. 0.996 | 0.002 |
p-values from generalized linear mixed models
Cost expressed as least squares mean (Lower and Upper 95% confidence interval), inflated to 2016 dollars
Total costs based on area of spending are shown in Figure 1. Clinical and other costs represent the largest expenditures for the total hospitalization. The highest cost services in these groups include organ acquisition and procurement, organ transplant service, and intensive care unit charges. Between Era-1 and Era-2 there were no significant changes in total costs across any area of spending (ie, pharmacy, laboratory, imaging, supply, clinical, and other costs). Between Era-2 and Era-3 there were significant decreases in total laboratory, supply, and other costs and a trend towards decreased total pharmacy and clinical costs. Imaging costs did not change during this timeframe.
Figure 1.
Adjusted total cost based on area of spending by era
Changes in VAD costs over time are shown in Table 3. There was a trend towards decreasing total hospitalization cost for patients supported with a VAD across eras.
Table 3.
Unadjusted and adjusted hospitalization costs based on VAD Utilization
| Era 1 (2002 – 2006) N=649 (22.4%) |
Era 2 (2007 – 2011) N=1028 (35.5%) |
Era 3 (2012 – 2016) N=1219 (42.1%) |
p-value* | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| Overall | Era 1 vs. Era 2 | Era 2 vs. Era 3 | |||||||
|
|
|||||||||
| Unadjusted costs | |||||||||
| No VAD | $661,353 | ($566,546– $772,024) | $663,489 | ($572,798– $768,540) | $596,058 | ($517,596– $686,415) | 0.056 | 0.953 | 0.029 |
| With VAD | $986,549 | ($723,476– $1,345,283) | $1,096,136 | ($917,845– $1,309,061) | $917,043 | ($788,634– $1,066,360) | 0.197 | 0.533 | 0.072 |
| Adjusted costs | |||||||||
| No VAD | $721,882 | ($471,513– $1,105,194 | $721,813 | ($470,717– $1,106,853 | $612,681 | ($399,713– $939,119) | 0.002 | 0.999 | < 0.001 |
| With VAD | $1,273,933 | ($542,009– $2,994,241) | $1,176,332 | ($512,326– $2,700,933) | $975,842 | ($425,680– $2,237,049) | 0.108 | 0.672 | 0.068 |
p-values from generalized linear mixed models
Cost expressed as least squares mean (Lower and Upper 95% confidence interval), inflated to 2016 dollars
Discussion
Our analysis demonstrates that, while pre-HT hospitalization costs increased between Era-1 and Era-2, overall costs have decreased in the most recent era. These improvements were seen during all periods of the transplant hospitalization including total, pretransplant, and posttransplant care. These findings suggest the evolution of more cost-effective management strategies, potentially impacted by advances in pediatric mechanical circulatory support (MCS).
The early increases seen in pre-HT costs are likely multifactorial; however, shifts in pediatric MCS strategies may play a critical role. VAD use in children has increased significantly over the past 10 years5. We also observed this change with VAD use increasing from 7.1% of patients to 22.2% of patients over the 15-year timeframe included in this study. Concurrently, there has been a decline in the use of ECMO for patients undergoing HT. While this shift in MCS strategy may contribute to increases in pre-HT costs, the superior outcomes of VAD support compared to ECMO10 may offset this added cost. In fact, our analysis demonstrates that the costs associated with HT have decreased in the most recent era. It is possible that the shift in MCS strategy from ECMO to VAD has resulted in patients being better supported prior to HT and leading to a less complicated pre- and post-HT course. Any cost savings associated with VAD support may be outweighed by increased utilization over time. Prior studies have demonstrated that as experience with VAD support increases, costs decrease11. We hypothesize that the costs associated with VAD support may be declining as centers gain more experience in successful candidate selection, device management, detection of device complications, and as the use of continuous flow devices expand further in the pediatric population. In fact, our analysis suggests that the total hospitalization costs for VAD supported patients may be decreasing over time. It is also important to note that with each successive era, pediatric heart transplant outcomes have improved.2 This improvement in early mortality may also influence hospitalization costs over time as early mortality may decrease costs.
The early increase in pre-HT costs may also be impacted by waitlist times. Survival on the waitlist has improved in the recent era7, which may be directly related to the increased availability of VADs, allowing a longer duration of support. While this likely represents overall improvement in management strategies, this also leads to longer waitlist times and the potential for higher acuity patients undergoing HT. Both of these factors may result in increased costs.
There are limited prior studies addressing the costs associated with pediatric HT and only one that addressed the changes in costs over time. Law et al reported a 160% increase in hospital charges associated with pediatric HT from 1997 to 20063. Our analysis provides more contemporary cost data compared to this prior study, and represents the largest reported U.S. cohort to date. Importantly, while our analysis confirms an increase in pre-HT costs in earlier eras, data from the most recent era suggest the evolution of more cost effective management strategies.
These data demonstrate that areas of cost expenditure have also changed over time. Cost improvements were seen in laboratory, supply, and other costs, while no significant decreases in cost were observed in pharmacy, imaging, and clinical costs. Any potential cost saving strategies for pharmacy and imaging costs may be outweighed by other factors. Pharmacy costs may be influenced by increases in medication costs as well as changes in medication prescribing patterns over time. There has been a shift toward greater utilization of tacrolimus and mycophenolate mofetil over time with fewer patients receiving cyclosporine, azathioprine, or steroids2,12. Tacrolimus and mycophenolate mofetil have increased drug acquisition costs compared to cyclosporine and azathioprine respectively, but prior studies indicate that these agents are more cost-effective secondary to improved patient outcomes13–15. Additionally, there has been an increase in the use of induction therapy, further impacting pharmacy costs2,12. In fact, T cell depleting agents are now the most commonly used induction agents following pediatric HT and also represent the most costly alternative compared to IL-2 receptor antagonists or no induction therapy15. Additionally, multiple studies have documented a significant increase in the utilization of imaging over time16,17, which may offset any potential cost savings in this area.
Prior studies have suggested that physicians have little awareness of the costs associated with the therapies they prescribe18–20. For this reason, any improvement in costs over time may not be related to physicians becoming more cost conscious, but instead related to improvements in patient management and quality of care. Further improvements in costs may be possible; however, these will likely require increased physician awareness and involvement in selecting the most cost-effective therapies.
Limitations
Our analysis has inherent limitations. Given the expected positive skewed distribution of cost data21, outliers may exist that overestimate the true cost. However, the use of generalized linear mixed effects models with an exponential distribution (to model skewed data) and reporting of least squares means represents the most statistically appropriate methodology. The linked PHIS and SRTR database only includes transplanted patients and therefore excludes patients who were listed but not transplanted. This may result in underestimation of costs, but is unlikely to significantly impact assessment of the changes in cost over time. Patients who were not hospitalized at the time of HT had negligible pre-HT costs, potentially resulting in underestimation of these costs. Additionally, we are only able to report data from hospitals that contribute to both databases. While this unique linkage has allowed an in-depth assessment of HT costs over time, the etiology for the changes remains unclear. There may be significant regional variation in costs, potentially impacting our analysis. To address this, a study investigating the differences in costs across HT centers is underway. As with any large dataset, there is the potential for missing or erroneous data. However, we believe that the merger of these 2 databases increases data granularity and helps to minimize this limitation.
Conclusion
The costs associated with pediatric HT have decreased in the most recent era, suggesting the evolution of more cost effective management strategies. These changes may be in part related to shifts in pediatric mechanical circulatory support.
Acknowledgments
Funding sources:
This project was supported through internal funding from the Katherine Dodd Faculty Scholar Program at Vanderbilt University (JG). Research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL123938 (Bethesda, MD) (JS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The authors would like to thank Bryn Thompson from SRTR for his role in facilitating the data linkage between SRTR and PHIS.
Abbreviations
- ECMO
extracorporeal membrane oxygenation
- HT
heart transplant
- MCS
mechanical circulatory support
- OPTN
Organ Procurement and Transplantation Network
- PHIS
Pediatric Health Information System
- SRTR
Scientific Registry of Transplant Recipients
- VAD
ventricular assist device
Footnotes
Authorship statement: Justin Godown: Participated in research design, data analysis, and manuscript preparation. Cary Thurm: Participated in research design, data analysis, and manuscript preparation. Matt Hall: Participated in research design, data linkage, data analysis, and manuscript preparation. Jonathan H. Soslow: Participated in research design and manuscript preparation. Brian Feingold: Participated in research design and manuscript preparation. Bret A. Mettler: Participated in research design and manuscript preparation. Andrew H. Smith: Participated in research design and manuscript preparation. David W. Bearl: Participated in research design and manuscript preparation. Debra A. Dodd: Participated in research design and manuscript preparation
Disclosures:
The data reported here have been supplied by the Minneapolis Medical Research Foundation (MMRF) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government.
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