Correspondence to Peter Lee; [email protected]
Strengths and limitations of this study
Multivariable generalised regression models were developed, allowing for the adjustment of cost models for key procedural and clinical characteristics and the identification of temporal trends in costs.
Real-world registry data from the Victorian Cardiac Outcomes Registry were linked to the Victorian Admitted Episodes Dataset, allowing for key hospitalisations and patient data across Victoria to be captured for direct, ‘bottom-up’ costing.
Analyses were limited to publicly admitted patients; the cost burden estimated in this study and findings may only be applicable to the public health sector across Australia and New Zealand.
Analyses did not consider the cost impacts attributed to major adverse cardiac and cerebrovascular events and, readmissions, medications use or mortality following discharge; however, these remained stable over the 4-year period of evaluation.
There was uncertainty around the extent to which changes in clinical practice had contributed to the stability observed in mean procedural costs over time.
Introduction
Cardiovascular disease (CVD), including coronary heart disease (CHD) and cerebrovascular disease, represents a significant cause of morbidity and mortality in Australia. In 2017–2018, the prevalence of CHD in Australia was estimated to be 3% of the adult population.1 Although cardiovascular mortality has declined significantly since the 1960s, it remains the second leading cause of death (approximately 26%) in Australia.1 The burden of CVD in terms of premature mortality and morbidity is also significant, with CHD and stroke comprising 11% and 5%, respectively, of the total disease burden in Australia in 2015.2
Of the estimated 580 300 Australians aged 18 years and over with CHD in 2017–2018, 40% had experienced angina and 74% had suffered acute coronary syndrome (ACS).1 Percutaneous coronary intervention (PCI) is the preferred means of revascularisation therapy for many patients presenting with ACS based on Australian and international guidelines.3 4 In non-ACS settings, elective PCI may also be indicated for the symptom management of stable angina.3 4 In 2017–2018, 44 886 PCIs were performed in Australia.1 In Victoria, 12 447 patients underwent PCI in 2018, 49% of which was for ACS.5
The costs attributed to the management of CVD are expectedly high. Based on estimates from the Australian Institute of Health and Welfare, in 2018–2019, expenditure for CVD amounted to $A11.8 billion, comprising 9% of the total health expenditure.6 Of this, 68% was attributed to hospital admissions for CVD.6
Government estimates of health expenditure are generally generated via combination of ‘top-down’ and ‘bottom-up’ approaches.7 In a ‘top-down’ approach, total expenditure is apportioned to various disease states according to epidemiological data.8 9 However, this approach is designed for aggregate analysis of government expenditure and does not allow for the estimation of cost burden and identification of key cost drivers at the microlevel.10 In contrast, a ‘bottom-up’ costing approach, in which individual healthcare resources are determined and then aggregated, allows for a greater understanding of patient factors which drive health systems costs.10
There are currently limited data on the direct costs of PCI in Australia based on a ‘bottom-up’ approach. In this study, we aimed to estimate the economic burden of PCI in Victoria for the public healthcare system, and explore key drivers of procedural costs across ACS and non-ACS indications for PCI using data from the Victorian Cardiac Outcomes Registry (VCOR).
Methods
Data sources
VCOR is a cardiac clinical quality registry established in 2012 for the purposes of monitoring and benchmarking hospital performance and outcomes in terms of PCIs undertaken in Victoria, Australia, and has previously been described in detail.11 Since 2017, all public and private PCI-capable centres in Victoria have contributed data to VCOR. However; for the purposes of this study, our analyses were limited to publicly admitted patients.5 Patient in-hospital baseline, demographic and clinical characteristics, as well as procedural outcomes, are collected from participating hospital sites through hospital-appointed data managers. Furthermore, patient follow-up is performed at 30 days to collect data on patient outcomes, including mortality and major adverse cardiac and cerebrovascular events (MACCE).5 11
To obtain relevant cost data, VCOR was linked to the Victorian Admitted Episodes Dataset (VAED), which contains data on all admissions into all Victorian hospitals, as well as diagnostic and procedural data.12 VAED variables reflect hospital activity for funding purposes.13 14 Of 32 852 patients enrolled in VCOR between 1 January 2013 and 31 December 2017 who were alive at 30 days following PCI, 194 were excluded from linkage due to insufficient case information, and successful matching was achieved for 28 488 (87%) patients.15 For the purposes of this study, we analysed data for all PCIs undertaken in Victorian public hospitals from 1 January 2014 to 31 December 2017. Data from 2013, the year of commencement of data collection, were excluded as it was an incomplete dataset, with several sites only contributing data for 1 month.16 17 Cost estimates were limited to the public hospital setting, as there are differences in cost reporting between public and private hospitals and discrepancies in the relative financial efficiency between public and private hospitals.18 Further, cost data were not available from private hospitals contributing data to VCOR.
Costs
In Victoria, public hospitals are funded through casemix funding.19 20 The basic casemix funding method allocates cost weights according to diagnosis-related groups (DRGs), which classify patients who have similar conditions and similar resource use.19 20 The DRG cost weight is calculated as the ratio of the average cost of all episodes in a DRG to the average cost of all episodes across all DRGs.19 20 As such, every episode for a DRG is funded at a flat rate determined by the DRG cost weight and the price paid per cost weight. This basic casemix funding method has been refined to account for differences in hospital length of stay as patients in a given DRG need various levels of care. This improved casemix funding is known as the ‘weighted inlier-equivalent separation (WIES).19 20
The cost attributed to PCIs undertaken in Victorian public hospitals was estimated using the WIES casemix funding method, in which a WIES weight18 21 was multiplied by the WIES price set for a given financial year to estimate the cost for an episode of care.19 Additional details pertaining to the WIES weights used to inform procedural costs are presented in the online supplemental appendices. All costs were adjusted for inflation to 2020 Australian dollars (A$) based on the Health Price Index.7
Henceforth, the per-person cost of PCI will be referred to as the procedural cost, which is comprised of all costs by the hospital during a patient’s stay, including the PCI procedure itself, hospital length of stay, critical care and medications. However, as the DRG and WIES weights represent a relative measure of resources use for each episode of care, the cost components cannot be assessed separately.
Statistical analysis
Continuous variables were expressed as mean±SD or median (IQR) where relevant, while categorical variables were expressed as frequencies (percentages). Differences in patient and procedural characteristics between years were compared using univariable linear regression, or univariable generalised linear regression models (GLM) with gamma distributions and log link where appropriate, for continuous variables. Generalised linear regression modelling with gamma distribution and log-link was used to account for the positive skew associated with length-of-stay and door-to-balloon/device time parameters.22 Pearson’s χ2 tests were used to assess trends over time for categorical variables.
Multivariable GLM with gamma distribution and log link was performed to establish key drivers in patient costs.22 Multivariable GLM was also performed to identify trends in patient procedural costs over time across the ACS groups, and for patients with non-ACS indications for PCI. To identify potential confounders of procedural costs, univariable GLMs with gamma distribution and log links were performed across the following variables: age (<75 years and ≥75 years); sex; indigenous status; body mass index (BMI); in-hours hospital arrival (between 08:00 and 18:00 hours on a workday); ACS status (non-ACS, unstable angina (UA), non-ST elevation myocardial infarction (NSTEMI), STEMI); cardiogenic shock or intubated out-of-hospital cardiac arrest (OHCA); left ventricular ejection fraction (LVEF); medicated diabetes mellitus; peripheral vascular disease; cerebrovascular disease; chronic oral anticoagulation therapy; prior coronary artery bypass grafting (CABG); previous PCI; use of glycoprotein IIb/IIIa inhibitors; use of thienopyridine or ticagrelor; estimated glomerular filtration rate (eGFR); percutaneous access site selection; required mechanical ventricular support; lesion complexity (American College of Cardiology/American Heart Association type A/B1 vs type B2/C lesions); unprotected left main PCI; chronic total occlusion PCI and in-stent restenosis PCI.23 24 Additionally, univariable GLM was performed using the variable for the year of procedure to evaluate the impact of temporal changes in patient and procedural characteristics. Variables found to have a significant impact on patient costs (p<0.05) were included in the final multivariable model to explore key drivers of patient costs.
All statistical analyses were performed using Stata V.14 (StataCorp).
Patient and public involvement
No patients or the public were involved in this study.
Results
From 1 January 2014 to 31 December 2017, 20,345 PCIs for non-ACS or ACS indications undertaken in Victorian public hospitals were captured in VCOR. Baseline clinical and preprocedural characteristics are presented in table 1. For comparison, patient clinical characteristics for PCIs undertaken across Victorian private hospitals are presented in the online supplemental appendix.
Table 1Characteristics of patients undergoing PCI in Victorian public hospitals
| Variable | 2014 (N=4424) | 2015 (N=4838) | 2016 (N=5225) | 2017 (N=5858) | Total (N=20 345) | P value* |
| Age (years) | <0.001 | |||||
| 62.95 (11.82) | 63.25 (11.74) | 63.56 (12.00) | 64.32 (12.05) | 63.57 (11.93) | ||
| 63 (18) | 63 (17) | 64 (17) | 65 (17) | 64 (18) | ||
| Age group (n, N%) | ||||||
| 3601 (81.40) | 3891 (80.43) | 4190 (80.19) | 4559 (77.83) | 16 241 (79.83) | ||
| 823 (18.60) | 947 (19.57) | 1035 (19.81) | 1299 (22.17) | 4104 (20.17) | ||
| Aboriginal/Torres strait Islander (n, N%) | 31 (0.70) | 26 (0.54) | 27 (0.52) | 47 (0.80) | 131 (0.64) | 0.195 |
| Sex (n, N%) | 0.286 | |||||
| 3433 (77.60) | 3749 (77.49) | 4057 (77.65) | 4471 (76.32) | 15 710 (77.22) | ||
| 991 (22.40) | 1089 (22.51) | 1168 (22.35) | 1387 (23.68) | 4635 (22.78) | ||
| BMI (n, N%) | 0.015 | |||||
| 30 (0.68) | 28 (0.58) | 57 (1.09) | 37 (0.63) | 152 (0.75) | ||
| 944 (21.34) | 1060 (21.91) | 1091 (20.88) | 1331 (22.72) | 4426 (21.75) | ||
| 1783 (40.30) | 1886 (38.98) | 2068 (39.58) | 2245 (38.32) | 7982 (39.23) | ||
| 1601 (36.19) | 1786 (36.92) | 1957 (37.45) | 2180 (37.21) | 7524 (36.98) | ||
| 66 (1.49) | 78 (1.61) | 52 (1.00) | 65 (1.11) | 261 (1.28) | ||
| ACS type (n, N%) | <0.001 | |||||
| 1574 (35.58) | 1787 (36.94) | 1913 (36.61) | 2320 (39.60) | 7594 (37.33) | ||
| 325 (7.35) | 305 (6.30) | 351 (6.72) | 301 (5.14) | 1282 (6.30) | ||
| 1247 (28.19) | 1381 (28.54) | 1518 (29.05) | 1610 (27.48) | 5756 (28.29) | ||
| 1278 (28.89) | 1365 (28.21) | 1443 (27.62) | 1627 (27.77) | 5713 (28.08) | ||
| Cardiogenic shock (n, N%) | 131 (2.96) | 169 (3.49) | 198 (3.79) | 175 (2.99) | 673 (3.31) | 0.049 |
| Intubated OHCA (n, N%) | 70 (1.58) | 78 (1.61) | 96 (1.84) | 109 (1.86) | 353 (1.74) | 0.594 |
| Pre-procedure cardiac arrest (n, N%) | 108 (2.44) | 104 (2.15) | 117 (2.24) | 95 (1.62) | 424 (2.08) | 0.022 |
| LVEF grade (n, N%) | 0.002 | |||||
| 1931 (43.65) | 2081 (43.01) | 2310 (44.21) | 2496 (42.61) | 8818 (43.34) | ||
| 744 (16.82) | 938 (19.39) | 841 (16.10) | 997 (17.02) | 3520 (17.30) | ||
| 355 (8.02) | 364 (7.52) | 467 (8.94) | 502 (8.57) | 1688 (8.30) | ||
| 178 (4.02) | 166 (3.43) | 211 (4.04) | 224 (3.82) | 779 (3.83) | ||
| 1216 (27.49) | 1289 (26.64) | 1396 (26.72) | 1639 (27.98) | 5540 (27.23) | ||
| Medicated diabetes (n, N%) | 966 (21.84) | 1134 (23.44) | 1153 (22.07) | 1265 (21.59) | 4518 (22.21) | 0.115 |
| Peripheral vascular disease (n, N%) | 168 (3.80) | 166 (3.43) | 189 (3.62) | 202 (3.45) | 725 (3.56) | 0.744 |
| Cerebrovascular disease (n, N%) | 137 (3.10) | 225 (4.65) | 175 (3.35) | 266 (4.54) | 803 (3.95) | <0.001 |
| Chronic oral anticoagulant therapy (n, N%) | 146 (3.30) | 186 (3.84) | 261 (5.00) | 318 (5.43) | 911 (4.48) | <0.001 |
| Previous CABG (n, N%) | 300 (6.78) | 321 (6.63) | 335 (6.41) | 378 (6.45) | 1334 (6.56) | 0.873 |
| Previous PCI (n, N%) | 1201 (27.15) | 1438 (29.72) | 1513 (28.96) | 1767 (30.16) | 5919 (29.09) | 0.006 |
| Dialysis (n, N%) | 51 (1.15) | 69 (1.43) | 80 (1.53) | 67 (1.14) | 267 (1.31) | 0.206 |
| Renal transplant (n, N%) | 13 (0.29) | 15 (0.31) | 21 (0.40) | 25 (0.43) | 74 (0.36) | 0.608 |
| Renal replacement therapy (n, N%) | 0.207 | |||||
| 4371 (98.80) | 4762 (98.43) | 5139 (98.35) | 5788 (98.81) | 20 060 (98.60) | ||
| 1 (0.02) | 5 (0.10) | 6 (0.12) | 3 (0.05) | 15 (0.07) | ||
| 52 (1.18) | 71 (1.47) | 80 (1.53) | 67 (1.14) | 270 (1.33) | ||
| Fibrinolytic therapy (n, N%) | 167 (3.77) | 219 (4.53) | 233 (4.46) | 240 (4.10) | 859 (4.22) | 0.236 |
| eGFR | 0.011 | |||||
| 95.00 (38.28) | 95.31 (38.92) | 95.90 (40.57) | 93.16 (39.68) | 94.78 (39.44) | ||
| 91.37 (48.84) | 91.34 (49.98) | 91.74 (50.35) | 89.09 (49.64) | 90.77 (49.62) | ||
| eGFR group (n, N%) | 0.010 | |||||
| 3385 (76.51) | 3705 (76.58) | 3974 (76.06) | 4339 (74.07) | 15 403 (75.71) | ||
| 632 (14.29) | 726 (15.01) | 741 (14.18) | 908 (15.50) | 3007 (14.78) | ||
| 93 (2.10) | 109 (2.25) | 117 (2.24) | 141 (2.41) | 460 (2.26) | ||
| 314 (7.10) | 298 (6.16) | 393 (7.52) | 470 (8.02) | 1475 (7.25) |
There were 1 missing case for medicated diabetes status, 4 for OHCA, 1 for in-hospital preprocedure cardiac arrest, 3 for peripheral vascular disease, 2 for cerebrovascular disease or chronic oral anticoagulant therapy and 1 for renal transplant.
*P value for year-to-year trend.
ACS, acute coronary syndrome; BMI, body mass index; CABG, coronary artery bypass graft; eGFR, estimated glomerular filtration rate; LVEF, left ventricular ejection fraction; NSTEMI, non-STEMI; OHCA, out-of-hospital cardiac arrest; PCI, percutaneous coronary intervention; STEMI, ST-elevation myocardial infarction; UA, unstable angina.;
Compared with patients in 2014, patients in 2017 had higher baseline risk. They were more likely to be aged ≥75 years (p<0.001), had greater average BMI (p=0.015) and lower mean eGFR (p=0.011) and LVEF (p=0.002). Over time, a greater proportion of patients were treated with PCI for NSTEMI or STEMI indications (p<0.001) and experienced cardiogenic shock (p=0.049) and preprocedural cardiac arrest (p=0.022). The proportion of patients with a prior history of cerebrovascular disease, patients being treated with chronic oral anticoagulant therapy, and patients presenting with a prior PCI also increased over the 4-year period (all p<0.05).
Patient procedural characteristics, door-to-balloon time metrics for patients with STEMI (excluding in-hospital transfers and inpatients) and 30-day outcomes are presented in table 2.
Table 2Procedural characteristics of PCI in Victorian public hospitals
| Variable | Year | P value* | ||||
| 2014 (N=4424) | 2015 (N=4838) | 2016 (N=5225) | 2017 (N=5858) | Total (N=20 345) | ||
| Access site (n, N%) | <0.001 | |||||
| 1888 (42.68) | 2491 (51.49) | 3287 (62.91) | 4010 (68.45) | 11 676 (57.39) | ||
| 2536 (57.32) | 2347 (48.51) | 1938 (37.09) | 1848 (31.55) | 8669 (42.61) | ||
| Peri-procedural medications (n, N%) | <0.001 | |||||
| 754 (17.04) | 709 (14.65) | 628 (12.02) | 633 (10.81) | 2724 (13.39) | ||
| 3531 (79.81) | 3644 (75.32) | 3794 (72.61) | 4064 (69.38) | 15 033 (73.89) | ||
| 73.89 (76.85) | 4154 (85.86) | 4755 (91.00) | 5655 (96.53) | 17 964 (88.30) | ||
| 4070 (92.00) | 4567 (94.40) | 4814 (92.13) | 5349 (91.31) | 18 800 (92.41) | ||
| Lesion characteristics (n, %N) | ||||||
| Treated vessel | ||||||
| 62 (1.40) | 64 (1.32) | 88 (1.68) | 114 (1.95) | 328 (1.61) | 0.044 | |
| 637 (14.40) | 811 (16.76) | 908 (17.38) | 1121 (19.14) | 3477 (17.09) | <0.001 | |
| 228 (5.15) | 297 (6.14) | 360 (6.89) | 450 (7.68) | 1335 (6.56) | <0.001 | |
| Lesion complexity | <0.001 | |||||
| 2054 (46.43) | 2415 (49.92) | 2436 (46.62) | 2480 (42.34) | 9385 (46.13) | ||
| 2370 (53.57) | 2423 (50.08) | 2789 (53.38) | 3378 (57.66) | 10 960 (53.87) | ||
| Unprotected left main PCI (n, %N) | 36 (0.81) | 35 (0.72) | 63 (1.21) | 77 (1.31) | 211 (1.04) | 0.006 |
| Chronic total occlusion (n, %N) | 151 (3.41) | 203 (4.20) | 223 (4.27) | 221 (3.77) | 798 (3.92) | 0.111 |
| In-stent restenosis (n, %N) | 221 (5.00) | 257 (5.31) | 251 (4.80) | 289 (4.93) | 1018 (5.00) | 0.690 |
| Device used (n, %N) | ||||||
| 1143 (25.84) | 951 (19.66) | 604 (11.56) | 300 (5.12) | 2998 (14.74) | <0.001 | |
| 2991 (67.61) | 3477 (71.87) | 4296 (82.22) | 5170 (88.26) | 15 934 (78.32) | <0.001 | |
| 270 (6.10) | 332 (6.86) | 291 (5.57) | 380 (6.49) | 1273 (6.26) | 0.047 | |
| Postprocedural characteristics | ||||||
| 4019 (90.85) | 4338 (89.67) | 4739 (90.70) | 5271 (89.98) | 18 367 (90.28) | 0.148 | |
| 110 (2.49) | 148 (3.06) | 148 (2.83) | 234 (3.99) | 640 (3.15) | <0.001 | |
| Discharge characteristics | ||||||
| 0.715 | ||||||
| 3 (3) | 3 (3) | 3 (3) | 3 (3) | 3 (3) | ||
| Referred to cardiac rehabilitation (n, %N) | 3274 (74.01) | 3923 (81.09) | 4326 (82.79) | 4438 (75.76) | 15 961 (78.45) | <0.001 |
| Door to balloon time (STEMI only)† | Year | |||||
| 2014 (n=775) | 2015 (n=819) | 2016 (n=846) | 2017 (n=1042) | Total (n=3482) | ||
| Door-to-balloon/device time (minutes, median (IQR)) | 70 (50) | 72 (52) | 68 (49) | 63 (43) | 68 (48) | 0.008 |
| Door-to-balloon/device time (n, %N) | <0.001 | |||||
| 529 (68.26) | 563 (68.74) | 611 (72.22) | 814 (78.12) | 2517 (72.29) | ||
| 244 (31.48) | 252 (30.77) | 235 (27.78) | 227 (21.79) | 958 (27.51) | ||
| 2 (0.26) | 4 (0.49) | 0 (0.00) | 1 (0.10) | 7 (0.20) | ||
| Outcomes (0–30 days) | ||||||
| 133 (3.01) | 123 (2.54) | 147 (2.81) | 164 (2.80) | 567 (2.79) | 0.600 | |
| 252 (5.70) | 242 (5.00) | 252 (4.82) | 286 (4.88) | 1032 (5.07) | 0.192 | |
| 249 (5.63) | 300 (6.20) | 329 (6.30) | 400 (6.83) | 1278 (6.28) | 0.101 | |
*P value for year-to-year trend.
†Excluding all inter-hospital transfer arrivals and patients with STEMI onset while a current in-patient.
BMS, bare metal stent; DES, drug-eluting stent; MACCE, major adverse cardiac or cerebrovascular event; PCI, percutaneous coronary intervention; POBA, plain old balloon angioplasty; STEMI, ST-elevation myocardial infarction.
Over time, patients were more likely to be managed with radial access PCI (p<0.001). Additionally, patients were likely to present with greater case complexity over time. There were increases in the proportion of patients being treated with multivessel coronary artery disease (5.15% in 2014 to 7.68% in 2017), presenting with American College of Cardiology/American Heart Association B2/C lesion complexity (53.57% in 2014 to 57.66% in 2017) or with unprotected left main artery disease (0.81% in 2014 to 1.31% in 2017), and a greater occurrence of postprocedural renal impairment (2.49% in 2014 to 3.99% in 2017) observed across public hospitals over time in Victoria (all p<0.05). Drug-eluting stent (DES) use increased over the 4-year period (68% in 2014 to 88% in 2017), and STEMI patients were more likely to be treated within parameters for timely reperfusion (all p<0.05). Procedural success rates did not change (p=0.148), and patients were more likely to be referred to cardiac rehabilitation services over the 4-year period (p<0.001). There was no trend in patient mortality, MACCE and cardiac readmissions at 30 days throughout the study period (all p>0.05).
Table 3 summarises the total costs of PCI over the 4 years of evaluation, and the change in mean costs over time stratified by non-ACS, UA, NSTEMI and STEMI indications.
Table 3Total annual cost burden and mean cost per procedure over time and indication
| Variable | Year | |||
| 2014 | 2015 | 2016 | 2017 | |
| Total annual cost | $55 569 740 | $59 958 968 | $67 189 976 | $72 179 656 |
| Mean procedural cost | ||||
| $12 629 ($12 294–$12 965) | $12 468 ($12 135–$12 801) | $12 936 ($12 563–$13 310) | $12 473 ($12 120–$12 825) | |
| $12 521 ($12 323–$12 720) | $12 407 ($12 210–$12 603) | $12 745 ($12 552–$12 938) | $12 185 ($11 986–$12 384) | |
| Stratified mean procedural cost | ||||
| Non-ACS | ||||
| Crude | $10 212 ($9804–$10 621) | $10 325 ($10 058–$10 593) | $10 439 ($10 195–$10 683) | $10 554 ($10 184–$10 924) |
| Adjusted* | $10 144 ($9975–$10 313) | $9996 ($9824–$10 168) | $10 291 ($10127–$10455) | $10 127 ($9956–$10 298) |
| UA | ||||
| Crude | $9622 ($9093–$10 151) | $9766 ($9419–$10 114) | $9913 ($9555–$10 271) | $10 061 ($9498–$10 6245) |
| Adjusted* | $9607 ($9375–$9838) | $9826 ($9575–$10 078) | $10 363 ($10 103–$10 623) | $9619 ($9373–$9866) |
| NSTEMI | ||||
| Crude | $12 266 ($11 781–$12 751) | $12 529 ($12 208–$12 850) | $12 797 ($12 491–$13 103) | $13 071 ($12 598– $13 545) |
| Adjusted* | $12 598 ($12 366–$12 830) | $12 737 ($12 518–$12 957) | $12 879 ($12 665–$13 093) | $12 497 ($12 238–$12 757) |
| STEMI | ||||
| Crude | $16 792 ($15 991–$17 592) | $16 383 ($15 875–$16 891) | $15 983 ($15 519–$16 448) | $15 594 ($14 910–$16 278) |
| Adjusted* | $16 204 ($15 812–$16 597) | $15 811 ($15 439–$16 184) | $16 514 ($16 118–$16 910) | $15 301 ($14 959–$15 644) |
*Based on multivariable generalised linear regression modelling, adjusted for key confounding variables. All costs are reported in 2020 Australian Dollars (A$).
Non-ACS, non-acute coronary syndrome; NSTEMI, non-STEMI; STEMI, ST-elevation myocardial infarction; UA, unstable angina.
The total costs of PCI increased considerably over the 4-year period, from $A55 569 740 in 2014 to $A72 179 656 in 2017. However, based on multivariable GLM, adjusted mean procedural costs remained stable over time, from $A12 521 (95% CI $A12 323 to $A12,720) in 2014 to $A12 185 (95% CI $A11 986 to $A12 384) in 2017. Procedural costs also remained stable across the ACS subgroups and for patients undergoing PCI for non-ACS indications over time (table 3). The results of the univariable and multivariable regression analyses are presented in table 4.
Table 4Results of univariable and multivariable generalised linear regression modelling
| Variable | Univariable | Multivariable | ||||||
| B-coefficient | % Change | 95% CI | P value | B-coefficient | % Change | 95% CI | P value | |
| Year | ||||||||
| REF | REF | |||||||
| −0.013 | −1.28% | −0.050 to 0.025 | 0.503 | −0.011 | −1.09% | −0.030 to 0.008 | 0.313 | |
| 0.024 | 2.43% | −0.015 to 0.063 | 0.231 | 0.013 | 1.30% | −0.005 to 0.031 | 0.054 | |
| −0.013 | −1.24% | −0.051 to 0.026 | 0.528 | −0.016 | −1.63% | −0.036 to 0.003 | 0.409 | |
| Socioeconomic status | – | – | – | – | ||||
| −0.0246 | −2.43% | −0.065 to 0.016 | 0.237 | |||||
| Sex | ||||||||
| −0.008 | −0.82% | −0.038 to 0.021 | 0.585 | – | – | – | – | |
| Aboriginal/Torres Strait Islander | 0.165 | 17.89% | 0.094 to 0.235 | <0.001 | 0.233 | 26.18% | 0.189 to 0.276 | <0.001 |
| ACS | ||||||||
| REF | ||||||||
| −0.056 | −5.41% | −0.095 to −0.016 | 0.006 | −0.179 | −16.35% | −0.207 to −0.150 | <0.001 | |
| 0.199 | 22.05% | 0.167 to 0.231 | <0.001 | 0.031 | 3.18% | 0.006 to 0.057 | 0.016 | |
| 0.440 | 55.27% | 0.404 to 0.475 | <0.001 | 0.072 | 7.50% | 0.042 to 0.103 | <0.001 | |
| Age | ||||||||
| 0.000 | −0.02% | −0.029 to 0.028 | 0.987 | – | – | – | – | |
| In hours arrival | −0.108 | −10.25% | −0.136 to −0.080 | <0.001 | −0.020 | −2.02% | −0.034 to −0.007 | 0.004 |
| Length-of-stay (quartiles) | ||||||||
| REF | ||||||||
| 0.208 | 23.07% | 0.193 to 0.221 | <0.001 | 0.210 | 23.42% | 0.190 to 0.230 | <0.001 | |
| 0.306 | 35.76% | 0.295 to 0.316 | <0.001 | 0.269 | 30.88% | 0.247 to 0.291 | <0.001 | |
| 0.865 | 137.49% | 0.828 to 0.902 | <0.001 | 0.640 | 90.27% | 0.609 to 0.677 | <0.001 | |
| LVEF grade | ||||||||
| REF | ||||||||
| 0.123 | 13.06% | 0.092 to 0.154 | <0.001 | −0.005 | −0.47% | −0.022 to 0.013 | 0.602 | |
| 0.311 | 36.49% | 0.254 to 0.368 | <0.001 | 0.037 | 3.79% | 0.007 to 0.067 | 0.014 | |
| 0.712 | 103.76% | 0.615 to 0.809 | <0.001 | 0.146 | 15.71% | 0.088 to 0 .203 | <0.001 | |
| Medicated diabetes mellitus | 0.028 | 2.81% | −0.006 to 0 .061 | 0.105 | – | – | – | – |
| PVD | 0.178 | 19.46% | 0.109 to 0.246 | <0.001 | 0.073 | 7.59% | 0.031 to 0.116 | 0.001 |
| CBVD | 0.167 | 18.20% | 0.071 to 0.263 | 0.001 | 0.016 | 1.65% | −0.029 to 0.063 | 0.489 |
| Chronic oral anticoagulant therapy | 0.087 | 9.12% | 0.016 to 0.159 | 0.017 | 0.033 | 3.33% | −0.003 to 0.069 | 0.076 |
| Prior PCI | −0.109 | −10.31% | −0.137 to −0.081 | <0.001 | 0.031 | 3.14% | 0.014 to 0.048 | <0.001 |
| Medications | ||||||||
| 0.273 | 31.34% | 0.231 to 0.314 | <0.001 | 0.009 | 0.88% | −0.014 to 0.031 | 0.453 | |
| −0.045 | −4.42% | −0.079 to −0.011 | 0.010 | −0.005 | −0.57% | −0.021 to 0.010 | 0.465 | |
| −0.004 | −0.41% | −0.041 to 0.032 | 0.827 | – | – | – | – | |
| 0.134 | 14.37% | 0.091 to 0.178 | <0.001 | 0.032 | 3.27% | 0.009 to 0.055 | 0.005 | |
| eGFR group | ||||||||
| REF | ||||||||
| 0.134 | 14.33% | 0.093 to 0.174 | <0.001 | 0.004 | 0.43% | −0.015 to 0.024 | 0.666 | |
| 0.377 | 45.75% | 0.281 to 0.472 | <0.001 | 0.100 | 10.47% | 0.032 to 0.167 | 0.004 | |
| Access | ||||||||
| 0.144 | 15.46% | 0.115 to 0.172 | <0.001 | 0.028 | 2.82% | 0.014 to 0.041 | <0.001 | |
| Mechanical ventricular support | 1.341 | 282.47% | 1.178 to 1.505 | <0.001 | 0.510 | 66.58% | 0.357 to 0.664 | <0.001 |
| Multivessel disease | 0.183 | 20.08% | 0.112 to 0.254 | <0.001 | 0.033 | 3.36% | −0.005 to 0.071 | 0.090 |
| Lesion complexity | ||||||||
| 0.132 | 14.17% | 0.106 to 0.159 | <0.001 | 0.007 | 0.68% | −0.006 to 0.020 | 0.305 | |
| Unprotected left main PCI | 0.492 | 63.63% | 0.290 to 0.695 | <0.001 | −0.067 | −6.47% | −0.158 to 0.025 | 0.155 |
| Shock or OHCA | 1.183 | 226.73% | 1.102 to 1.266 | <0.001 | 0.695 | 100.00% | 0.621 to 0.769 | <0.001 |
| Chronic total occlusion | 0.018 | 1.82% | −0.084 to 0.120 | 0.729 | – | – | – | – |
| ISR | 0.000 | −0.18% | −0.077 to 0.073 | 0.962 | – | – | – | – |
ACS, acute coronary syndrome; BMI, body mass index; CABG, coronary artery bypass graft; CBVD, cerebrovascular disease; eGFR, estimated glomerular filtration rate; ISR, in-stent restenosis; LVEF, left ventricular ejection fraction; NSTEMI, non-STEMI; OHCA, out-of-hospital cardiac arrest; PCI, percutaneous coronary intervention; PVD, peripheral vascular disease; REF, Reference; STEMI, ST-elevation myocardial infarction; UA, unstable angina.
Factors associated with lower costs were PCI for UA and in-hours arrival (all p<0.001). Costs were higher for femoral access (vs radial access) PCI (3% increase, p<0.001) and increasing case complexity, as evidenced by the considerable percentage increase in mean costs with increasing patient length-of-stay (p<0.001). Other indicators of case complexity, including NSTEMI/STEMI indications for PCI, OHCA, multivessel disease and required mechanical ventricular support were also associated with increases in costs (all p<0.05). Adjusted mean procedural costs were highest for patients with STEMI (4-year adjusted mean: $A15 930, 95% CI: $A15 606 to $A16 254). Patients with NSTEMI (4-year adjusted mean: $A12 677, 95% CI $A12 495 to $A12 860) or UA (4-year adjusted mean: $A9872, 95% CI $A9653 to $A10 091) and patients undergoing PCI for non-ACS indications (4-year adjusted mean: $A10 142, 95% CI: $A10 019 to $A10 264) incurred lower costs.
Discussion
To the best of our knowledge, our study is the first to evaluate the costs of PCI in Victorian public hospitals. The cost burden of PCI increased from 2013 to 2017, but mean procedural costs remained stable over this period. Expectedly, patient factors which significantly increased costs included moderate and severe reductions in LVEF, declining eGFR, the need for mechanical ventricular support and cardiogenic shock or intubated OHCA. Higher acuity ACS presentations were also associated with greater costs. The observed increase in total costs of PCI over time may be attributed to the increased proportion of patients presenting with multiple comorbidities or NSTEMI/STEMI, and an overall increase in the number of procedures performed in Victoria.5 Similar trends in the evolution of patient risk in contemporary practice have been observed across other registries in Australia, the UK and the USA.25–27
Adjusted mean procedural costs remained stable over time across non-ACS/ACS indications (see online supplemental appendix). This is despite the greater number of patients presenting with multiple comorbidities for PCI, and may be attributed to changes in patient management over time and across institutions. Previous Australian studies have explored the cost and clinical impacts of routine PCI in multivessel coronary artery disease compared with CABG, the appropriate use of DESs, and intravascular ultrasound guidance for PCI.28–30 These studies explore the cost consequences of evidence-based changes in clinical practice, and highlight the value of economic analyses for improving efficiency in cardiac care.28–30
In our study, a key cost driver identified was hospital length of stay. Across our cohort, patients were more likely to be treated with radial access (43% in 2014, increasing to 68% in 2017, p<0.001) PCI (see online supplemental appendix), which is associated with improved patient outcomes as well as shorter length of stay. Our results demonstrate that radial access is also associated with lower procedural costs. Our group recently published a cost-effectiveness analysis of radial access PCI using trial-based data.31 We found that radial access was cost-saving compared with femoral access PCI, via a reduction in major bleeding and mortality.31
Our study highlights the importance of ongoing performance benchmarking and feedback in reducing the cost burden attributed to PCI. Recent data from VCOR have highlighted that despite evidence for the safety of same-day discharge of patients undergoing elective PCI, same-day discharge remains uncommon in Victoria, being implemented in only 3% of elective PCIs from 2014 to 2017.32 As length of stay was a significant driver of patient costs in our multivariable model, increasing the rate of same-day discharge for elective PCI is likely to result in further cost savings.32–35 An economic evaluation using registry-based data is warranted to explore the impacts of increasing the uptake of same-day discharge for elective PCI on costs.32 Timely reperfusion (as indicated by door-to-balloon/device times ≤90 min) was associated with improved outcomes for STEMI patients.36 In our dataset, the proportion of STEMI patients undergoing timely reperfusion increased significantly over the 4-year period (68% in 2014 increasing to 78% in 2017, p<0.001) (see online supplemental appendix). A future study exploring the clinical and cost impacts attributed to improvements in this metric is warranted.
A key strength of our study lies in the use of linked data from VCOR and the VAED. VCOR collects granular data on all PCIs undertaken in Victoria and the VAED is complete for all hospitalisations occurring in the state, with relevant variables to allow for ‘bottom-up’ costing. A key limitation to our study was that we limited our analyses to explore the costs of PCI attributed to publicly admitted patients only. Hence, the results are confined to the public hospital setting and likely underestimates the true costs of PCI in Victoria. While the majority of patients presenting with ACS are treated in public hospitals in Australia (64% of ACS cases in public hospitals vs 30% in the private sector,37 indications for PCI performed at private hospitals are more likely to be for non-ACS or elective cases for older patients (see online supplemental appendix).5 38 As demonstrated in our analyses, case complexity and ACS-indications are key cost drivers. Hence, although our estimates are only applicable to the public health sector, our analysis likely captures the majority of the cost burden attributed to PCI in Victoria. Our findings may be generalised across PCI-capable centres in Australia and New Zealand, as the Australian and New Zealand healthcare systems are both heavily subsidised by government.5 39 Furthermore, both countries have commonality in physician training and guidelines for the management of ACS and PCI.3 38 40 41 Importantly, other registry-based studies conducted in the UK, Sweden and the USA on the evolution of PCI over time have found considerable improvements in both hospital adherence to guideline-recommended practices and patient outcomes.25 42–45 Although a limited number of studies have explored the cost impacts attributed to these trends, it is likely that findings of improved health services efficiency will be observed against a background of improved patient management, in line with our analyses.
Additionally, our analyses did not consider the cost impacts attributed to patient MACCE, readmissions, medications use and patient mortality following discharge, as these cost inputs were not captured in the dataset and our analyses were limited to exploring drivers of procedural costs. However, MACCE, readmissions and mortality remained stable and low throughout the period of evaluation, in line with findings from VCOR annual reporting and similar studies using registry datasets (table 2).5 16 27 Finally, WIES weights are adjusted annually using cost data reported from the previous financial year to the Victorian Cost Data Collection.19 Therefore, there was uncertainty around the extent to which changes in clinical practice, such as reduced length of stay attributed to greater uptake of radial access PCI, had contributed to the stability observed in mean procedural costs over time. Future studies which capture the direct cost and clinical impacts attributed to improved adherence to guideline-recommended practices over time are therefore warranted. Ultimately, although conservative, our study provides important insight into key cost drivers of PCI in Australia.
Conclusions
Although the cost burden of PCI in Victoria is considerable and rising over time, mean procedural costs remain stable. The latter is likely attributable to changes in the clinical management of patients managed with PCI which better reflect evidence-based guidelines, which are facilitated through the ongoing monitoring and benchmarking of patient outcomes through VCOR.
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study received ethical approval from Monash University Human Research Ethics Committee (13882).
Contributors PL and DL had full access to all of the data in this study and take responsibility for the integrity of the data and accuracy of the data analysis. PL, EZ and DL were responsible for the study concept and design, the acquisition, analysis and interpretation of data and drafting of the manuscript. PL, ALB, DS, DTD, JL, CMR, EZ and DL made significant contribution to drafting the work, or revising it critically for intellectual content. PL, ALB, DS, DTD, JL, CMR, EZ and DL provided final approval of the version to be published. PL is the guarantor of this paper.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests PL is supported by an Australian Government Research Training Program (RTP) scholarship (Award/Grant number is not applicable). EZ has received grants from Amgen, Astra Zeneca, Pfizer, Shire and Zoll Medical Corporation outside of the submitted work. DL has received honoraria or study grants from Abbvie, Amgen, Astellas, AstraZeneca, Bohringer Ingelheim, Bristol Myers Squibb, Novartis, Pfizer, Sanofi, Shire and Zoll Medical Corporation, outside the submitted work. DS is supported by the National Heart Foundation Fellowship and Viertel Foundation Award. CMR is supported by a National Health and Medical Research Council Principal Research Fellowship (GNT1136372). All other authors have no conflicts of interest to disclose.
Provenance and peer review Not commissioned; externally peer reviewed.
Author note All authors takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
1 Australian Institute of Health and Welfare. Data tables: cardiovascular disease 2020 2020.
2 Australian Institute of Health and Welfare. Australian burden of disease study: impact and causes of illness and death in Australia 2015. Australian burden of disease study series no. 3. BOD 4. Canberra: AIHW, 2019.
3 Chew DP, Scott IA, Cullen L, et al. National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand: Australian clinical guidelines for the management of acute coronary syndromes 2016. Med J Aust 2016; 205: 128–33. doi:10.5694/mja16.00368 http://www.ncbi.nlm.nih.gov/pubmed/27465769
4 Ibanez B, James S, Agewall S, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the task force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of cardiology (ESC). Eur Heart J 2018; 39: 119–77. doi:10.1093/eurheartj/ehx393 http://www.ncbi.nlm.nih.gov/pubmed/28886621
5 Lefkovits J, Brennan A, Dinh DT. The Victorian cardiac outcomes registry annual report. Monash University, SPHPM, 2018.
6 Australian Institute of Health and Welfare. Disease expenditure in Australia 2018-19. Canberra: AIHW, 2021.
7 Australian Institute of Health and Welfare. Health expenditure Australia 2017 - 2018. Health and welfare expenditure series no. 65. Cat. no. HWE 77. Canberra: AIHW, 2019.
8 Australian Institute of Health and Welfare. Health-care expenditure on cardiovascular diseases 2008–09. Cat. no. CVD 65. Canberra: AIHW, 2014.
9 Australian Institute of Health and Welfare. Australian health expenditure—demographics and diseases: hospital admitted patient expenditure 2004–05 to 2012–13. Health and welfare expenditure series no. 59. Cat. no. HWE 69. Canberra: AIHW, 2017.
10 Ademi Z, Liew D, Hollingsworth B, et al. The economic implications of treating atherothrombotic disease in Australia, from the government perspective. Clin Ther 2010; 32: 119–32. doi:10.1016/j.clinthera.2010.01.009 http://www.ncbi.nlm.nih.gov/pubmed/20171418
11 Stub D, Lefkovits J, Brennan AL, et al. The establishment of the Victorian cardiac outcomes registry (VCOR): monitoring and optimising outcomes for cardiac patients in Victoria. Heart Lung Circ 2018; 27: 451–63. doi:10.1016/j.hlc.2017.07.013 http://www.ncbi.nlm.nih.gov/pubmed/29291960
12 Department of Health and Human Services. Victorian admitted episodes dataset manual, 30th Edition, Section 1 - Introduction 2020. Available: https://www2.health.vic.gov.au/hospitals-and-health-services/data-reporting/health-data-standards-systems/data-collections/vaed [Accessed 2 Aug 2020 ].
13 Biswas S, Tacey M, Dinh D, et al. Factors associated with unplanned Hospital readmissions and emergency presentation following percutaneous coronary intervention: insights from the Victorian cardiac outcomes registry. Heart, Lung and Circulation 2018; 27: S449–S49. doi:10.1016/j.hlc.2018.06.920
14 Meehan E, Reid SM, Williams K, et al. Hospital admissions in children with cerebral palsy: a data linkage study. Dev Med Child Neurol 2017; 59: 512–9. doi:10.1111/dmcn.13350 http://www.ncbi.nlm.nih.gov/pubmed/27900776
15 Biswas S, Dinh D, Lucas M. Incidence and predictors of unplanned hospital readmission after percutaneous coronary intervention. doi:10.3390/jcm9103242
16 Lefkovits J, Brennan A, Dinh D. The Victorian cardiac outcomes registry annual report 2014. Monash University, DEPM June 2015, Report No 2, pages 60.
17 Lefkovits J, Brennan A, Dinh D. The Victorian cardiac outcomes registry annual report 2013. Monash University, DEPM July 2014, Report No 1, pages 42.
18 Ansari H, Ansari Z, Hutson JM, et al. Potentially avoidable hospitalisation for constipation in Victoria, Australia in 2010-11. BMC Gastroenterol 2014; 14: 125. doi:10.1186/1471-230X-14-125 http://www.ncbi.nlm.nih.gov/pubmed/25015386
19 Department of Health and Human Services. Policy and funding guidelines for health services 2018, 2020. Available: https://www.dhhs.vic.gov.au/policy-and-funding-guidelines-health-services2020
20 Department of Health and Human Services. Casemix funding. Victoria. Available: https://www2.health.vic.gov.au/hospitals-and-health-services/funding-performance-accountability/activity-based-funding/casemix-funding
21 Lingaratnam S, Thursky KA, Slavin MA, et al. The disease and economic burden of neutropenic fever in adult patients in Australian cancer treatment centres 2008: analysis of the Victorian admitted episodes dataset. Intern Med J 2011; 41: 121–9. doi:10.1111/j.1445-5994.2010.02343.x http://www.ncbi.nlm.nih.gov/pubmed/21272176
22 Malehi AS, Pourmotahari F, Angali KA. Statistical models for the analysis of skewed healthcare cost data: a simulation study. Health Econ Rev 2015; 5: 11. doi:10.1186/s13561-015-0045-7 http://www.ncbi.nlm.nih.gov/pubmed/26029491
23 Krone RJ, Shaw RE, Klein LW, et al. Evaluation of the American College of Cardiology/American Heart Association and the Society for Coronary Angiography and Interventions lesion classification system in the current "stent era" of coronary interventions (from the ACC-National Cardiovascular Data Registry). Am J Cardiol 2003; 92: 389–94. doi:10.1016/S0002-9149(03)00655-6 http://www.ncbi.nlm.nih.gov/pubmed/12914867
24 Tacey M, Dinh DT, Andrianopoulos N, et al. Risk-Adjusting key outcome measures in a clinical quality PCI registry: development of a highly predictive model without the need to exclude high-risk conditions. JACC Cardiovasc Interv 2019; 12: 1966–75. doi:10.1016/j.jcin.2019.07.002 http://www.ncbi.nlm.nih.gov/pubmed/31601391
25 Alkhouli M, Alqahtani F, Kalra A, et al. Trends in characteristics and outcomes of patients undergoing coronary revascularization in the United States, 2003-2016. JAMA Netw Open 2020; 3: e1921326. doi:10.1001/jamanetworkopen.2019.21326 http://www.ncbi.nlm.nih.gov/pubmed/32058558
26 Hulme WJ, Sperrin M, Martin GP, et al. Temporal trends in relative survival following percutaneous coronary intervention. BMJ Open 2019; 9: e024627. doi:10.1136/bmjopen-2018-024627 http://www.ncbi.nlm.nih.gov/pubmed/30782913
27 Yeoh J, Yudi MB, Andrianopoulos N, et al. Evolution of Australian Percutaneous Coronary Intervention (from the Melbourne Interventional Group [MIG] Registry). Am J Cardiol 2017; 120: 47–54. doi:10.1016/j.amjcard.2017.03.258 http://www.ncbi.nlm.nih.gov/pubmed/28495431
28 Ariyaratne TV, Ademi Z, Huq M. The real-world cost-effectiveness of coronary artery bypass surgery versus stenting in high-risk patients: propensity score-matched analysis of a single-centre experience. (1179-1896 (Electronic)).
29 Zhou J, Liew D, Duffy SJ. Intravascular ultrasound versus angiography-guided drug-eluting stent implantation: a health economic analysis. (1941-7705 (Electronic)).
30 Ariyaratne TV, Ademi Z, Ofori-Asenso R. The cost-effectiveness of guideline-driven use of drug-eluting stents: propensity-score matched analysis of a seven-year multicentre experience. (1473-4877 (Electronic)).
31 Lee P, Liew D, Brennan A, et al. Cost-effectiveness of radial access percutaneous coronary intervention in acute coronary syndrome. Am J Cardiol 2021; 156: 44–51. doi:10.1016/j.amjcard.2021.06.034 http://www.ncbi.nlm.nih.gov/pubmed/34325876
32 Liew S, Dinh D, Liew D, et al. Prevalence, outcomes and cost implications of patients undergoing same day discharge after elective percutaneous coronary intervention in Australia. Heart Lung Circ 2020; 29: e185–93. doi:10.1016/j.hlc.2019.09.005 http://www.ncbi.nlm.nih.gov/pubmed/31791887
33 Amin AP, Pinto D, House JA, et al. Association of same-day discharge after elective percutaneous coronary intervention in the United States with costs and outcomes. JAMA Cardiol 2018; 3: 1041–9. doi:10.1001/jamacardio.2018.3029 http://www.ncbi.nlm.nih.gov/pubmed/30267035
34 Madan M, Bagai A, Overgaard CB, et al. Same-Day discharge after elective percutaneous coronary interventions in Ontario, Canada. J Am Heart Assoc 2019; 8: e012131. doi:10.1161/JAHA.119.012131 http://www.ncbi.nlm.nih.gov/pubmed/31498023
35 Writing Committee, Rao SV, Vidovich MI, et al. 2021 ACC expert consensus decision pathway on same-day discharge after percutaneous coronary intervention: a report of the American College of cardiology solution set oversight Committee. J Am Coll Cardiol 2021; 77: 811–25. doi:10.1016/j.jacc.2020.11.013 http://www.ncbi.nlm.nih.gov/pubmed/33423859
36 Yudi MB, Ramchand J, Farouque O, et al. Impact of door-to-balloon time on long-term mortality in high- and low-risk patients with ST-elevation myocardial infarction. Int J Cardiol 2016; 224: 72–8. doi:10.1016/j.ijcard.2016.09.003 http://www.ncbi.nlm.nih.gov/pubmed/27631718
37 Lefkovits J, Brennan A, Dinh DT. The Victorian cardiac outcomes registry annual report 2017. Monash University, SPHPM August 2018, Report No 5.
38 Chew DP, French J, Briffa TG, et al. Acute coronary syndrome care across Australia and New Zealand: the SNAPSHOT ACS study. Med J Aust 2013; 199: 185–91. doi:10.5694/mja12.11854 http://www.ncbi.nlm.nih.gov/pubmed/23909541
39 Kerr AJ, Lee M, Jiang Y. High level of capture of coronary intervention and associated acute coronary syndromes in the all New Zealand acute coronary syndrome quality improvement cardiac registry and excellent agreement with national administrative datasets (ANZACS-QI 25). (1175-8716 (Electronic)).
40 Australian Institute of Health and Welfare. Disease expenditure in Australia Canberra: AIHW, 2019. Available: https://www.aihw.gov.au/reports/health-welfare-expenditure/disease-expenditure-australia
41 Barker SF, Zomer E, O'Toole J, et al. Cost of gastroenteritis in Australia: a healthcare perspective. PLoS One 2018; 13: e0195759. doi:10.1371/journal.pone.0195759 http://www.ncbi.nlm.nih.gov/pubmed/29649285
42 Szummer K, Wallentin L, Lindhagen L, et al. Improved outcomes in patients with ST-elevation myocardial infarction during the last 20 years are related to implementation of evidence-based treatments: experiences from the SWEDEHEART registry 1995-2014. Eur Heart J 2017; 38: 3056–65. doi:10.1093/eurheartj/ehx515 http://www.ncbi.nlm.nih.gov/pubmed/29020314
43 Szummer K, Wallentin L, Lindhagen L, et al. Relations between implementation of new treatments and improved outcomes in patients with non-ST-elevation myocardial infarction during the last 20 years: experiences from SWEDEHEART registry 1995 to 2014. Eur Heart J 2018; 39: 3766–76. doi:10.1093/eurheartj/ehy554 http://www.ncbi.nlm.nih.gov/pubmed/30239671
44 McNamara RL, Chung SC, Jernberg T, et al. International comparisons of the management of patients with non-ST segment elevation acute myocardial infarction in the United Kingdom, Sweden, and the United States: the MINAP/NICOR, SWEDEHEART/RIKS-HIA, and ACTION Registry-GWTG/NCDR registries. Int J Cardiol 2014; 175: 240–7. doi:10.1016/j.ijcard.2014.04.270 http://www.ncbi.nlm.nih.gov/pubmed/24882696
45 Mamas MA, Nolan J, de Belder MA, et al. Changes in arterial access site and association with mortality in the United Kingdom: observations from a national percutaneous coronary intervention database. Circulation 2016; 133: 1655–67. doi:10.1161/CIRCULATIONAHA.115.018083 http://www.ncbi.nlm.nih.gov/pubmed/26969759
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Abstract
Objectives
In this study, we sought to evaluate the costs of percutaneous coronary intervention (PCI) across a variety of indications in Victoria, Australia, using a direct per-person approach, as well as to identify key cost drivers.
Design
A cost-burden study of PCI in Victoria was conducted from the Australian healthcare system perspective.
Setting
A linked dataset of patients admitted to public hospitals for PCI in Victoria was drawn from the Victorian Cardiac Outcomes Registry (VCOR) and the Victorian Admitted Episodes Dataset. Generalised linear regression modelling was used to evaluate key cost drivers. From 2014 to 2017, 20 345 consecutive PCIs undertaken in Victorian public hospitals were captured in VCOR.
Primary outcome measures
Direct healthcare costs attributed to PCI, estimated using a casemix funding method.
Results
Key cost drivers identified in the cost model included procedural complexity, patient length of stay and vascular access site. Although the total procedural cost increased from $A55 569 740 in 2014 to $A72 179 656 in 2017, mean procedural costs remained stable over time ($A12 521 in 2014 to $A12 185 in 2017) after adjustment for confounding factors. Mean procedural costs were also stable across patient indications for PCI ($A9872 for unstable angina to $A15 930 for ST-elevation myocardial infarction) after adjustment for confounding factors.
Conclusions
The overall cost burden attributed to PCIs in Victoria is rising over time. However, despite increasing procedural complexity, mean procedural costs remained stable over time which may be, in part, attributed to changes in clinical practice.
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Details
; Brennan, Angela L 1 ; Stub, Dion 2 ; Dinh, Diem T 1 ; Lefkovits, Jeffrey 3 ; Reid, Christopher M 4 ; Zomer, Ella 1 ; Liew, Danny 1 1 School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
2 School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Department of Cardiology, Alfred Hospital, Melbourne, Victoria, Australia
3 School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Department of Cardiology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
4 School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Curtin University, Perth, Western Australia, Australia