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Implantable cardioverter defibrillator therapy is cost effective for primary prevention patients in Taiwan: An analysis from the Improve SCA trial

  • Reece Holbrook,

    Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

    Affiliation Medtronic, plc, Dublin, Ireland

  • Lucas Higuera,

    Roles Validation, Writing – review & editing

    Affiliation Medtronic, plc, Dublin, Ireland

  • Kael Wherry,

    Roles Validation, Writing – review & editing

    Affiliation Medtronic, plc, Dublin, Ireland

  • Dave Phay,

    Roles Conceptualization, Data curation, Writing – review & editing

    Affiliation Medtronic, plc, Dublin, Ireland

  • Yu-Cheng Hsieh,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliation Department of Cardiology, Taichung Veterans General Hospital and National Yan-Ming University School of Medicine, Taichung, Taiwan

  • Kuo-Hung Lin,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliation Department of Cardiology, China Medical Center University Hospital

  • Yen-Bin Liu

    Roles Conceptualization, Supervision, Writing – review & editing

    yenbin@ntu.edu.tw

    Affiliation Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan

Abstract

Objective

Implantable cardiac defibrillators (ICDs) for primary prevention (PP) of sudden cardiac arrest (SCA) are well-established but underutilized globally. The Improve SCA study has identified a cohort of patients called 1.5 primary prevention (1.5PP) based on PP patients with the presence of certain risk factors. We evaluated the cost-effectiveness of ICD therapy compared to no ICD among the PP population and the subset of 1.5PP patients in Taiwan.

Methods

A Markov model was run over a lifetime time horizon from the Taiwan payer perspective. Mortality and utility estimates were obtained from the literature (PP) and the IMPROVE SCA trial (1.5PP). Cost inputs were obtained from the Taiwan National Health Insurance Administration (NHIA), Ministry of Health and Welfare. We used a willingness-to-pay (WTP) threshold of NT$2,100,000, as established through standard WTP research methods and in alignment with World Health Organization recommendations.

Results

The total discounted costs for ICD therapy and no ICD therapy were NT$1,664,259 and NT$646,396 respectively for PP, while they were NT$2,410,603 and NT$905,881 respectively for 1.5PP. Total discounted QALYs for ICD therapy and no ICD therapy were 6.48 and 4.98 respectively for PP, while they were 10.78 and 7.71 respectively for 1.5PP. The incremental cost effectiveness ratio was NT$708,711 for PP and NT$441,153 for 1.5PP, therefore ICD therapy should be considered cost effective for PP and highly cost effective for 1.5PP.

Conclusions

ICD therapy compared to no ICD therapy is cost-effective in the whole PP population and highly cost-effective in the subset 1.5PP population in Taiwan.

Introduction

Evidence for the use of implantable cardioverter defibrillators (ICDs) for primary prevention of sudden cardiac arrest (SCA) in patients with moderately symptomatic heart failure and reduced systolic function is well-established through multiple randomized clinical trials [1, 2] and confirmed in real-world observational evidence [3, 4]. This evidence has led to strong recommendations for ICD use in society guidelines [5, 6] and has been leveraged to establish the cost-effectiveness of ICD therapy in multiple healthcare systems [7, 8]. Despite this strong evidence base, ICD therapy remains underutilized globally, due at least in part to cost considerations and lack of reimbursement [9]. ‎

The Improve SCA study has identified a high-risk subset of primary prevention patients called 1.5 primary prevention based on the presence of at least one of the following documented risk factors: non-sustained ventricular tachycardia (NSVT), frequent premature ventricular contractions (PVCs) >10/h, left ventricular ejection fraction (LVEF) <25%, pre-syncope or syncope [10]. Improve SCA patients with 1.5 primary prevention characteristics were found to have a higher rate of treatment with appropriate therapy than primary prevention patients, and when treated with an ICD, 1.5 primary prevention patients experienced a 49% relative risk reduction in all-cause mortality.

While the cost-effectiveness of ICD therapy for primary prevention patients has been established in western countries, it has not been previously established for the healthcare system in Taiwan. Furthermore, the cost-effectiveness of ICD therapy for 1.5 primary prevention patients is not well known. The 1.5 primary prevention cohort could be used to prioritize health care resources in geographies where such resources are insufficient to cover the full primary prevention population. The aim of the present study was to critically evaluate the cost-effectiveness models of ICD therapy for both the superset of primary prevention and the subset 1.5 of primary prevention patients with heart failure in the Taiwan healthcare system, and to identify the main factors influencing the cost-effectiveness of ICD therapy [11].

Methods

We used an existing Markov decision model [7] to estimate the lifetime cost, quality of life, survival, and incremental cost-effectiveness of ICD therapy versus no ICD therapy for a Taiwanese population at risk for SCA (both primary prevention and 1.5 primary prevention). The Improve SCA study [11] protocol was approved by the Institutional Review Board or Medical Ethics Committee of each respective study center. This analysis is a modeling exercise based on previously published data from the Improve SCA study [11] and does not involve any additional human research. No ICD therapy was selected as the control instead of pharmacologic therapy based SCD-HeFT study findings that indicated no significant difference in the risk of death between treatment with amiodarone and treatment with a placebo [1]. Model inputs are shown in Table 1 and described in detail, below. The model was implemented in Microsoft Excel, as described previously [7].

Model structure

The model is structured as a decision tree with two treatment arms, ICD therapy or no ICD therapy, followed by consecutive Markov models (Fig 1). The model was run in two separate scenarios, following a simulated cohort of 1,000 patients with a standard indication for primary prevention ICD therapy, and for patients with a standard indication for primary prevention ICD therapy with one or more 1.5 primary prevention risk factors. Patients who enter the model in the ICD arm are at an initial risk of operative death or survival. Patients who survive the ICD surgery enter the Markov model in the well state. From the well state, ICD patients stay well or progress to ICD complications, sudden cardiac death, non-sudden cardiac death, non-cardiac death, unknown death. Patients remain in the same state or progress to a different state at the beginning of each cycle, except for the complication state. Patients who experience an ICD complication remain in the complication state for only one cycle, then progress to continued ICD therapy or discontinued ICD therapy. In the event of therapy discontinuation, ICD patients stay well without ICD treatment or progress to sudden cardiac death, non-sudden cardiac death, non-cardiac death, unknown death. Patients in the no ICD arm enter the model in the well state and remain well or progress to sudden cardiac death, non-sudden cardiac death, non-cardiac death, unknown death.

Patients incur costs and effects by progressing through the model in monthly increments over a lifetime (420 months); a lifetime perspective allows the model to account for all costs incurred by patients that survive without a sudden cardiac arrest event. Patients in both treatment arms incur monthly inpatient and outpatient costs. In the ICD therapy arm, patients also incur the cost of the device and ICD implant procedure. ICD patients who remain alive long enough to require a device replacement incur additional device and procedure costs at the time of replacement. ICD patients may receive an inappropriate shock or other ICD-related complication that incurs a cost and affects treatment adherence. After experiencing an inappropriate shock or other ICD-related complication, patients remain in the ICD therapy arm with ICD treatment or progress to discontinued use of ICD therapy. We assumed ICD patients who discontinue use of ICD therapy have the same mortality risk as patients in the no ICD arm.

Clinical data

Mortality inputs to the model for primary prevention patients were based on a previously published meta-analysis and for 1.5 primary prevention patients were based on Improve SCA clinical study results. Primary prevention patients had a mean age of 61.1 years and were 76.3% male, while 1.5 primary prevention patients had a mean age of 61.1 years and were 79.5% male; other characteristics for each population are included in the original study publications [7, 11]. Other clinical inputs to the model for both ICD cohorts were based on the United States (US) National ICD registry, literature, and administrative claims-based analyses. The probability of implant-related operative death (0.0002) was based on the US National ICD Registry and applied only to the ICD treatment arms [12].‎ Inappropriate shock probability was derived from a weighted average based on the MADIT RIT, ADVANCE III, PROVIDE, and PainFree SST clinical trials that demonstrated a reduction in inappropriate shock rates due to device programming [1316]. Probabilities of lead failure or dislodgement after initial implant were based on studies of annual incidence of lead failure and ICD lead dislodgement at one year after implant, 0.45% and 1.8% respectively [18, 19]. ‎ Probability of lead dislodgement or replacement after ICD replacement was based on data from the REPLACE registry that reported a 1% combined dislodgement and replacement rate [20]. We assumed half of the combined rate reported in the REPLACE registry could be attributed to lead failure (0.5%) and half could be attributed to lead dislodgement (0.5%). We estimated the one-year probability of lead infection after initial implant (1.22%) and after device replacement (2.16%) with a retrospective data analysis based on administrative claims from a large US insurer [21]. The lifetime risk of lead infection after the first year of an initial or replacement implant was double the value of the one-year claims-based probability [22].

Economic data

Device related costs and long-term health care utilization costs associated with heart disease were modeled over a lifetime. Costs for individual events were assumed to be the same regardless of the indication (primary prevention or 1.5 primary prevention) for the ICD. The procedural costs of an initial ICD implant, subsequent revision or replacement, and ICD-related complications (infection and dislodgement) which including cost of devices, admission fee, drug fee, examination etc. were derived from the Taiwan Diagnosis-Related Group (Tw-DRG), edition 3.4 of the National Health Insurance Administration (NHIA), Ministry of Health and Welfare [23]. The procedural cost of inappropriate shocks and monthly long-term inpatient and outpatient costs were derived for the base case by evaluating the practice and calculated the cost base on "Fee Schedule for Medical Services of National Health Insurance" of the National Health Insurance Administration (NHIA), Ministry of Health and Welfare [24, 25]. Monthly inpatient and outpatient costs related to heart failure were estimated from the NHIA inpatient expenditures by admissions (DD) database and NHIA outpatient expenditures (OD) database, respectively.

Health-related quality of life

Quality of life was based on an analysis of EQ-5D data collected in the PainFree SST clinical trial [28]. Taiwan-specific utilities were derived by mapping each patient’s EQ-5D state using country specific societal preferences (S2 Table) [29]. We assumed the baseline utility for ICD patients and no ICD patients was the same [30]. Patients who experienced an ICD-related complication received a short-term utility decrement of 0.096 that is equivalent to 3.5 days [31].

Construction of the ICER (w/WTP) and sensitivity analysis

Total lifetime costs and quality-adjusted life years (QALYs) between ICD therapy and no ICD therapy were simulated to calculate the incremental cost effectiveness ratio (ICER). Both undiscounted and discounted results were calculated to best represent the time value of costs and outcomes. We conducted one-way sensitivity and probabilistic sensitivity analyses to assess the impact of model inputs and parameter uncertainty. Beta distributions were used for probabilities and utilities in the probabilistic sensitivity analysis. We used a willingness-to-pay (WTP) threshold value of NT$2.1 million for this analysis [32].

Results

Base case scenario for primary prevention

Table 2 shows the results of the base-case scenario for primary prevention.

thumbnail
Table 2. Base case scenario results (primary prevention and 1.5 primary prevention).

https://doi.org/10.1371/journal.pone.0241697.t002

ICD therapy for primary prevention resulted in a benefit of 8.88 (discounted) and 9.82 (undiscounted) life-years saved, while no ICD therapy resulted in a benefit of 6.80 and 7.36 life-years saved, respectively. Measured in QALYs, the discounted benefit from ICD therapy is 6.48 and 4.98 from no ICD therapy, resulting in an incremental effectiveness of 1.51 QALYs. Discounted costs from ICD therapy and no ICD therapy account for NT$1,664,259 and NT$597,087, respectively. The ICER for ICD therapy is NT$708,711 per QALY; ICD therapy for primary prevention is cost-effective at NT$2.1 million.

Base case scenario for 1.5 primary prevention

Table 2 (above) also shows the results of the base-case scenario for 1.5 primary prevention. ICD therapy for 1.5 primary prevention resulted in a benefit of 12.45 (discounted) and 14.17 (undiscounted) life-years saved, while no ICD therapy resulted in a benefit of 8.88 and 9.82 life-years saved, respectively. Measured in QALYs, the discounted benefit from ICD therapy is 10.78 and 7.71 from no ICD therapy, resulting in an incremental effectiveness of 3.08 QALYs. Discounted costs from ICD therapy and no ICD therapy account for NT$2,175,478 and NT$818,782, respectively. The ICER for ICD therapy is NT$441,153 per QALY; ICD therapy for 1.5 prevention is below one third of the WTP and is highly cost-effective at NT$2.1 million.

Sensitivity analyses

Results of the sensitivity analyses are presented for the 1.5 primary prevention indication only (results for the primary prevention indication are in the S1 Table). Results of the one-way sensitivity analyses show that costs per QALY are more responsive to the age at implant, conventional mortality, replacement period and quality of life (Fig 2, Panel A). The incremental costs per QALY remained below the WTP threshold for all values of the one-way sensitivity analysis.

Fig 2, Panel B shows the simulated costs per QALY of the probabilistic sensitivity analysis. Results for 1.5 primary prevention show a mean cost per QALY of NT$434,053 (median cost per QALY of NT$435,301, 95-percent Credible Interval [NT$291,938 –NT$1,115,321] per QALY) after 1,000 iterations. For 1.5 primary prevention, 99.5 percent of simulations result in costs per QALY below the WTP threshold, and 90 percent of simulations result in costs per QALY below one third of the WTP threshold, indicating ICD therapy is highly cost effective for this population.

Discussion

Our results indicate that ICD therapy is cost effective for the whole primary prevention patient population and highly cost effective for the subset of 1.5 primary prevention patients in the Taiwan healthcare system. The primary prevention and 1.5 primary prevention are at ICERs of NT$708,711 per QALY and NT$441,153 per QALY respectively; while 1.5 primary prevention was more cost effective, both are less than the WTP value of NT$2.1 million. This finding is robust, with sensitivity analyses indicating that the cost effectiveness is preserved in nearly all reasonable variations of model inputs. To our knowledge, this is the first evaluation of the cost-effectiveness of ICD therapy compared to no ICD therapy among the whole primary prevention patient population and the subset of 1.5 primary prevention patients from the perspective of the public healthcare system in Taiwan.

Prior estimates of the cost effectiveness of ICD therapy have been performed in the primary prevention population. Mark et al [8]‎ performed an analysis of the randomized SCD-HeFT trial and found ICD therapy to be economically attractive at $41,530/QALY (at a WTP of $100,000) in the US healthcare system. An analysis in the healthcare system of a European country using a meta-analysis of six randomized primary prevention trials and the same model used in this study showed similar results [7].‎ The cost-effectiveness of ICD therapy has also been confirmed in a real world setting outside of clinical trials [33].‎ Our study shows that ICD therapy for primary prevention patients cost less than 1 GDP per capita per QALY and appear even more cost-effective in the Taiwan healthcare system compared to previous reports in other countries (S1 Table).

Despite convincing evidence from multiple randomized clinical trials [13], and strong recommendations in international society guidelines [5, 6], ICD therapy remains underutilized in Asian countries [34, 35]. In particular, a report by Chia et al in 2017 [9] found that among guideline recommended ICD-eligible patients in Taiwan, only 7.7% had received ICD therapy, and by comparison, in Japan 52.5% of ICD-eligible patients had received ICD therapy [14]. National health expenditure (NHE) in Taiwan was 6.1% of GDP in 2017 [36] approximately one-third of the US (17.1%) and 69% of the average for OECD (Organization for Economic Cooperation and Development) countries (8.8%). Health spending per capita in 2017 in Taiwan was PPPUS$3,047 [36] less than one-third (30%) of the US total (PPPUS$10,209) and 76% of the average for OECD countries (PPPUS$3,992). To the extent that economic factors play a role, this study provides information for decision makers to direct scarce resources first toward those who can benefit the most. While it remains cost effective to treat the entire primary prevention population with ICD therapy, from an economic standpoint a priority could be placed on treating patients with a 1.5 primary prevention indication.

It is important to acknowledge the limitations of this analysis. The Improve SCA trial was not randomized, however the mortality benefit from the trial remained significant after adjusting for baseline characteristics likely to have an impact on mortality, and the effectiveness of ICD therapy for the primary prevention population has been shown to be replicated in both randomized and non-randomized observational trials. Costs and benefits were modeled beyond the timeline of direct observation in the Improve SCA trial, however this is a standard approach in economic modeling and necessary for the proper perspective for decision makers. For the full primary prevention analysis ICD effectiveness data were taken from a global meta-analysis that did not include Taiwan, however such data from Taiwan were not available. For the 1.5 primary prevention analysis patients in the Improve SCA trial were not all from Taiwan, yet the majority were from Asia and ICD therapy application is well developed and largely standardized around the world. Conclusions from this report may not be generalizable beyond the Taiwan healthcare system.

Conclusion

ICD therapy is cost effective for primary prevention patients in the Taiwan healthcare system, and highly cost effective for 1.5 prevention patients. These data provide guidance as to an efficient way to address underutilization of ICD therapy in indicated patients in Taiwan.

Supporting information

S1 Table. Characteristics and result of economic evaluations of ICD for primary prevention.

https://doi.org/10.1371/journal.pone.0241697.s003

(DOCX)

S1 File. We have included a trace of the base case calculations for each patient population as a supplementary files, which give transparent and granular information on the calculations performed to produce the results.

This, together with the information in the manuscript (the specific model inputs and sources, and the structure of the model) is meant to allow for sufficient transparency to understand how the results were produced. S1 File, PP patients. S2 File, 1.5PP patients.

https://doi.org/10.1371/journal.pone.0241697.s005

(XLS)

S2 File. We have included a trace of the base case calculations for each patient population as a supplementary files, which give transparent and granular information on the calculations performed to produce the results.

This, together with the information in the manuscript (the specific model inputs and sources, and the structure of the model) is meant to allow for sufficient transparency to understand how the results were produced. S1 File, PP patients. S2 File, 1.5PP patients.

https://doi.org/10.1371/journal.pone.0241697.s006

(XLS)

Acknowledgments

We are grateful to Brian Van Dorn, MS for providing the utility estimates based on the IMPROVE SCA quality of life data and Janet E O’Brien, MS for medical writing assistance (from Medtronic).

References

  1. 1. Bardy GH, Lee KL, Mark DB, Poole JE, Packer DL, Boineau R, et al. Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N Engl J Med. 2005;352(3):225–37. Epub 2005/01/22. pmid:15659722.
  2. 2. Moss AJ, Zareba W, Hall WJ, Klein H, Wilber DJ, Cannom DS, et al. Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med. 2002;346(12):877–83. Epub 2002/03/22. pmid:11907286.
  3. 3. Al-Khatib SM, Hellkamp A, Bardy GH, Hammill S, Hall WJ, Mark DB, et al. Survival of patients receiving a primary prevention implantable cardioverter-defibrillator in clinical practice vs clinical trials. JAMA. 2013;309(1):55–62. Epub 2013/01/03. pmid:23280225; PubMed Central PMCID: PMC3638257.
  4. 4. Schrage B, Uijl A, Benson L, Westermann D, Stahlberg M, Stolfo D, et al. Association Between Use of Primary-Prevention Implantable Cardioverter-Defibrillators and Mortality in Patients With Heart Failure: A Prospective Propensity Score-Matched Analysis From the Swedish Heart Failure Registry. Circulation. 2019;140(19):1530–9. Epub 2019/09/04. pmid:31476893.
  5. 5. Priori SG, Blomstrom-Lundqvist C, Mazzanti A, Blom N, Borggrefe M, Camm J, et al. 2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC). Eur Heart J. 2015;36(41):2793–867. Epub 2015/09/01. pmid:26320108.
  6. 6. Epstein AE, DiMarco JP, Ellenbogen KA, Estes NA 3rd, Freedman RA, Gettes LS, et al. ACC/AHA/HRS 2008 Guidelines for Device-Based Therapy of Cardiac Rhythm Abnormalities: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the ACC/AHA/NASPE 2002 Guideline Update for Implantation of Cardiac Pacemakers and Antiarrhythmia Devices): developed in collaboration with the American Association for Thoracic Surgery and Society of Thoracic Surgeons. Circulation. 2008;117(21):e350–408. Epub 2008/05/17. pmid:18483207.
  7. 7. Cowie MR, Marshall D, Drummond M, Ferko N, Maschio M, Ekman M, et al. Lifetime cost-effectiveness of prophylactic implantation of a cardioverter defibrillator in patients with reduced left ventricular systolic function: results of Markov modelling in a European population. Europace. 2009;11(6):716–26. Epub 2009/04/11. pmid:19359333.
  8. 8. Mark DB, Nelson CL, Anstrom KJ, Al-Khatib SM, Tsiatis AA, Cowper PA, et al. Cost-effectiveness of defibrillator therapy or amiodarone in chronic stable heart failure: results from the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). Circulation. 2006;114(2):135–42. Epub 2006/07/05. pmid:16818817.
  9. 9. Chia YMF, Teng TK, Tan ESJ, Tay WT, Richards AM, Chin CWL, et al. Disparity Between Indications for and Utilization of Implantable Cardioverter Defibrillators in Asian Patients With Heart Failure. Circ Cardiovasc Qual Outcomes. 2017;10(11). Epub 2017/11/19. pmid:29150533.
  10. 10. Zhang S, Singh B, Rodriguez DA, Chasnoits AR, Hussin A, Ching CK, et al. Improve the prevention of sudden cardiac arrest in emerging countries: the Improve SCA clinical study design. Europace. 2015;17(11):1720–6. Epub 2015/06/04. pmid:26037794; PubMed Central PMCID: PMC4654425.
  11. 11. Zhang S, Ching CK, Huang D, Liu YB, Rodriguez-Guerrero DA, Hussin A, et al. Utilization of implantable cardioverter-defibrillators for the prevention of sudden cardiac death in emerging countries: Improve SCA clinical trial. Heart Rhythm. 2020;17(3):468–75. Epub 2019/09/29. pmid:31561030.
  12. 12. Hammill SC, Kremers MS, Stevenson LW, Heidenreich PA, Lang CM, Curtis JP, et al. Review of the registry's fourth year, incorporating lead data and pediatric ICD procedures, and use as a national performance measure. Heart Rhythm. 2010;7(9):1340–5. Epub 2010/07/22. pmid:20647056.
  13. 13. Gasparini M, Proclemer A, Klersy C, Kloppe A, Lunati M, Ferrer JB, et al. Effect of long-detection interval vs standard-detection interval for implantable cardioverter-defibrillators on antitachycardia pacing and shock delivery: the ADVANCE III randomized clinical trial. JAMA. 2013;309(18):1903–11. Epub 2013/05/09. pmid:23652522.
  14. 14. Auricchio A, Schloss EJ, Kurita T, Meijer A, Gerritse B, Zweibel S, et al. Low inappropriate shock rates in patients with single- and dual/triple-chamber implantable cardioverter-defibrillators using a novel suite of detection algorithms: PainFree SST trial primary results. Heart Rhythm. 2015;12(5):926–36. Epub 2015/02/01. pmid:25637563.
  15. 15. Saeed M, Hanna I, Robotis D, Styperek R, Polosajian L, Khan A, et al. Programming implantable cardioverter-defibrillators in patients with primary prevention indication to prolong time to first shock: results from the PROVIDE study. J Cardiovasc Electrophysiol. 2014;25(1):52–9. Epub 2013/10/12. pmid:24112717.
  16. 16. Moss AJ, Schuger C, Beck CA, Brown MW, Cannom DS, Daubert JP, et al. Reduction in inappropriate therapy and mortality through ICD programming. N Engl J Med. 2012;367(24):2275–83. Epub 2012/11/08. pmid:23131066.
  17. 17. Li H, Natale A, Zhu W, Greenfield RA, Easley A, Barrington W, et al. Causes and consequences of discontinuation of the implantable cardioverter-defibrillator therapy in non-terminally ill patients. Am J Cardiol. 1998;81(10):1203–5. Epub 1998/05/30. pmid:9604946.
  18. 18. Ghani A, Delnoy PP, Ramdat Misier AR, Smit JJ, Adiyaman A, Ottervanger JP, et al. Incidence of lead dislodgement, malfunction and perforation during the first year following device implantation. Neth Heart J. 2014;22(6):286–91. Epub 2014/05/09. pmid:24807834; PubMed Central PMCID: PMC4031363.
  19. 19. Providencia R, Kramer DB, Pimenta D, Babu GG, Hatfield LA, Ioannou A, et al. Transvenous Implantable Cardioverter-Defibrillator (ICD) Lead Performance: A Meta-Analysis of Observational Studies. J Am Heart Assoc. 2015;4(11). Epub 2015/11/01. pmid:26518666; PubMed Central PMCID: PMC4845221.
  20. 20. Poole JE, Gleva MJ, Mela T, Chung MK, Uslan DZ, Borge R, et al. Complication rates associated with pacemaker or implantable cardioverter-defibrillator generator replacements and upgrade procedures: results from the REPLACE registry. Circulation. 2010;122(16):1553–61. Epub 2010/10/06. pmid:20921437.
  21. 21. Eby EL, Bengtson LGS, Johnson MP, Burton ML, Hinnenthal J. Economic impact of cardiac implantable electronic device infections: cost analysis at one year in a large U.S. health insurer. J Med Econ. 2020:1–8. Epub 2020/04/08. pmid:32255386.
  22. 22. Hussein AA, Baghdy Y, Wazni OM, Brunner MP, Kabbach G, Shao M, et al. Microbiology of Cardiac Implantable Electronic Device Infections. JACC Clin Electrophysiol. 2016;2(4):498–505. Epub 2016/08/01. pmid:29759872.
  23. 23. National Health Insurance Administration Ministry of Health and Welfare Taipei 10634, Taiwan National Health Insurance Administration; 2015 [cited 2019 9 September 2019]. Available from: https://www.nhi.gov.tw/Content_List.aspx?n=150B11D4DCAB0FFD&topgdrtn=5FE8C9FEAE863B46.
  24. 24. Taiwan Ministry of Health and Welfare [cited 2019 October 21]. Available from: https://www.nhi.gov.tw/Content_List.aspx?n=150B11D4DCAB0FFD&topgdrtn=5FE8C9FEAE863B46.
  25. 25. National Health Insurance Administration Ministry of Health and Welfare [Internet]. National Health Insurance Administration. 2019 [cited 19 September 2019]. Available from: https://www.nhi.gov.tw/Content_List.aspx?n=58ED9C8D8417D00B&topn=5FE8C9FEAE863B46.
  26. 26. Central Bank of the Republic of China (Taiwan) [Internet]. 2019 [cited 01 December 2017]. Available from: http://www.cbc.gov.tw/lp.asp?CtNode=695&CtUnit=303&BaseDSD=32&mp=2.
  27. 27. Zhang S, Ching CK, Huang D, Liu YB, Rodriguez-Guerrero DA, Hussin A, et al. Utilization of implantable cardioverter-defibrillators for the prevention of sudden cardiac death in emerging countries: Improve SCA clinical trial. Heart Rhythm. 2019. Epub 2019/09/29. pmid:31561030.
  28. 28. Sears SF, Rosman L, Sasaki S, Kondo Y, Sterns LD, Schloss EJ, et al. Defibrillator shocks and their effect on objective and subjective patient outcomes: Results of the PainFree SST clinical trial. Heart Rhythm. 2018;15(5):734–40. Epub 2017/12/27. pmid:29277687.
  29. 29. Lee HY, Hung MC, Hu FC, Chang YY, Hsieh CL, Wang JD. Estimating quality weights for EQ-5D (EuroQol-5 dimensions) health states with the time trade-off method in Taiwan. J Formos Med Assoc. 2013;112(11):699–706. Epub 2013/11/05. pmid:24183199.
  30. 30. Bundgaard JS, Thune JJ, Nielsen JC, Videbaek R, Haarbo J, Bruun NE, et al. The impact of implantable cardioverter-defibrillator implantation on health-related quality of life in the DANISH trial. Europace. 2019;21(6):900–8. Epub 2019/02/24. pmid:30796456.
  31. 31. Sanders GD, Hlatky MA, Owens DK. Cost-effectiveness of implantable cardioverter-defibrillators. N Engl J Med. 2005;353(14):1471–80. Epub 2005/10/07. pmid:16207849.
  32. 32. Shiroiwa T, Sung YK, Fukuda T, Lang HC, Bae SC, Tsutani K. International survey on willingness-to-pay (WTP) for one additional QALY gained: what is the threshold of cost effectiveness? Health Econ. 2010;19(4):422–37. Epub 2009/04/22. pmid:19382128.
  33. 33. Thijssen J, van den Akker van Marle ME, Borleffs CJ, van Rees JB, de Bie MK, van der Velde ET, et al. Cost-effectiveness of primary prevention implantable cardioverter defibrillator treatment: data from a large clinical registry. Pacing Clin Electrophysiol. 2014;37(1):25–34. Epub 2013/09/04. pmid:23998638.
  34. 34. Satake H, Fukuda K, Sakata Y, Miyata S, Nakano M, Kondo M, et al. Current status of primary prevention of sudden cardiac death with implantable cardioverter defibrillator in patients with chronic heart failure—a report from the CHART-2 Study. Circ J. 2015;79(2):381–90. Epub 2014/12/06. pmid:25476195.
  35. 35. Lee JH, Lee SR, Choi EK, Jeong J, Park HD, You SJ, et al. Temporal Trends of Cardiac Implantable Electronic Device Implantations: a Nationwide Population-based Study. Korean Circ J. 2019;49(9):841–52. Epub 2019/05/11. pmid:31074230; PubMed Central PMCID: PMC6713826.
  36. 36. Taiwan Ministry of Health and Welfare Available from: https://www.nhi.gov.tw/Content_List.aspx?n=150B11D4DCAB0FFD&topgdrtn=5FE8C9FEAE863B46.