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Evolution of Modern Cardiovascular Quality Metrics

  • Ifeany David Chinedozi
  • Benjamin WesslerEmail author
Chapter
  • 24 Downloads

Abstract

It is the winter of 1990 and a 68-year-old woman with a history of hypertension presents with a syndrome of stuttering chest pain and associated dyspnea. She was working at the time and was encouraged by her coworkers to go to the emergency room. Ultimately, she was admitted to her local hospital and treated for a non-ST elevation myocardial infarction and discharged home. She was told—and believed—that the care she received was excellent.

Keywords

Cardiovascular Cardiac Care Primordial prevention Cardiovascular diseases National Myocardial Infarction Registry 

References

  1. 1.
    Anon. Secondary prevention of vascular disease by prolonged antiplatelet treatment. Antiplatelet Trialists’ Collaboration. Br Med J (Clin Res Ed). 1988;296:320–31.CrossRefGoogle Scholar
  2. 2.
    Ellerbeck EF, Jencks SF, Radford MJ, et al. Quality of care for Medicare patients with acute myocardial infarction. A four-state pilot study from the Cooperative Cardiovascular Project. JAMA. 1995;273:1509–14.CrossRefGoogle Scholar
  3. 3.
    Tooley SA. The life of Florence Nightingale. London: S.H. Bousfield & Co., Ltd.; 1905. ISBN-13: 978-1104449629.Google Scholar
  4. 4.
    Higginson IJ, Carr AJ. Measuring quality of life: using quality of life measures in the clinical setting. BMJ. 2001;322:1297–300.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lloyd-Jones DM. Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation. 2010;121:1768–77.CrossRefGoogle Scholar
  6. 6.
    Strasser T. Reflections on cardiovascular diseases. Interdiscip Sci Rev. 1978;3:225–30.CrossRefGoogle Scholar
  7. 7.
    Gillman MW. Primordial prevention of cardiovascular disease. Circulation. 2015;131:599–601.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N Engl J Med. 2007;356:2388–98.CrossRefGoogle Scholar
  9. 9.
    Anon. To err is human. Washington, D.C.: National Academies Press; 2000.Google Scholar
  10. 10.
    Anon. Crossing the quality chasm. Washington, D.C.: National Academies Press; 2001.Google Scholar
  11. 11.
    Berwick DM. A user’s manual for the IOM’s ‘quality chasm’ report. Health Aff. 2002;21:80–90.CrossRefGoogle Scholar
  12. 12.
    Blendon RJ, Altman DE, Benson JM, Brodie M. Health care in the 2004 presidential election. N Engl J Med. 2004;351:1314–22.CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Chatterjee P, Joynt KE. Do cardiology quality measures actually improve patient outcomes? J Am Heart Assoc. 2014;3:e000404.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Wasfy JH, Borden WB, Secemsky EA, McCabe JM, Yeh RW. Public reporting in cardiovascular medicine: accountability, unintended consequences, and promise for improvement. Circulation. 2015;131:1518–27.CrossRefGoogle Scholar
  16. 16.
    Hannan EL, Kilburn H, Racz M, Shields E, Chassin MR. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA. 1994;271:761–6.CrossRefGoogle Scholar
  17. 17.
    Omoigui NA, Miller DP, Brown KJ, et al. Outmigration for coronary bypass surgery in an era of public dissemination of clinical outcomes. Circulation. 1996;93:27–33.CrossRefGoogle Scholar
  18. 18.
    Peberdy MA, Donnino MW, Callaway CW, et al. Impact of percutaneous coronary intervention performance reporting on cardiac resuscitation centers: a scientific statement from the American Heart Association. Circulation. 2013;128:762–73.CrossRefGoogle Scholar
  19. 19.
    Rowe R, Iqbal J, Murali-krishnan R, et al. Role of frailty assessment in patients undergoing cardiac interventions. Open Heart. 2014;1:e000033–e000033.CrossRefGoogle Scholar
  20. 20.
    Green J, Wintfeld N. Report cards on cardiac surgeons. Assessing New York State’s approach. N Engl J Med. 1995;332:1229–32.CrossRefGoogle Scholar
  21. 21.
    Kent DM, Hayward RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA. 2007;298:1209–12.CrossRefGoogle Scholar
  22. 22.
    Forman DE, Maurer MS, Boyd C, et al. Multimorbidity in older adults with cardiovascular disease. J Am Coll Cardiol. 2018;71:2149–61.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Rogers WJ, Bowlby LJ, Chandra NC, et al. Treatment of myocardial infarction in the United States (1990 to 1993). Observations from the National Registry of Myocardial Infarction. Circulation. 1994;90:2103–14.CrossRefGoogle Scholar
  24. 24.
    Yusuf S, Sleight P, Held P, McMahon S. Routine medical management of acute myocardial infarction. Lessons from overviews of recent randomized controlled trials. Circulation. 1990;82:II117–34.PubMedGoogle Scholar
  25. 25.
    Gunnar RM, Bourdillon PD, Dixon DW, et al. ACC/AHA guidelines for the early management of patients with acute myocardial infarction. A report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (subcommit). Circulation. 1990;82:664–707.CrossRefGoogle Scholar
  26. 26.
    Rosamond WD, Shahar E, McGovern PG, Sides TL, Luepker RV. Trends in coronary thrombolytic therapy for acute myocardial infarction (the Minnesota Heart Survey Registry, 1990 to 1993). Am J Cardiol. 1996;78:271–7.CrossRefGoogle Scholar
  27. 27.
    Gunter N, Moore L, Odom P. Cooperative cardiovascular project. J S C Med Assoc. 1997;93:177–9.PubMedGoogle Scholar
  28. 28.
    Marciniak TA, Ellerbeck EF, Radford MJ, et al. Improving the quality of care for Medicare patients with acute myocardial infarction: results from the Cooperative Cardiovascular Project. JAMA. 1998;279:1351–7.CrossRefGoogle Scholar
  29. 29.
    Masoudi FA, Ralston DL, Wolfe P, et al. Baseline quality indicator rates from the National Heart Failure Project: a HCFA initiative to improve the care of medicare beneficiaries with heart failure. Congest Heart Fail. 7:53–6.Google Scholar
  30. 30.
    McNaughton H, McPherson K, Taylor W, Weatherall M. Relationship between process and outcome in stroke care. Stroke. 2003;34:713–7.CrossRefGoogle Scholar
  31. 31.
    Peterson ED, Roe MT, Mulgund J, et al. Association between hospital process performance and outcomes among patients with acute coronary syndromes. JAMA. 2006;295:1912–20.CrossRefGoogle Scholar
  32. 32.
    Bradley EH, Herrin J, Elbel B, et al. Hospital quality for acute myocardial infarction: correlation among process measures and relationship with short-term mortality. JAMA. 2006;296:72–8.CrossRefGoogle Scholar
  33. 33.
    Fonarow GC, Abraham WT, Albert NM, et al. Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA. 2007;297:61–70.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Heidenreich PA, Hernandez AF, Yancy CW, Liang L, Peterson ED, Fonarow GC. Get with the guidelines program participation, process of care, and outcome for medicare patients hospitalized with heart failure. Circ Cardiovasc Qual Outcomes. 2012;5:37–43.CrossRefGoogle Scholar
  35. 35.
    Anon. No title. Available at: http://www.qualityforum.org/Measures_List.aspx.
  36. 36.
    National Quality Forum. NQF‐Endorsed® Standards. 1999. Available at: http://www.qualityforum.org/Measures_List.aspx.
  37. 37.
    Berthiaume JT, Chung RS, Ryskina KL, Walsh J, Legorreta AP. Aligning financial incentives with quality of care in the hospital setting. J Healthc Qual. 2006;28:36–44, 51.CrossRefGoogle Scholar
  38. 38.
    Nahra TA, Reiter KL, Hirth RA, Shermer JE, Wheeler JRC. Cost-effectiveness of hospital pay-for-performance incentives. Med Care Res Rev. 2006;63:49S–72S.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Lindenauer PK, Remus D, Roman S, et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007;356:486–96.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Glickman SW, Ou F-S, DeLong ER, et al. Pay for performance, quality of care, and outcomes in acute myocardial infarction. JAMA. 2007;297:2373–80.CrossRefGoogle Scholar
  41. 41.
    Centers for Medicare & Medicaid Services (CMS) H. Medicare program; hospital inpatient value-based purchasing program. Final rule. Fed Regist. 2011;76:26490–547.Google Scholar
  42. 42.
    Ryan AM, Blustein J, Casalino LP. Medicare’s flagship test of pay-for-performance did not spur more rapid quality improvement among low-performing hospitals. Health Aff. 2012;31:797–805.CrossRefGoogle Scholar
  43. 43.
    Hayward RA. Kent DM. 6 EZ steps to improving your performance: (or how to make P4P pay 4U!). JAMA. 2008;300:255–6.CrossRefGoogle Scholar
  44. 44.
    Figueroa JF, Tsugawa Y, Zheng J, Orav EJ, Jha AK. Association between the Value-Based Purchasing pay for performance program and patient mortality in US hospitals: observational study. BMJ. 2016;353:i2214.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Papanicolas I, Figueroa JF, Orav EJ, Jha AK. Patient hospital experience improved modestly, but no evidence medicare incentives promoted meaningful gains. Health Aff (Millwood). 2017;36:133–40.CrossRefGoogle Scholar
  46. 46.
    Jha AK. Time to get serious about pay for performance. JAMA. 2013;309:347.CrossRefGoogle Scholar
  47. 47.
    Keenan PS, Normand ST, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1:29–37.CrossRefGoogle Scholar
  48. 48.
    Zuckerman RB, Sheingold SH, Orav EJ, Ruhter J, Epstein AM. Readmissions, observation, and the hospital readmissions reduction program. N Engl J Med. 2016;374:1543–51.CrossRefGoogle Scholar
  49. 49.
    Konstam MA. Heart failure in the lifetime of Musca Domestica (the common housefly). JACC Heart Fail. 2013;1:178–80.CrossRefGoogle Scholar
  50. 50.
    Wadhera RK, Joynt Maddox KE, Wasfy JH, Haneuse S, Shen C, Yeh RW. Association of the hospital readmissions reduction program with mortality among medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia. JAMA – J Am Med Assoc. 2018;320:2542–52.CrossRefGoogle Scholar
  51. 51.
    Gupta A, Allen LA, Bhatt DL, et al. Association of the hospital readmissions reduction program implementation with readmission and mortality outcomes in heart failure. JAMA Cardiol. 2018;3:44.CrossRefGoogle Scholar
  52. 52.
    Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J. Do non-clinical factors improve prediction of readmission risk? JACC Heart Fail. 2016;4:12–20.CrossRefGoogle Scholar
  53. 53.
    Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the hospital readmissions reduction program. JAMA. 2013;309:342.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Figueroa JF, Joynt KE, Zhou X, Orav EJ, Jha AK. Safety-net hospitals face more barriers yet use fewer strategies to reduce readmissions. Med Care. 2017;55:229–35.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Rathi VK, McWilliams JM. First-year report cards from the Merit-Based Incentive Payment System (MIPS). JAMA. 2019;321:1157.CrossRefGoogle Scholar
  56. 56.
    MacLean CH, Kerr EA, Qaseem A. Time out — charting a path for improving performance measurement. N Engl J Med. 2018;378:1757–61.CrossRefGoogle Scholar
  57. 57.
    O’Brien SM, Shahian DM, Filardo G, et al. The society of thoracic surgeons 2008 cardiac surgery risk models: part 2—isolated valve surgery. Ann Thorac Surg. 2009;88:S23–42.CrossRefGoogle Scholar
  58. 58.
    Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol. 2016;74:167–76.CrossRefGoogle Scholar
  59. 59.
    Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115:928–35.CrossRefGoogle Scholar
  60. 60.
    Steyerberg EW, Borsboom GJJM, van Houwelingen HC, Eijkemans MJC, Habbema JDF. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med. 2004;23:2567–86.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tufts University School of MedicineBostonUSA
  2. 2.Division of CardiologyTufts Medical CenterBostonUSA
  3. 3.Predictive Analytic sand Comparative Effectiveness (PACE) CenterTufts Medical CenterBostonUSA

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