Student Heart Failure Intervention Pilot (SHIP): A Study of Risk Factor Analytics and Population Outreach

Authors

  • William Byron Reichert University Medical Center Phoenix, Internal Medicine Department, 1111 E. McDowell Rd, Phoenix, AZ 85006Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Gerard Hoatam Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Emily Schmidt Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Michael Leher Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Arathi Gorur Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Anna Jones Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Anantharam Kalya Norton Thoracic Institute-St. Joseph’s Hospital & Medical Center, Phoenix, USA.
  • Priya Radhakrishnan Creighton School of Medicine, Phoenix Regional Campus - St. Joseph’s Hospital & Medical Center, Phoenix, USA.

DOI:

https://doi.org/10.5195/ijms.2017.18

Keywords:

Heart failure, population characteristics, risk assessment, quality improvement, medical student

Abstract

Background: Heart failure (HF), the leading cause of hospitalization in adults over the age of 65, is a difficult-to-treat syndrome associated with high morbidity and mortality. Home-monitoring programs may help reduce HF-associated morbidity, but can be difficult to establish in smaller clinical settings. In this quality improvement project, we identified local patients at high risk of HF-related morbidity and hospitalizations, then implemented a medical student-based constant-contact program to encourage their follow-through on self-care.

Methods: Between June 2012 and September 2014, our clinic treated 197 patients for systolic or diastolic HF. These patients’ baseline characteristics were evaluated for trends that increased their risk for hospitalization. Of the high-risk patients identified (n=80), 12 (15%) were enrolled in the project. An 8-week constant-contact intervention was initiated through weekly calls. Patients’ health statuses were recorded and the importance of self-care was reiterated.

Results: High-risk HF patients were identified based on >10 clinic visits during the study period; 3 were lost to follow-up. Each patient completed two questionnaires at the study’s beginning and conclusion, with response rates of 67% (6/9) and 56% (5/9). Most participants reported symptom improvement and increased knowledge about their conditions.

Conclusion: Our preliminary population-guided, medical-student initiated intervention in a small clinical setting was designed to increase patient understanding and compliance and to improve HF symptoms. Although the study was limited by its low participation rate, drastic improvements in self-reported outcomes were noted among participants. A larger study with similar positive outcomes could ultimately influence follow-up methods.

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References

1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. Heart disease and stroke statistics--2014 update: a report from the American Heart Association. Circulation. 2014; 129(3): e28-e292.
2. Lepage S. Acute decompensated heart failure. Canadian J Cardiol. 2008; 24 Suppl B: 6B-8B.
3. Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, et al. Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009; 119(3): 480-6.
4. Writing Group M, Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, et al. Heart disease and stroke statistics--2010 update: a report from the American Heart Association. Circulation. 2010; 121(7): e46-e215.
5. Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013; 6(3): 606-19.
6. Cohen S. Differentials in the concentration of health expenditures across population subgroups in the U.S., 2012: Agency for Healthcare Research and Quality; 2014.
7. Hasselman D. Super-utilizer Summit Common Themes from Innovative Complex Care Management Programs: Center for Health Care Strategies; 2013.
8. Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for High-Need, High-Cost Patients - An Urgent Priority. New Engl J Med. 2016; 375(10): 909-11.
9. Borde D, Pinkney J, Leverence R. How we promoted sustainable super-utilizer care through teamwork and taking time to listen. N Engl J Med Catalystp.
10. Mercer T, Bae J, Kipnes J, Velazquez M, Thomas S, Setji N. The highest utilizers of care: individualized care plans to coordinate care, improve healthcare service utilization, and reduce costs at an academic tertiary care center. J Hosp Med. 2015; 10(7): 419-24.
11. Fried LP, Kronmal RA, Newman AB, Bild DE, Mittelmark MB, Polak JF, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA. 1998; 279(8): 585-92.
12. Krieger N. Racial and gender discrimination: risk factors for high blood pressure? Soc Sci Med. 1990; 30(12): 1273-81.
13. Lantz PM, Lynch JW, House JS, Lepkowski JM, Mero RP, Musick MA, et al. Socioeconomic disparities in health change in a longitudinal study of US adults: the role of health-risk behaviors. Soc Sci Med. 2001; 53(1): 29-40.
14. Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003; 129(5): 674-97.
15. Rozanski A, Blumenthal JA, Davidson KW, Saab PG, Kubzansky L. The epidemiology, pathophysiology, and management of psychosocial risk factors in cardiac practice: the emerging field of behavioral cardiology. J Am Coll Cardiol. 2005; 45(5): 637-51.
16. Jaarsma T, Halfens R, Huijer Abu-Saad H, Dracup K, Gorgels T, van Ree J, et al. Effects of education and support on self-care and resource utilization in patients with heart failure. Eur Heart J. 1999; 20(9): 673-82.
17. Koelling TM, Johnson ML, Cody RJ, Aaronson KD. Discharge education improves clinical outcomes in patients with chronic heart failure. Circulation. 2005; 111(2): 179-85.
18. Krumholz HM, Amatruda J, Smith GL, Mattera JA, Roumanis SA, Radford MJ, et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol. 2002; 39(1): 83-9.
19. Ni H, Nauman D, Burgess D, Wise K, Crispell K, Hershberger RE. Factors influencing knowledge of and adherence to self-care among patients with heart failure. Arch Intern Med. 1999; 159(14): 1613-9.
20. Rich MW, Beckham V, Wittenberg C, Leven CL, Freedland KE, Carney RM. A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. New Engl J Med. 1995; 333(18): 1190-5.
21. Stromberg A. The crucial role of patient education in heart failure. Eur J Heart Fail. 2005; 7(3): 363-9.
22. Yip MP, Chang AM, Chan J, MacKenzie AE. Development of the Telemedicine Satisfaction Questionnaire to evaluate patient satisfaction with telemedicine: a preliminary study. J Telemed Telecare. 2003; 9(1): 46-50.
23. de Lorgeril M, Salen P, Martin JL, Monjaud I, Delaye J, Mamelle N. Mediterranean diet, traditional risk factors, and the rate of cardiovascular complications after myocardial infarction: final report of the Lyon Diet Heart Study. Circulation. 1999; 99(6): 779-85.
24. Forman DE, Butler J, Wang Y, Abraham WT, O'Connor CM, Gottlieb SS, et al. Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol. 2004; 43(1): 61-7.
25. Giamouzis G, Kalogeropoulos A, Georgiopoulou V, Laskar S, Smith AL, Dunbar S, et al. Hospitalization epidemic in patients with heart failure: risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail. 2011; 17(1): 54-75.
26. Kalantar-Zadeh K, Block G, Horwich T, Fonarow GC. Reverse epidemiology of conventional cardiovascular risk factors in patients with chronic heart failure. J Am Coll Cardiol. 2004; 43(8): 1439-44.
27. Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation. 2003; 108(17): 2154-69.
28. Clark RA, Inglis SC, McAlister FA, Cleland JG, Stewart S. Telemonitoring or structured telephone support programmes for patients with chronic heart failure: systematic review and meta-analysis. BMJ. 2007; 334(7600): 942.
29. Shah NB, Der E, Ruggerio C, Heidenreich PA, Massie BM. Prevention of hospitalizations for heart failure with an interactive home monitoring program. Am Heart J. 1998; 135(3): 373-8.
30. Wheeler EC, Waterhouse JK. Telephone interventions by nursing students: improving outcomes for heart failure patients in the community. J Community Health Nurs. 2006; 23(3): 137-46.

Published

2017-08-31

How to Cite

Reichert, W. B., Hoatam, G., Schmidt, E., Leher, M., Gorur, A., Jones, A., Kalya, A., & Radhakrishnan, P. (2017). Student Heart Failure Intervention Pilot (SHIP): A Study of Risk Factor Analytics and Population Outreach. International Journal of Medical Students, 5(2), 68–73. https://doi.org/10.5195/ijms.2017.18