Potentially inappropriate medications for patients with heart failure and risk of hospitalization from heart failure: A casecontrol study from Thailand

Main Article Content


Potentially Inappropriate Medication List, Heart Failure, In-hospital mortality, Hospitalization, Case-Control Studies


Background: Thailand have developed a list of potentially inappropriate medications for patients with heart failure (PIMHF). However, its association with clinical outcomes has not been evaluated in real-world clinical practice. Objective: To examine the association between the prescription of any PIMHF and hospitalization from heart failure (HF). Methods: A 1:1 matched case-control study was conducted. Data on HF patients visiting the study hospitals during 2017-2019 were obtained from the electronic medical record database. Patients with a history of first hospitalization due to HF and those with a history of outpatient department visits or non-HF hospitalization were defined as cases and controls, respectively. The association of hospitalization from HF with the prescription of any PIMHF was expressed as the adjusted odds ratio (aOR) and 95% confidence interval (95%CI), calculated using a conditional logistic regression (CLR) model. Results: After matching, 1,603 pairs of case and control were generated for the analysis. In total, 21 of 47 PIMHF were found to have been prescribed. Compared with the reference group of patients not prescribed any of the 21 PIMHF, those who had been prescribed a PIMHF had an aOR of 1.47 [95%CI 1.02:2.13]. NSAIDs/COX-2 inhibitors, oral short-acting beta-2 agonists, medications that promote fluid overload, and medications that elevate blood pressure were the four medication classes associated with the increased risk of hospitalization from HF (aOR = 2.64, [95%CI 1.30:5.38], aOR = 4.87, [95%CI 1.17:20.29], aOR = 1.50, [95%CI 1.01:2.22], and aOR = 2.51, [95%CI 1.26:4.99], respectively). Conclusions: The prescription of any of the 21 PIMHF found to have been prescribed in this study may increase the risk of hospitalization from HF. The Thai PIMHF list may be used in pharmacy practice as an assessment tool for the appropriate use of medication in HF patients.


Download data is not yet available.
Abstract 289 | pdf Downloads 298


1. Virani SS, Alonso A, Benjamin EJ, et al. The American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2020 update: a report from the American Heart Association. Circulation. 2020;141(9):e139-e596. https://doi.org/10.1161/cir.0000000000000746
2. Strategy and planning division. Pubic health statistics A.D.2018. Bangkok. Samcharoen Panich. 2018;157p.
3. Laothavorn P, Hengrussamee K, Kanjanavanit R, et al. Thai acute decompensated heart failure registry (Thai ADHERE). CVD Prev Control. 2010;5:89-95. https://doi.org/10.1016/j.cvdpc.2010.06.001
4. Dunlay SM, Redfield MM, Weston SA, et al. Hospitalizations after heart failure diagnosis: a community perspective. J Am Coll Cardiol. 2009;54(18):1695-702.
5. Chun S, Tu JV, Wijeysundera HC, et al. Lifetime analysis of hospitalizations and survival of patients newly admitted with heart failure. Circ Heart Fail. 2012;5(4):414-21. https://doi.org/10.1161/circheartfailure.111.964791
6. Jenghua K, Jedsadayanmata A. Rate and predictors of early readmission among Thai patients with heart failure. J Med Assoc Thai. 2011;94(7):782-8.
7. Boonrat S, Moleerergpoom W. Predictors of 6-month recurrent admission or death in patients with acute decompensated heart failure in police general hospital. Thai Heart J. 2012;25:87-94.
8. Gheorghiade M, Vaduganathan M, Fonarow GC, et al. Rehospitalization for heart failure: problems and perspectives. J Am Coll Cardiol. 2013;61(4):391-403. https://doi.org/10.3410/f.717973342.793476620
9. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-28. https://doi.org/10.1016/s0749-4041(09)79176-6
10. Kossovsky MP, Sarasin FP, Perneger TV, et al. Unplanned readmissions of patients with congestive heart failure: do they reflect in-hospital quality of care or patient characteristics? Am J Med. 2000;109(5):386-90. https://doi.org/10.1016/s0002-9343(00)00489-7
11. Arora S, Patel P, Lahewala S, et al. Etiologies, trends, and predictors of 30-day readmission in patients with heart failure. Am J Cardiol. 2017;119(5):760-9. https://doi.org/10.3410/f.727348423.793530293
12. Metra M, Cotter G, El-Khorazaty J, et al. Acute heart failure in the elderly: differences in clinical characteristics, outcomes, and prognostic factors in the VERITAS Study. J Card Fail. 2015;21(3):179-88. https://doi.org/10.1016/j.cardfail.2014.12.012
13. Corrao G, Ghirardi A, Ibrahim B, et al. Short-and long-term mortality and hospital readmissions among patients with new hospitalization for heart failure: A population-based investigation from Italy. Int J Cardiol. 2015;181:81-7. https://doi.org/10.1016/j.ijcard.2014.12.004
14. Davison BA, Metra M, Senger S, et al. Patient journey after admission for acute heart failure: length of stay, 30‐day readmission and 90‐day mortality. Eur J Heart Fail. 2016;18(8):1041-50. https://doi.org/10.1002/ejhf.540
15. Cowie MR, Anker SD, Cleland JGF, et al. Improving care for patients with acute heart failure: before, during and after hospitalization. ESC Heart Fail. 2014;1(2):110-45. https://doi.org/10.1002/ehf2.12021
16. Braunstein JB, Anderson GF, Gerstenblith G, et al. Noncardiac comorbidity increases preventable hospitalizations and mortality among Medicare beneficiaries with chronic heart failure. J Am Coll Cardiol. 2003;42(7):1226-33. https://doi.org/10.1016/s0735-1097(03)00947-1
17. Lang CC, Mancini DM. Non-cardiac comorbidities in chronic heart failure. Heart. 2007;93(6):665-71. https://doi.org/10.1136/hrt.2005.068296
18. Dahlstrom U. Frequent non-cardiac comorbidities in patients with chronic heart failure. Eur J Heart Fail. 2005;7(3):309-16. https://doi.org/10.1016/j.ejheart.2005.01.008
19. Abete P, Testa G, Della-Morte D, et al. Treatment for chronic heart failure in the elderly: current practice and problems. Heart Fail Rev. 2013;18(4):529-51. https://doi.org/10.1007/s10741-012-9363-6
20. Dunlay SM, Eveleth JM, Shah ND, et al. Medication adherence among community-dwelling patients with heart failure. Mayo Clin Proc. 2011;86(4):273-81. https://doi.org/10.4016/27977.01
21. Niriayo YL, Kumela K, Kassa TD, et al. Drug therapy problems and contributing factors in the management of heart failure patients in Jimma University Specialized Hospital, Southwest Ethiopia. PloS one. 2018;13(10):e0206120. https://doi.org/10.1371/journal.pone.0206120
22. Kennel PJ, Kneifati-Hayek J, Bryan J, et al. Prevalence and determinants of hyperpolypharmacy in adults with heart failure:an observational study from the National Health and Nutrition Examination Survey (NHANES). BMC Cardiovasc Disord. 2019;19(1):76. https://doi.org/10.1186/s12872-019-1058-7
23. Reed BN, Rodgers JE, Sueta CA. Polypharmacy in heart failure: drugs to use and avoid. Heart Fail Clin. 2014;10(4):577-90. https://doi.org/10.1016/j.hfc.2014.07.005
24. Page II RL, O’Bryant CL, Cheng D, et al. The American Heart Association Clinical Pharmacology and Heart Failure and Transplantation Committees of the Council on Clinical Cardiology; Council on Cardiovascular Surgery and Anesthesia; Council on Cardiovascular and Stroke Nursing; and Council on Quality of Care and Outcomes Research.Drugs that may cause or exacerbate heart failure: a scientific statement from the American Heart Association. Circulation. 2016;134(6):e32-69.
25. Page RL, Lindenfeld J. The comorbidity conundrum: a focus on the role of noncardiovascular chronic conditions in the heart failure patient. Curr Cardiol Rep. 2012;14(3):276-84. https://doi.org/10.1007/s11886-012-0259-9
26. Masoudi FA, Baillie CA, Wang Y, et al. The complexity and cost of drug regimens of older patients hospitalized with heart failure in the United States, 1998-2001. Arch Intern Med. 2005;165(18):2069-76. https://doi.org/10.1001/archinte.165.18.2069
27. Bermingham M, Ryder M, Travers B, et al. The St Vincent’s potentially inappropriate medicines study: development of a disease-specific consensus list and its evaluation in ambulatory heart failure care. Eur J Heart Fail. 2014;16(8):915-22. https://doi.org/10.1002/ejhf.132
28. Jenghua K, Chinwong S, Chinwong D, et al. Development of a list of potentially inappropriate medications for patients with heart failure (PIMHF). Res Social Adm Pharm. 2021;17(5):894-903. https://doi.org/10.1016/j.sapharm.2020.07.021
29. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;1130-9. https://doi.org/10.1097/01.mlr.0000182534.19832.83
30. Saczynski JS, Andrade SE, Harrold LR, et al. A systematic review of validated methods for identifying heart failure using administrative data. Pharmacoepidemiol Drug Saf. 2012;21(S1):129-40. https://doi.org/10.1002/pds.2313
31. World Health Organization. International Statistical Classification ofDiseases and Related Health Problems 10th Revision Version for 2016. Geneva2016.
32. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. https://doi.org/10.1016/0021-9681(87)90171-8
33. Buakhamsri A, Chirakarnjanakorn S, Sanguanwong S, et al. Heart Failure Council of Thailand (HFCT) 2019 heart failure guideline: pharmacologic treatment of chronic heart failure-part I. J Med Assoc Thai. 2019;102:240-4.
34. Yingchoncharoen T, Kanjanavanich R. Heart Failure Council of Thailand (HFCT) 2019 heart failure guideline: pharmacologic treatment of chronic heart failure-part II. J Med Assoc Thai. 2019;102(3):368-72.
35. Dupont WD. Power calculations for matched case-control studies. Biometrics. 1988;1157-68. https://doi.org/10.2307/2531743 
36. Intarut N, Lertmaharit S. Analysis of matched case-control study using conditional Logistic Regression Model. DMBN E Journal. 2006;2(1):19-26.
37. Kosainate S. Multicolinearity: examples in binary logistic regression. DMBN E Journal. 2006;2(1):9-17.
38. Mann C. Observational research methods-Cohort studies, cross sectional studies, and case-control studies. Afr J Emerg Med. 2012;2(1):38-46. https://doi.org/10.1016/j.afjem.2011.12.004
39. Quartey G, Feudjo‐Tepie M, Wang J, et al. Opportunities for minimization of confounding in observational research. Pharm Stat. 2011;10(6):539-47. https://doi.org/10.1002/pst.528
40. Bermingham M, O’Callaghan E, Dawkins I, et al. Are beta2‐agonists responsible for increased mortality in heart failure?. Eur J Heart Fail. 2011;13(8):885-91. https://doi.org/10.1093/eurjhf/hfr063
41. Hawkins NM, Petrie MC, MacDonald MR, et al. Heart failure and chronic obstructive pulmonary disease: the quandary of betablockers and beta-agonists. J Am Coll Cardiol. 2011;57(21):2127-38.
42. Chirakarnjanakorn S, Krittayaphong R, Chantrarat T, et al. Heart Failure Council of Thailand (HFCT) 2019 Heart Failure Guideline: Comorbidity in Heart Failure. J Med Assoc Thai. 2019;102(4):508-12.