open access

Vol 25, No 6 (2018)
Original articles — Clinical cardiology
Published online: 2018-12-31
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Impact of non-cardiovascular disease burden on thirty-day hospital readmission in heart failure patients

Valentina Kutyifa, John Rice, Roy Jones, Andrew Mathias, Ayhan Yoruk, Katherine Vermilye, Brent Johnson, Robert Strawderman, Charles Lowenstein
DOI: 10.5603/CJ.2018.0147
·
Pubmed: 30600831
·
Cardiol J 2018;25(6):691-700.

open access

Vol 25, No 6 (2018)
Original articles — Clinical cardiology
Published online: 2018-12-31

Abstract

Background: Little is known about the impact of non-cardiovascular disease (CVD) burden on 30- -day readmission in heart failure (HF) patients. The aim of the study was to assess the role of non-CVD burden on 30-day readmission in HF patients. \

Methods: We analyzed the effect of non-CVD burden by frequency of ICD-9 code categories on readmis­sions of patients discharged with a primary diagnosis of HF. We first modeled the probability of readmis­sion within 30 days as a function of demographic and clinical covariates in a randomly selected training dataset of the total cohort. Variable selection was carried out using a bootstrap LASSO procedure with 1000 bootstrap samples, the final model was tested on a validation dataset. Adjusted odds ratios and confidence intervals were reported in the validation dataset.

Results: There were a total of 6228 HF hospitalizations, 1523 (24%) with readmission within 30 days of discharge. The strongest predictor for 30-day readmissions was any hospital admission in the prior year (p < 0.001). Cardiovascular risk factors did not enter the final model. However, digestive system diseases increased the risk for readmission by 17% for each diagnosis (p = 0.046), while respiratory diseases and genitourinary diseases showed a trend toward a higher risk of readmission (p = 0.07 and p = 0.09, respectively). Non-CVDs out-competed cardiovascular covariates previously reported to predict readmission.

Conclusions: In patients with HF hospitalization, prior admissions predicted 30-day readmission. Diseases of the digestive system also increase 30-day readmission rates. Assessment of non-CVD burden in HF patients could serve as an important risk marker for 30-day readmissions.

Abstract

Background: Little is known about the impact of non-cardiovascular disease (CVD) burden on 30- -day readmission in heart failure (HF) patients. The aim of the study was to assess the role of non-CVD burden on 30-day readmission in HF patients. \

Methods: We analyzed the effect of non-CVD burden by frequency of ICD-9 code categories on readmis­sions of patients discharged with a primary diagnosis of HF. We first modeled the probability of readmis­sion within 30 days as a function of demographic and clinical covariates in a randomly selected training dataset of the total cohort. Variable selection was carried out using a bootstrap LASSO procedure with 1000 bootstrap samples, the final model was tested on a validation dataset. Adjusted odds ratios and confidence intervals were reported in the validation dataset.

Results: There were a total of 6228 HF hospitalizations, 1523 (24%) with readmission within 30 days of discharge. The strongest predictor for 30-day readmissions was any hospital admission in the prior year (p < 0.001). Cardiovascular risk factors did not enter the final model. However, digestive system diseases increased the risk for readmission by 17% for each diagnosis (p = 0.046), while respiratory diseases and genitourinary diseases showed a trend toward a higher risk of readmission (p = 0.07 and p = 0.09, respectively). Non-CVDs out-competed cardiovascular covariates previously reported to predict readmission.

Conclusions: In patients with HF hospitalization, prior admissions predicted 30-day readmission. Diseases of the digestive system also increase 30-day readmission rates. Assessment of non-CVD burden in HF patients could serve as an important risk marker for 30-day readmissions.

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Keywords

heart failure readmission, non-cardiovascular disease burden, predictive model

About this article
Title

Impact of non-cardiovascular disease burden on thirty-day hospital readmission in heart failure patients

Journal

Cardiology Journal

Issue

Vol 25, No 6 (2018)

Pages

691-700

Published online

2018-12-31

DOI

10.5603/CJ.2018.0147

Pubmed

30600831

Bibliographic record

Cardiol J 2018;25(6):691-700.

Keywords

heart failure readmission
non-cardiovascular disease burden
predictive model

Authors

Valentina Kutyifa
John Rice
Roy Jones
Andrew Mathias
Ayhan Yoruk
Katherine Vermilye
Brent Johnson
Robert Strawderman
Charles Lowenstein

References (36)
  1. Go A, Mozaffarian D, Roger V, et al. Heart disease and stroke statistics — 2014 update. Circulation. 2014; 129(3): e28–e292.
  2. Ross JS, Chen J, Lin Z, et al. Recent national trends in readmission rates after heart failure hospitalization. Circ Heart Fail. 2010; 3(1): 97–103.
  3. Dharmarajan K, Wang Y, Lin Z, et al. Association of changing hospital readmission rates with mortality rates after hospital discharge. JAMA. 2017; 318(3): 270–278.
  4. Krumholz H, Lin Z, Keenan P, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013; 309(6): 587–593.
  5. Rathore SS, Masoudi FA, Wang Y, et al. Socioeconomic status, treatment, and outcomes among elderly patients hospitalized with heart failure: findings from the National Heart Failure Project. Am Heart J. 2006; 152(2): 371–378.
  6. Kutyifa V, Moss AJ, Klein H, et al. Use of the wearable cardioverter defibrillator in high-risk cardiac patients: data from the Prospective Registry of Patients Using the Wearable Cardioverter Defibrillator (WEARIT-II Registry). Circulation. 2015; 132(17): 1613–1619.
  7. Team RCR. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2015.
  8. Fitzmaurice GM, Laird NM, Ware H. Applied longitudinal analysis Hoboken, N J. 2004.
  9. Efron B. Bootstrap Methods: Another Look at the Jackknife. Ann Statist. 1979; 7(1): 1–26.
  10. Tibshirani R. Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met. 1996; 58: 267–88.
  11. Bristow M, Saxon L, Boehmer J, et al. Cardiac-Resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure. N Engl J Med. 2004; 350(21): 2140–2150.
  12. Keenan PS, Normand SLT, 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(1): 29–37.
  13. Krumholz HM, Chen YT, Wang Y, et al. Predictors of readmission among elderly survivors of admission with heart failure. Am Heart J. 2000; 139(1 Pt 1): 72–77.
  14. Davis JD, Olsen MA, Bommarito K, et al. All-Payer analysis of heart failure hospitalization 30-day readmission: comorbidities matter. Am J Med. 2017; 130(1): 93.e9–93.e28.
  15. Chen LM, Jha AK, Guterman S, et al. Hospital cost of care, quality of care, and readmission rates: penny wise and pound foolish? Arch Intern Med. 2010; 170(4): 340–346.
  16. Tokatli A, Ural D. Previous hospitalizations predict both hospital readmissions and mortality in patients with heart failure. Cardiol J. 2016; 23(2): 224–224.
  17. Swindle JP, Chan WW, Waltman Johnson K, et al. Evaluation of mortality and readmissions following hospitalization with heart failure. Curr Med Res Opin. 2016 [Epub ahead of print]: 1–11.
  18. Desai A, Stevenson L. Rehospitalization for heart failure. Circulation. 2012; 126(4): 501–506.
  19. Sherer AP, Crane PB, Abel WM, et al. Predicting heart failure readmissions. J Cardiovasc Nurs. 2016; 31(2): 114–120.
  20. Rigolli M, Whalley GA. Heart failure with preserved ejection fraction. J Geriatr Cardiol. 2013; 10(4): 369–376.
  21. Sharma K, Kass DA. Heart failure with preserved ejection fraction: mechanisms, clinical features, and therapies. Circ Res. 2014; 115(1): 79–96.
  22. Vasko MR, Cartwright DB, Knochel JP, et al. Furosemide absorption altered in decompensated congestive heart failure. Ann Intern Med. 1985; 102(3): 314–318.
  23. Sica DA, Sica DA. Drug absorption in congestive heart failure: impact on management. Prog Cardiovasc Nurs. 1999; 14(1): 30–32.
  24. Smilde TDJ, Hillege HL, Voors AA, et al. Prognostic importance of renal function in patients with early heart failure and mild left ventricular dysfunction. Am J Cardiol. 2004; 94(2): 240–243.
  25. Lang CC, Mancini DM. Non-cardiac comorbidities in chronic heart failure. Heart. 2007; 93(6): 665–671.
  26. Liang KV, Williams AW, Greene EL, et al. Acute decompensated heart failure and the cardiorenal syndrome. Crit Care Med. 2008; 36(1 Suppl): S75–S88.
  27. Ross JS, Mulvey GK, Stauffer B, et al. Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med. 2008; 168(13): 1371–1386.
  28. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011; 306(15): 1688–1698.
  29. Frizzell JD, Liang Li, Schulte PJ, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol. 2017; 2(2): 204–209.
  30. Krumholz H, Bernheim S. The Role of Socioeconomic Status in Hospital Outcomes Measures. Ann Intern Med. 2015; 162(9): 670.
  31. Priori SG, Blomstrom-Lundqvist C, Mazzanti A, 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: 2793–2867.
  32. Huynh Q, Negishi K, Blizzard L, et al. Predictive score for 30-day readmission or death in heart failure. JAMA Cardiol. 2016; 1(3): 362–364.
  33. Calvillo-King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013; 28(2): 269–282.
  34. Mesquita ET, Cruz LN, Mariano BM, et al. Post-Hospital syndrome: a new challenge in cardiovascular practice. Arq Bras Cardiol. 2015; 105(5): 540–544.
  35. Krumholz HM. Post-hospital syndrome--an acquired, transient condition of generalized risk. N Engl J Med. 2013; 368(2): 100–102.
  36. Khan H, Kalogeropoulos AP, Georgiopoulou VV, et al. Frailty and risk for heart failure in older adults: the health, aging, and body composition study. Am Heart J. 2013; 166(5): 887–894.

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