Skip to main content

A multi-state approach to patients affected by chronic heart failure

The value added by administrative data


Healthcare administrative databases are becoming more and more important and reliable sources of clinical and epidemiological information. They are able to track several interactions between a patient and the public healthcare system. In the present study, we make use of data extracted from the administrative data warehouse of Regione Lombardia, a region located in the northern part of Italy whose capital is Milan. Data are within a project aiming at providing a description of the epidemiology of Heart Failure (HF) patients at regional level, to profile health service utilization over time, and to investigate variations in patient care according to geographic area, socio-demographic characteristic and other clinical variables. We use multi-state models to estimate the probability of transition from (re)admission to discharge and death adjusting for covariates which are state dependent. To the best of our knowledge, this is the first Italian attempt of investigating which are the effects of pharmacological and outpatient cares covariates on patient’s readmissions and death. This allows to better characterise disease progression and possibly identify what are the main determinants of a hospital admission and death in patients with Heart Failure.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    We record the following procedures: heart circulatory system shock, ICD (Inplantable Cardioverter Defribillator), CABG (Coronary Artery Bypass Grafting) or PTCA (Percutaneous Transluminal Coronary Angioplasty)

  2. 2.

    We record many comorbidities like: metastatic, dementia, renal, weight loss, hemiplegia, alcohol abuse, tumor, arrhythmia, hypertension, pulmonary disease, coagulopathy, diabetes, anemia, electrolytes, liver, Peripheral Arterial Disease (PVD), psychosis, pulmonary circulation, HIV/AIDS.

  3. 3.

    Direct link to download the package:

  4. 4.

    We fictitiously indicate the last state in which we see a patient.

  5. 5.

    The comorbidity score does not intervene in this transition.


  1. 1.

    AHRQ (2015) Quality indicators, heart failure mortality rate, technical specifications, version 5.0, 2015;.

  2. 2.

    Andersen PK, Keiding N (2002) Multi-state models for event history analysis. Stat Methods Med Res 11 (2):91–115

    Article  Google Scholar 

  3. 3.

    Castañeda J, Gerritse B (2010) Appraisal of several methods to model time to multiple events per subject: modeling time to hospitalizations and death. Revista Colombiana de Estadística 33(1):43–61

    Google Scholar 

  4. 4.

    Cox DR, Miller H (1965) The theory of stochastic processes. Chapman and Hall

  5. 5.

    Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B Methodol 34(2):187–220

  6. 6.

    de Wreede LC, Fiocco M, Putter H, et al. (2011) mstate: an R package for the analysis of competing risks and multi-state models. J Stat Softw 38(7):1–30

  7. 7.

    Dowle M, Short T, Lianoglou S (2014) With contributions from R. Saporta, A.S., Antonyan, E.: data.table: extension of data.frame.

  8. 8.

    Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S (2011) A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol 64(7):749–759

    Article  Google Scholar 

  9. 9.

    Gavrielov-Yusim N, Friger M (2013) Use of administrative medical databases in population-based research. J Epidemiol Community Health pp jech–2013

  10. 10.

    Grimes DA (2010) Epidemiologic research using administrative databases: garbage in, garbage out. Obstet Gynecol 116(5):1018–1019

    Article  Google Scholar 

  11. 11.

    Grossetti F (2016) Building augmented data for multi-state models: the msmtools package (work in progress)

  12. 12.

    Hoover K, Tao G, Kent C, Aral S (2011) Epidemiologic research using administrative databases: garbage in, garbage out. Obstet Gynecol 117(3):729–730

    Article  Google Scholar 

  13. 13.

    Hougaard P (1999) Multi-state models: a review. Lifetime Data Anal 5(3):239–264

    Article  Google Scholar 

  14. 14.

    Ieva F, Gale CP, Sharples LD (2014) Contemporary roles of registries in clinical cardiology: when do we need randomized trials? Expert Rev Cardiovasc Ther 12(12):1383–1386

    Article  Google Scholar 

  15. 15.

    Ieva F, Jackson CH, Sharples LD (2015) Multi-state modeling of repeated hospitalisation and death in patients with heart failure: the use of large administrative databases in clinical epidemiology. Statistical Methods in Medical Research, SAGE publications. doi:10.1177/0962280215578777

  16. 16.

    Jackson CH (2011) Multi-state models for panel data: the msm package for R. J Stat Softw 38(8):1–29

    Article  Google Scholar 

  17. 17.

    Jackson CH Multi-state modeling with r: the msm package. msm package vignette available at

  18. 18.

    Jackson CH (2016) flexsurv: a platform for parametric survival modeling in R. J Stat Softw 70(8):1–33

    Article  Google Scholar 

  19. 19.

    Maggioni AP (2015) Epidemiology of heart failure in Europe. Heart Fail Clin 11(4):625–635

    Article  Google Scholar 

  20. 20.

    Mazzali C, Duca P (2015) Use of administrative data in healthcare research. Intern Emerg Med 1–8

  21. 21.

    Ministero della Salute (1997) ICD9-CM Italian version.

  22. 22.

    Nash JC (1990) Compact numerical methods for computers: linear algebra and function minimisation. CRC Press

  23. 23.

    Nguyen LL, Barshes NR (2010) Analysis of large databases in vascular surgery. J Vasc Surg 52(3):768–774

    Article  Google Scholar 

  24. 24.

    Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, Falk V, González-Juanatey JR, Harjola V, Jankowska EA,, et al. (2015) 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure

  25. 25.

    Pope GC, Kautter J, Ingber MJ, Freeman S, Sekar R, Newhart C (2011) Evaluation of the cms-hcc risk adjustment model final report.

  26. 26.

    Putter H, Fiocco M, Geskus R, et al. (2007) Tutorial in biostatistics: competing risks and multi-state models. Stat Med 26(11):2389

    Article  Google Scholar 

  27. 27.

    R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria -

  28. 28.

    Therneau TM (2015) A package for survival analysis in S.

  29. 29.

    Titman AC, Sharples LD (2009) Model diagnostics for multi-state models. Stat Methods Med Res 19(6):621–651

  30. 30.

    World Health Organization (2015) The international classification of diseases system used to classify the different type of diagnosis.

Download references

Author information



Corresponding author

Correspondence to Francesco Grossetti.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Grossetti, F., Ieva, F. & Paganoni, A.M. A multi-state approach to patients affected by chronic heart failure. Health Care Manag Sci 21, 281–291 (2018).

Download citation


  • Administrative data
  • Chronic heart failure
  • Multi-state models
  • Survival analysis
  • Data management