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.
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We record the following procedures: heart circulatory system shock, ICD (Inplantable Cardioverter Defribillator), CABG (Coronary Artery Bypass Grafting) or PTCA (Percutaneous Transluminal Coronary Angioplasty)
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.
Direct link to download the package: https://CRAN.R-project.org/package=msmtools
We fictitiously indicate the last state in which we see a patient.
The comorbidity score does not intervene in this transition.
AHRQ (2015) Quality indicators, heart failure mortality rate, technical specifications, version 5.0, 2015;. http://www.qualityindicators.ahrq.gov/downloads/modules/IQI/v50/techspecs/IQI_16_heart_failure_mortality_rate.pdf
Andersen PK, Keiding N (2002) Multi-state models for event history analysis. Stat Methods Med Res 11 (2):91–115
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
Cox DR, Miller H (1965) The theory of stochastic processes. Chapman and Hall
Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B Methodol 34(2):187–220
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
Dowle M, Short T, Lianoglou S (2014) With contributions from R. Saporta, A.S., Antonyan, E.: data.table: extension of data.frame. http://CRAN.r-project.org/package=data.table
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
Gavrielov-Yusim N, Friger M (2013) Use of administrative medical databases in population-based research. J Epidemiol Community Health pp jech–2013
Grimes DA (2010) Epidemiologic research using administrative databases: garbage in, garbage out. Obstet Gynecol 116(5):1018–1019
Grossetti F (2016) Building augmented data for multi-state models: the msmtools package (work in progress)
Hoover K, Tao G, Kent C, Aral S (2011) Epidemiologic research using administrative databases: garbage in, garbage out. Obstet Gynecol 117(3):729–730
Hougaard P (1999) Multi-state models: a review. Lifetime Data Anal 5(3):239–264
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
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
Jackson CH (2011) Multi-state models for panel data: the msm package for R. J Stat Softw 38(8):1–29
Jackson CH Multi-state modeling with r: the msm package. msm package vignette available at https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf
Jackson CH (2016) flexsurv: a platform for parametric survival modeling in R. J Stat Softw 70(8):1–33
Maggioni AP (2015) Epidemiology of heart failure in Europe. Heart Fail Clin 11(4):625–635
Mazzali C, Duca P (2015) Use of administrative data in healthcare research. Intern Emerg Med 1–8
Ministero della Salute (1997) ICD9-CM Italian version. http://www.salute.gov.it
Nash JC (1990) Compact numerical methods for computers: linear algebra and function minimisation. CRC Press
Nguyen LL, Barshes NR (2010) Analysis of large databases in vascular surgery. J Vasc Surg 52(3):768–774
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
Pope GC, Kautter J, Ingber MJ, Freeman S, Sekar R, Newhart C (2011) Evaluation of the cms-hcc risk adjustment model final report. https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/downloads/evaluation_risk_adjmodel_2011.pdf
Putter H, Fiocco M, Geskus R, et al. (2007) Tutorial in biostatistics: competing risks and multi-state models. Stat Med 26(11):2389
R Core Team (2016) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria - http://www.r-project.org/
Therneau TM (2015) A package for survival analysis in S. http://CRAN.r-project.org/package=survival
Titman AC, Sharples LD (2009) Model diagnostics for multi-state models. Stat Methods Med Res 19(6):621–651
World Health Organization (2015) The international classification of diseases system used to classify the different type of diagnosis. http://www.who.int/classifications/icd/en/
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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). https://doi.org/10.1007/s10729-017-9400-z
- Administrative data
- Chronic heart failure
- Multi-state models
- Survival analysis
- Data management