PLoS One. 2017 Jun 7;12(6):e0179176. doi: 10.1371/journal.pone.0179176.

Multi-state modelling of heart failure care path: A population-based investigation from Italy.

Gasperoni F1, Ieva F1, Barbati G2,3, Scagnetto A2, Iorio A3,4, Sinagra G5, Di Lenarda A3.

1 MOX-Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milano, Italy.
2 Department of Medical Sciences, Università di Trieste, Trieste, Italy.
3 Cardiovascular Center, Trieste, Italy.
4 Cardiology Unit, Papa Giovanni XXIII Hospital, Bergamo, Italy.
5 Cardiovascular Department, Azienda Sanitaria-Universitaria Integrata Trieste ‘ASUITS’, Trieste, Italy.

 

Abstract

BACKGROUND:

How different risk profiles of heart failure (HF) patients can influence multiple readmissions and outpatient management is largely unknown. We propose the application of two multi-state models in real world setting to jointly evaluate the impact of different risk factors on multiple hospital admissions, Integrated Home Care (IHC) activations, Intermediate Care Unit (ICU) admissions and death.

METHODS AND FINDINGS:

The first model (model 1) concerns only hospitalizations as possible events and aims at detecting the determinants of repeated hospitalizations. The second model (model 2) considers both hospitalizations and ICU/IHC events and aims at evaluating which profiles are associated with transitions in intermediate care with respect to repeated hospitalizations or death. Both are characterized by transition specific covariates, adjusting for risk factors. We identified 4,904 patients (4,129 de novo and 775 worsening heart failure, WHF) hospitalized for HF from 2009 to 2014. 2,714 (55%) patients died. Advanced age and higher morbidity load increased the rate of dying and of being rehospitalized (model 1), decreased the rate of being discharged from hospital (models 1 and 2) and increased the rate of inactivation of IHC (model 2). WHF was an important risk factor associated with hospital readmission.

CONCLUSION:

Multi-state models enable a better identification of two patterns of HF patients. Once adjusted for age and comorbidity load, the WHF condition identifies patients who are more likely to be readmitted to hospital, but does not represent an increasing risk factor for activating ICU/IHC. This highlights different ways to manage specific patients’ patterns of care. These results provide useful healthcare support to patients’ management in real world context. Our study suggests that the epidemiology of the considered clinical characteristics is more nuanced than traditionally presented through a single event.

PMID: 28591172

 

Supplement

The use of multi state models for the analysis of the patterns of care in chronic diseases is a very promising approach. In fact, the joint modeling of process indicators and outcomes of interest allow for more complete investigations of complex phenomena like those produced in elder patients by chronic complex diseases like Heart Failure (HF).

This contribution is important because targets clinical databases as a specific class of administrative databases, showing a concrete example of how integration among clinical and administrative data make epidemiological investigations possible, meaningful and effective [1].

In fact, in countries where a public/national health service is present, healthcare administrative data come from the automatic storage of billing records, drugs receipts, hospitals admissions to be reimbursed, and so on. They address then firstly operational goals, but are increasingly used for clinical and epidemiological ones (see, among others, [2], [3], [4], [5] and [6]). Extracting meaningful information from such data holds unparalleled potential for epidemiology and healthcare management, since they can inform policies, driving healthcare decisions towards good practices which improve patients health status and assistance and, at the same time, optimize costs. Indeed, data like those analyzed into the paper can give a complete frame of the whole healthcare system, both from patients’ (therapies, prognosis and pharmacoepidemiologic information) and providers’ side (policies’ efficiency, cost-effectiveness of hospitalizations and procedures, providers profiling). Moreover, the size of the sample collected in these data warehouses allows to reach real world conclusions.

Despite the advantages of such research practice, a big issue when administrative data have to be used in clinical research is their accessibility. For this reason, we would like to thank Regione Friuli Venezia Giulia and Dr. Loris Zanier for providing data access.

 

References

[1] Schneeweiss, S. (2014) Learning from Big Healthcare Data. N Engl J Med, 370(23).

[2] Motheral, B. R., Fairman, K.A. (1997) The Use of Claims Databases for Outcomes Research: Rationale, Challenges, and Strategies. Clin Ther; 19(2).

[3] Ieva, F., Jackson, C.H., Sharples, L.D. (2017) Multi-State modelling of repeated hospitalisation and death in patients with Heart Failure: the use of large administrative databases in clinical epidemiology. Stat Methods Med Res, 26(3): 1350-1372

[4] Mazzali, C., Paganoni, A.M., Ieva, F., Masella, C., Maistrello, M., Agostoni, O., Scalvini, S., Frigerio, M. (2016) Methodological issues on the use of administrative data in healthcare research: the case of heart failure hospitalizations in Lombardy Region, 2000 to 2012. BMC Health Serv. Res., 16 (1): 234

[5] Ekin, T., Ieva, F., Ruggeri, F., Soyer, R. (2017) On the Use of the Concentration Function in Medical Fraud Assessment. The American Statistician, 71(3): 236-241.

[6] Iorio, A., Senni, M., Barbati, G., Greene, S.J., Poli, S., Zambon, E., Di Nora, C., Cioffi, G., Tarantini, L., Gavazzi, A., Sinagra, G., Di Lenarda, A.  (2018) Prevalence and Prognostic Impact of Noncardiac Comorbidities in Heart Failure Outpatients with Preserved and Reduced Ejection Fraction: A Community-Based Study. European Journal of Heart Failure, to appear.