RATIONALE, AIMS AND OBJECTIVES: Some objective indicators like symptoms, @entity137 , performance status, rate of hospitalization or re-employment have been already employed in scientific literature as proxies of Quality of Life assessment, and, in spite of the intrinsic limitations of such a measurement, they represent a valuable source of information in all the situations where a formal assessment is impossible, due to budget, time or @entity1 resources constrains. We concentrate here on some models for the analysis of frequency of hospitalization data and we discuss an application to the @entity121 Disease Study Group data. METHODS: A sample of 235 @entity1 with @entity384 ( @entity384 ) prospectively treated at the Department of Cardiology (Trieste, Italy) have been observed during a period of 18 years, from 1978 to 1992 and data regarding hospitalization history were collected. The hospitalization process depends on the time since the last event, and usually is a function of a set of explanatory variables, such as the current state of the @entity1 , treatments he has been receiving and the severity of disease. We propose here a semi-Markov representation of the hospitalization process, and we analyze data regarding @entity384 , implementing Exponential, Weibull, and Cox models; in Cox models we take care also of the stratification according to the duration or to the levels of the state factor. RESULTS: The probability of experiencing a second hospitalization within one year after the first one is estimated about 0.50, and within two years about 0.30. After this point the probability remains constant at a 0.10 level. The same pattern is observed for the second hospitalization, while things are getting worse after the third hospitalization, when the probability of not having a subsequent hospitalization is about 0.10 within one year. Betablockers have a strong influence in enlarging the time interval spent between an hospitalization and the other. CONCLUSIONS: The hospitalization process can be viewed only as a rough approximation of the good standing of the @entity1 . However, for diseases like @entity384 can be reasonable, because of the relatively fast increment in the worsening conditions of the @entity1 and the consequently high chances of observing new hospitalizations up to the absorbing state (the death). Moreover a very detailed modeling of the process leads to extract as much information as possible from the data.

Hospitalization rate as a proxy for the quality of life evaluation: the case of XXXX .

@entity1 :: ('9606', 'Species') :: ['human', 'patients', 'patient'] @entity384 :: ('MESH:D002311', 'Disease') :: ['DCM', 'dilated cardiomyopathy'] @entity137 :: ('MESH:D064420', 'Disease') :: ['toxicity'] @entity121 :: ('MESH:D009135', 'Disease') :: ['Hearth Muscle']
@entity384 :: (MESH:D002311,Disease) :: ['dilated cardiomiopathy']
