Introduction. Traditionally, predictive models of in-hospital mortality in ischemic stroke have focused on individual patient variables, to the neglect of in-hospital contextual variables. In addition, frequently used scores predict more the risk of sequelae than mortality, and, to date, there has only been anecdotal use of structural equations in elaborating such measures.
Aims. The aim of this paper was to analyze the joint predictive weight of the following: (1) individual factors (age, gender, obesity and epilepsy) on the mediating factors of arrhythmias, dyslipidemia, hypertension) and ultimately death (exitus); (2) contextual in-hospital factors (year and existence of a stroke unit), on mediating factors (number of diagnoses, procedure and length of stay) and re-admission, as determinants of death; (3) certain factors in predicting others.
Material and methods. Retrospective cohort study through observational analysis of all hospital stays of Diagnosis Related Group (DRG) 14, nonlysed ischemic stroke, during the time period 2008-2012. The sample consisted of a total of 186,245 hospital stays, taken from the Minimum Basic Data Set (MBDS) upon discharge from Spanish hospitals. MANOVAs were carried out to establish the linear effect of certain variables on others. These formed the basis for building the SEM model, with the corresponding parameters and restrictive indicators.
Results. A consistent model of causal predictive relationships between the postulated variables was obtained. One of the most interesting effects was the predictive value of contextual variables on individual ones, especially the indirect effect of the existence of stroke units on reducing procedures, readmission and in-hospital mortality.
Conclusion. Contextual variables, and specifically the availability of stroke units, make a positive impact on individual variables that affect prognosis and mortality in ischemic stroke. Moreover, the determination of this impact is feasible through the use of structural equation methodology. We analyze the methodological and clinical implications of this type of study for hospital policies.
Key words: Stroke; mortality; structural equation model; predictive model