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This paper presents the development of a new predictive model of elders at risk of suffering hospital admissions in the subsequent year in Valencia (Spain), based on primary care experts’ discussions and consensus. The study involves three main stages. Firstly, Focus Group methodology with six primary care experts to design the first set of variables of the model. Subsequently, two retrospective studies to analyse the performance of the selected variables in a pilot sample (n=107) and to design the predictive model in a development sample (n=343). Data was collected from electronic medical records and consulting the professional of reference. Logistic regression analysis identified five variables as predictors of hospital admissions during the subsequent year of the development cohort: diagnosis of chronic heart diseases, chronic respiratory diseases, diabetes, presence of palliative care and number of previous visits to the hospital emergency department. A risk scoring system was developed for each patient from 0 to 1, with a cut-off point of 0. 5. The model had a sensitivity of 42%, specificity of 96% and AUC of 0. 764. Our predictive model identifies with moderate efficiency elderly patients at risk of suffering future hospital admissions. Additionally, this first screening could be extended through a second phase aimed to assess social variables which are very relevant in the current economic context in Spain. Further research is needed to validate these results with larger samples, and to explore their applicability in other health and social care settings.

Development of a Predictive Model of Elderly Patients at Risk of Future Hospital Admission at Primary Care Centres in Valencia (Spain)