Identifying risk individuals for heart failure diagnosis within two years in the adult population in southern Sweden using gender, age, multimorbidity level, and socioeconomic status

ObjectiveHeart failure (HF) is a common disease among elderly individuals and is associated with poor quality of life and prognosis. Individuals at risk of developing HF are often already patients in primary healthcare, but it is often a difficult diagnosis at an early stage. Identifying patients at high risk for HF and initiating early treatment is crucial for their outcomes. Using the variables gender, age, multimorbidity (MM) level, and socioeconomic status (SES), we aimed to study the possibility of identifying individuals at high risk for HF diagnosis within two years in the adult population in southern Sweden. DesignA register-based, cross-sectional cohort study. Setting and subjects961,190 inhabitants aged 20 years and older without a HF diagnosis living in southern Sweden (Region Sk[a]ne) in 2015. Main outcome measurePredicting the risk for HF diagnosis within two years in Southern Sweden using the variables gender, age, MM level, and SES. SES was measured as CNI (Care Need Index) percentiles depending on the individuals listed at the primary healthcare centre. ResultsAge, MM level, and SES were added to the logistic model along with gender in steps. Each model was compared with the previous model using a likelihood-ratio test, which were all significant, resulting in an increased AUC (area under the curve) from 0.5144 to 0.9379. Age was the most important factor to predict the probability of HF diagnosis within two years. The second most important factor in our study was MM level. Both gender and SES improved the model significantly. ConclusionsAge was the most important factor to predict probability of HF diagnosis within two years. The second most important factor in our study was MM level. Both gender and SES improved the model and its AUC significantly. Using the combination of these four variables was shown to be a possible tool to identify elderly individuals at high risk for HF diagnosis with high positive and negative predictive values in the adult population aged 70 years and older.