Arq. Bras. Cardiol. 2021; 117(4): 624-625
Predictive Models to Enhance our Knowledge: Is It Necessary?
Marcia M. Noya-Rabelo
, Diogo Freitas Cardoso de Azevedo, Olga Ferreira de Souza
This Short Editorial is referred by the Research article "A Simple Clinical Risk Score to Predict Post-Discharge Mortality in Chinese Patients Hospitalized with Heart Failure".
Heart Failure (HF) is a condition with an adverse prognosis; 1-year mortality rates in population-based studies have been reported to be 35% to 40%. – In Brazil, BREATHE registry, large sample of hospitalized patients with decompensated heart failure from different regions, demonstrated high intrahospital mortality (12.6%). Although HF admission decreased from 2008 to 2017, it was observed increased rate of length of stay for heart failure in hospitalized patients. Accurate prognostic information may enhance our ability to predict outcomes, thus informing decisions for patients. Numerous demographics, clinical and biochemical variables have been reported to provide important prognostic value in patients with heart failure, and several predictive models have been developed. Therefore, knowledge of mortality predictors can be used to generate predictive models that can aid clinicians’ decision making by identifying patients who are at high or low risk of death.
Although most previous models are comprehensive and well-validated, there are still some limitations. First, some models were constructed in the period before the recent guideline-directed pharmacological treatment. Second, most previous models aimed to predict the short-term mortality rate. In addition, most were built using population in Western countries. As we have already known, factors that predict mortality in the community setting may differ.
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