Search

BACKGROUNDThe use of traditional models to predict heart failure (HF) has limitations in preventing HF hospitalizations. Artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine only have limited data published regarding HF populations, with none assessing the favorability of decongestive therapy aquapheresis (AQ). AI and ML can be leveraged to design non-traditional models to identify those who are at high risk of HF readmissions.OBJECTIVESThis study aimed to develop a model for pretreatment identification of risk for 90-day HF events among HF patients who have undergone AQ.METHODSUsing data from the AVOID-HF (Aquapheresis versus Intravenous Diuretics and Hospitalization for Heart Failure) trial, we designed a ML-based predictive model that can be used before initiating AQ to anticipate who will respond well to AQ and who will be at high risk of future HF events.RESULTSUsing ML we identified the top ten predictors for 90-day HF events. Interestingly, the variable for ‘intimate relationships with loved ones’ strongly predicted response to therapy. This ML-model was more successful in predicting the outcome in HF patients who were treated with AQ. In the original AVOID-HF trial, the overall 90-day HF event rate in the AQ arm was 32%. Our proposed predictive model was accurate in anticipating 90-day HF events with better statistical accuracy (area under curve 0.88, sensitivity 80%, specificity 75%, negative predictive value 90%, and positive predictive value 57%).CONCLUSIONSML can help identify HF patients who will respond to AQ therapy. Our model can identify super-respondents to AQ therapy and predict 90-day HF events better than currently existing traditional models.CONDENSED ABSTRACTUtilizing data from the AVOID-HF trial, we designed a ML-predictive model that can be used before initiating AQ to anticipate who will respond well to AQ and who will be at high risk of future HF events. Using ML, we identified the top 10 predictors for 90-day HF events. Our model can identify super-respondents to ultrafiltration therapy and predict 90-day HF events better than currently existing traditional models.

How do political communities stick together as polities in times of crisis? What is needed to motivate their citizens work together to deter threats, maintain state monopolies of violence, and sustain different forms of military alliances and cooperation? The answer, in large part, is patriotism. Patriotic thinking—both as a sentiment and as an element of political ideology—is a central tenet in building states and conducting state affairs. From the perspective of political philosophy, patriotism is often characterized as love for one’s country and as an affective relationship imbued with such emotions as pride, honor, and appreciation for the collective that it represents. Scholars from the tradition of republican patriotism argue that such love may be directed toward political institutions and that it helps form a way of life that sustains the liberty of a state’s population. This stands in contrast to nationalism, which regards thesame object of veneration, the homeland, with less tolerance of heterogeneity and political disunion. Both patriotism and nationalism are conceptualized in a variety of ways and can take on meanings as varied as benign civic unity and exclusionary chauvinism. Patriotism is often conceptualized as a form of civic virtue, stressing the way it prioritizes the community ahead of the individual.