How a New Risk Calculator Can Reduce CV Readmission After a Heart Failure Hospitalization

In this podcast, Parag Goyal, MD, MSc, talks about the association between cardiac comorbidities and cardiovascular readmission among patients who had been hospitalized for heart failure, including a cardiac comorbidity count that was found to be independently associated with an increased risk for CV readmission.

Additional Resources:

1. Visaria A, Balkan L, Pinheiro LC, et al. Relation of a simple cardiac co-morbidity count and cardiovascular readmission after a heart failure hospitalization. Am J Cardiol. 2020;125(10)1529-1535. doi:10.1016/j.amjcard.2020.02.018
2. Yale-New Haven Hospital. Center for Outcomes Research and Evaluation. Readmission risk score for heart failure. Accessed June 18, 2020. https://www.readmissionscore.org/heart_failure.php
3. Peterson PN, Rumsfeld JS, Liang L, et al; American Heart Association Get With the Guidelines–Heart Failure Program. Circ Cardiovasc Qual Outcomes. 2010;3(1):25-32. doi:10.1161/CIRCOUTCOMES.109.854877
4. Healthcare Cost and Utilization Project. Nationwide Readmissions Database overview. Accessed June 18, 2020. https://www.hcup-us.ahrq.gov/nrdoverview.jsp

Parag Goyal, MD, MSc, is an assistant professor of medicine and director of the Heart Failure with Preserved Ejection Fraction Program at Weill Cornell Medicine in New York, New York.

TRANSCRIPTION:

Cardiology Consultant: Hello everyone, and welcome to another installment of Podcasts360—your go-to resource for medical news and clinical updates. I'm your moderator, Colleen Murphy, with Consultant360 specialty network. According to the Centers for Disease Control and Prevention’s latest numbers, about 6.5 million adults in the United States have heart failure. Readmission following a heart failure hospitalization is not uncommon. So how can readmissions among this patient population be reduced? Researches recently evaluated whether a simple cardiac comorbidity counts could identify individuals at high risk for cardiovascular readmission following a heart failure hospitalization. Dr Parag Goyal was one of the coauthors of the study. Dr. Goyal, who is an assistant professor of medicine and the director of the Heart Failure with Preserve Rejection Fraction Program at Weill Cornell Medicine, joins me today to highlight the efficacy of this simply cardiac comorbidity count and why you may want to consider implementing the risk calculator into your practice. Thank you for talking with me today, Dr Goyal.

Parag Goyal: It’s a pleasure to be here and chat with you today. Thanks for having me.

C360: Can you briefly review the risk calculators that are currently available to determine risk for readmission following a heart failure hospitalization? And what are their shortcomings on providing information for cause-specific readmission?

PG: Yeah, sure. So, heart failure readmissions obviously is a pretty hot topic in cardiology and in heart failure, given the impact that it has on patients in terms of their quality of life, as well as cost for the health care system. And so there's been a lot of interest in trying to reduce our readmission rates specifically for heart failure. And one strategy that people have implemented is, “Well, let's figure out which patients are at highest risk of coming back to the hospital, of being readmitted.” So there are actually a host of risk calculators for readmissions. Yale has developed one. There's one from a large national cohort called Get With The Guidelines. And, I mean, I think broadly speaking, they're valuable and they can identify patients at high risk. But I think there are two major challenges with the preexisting risk calculators—and there are a host of other risk calculators that have been developed across the country. One is a lot of them include multiple different variables with waiting. And when I say that, what it means is you can't just at the bedside calculate a number and determine risk for readmission. It typically requires a calculator or a computer to help you figure out. So you plug in lots and lots of variables, and it spits out a number, and then you have a sense of what this particular patient risk is for readmission, And I think in reality, the extra steps that it takes means the calculators like that, unfortunately, are not frequently used by clinicians, And so, one of the limitations is that lots of calculators out there are simply not used. I think the more sophisticated, the more variables that are in there, the more accurate, it will be. But it comes at a cost of it not being as practical as it could be.

I think that if you if you had a calculator that could assess risk with a level of 90, 95% accuracy, then maybe these complex risk calculators would be value will be would be more valuable. But the reality is most of these risk calculators only are accurate about—depending on which calculator, depending on which setting—somewhere between 60% and 70% accurate. So it's only it's better than a flip of the coin, but not a ton better than a flip of the coin

C360: What would you say is the benefit to understanding cause-specific readmission risk?

PG: When people talk about readmission, it's important to understand well, why did they end up going back to the hospital? And for patients who have heart failure and are hospitalized with heart failure, when they come back into the hospital, the presumption, or the thought is that, “Oh, well, they're going back for heart failure.” But the reality is, data would show pretty consistently that only a quarter of the time—so only 1 out of 4 times—are they actually going back for heart failure. All the other times, they're not going back for heart failure, they're going back for some other reason. And it's a host of other reasons. It could be infection. It could be another cardiovascular cause like chest pain or an arrhythmia. And so, as we try to think about A, predicting who's going to be rehospitalized and B, developing strategies to prevent people from coming back to the hospital, I think it is really, really important to understand why they're coming back and start to think about which patients are being readmitted for heart failure, which patients are being readmitted for a nonheart failure but other cardiovascular reason, and which patients are being readmitted for a noncardiovascular reason. And by figuring out which patients are going to be readmitted for which problem, we can tailor our posthospitalization strategies or therapies, so we can minimize the risk they come back to the hospital.

C360: You kind of actually went into my next question of what was the goal of your research? And specifically, why did you choose to focus on a simple cardiac comorbidity count?

PG: Yeah, I think the prior comments started to address this. The goal here is—taking a step back, there are lots of calculators that exist. The calculators are complex, the calculators are not particularly good at predicting who's going to come back, and for the most part the calculators don't identify why they're going to come back. And so, if we could come up with a calculator that was simple, that performed reasonably well–at least similar to the preexisting calculators—and calculators that not just identified a readmission but cause-specific readmission, whether it was cardiovascular readmission or was it going to be a noncardiovascular readmission—it would allow us to then identify subgroups of patients and subsequently apply or implement tailored strategies for each individual patient.

So that is ultimately the goal, to ultimately move move away from a one-size-fits-all approach to trying to reduce heart failure readmissions, which I think we really done over the past several years and we've seen that it doesn't work. The same approach for everybody does not work. So we need to move toward a more tailored, personalized approach. And so this was a baby step in that in that direction.

C360: Can we talk a little bit about how you performed your evaluation of whether this count would be effective, and maybe some of your key findings?

PG: Again, we wanted to really focus on something simple and something that clinicians could calculate or figure out at the bedside. And I thought about my own practice and thought about, well, when I take care of patients in the hospital, what are the factors that make me more concerned that this patient is going to come back for a cardiovascular cause vs a patient who is going to come back for a noncardiovascular cause? And the 3 major factors that just clinically I felt would be important were the presence of coronary artery disease, the presence of an atrial arrhythmia like atrial fibrillation, and the presence of a ventricular arrhythmia. And so our hypothesis was that the presence of any of these conditions would increase the risk of a patient coming back to the hospital for a cardiovascular cause. And so we said, “Well, why don't we just create the simple count, where you just counted the number of these 3 cardiac comorbid conditions?” And then what we were interested in examining here was whether or not that simple count was associated with cause-specific readmission, in particular cardiovascular readmission. And so we looked at the nationwide Readmissions Database, which is a large nationally representative database of hospitalizations across the country that has data on readmissions. And we looked specifically at patients hospitalized with heart failure. And we essentially examined the association between this count and 3 different outcomes. We looked at a cardiovascular readmission, we looked at noncardiovascular readmission, and we looked at no remission. And ultimately what we found in our analysis is that this concept of a simple cardiac comorbidity count–which is from 0 to 3—was indeed associated with cardiovascular readmission. In other words, this count could be used to identify a population that was at higher risk of being readmitted for a cardiovascular reason, supporting the concept that this score could be used in clinical practice to figure out, OK, well, if this patient has a higher risk of being readmitted for a cardiovascular reason, then now I should implement processes or strategies that specifically hone in on this set of comorbid conditions for this patient.

C360: You also found that cardiac comorbidity count was not independently associated with noncardiovascular readmission. Can you touch on this facet of your results?

PG: Our goal was to examine the association between cardiac comorbidity, and specifically cardiovascular readmission. And I think that it was important to also examine whether this cardiac comorbidity count was associated with noncardiovascular readmission because if it was, then this count wouldn't help differentiate between these 2 types of readmission. The whole premise of this study was to identify the patients that were at highest risk specifically of cardiovascular as compared to noncardiovascular. And so in addition to looking at the association with cardiovascular, we also looked for an association with non-cardiovascular, and we found that the cardiac comorbidity count was not associated with noncardiovascular. All of that supports the concept that this count specifically hones in on cardiovascular readmission.

C360: ow taking everything that you just presented, how do you suggest that the listeners here implement these findings into their everyday practice?

PG: I mean I think point 1–and this has been established, but is worth emphasizing‑point 1 is that many among the heart failure patients, after a hospitalization, many of their readmissions are not for heart failure. In fact, half of them are for noncardiovascular causes, and so that's the first point. The second point is—and this is what was supported by our findings—is that the more cardiovascular conditions you have at baseline, the more likely it is that you will be readmitted for a cardiovascular cause. And I think that is intuitive, but I think it's a really important point because, as mentioned earlier, in order for us to start reducing readmissions for heart failure patients, it's time for us to move away from a one-size-fits-all and start to tailor our therapies to the likely reason that patients are going to be readmitted. And so if you have a patient that's going to likely be readmitted for heart failure or a cardiovascular cause, you might consider a more intense cardiovascular care after they leave the hospital. For example, having them see a cardiologist soon after discharge might make some sense in this patient. Implementing remote patient monitoring with perhaps a scale or a pulse oximeter which can check oxygen and heart rate, maybe a blood pressure cuff, might make some sense for a patient like that. On the other hand, if they have a low cardiac comorbidity count, where they are less likely to be readmitted for a cardiovascular cause but more likely to be readmitted for a noncardiovascular cause, then maybe, maybe, the cardiologist, the remote patient monitoring is less important, and what's more important is the primary care doctor who can help manage the diabetes, the chronic pain, the osteoarthritis, the chronic obstructive pulmonary disease. And so that's sort of how I would summarize the key findings here. And the hope is that this will stimulate further research that will promote this concept of personalized therapy postdischarge.

C360: I think that’s an interesting point to leave off on, that there is a need to move toward a more tailored, personalized approach and this count may be part of the answer in doing that. So, Dr Goyal, I want to thank you for joining me today. And I hope our listeners found what you presented here to be helpful.

PG: Thanks for the opportunity to chat today.