As a student at the University of Pittsburgh School of Nursing, Karina Kraevsky (PhD ’25) learned an alarming statistic that continues to drive her career.
“One of the things that was shocking to me was learning that it takes, on average, 17 years for research to be applied in a clinical setting,” she said. “Finding ways to translate research to the bedside faster is deeply meaningful to me because it allows us to apply the latest scientific evidence to patient care sooner.”
Now a fellow in Pitt’s School of Medicine and a critical care nurse at UPMC, Kraevsky has received national recognition for her efforts to put research into practice. In November 2025, she earned a competitive award from the American Heart Association (AHA) for proposing an algorithm-based tool that could improve the treatment of patients experiencing heart failure.
Awarded to one registered nurse with a PhD each year, the Martha N. Hill Early Career Investigator Award recognizes new investigators who have made significant contributions to the study, prevention and treatment of cardiovascular diseases. After submitting a manuscript, Kraevsky was invited to present her work as an award finalist at the AHA’s Scientific Sessions in New Orleans. Later that day, she learned she had won the prestigious award.
“I was there in real-time learning that I won, so it was pretty incredible to be on stage and hear my name called out,” she said. “To me, this award highlights the magnitude of progress that has been made in nursing research, and it was validating to learn that other people think the things I’m interested in are relevant and important.”
In her winning presentation, Kraevsky highlighted a decision support tool that could help clinicians identify the underlying cause of dyspnea, or difficulty breathing, in patients with heart failure who come to the emergency room. The tool would leverage machine learning methods and data collected by nurses within the first 15 minutes of a patient’s visit to provide quick insights, enabling health care providers to deliver more timely and personalized treatment and potentially saving more lives.
Millions of people around the world experience shortness of breath as a symptom of asthma, anxiety, pregnancy, heart attacks and dozens of other conditions, posing a challenge for clinicians tasked with identifying the problem. This is even trickier in patients with heart failure because they often have multiple co-existing chronic illnesses, or comorbidities, which can cause dyspnea. The clinical risk scores used in many emergency departments are limited in their ability to accurately identify the underlying cause of dyspnea, according to Kraevsky. Successful treatment hinges on rapidly leveraging the best available information—and that’s where an algorithm shines.
“If somebody comes into the ER with shortness of breath and the algorithm says they're more likely to be having heart failure exacerbation, that testing and treatment looks very different from somebody with pneumonia, for example,” Kraevsky explained.
Kraevsky first encountered this issue while working in a cardiac intensive care unit—her first job after earning her bachelor’s degree in nursing from the University of Tennessee at Chattanooga. She loved helping patients but felt frustrated by “questions that nobody else was answering.” After attending a conference and meeting Pitt Nursing Professor Emeritus Marilyn Hravnak, who studied cardiovascular acute and critical care, Kraevsky decided to confront these questions head-on by enrolling in the PhD program at Pitt Nursing.
“I knew that Pitt Nursing has been one of the top-performing schools for research, so that was definitely a big draw for me,” Kraevsky said.
As she continues her career at Pitt, Kraevsky plans to explore how data-driven methods can advance the understanding and care of patients with cardiovascular illness. While her presentation for the AHA award focused on training models, she said she would love the chance to test her decision support tool with new patient data someday. Ultimately, she hopes her work will yield practical applications that can improve patient care.
"I'm hoping to put all of this together by looking into risk stratification of patients with cardiovascular illness across clinical settings and outcomes,” she said. “I'm in the process of figuring out how to merge all of the things that I love.”