Diastolic blood pressure
Creatinine, a chemical waste product of creatine, an amino acid, excreted in urine
Blood urea nitrogen, a waste product produced as a result of digestion of protein; an indicator of kidney function
Hemoglobin, a protein responsible for transporting oxygen in blood
White blood cell count
Platelets, a type of blood cell that helps form clots to stop bleeding
Albumin, a liver-produced protein that helps keep fluid in the bloodstream and not leak into other tissues
Red blood cell distribution
Yagil said the newly developed model was able to accurately predict life expectancy 88 percent of the time and performed substantially better than other popular published models. The results are published online in the November 12, 2019 edition of European Journal of Heart Failure .
“This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year,” said Adler. “This is incredibly valuable. It allows us to make informed decisions based on a proven methodology and not have to look into a crystal ball.”
The tool was additionally tested using de-identified patient data from the University of California San Francisco and a data base derived from 11 European medical centers. “It was successful in those cohorts as well,” said Yagil. “Being able to repurpose our findings in independent populations is of utmost importance, thus validating our methodology and its results.”
“The development of the risk score marks an important step forward for us,” said Greenberg. “Not only did we demonstrate that we could accurately predict outcomes in heart failure patients, we were able to generate the score from the patient electronic medical record data base at UC San Diego Health. We now know how to utilize this data base to address other questions that are of vital importance to our patients.”
All three of the investigators said the partnership between physicists and cardiologists was critical to developing a reliable tool and extensive knowledge and experiences from both sides proved synergetic.
“It’s been a wonderful collaboration with two groups that don’t usually join forces,” said Adler. “Our findings need further validation, but we are thrilled to have these results to build upon. Avi has a first-hand perspective as a patient and a strong motivation to help improve existing medical strategies and approaches. Working with him has been a highlight of my career.”
“We taught Avi how to think as a cardiologist and he taught us how to think as a physicist would,” said Greenberg. “The insights learned have greatly influenced my perspective on how to utilize big data to accomplish important clinical research goals.”
