A new screening tool for electronic medical records accurately identifies patients who are at high risk of having or developing progressive scarring of the lungs, a condition called idiopathic pulmonary fibrosis, according to researchers from Weill Cornell Medicine, NewYork-Presbyterian , the University of Chicago, Brigham and Women’s Hospital and Mayo Clinic.
Idiopathic pulmonary fibrosis (IPF) is usually fatal, in part because it tends to be diagnosed late, when existing treatments are less effective. The new screening tool, described in a Sept. 29 article in Nature Medicine, has the potential to make early detection of IPF routine.
“Having a robust screening method based on parameters readily available from electronic medical records is a major step forward in ensuring early diagnosis,” said study co-author Dr. Fernando Martinez, professor of internal medicine. Bruce Webster at Weill Cornell Medicine and Chief of the Division of Pulmonary and Critical Care Medicine at Weill Cornell Medicine and NewYork-Presbyterian/Weill Cornell Medical Center.
“This tool literally takes no extra minutes [of patients’ time] and can recognize disease features before symptoms appear,” said study co-author Dr. Ishanu Chattopadhyay, assistant professor of medicine at the University of Chicago. “As soon as someone schedules a visit with their primary care physician, the program can run the screening tool and get the results before the patient even walks into the clinic.”
FPI affects tens of thousands of people in the United States and several million people worldwide. It is characterized by the progressive decline of lung function, due to chronic lung inflammation that transforms healthy lung tissue into scar-like fibrous tissue. The trigger for IPF is ‘idiopathic’ – unknown – but risk factors include older age, male sex and a history of smoking.
There are anti-fibrotic drug treatments for IPF that can prolong the lives of patients. However, these treatments tend to work better earlier in the course of the disease, whereas IPF is usually diagnosed a few years after the onset of symptoms. These symptoms, which are variable and nonspecific but often include slowly progressive shortness of breath on exertion, may not be noticed by patients or may be overlooked as part of natural aging. Additionally, the tests needed to confirm IPF are cumbersome and expensive, which tends to discourage patients from undergoing them until symptoms become evident.
“Traditionally, diagnosing IPF requires a multidisciplinary approach with pulmonary clinicians, radiologists, and laboratory and pathology specialists,” said study co-author Dr. Andrew Limper, professor of medicine and chairman. of the Division of Pulmonary and Critical Care Medicine at the Mayo Clinic. .
The new software screening tool is designed to streamline and speed up the diagnostic process by automatically detecting high risk of IPF, based on a patient’s electronic health records. In principle, it would primarily be used by primary care physicians, who would refer patients identified by the algorithm to the tests needed to confirm or rule out IPF.
“This work is novel in that information already captured in the medical record is used to identify patients in the system who may be at higher risk,” said co-author Dr. Gary “Matt” Hunninghake. Director of the Interstitial Lung Disease Program. at Brigham and Women’s Hospital.
The algorithm at the heart of the software, developed using machine learning processes, calculates an IPF risk score (Zero-burden Co-Morbidity Risk Score for IPF, or ZCoR-IPF) by looking for IPF risk factors known and IPF-related events — even IPF-related sequences of events — in the patient’s health records covering the previous two years.
The team trained the algorithm on a commercially available insurance claims database covering millions of patients in the United States between 2003 and 2018, then validated it using three datasets. additional claims. In total, the development of the tool was based on the files of nearly 3 million patients, including more than 54,000 cases of IPF.
Validation testing indicated that the tool was sensitive and specific enough to identify patients at high risk for IPF. Those with risk scores above the detection threshold were more than 30 times more likely to be diagnosed with IPF within the next year, compared to unscreened patients.
The researchers now hope to roll out the tool to primary care centers where it can be evaluated in real-world settings and formal clinical trials. They also expect the same approach, marrying machine learning algorithms and electronic health records, could be applied to other disorders where earlier diagnoses would save many lives.
“This approach is a paradigm shift in IPF screening,” said Dr. Martinez.
Many physicians and scientists at Weill Cornell Medicine maintain relationships and collaborate with external organizations to foster scientific innovation and provide expert advice. The institution makes this information public for the sake of transparency. For this information, see the profile of Dr. Fernando Martinez.