Cutting through the long coronavirus fog — Harvard Gazette

Although previous diagnostic studies suggest that 7% of the population suffers from long-term COVID-19 infection. General Brigham Mass Research has shown that it is much higher than 22.8%.

AI-based tools can sift through electronic medical records to help clinicians identify long-term COVID-19 cases. Often puzzling symptoms can include a series of symptoms. persistent symptomsfatigue, chronic cough, brain fog, etc. after SARS-CoV-2 infection.

The algorithm used was developed by extracting anonymized patient data from the clinical records of approximately 300,000 patients across 14 hospitals and 20 community health centers in the Massachusetts General Brigham System. of result, The findings, published in the journal Med, could identify more people who need to receive treatment for this potentially debilitating condition.

“Our AI tools have the potential to transform the foggy diagnostic process into something crisp and focused, giving clinicians the power to understand difficult conditions,” said the senior author. Hossein EstiriDirector of AI Research at MGB's Center for Learning Healthcare Systems AI and Biomedical Informatics (CAIBILS) and Associate Professor at Harvard Medical School. “With this study, we may finally know what long-term COVID-19 infections really are and, more importantly, how to treat them.”

For the purposes of the study, Estilli et al. defined long-lasting coronavirus as: Diagnosis of exclusion That too Infectious disease related. This means that the diagnosis could not be explained by the patient's specific medical records and was related to COVID-19. In addition, the diagnosis had to have been ongoing for at least 2 months during the 12-month follow-up period.

A new method called “precision phenotyping” developed by Estiri et al. scrutinizes an individual's records to identify symptoms and conditions associated with COVID-19 and to distinguish symptoms from other illnesses. Track symptoms over time. For example, the algorithm can detect whether shortness of breath is due to a pre-existing condition such as heart failure or asthma, rather than a long-term COVID-19 infection. Only when all other possibilities have been exhausted will the tool flag a patient as having long-term COVID-19 infection.

“Physicians are often faced with the challenge of juggling a complex web of symptoms and medical histories, unsure of which threads to pull, while balancing busy caseloads. If we had the tools to do that systematically on behalf of our patients, that could be a game-changer,” said co-lead author and internal medicine resident at Brigham and Women's Hospital, a founding member of the Massachusetts General Brigham Health System. A certain Arale Azir said. .

The new tool's patient-centered diagnosis may also help alleviate bias built into current diagnosis of long-term coronavirus, researchers say, adding official ICD-10 diagnosis codes for long-term coronavirus pointed out that diagnoses tend to be directed toward patients with easier access to medical care.

The researchers said their tool was about 3 percent more accurate, yet less biased, than the data captured by ICD-10 codes. Specifically, their study found that unlike long-term coronavirus algorithms that rely on a single diagnosis code or individual clinical experience, individuals identified as long-term coronavirus patients reflect the broader population composition of Massachusetts. and demonstrated biasing results toward certain groups, such as those with greater access to health care. care.

“This broader reach ensures that marginalized communities, who are often excluded from clinical research, are no longer visible,” Estilli said.

Limitations of the study and the AI ​​tools include that the health record data used by the algorithm to describe long-term coronavirus symptoms may be less complete than the data that doctors collect in clinical notes after a visit. It includes something. Another limitation is that the algorithm did not capture possible worsening of previous conditions that may have been symptoms of long-term coronavirus. For example, if a patient had COPD and it worsened before developing COPD-19, the algorithm could have removed that episode, even if it was a long-term COVID indicator. The decline in COVID-19 testing in recent years has also made it difficult to determine when a patient first became infected with COVID-19.

The study was limited to patients in Massachusetts.

Future studies may investigate the algorithm in cohorts of patients with specific conditions such as COPD or diabetes. The researchers also plan to make the algorithm publicly available in open access so that doctors and health systems around the world can use it on their patient populations.

In addition to opening the door to better clinical care, this study could lay the foundation for future research into the genetic and biochemical factors behind the different subtypes of the long-lasting coronavirus. be. “Questions about the true burden of long-term COVID-19, questions that have remained elusive until now, now seem more within reach,” Estilli said.

Support was provided by the National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIAID) R01AI165535, National Heart, Lung, and Blood Institute (NHLBI) OT2HL161847, and National Center for the Advancement of Translational Sciences (NCATS) UL1 TR003167, UL1 TR001881 . , U24TR004111. J. Hügel's research was partially funded by a fellowship within the IFI program of the German Academic Exchange Service (DAAD), the Federal Ministry of Education and Research (BMBF), and the German Research Foundation (426671079).