RECOVER—Researching COVID to Enhance Recovery—is a nationwide initiative dedicated to understanding why some people develop long-term symptoms following COVID-19 infection. Recently, for the first time, the project yielded outcomes that are expected to help standardize the definition of long COVID toward these goals.

The researchers created a scoring system based on the symptoms that most clearly distinguished patients previously infected with COVID-19 from those who had not. Their work was published online May 25 in the Journal of the American Medical Association.

“This initiative is driven by our shared vision to deepen our understanding of one of the most perplexing maladies of our times and to inform recovery and treatment of individuals who grapple with persistent symptoms following COVID-19 infection and learn why they do, so that we can find the most targeted solutions to help patients,” says Alexander Charney, MD, PhD, Associate Professor of Psychiatry, Genetics and Genomic Sciences, Neuroscience, and Neurosurgery, and Director of The Charles Bronfman Institute of Personalized Medicine at the Icahn School of Medicine at Mount Sinai.

Of the 37 symptoms studied, 12 were identified that set those with long COVID apart: post-exertional malaise, fatigue, brain fog, dizziness, gastrointestinal issues, heart palpitations, loss of sexual desire or performance, loss of smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Based on those symptoms, the investigators found that 23 percent of participants with a prior COVID-19 infection crossed the study threshold for long COVID.

“This is the first step of a series of studies that will be published using RECOVER data that will provide critical insights about the incidence, risk factors and pathophysiology of long COVID. This information will be vital for managing the large number of patients afflicted by this emerging condition,” says Juan Wisnivesky, MD, DrPH, Professor of Medicine at Icahn Mount Sinai, a clinical epidemiologist who is one of the primary leads of the RECOVER adult cohort study at the Mount Sinai site.  Dr. Wisnivesky is Chief of the Division of General Internal Medicine for the Mount Sinai Health System.

“Our overarching goal for RECOVER is to continue refining the definition of long COVID and to understand the biological causes of the condition. As a major hub for RECOVER enrollment and given the richly diverse communities that we serve—along with our grit—we are uniquely positioned to do just that.” — Alexander Charney, MD, PhD

The results were based on a survey in which 9,764 patients self-reported their symptoms. Next, data from the survey will be compared against lab and imaging results to validate the current findings.

“Until we establish a unifying framework to define it, it’s like we’re flying a plane without navigation,” Dr. Charney says when describing recent developments to come out of the Long COVID RECOVER multi-center study on which Mount Sinai is a co-author. “Defining long COVID in different ways will similarly get us varied answers. Only by creating a clear and uniform definition of long COVID can we accurately diagnose and begin the road to effective treatments. This study brings us closer to that goal of uniformity.”

On the hotly debated vaccination question—in another RECOVER study development—co-author Girish Nadkarni, MD, MPH, noted a preliminary signal in the data showing that being vaccinated was associated with a lower risk of long COVID. Dr. Nadkarni is the Irene and Dr. Arthur M. Fishberg Professor of Medicine, Director of The Charles Bronfman Institute of Personalized Medicine, and Chief, Division of Data Driven and Digital Medicine (D3M), Department of Medicine at Icahn Mount Sinai.

Mount Sinai colleagues in the Department of Psychiatry are leveraging RECOVER biospecimens to gain deeper insights into the cognitive and behavioral manifestations seen in long COVID.

Led by Scott Russo, PhD, Professor of Neuroscience and Director of the Center for Affective Neuroscience and Brain Body Research Center at Icahn Mount Sinai, the research will explore immune-mediated mechanisms underlying the emergence of depression and anxiety observed in this cohort. The work could inform the identification of biomarkers and enhance understanding of psychiatric symptoms associated with the disorder.

Dr. Russo and his team will also use RECOVER MRI data to measure changes in the penetrability of the blood-brain barrier associated with immune alterations and depression in long COVID.

Additional RECOVER brain initiatives, led by a team including Dr. Wisnivesky, Alex D. Federman, MD, MPH, and Jacqueline H. Becker, PhD, will conduct one of the first randomized clinical trials to investigate a potential therapeutic intervention for brain fog, a condition that affects many long COVID patients. Dr. Federman is Professor of Medicine and Director of Research for the Division of General Internal Medicine. Dr. Becker is a neuroscientist and Assistant Professor of Medicine.

Notably, Judith Aberg, MD, FIDA, FACP, the George Baehr Professor of Medicine, Dean of System Operations for Clinical Sciences, and Chief of Infectious Diseases, is a member of the RECOVER Pathobiology and Interventions Task Force. The task force recently published a white paper emphasizing the need for therapeutic interventions for individuals with long COVID. As the Principal Investigator, Dr. Aberg oversees the master contract for upcoming interventional trials sponsored by RECOVER.

“One of the exciting aspects of RECOVER is the collaboration of multi-disciplinary teams across Mount Sinai Health System engaged in interventional trials to improve the quality of life of those with long COVID,” says Dr. Aberg.

Sean Liu, MD, PhD, Medical Director of the COVID-19 Clinical Trials Unit, will lead a study evaluating the use of antivirals to treat long COVID based on the hypothesis that SARS-CoV-2 may persist in certain parts of the body and that a prolonged antiviral course may eliminate these hidden virus reservoirs.

Sarah Humphreys, MD, Assistant Professor of Medicine, will be the local Principal Investigator at Mount Sinai’s Center for Post-COVID Care. Other upcoming trials expected include interventions to improve cognitive function (the RECOVER-NEURO trial) and to address sleep disturbances (the RECOVER-SLEEP trial).

Mariana G. Figueiro, PhD, Professor at the Department of Population Health Science and Policy and Director of the Light and Health Research Center, is one of the Co-Principal Investigators nationally for RECOVER-SLEEP. The upcoming clinical trial will investigate the impact of light, alone and in combination with melatonin, on sleep in long COVID. The Center will serve as the core hub for this research protocol.

“Our overarching goal for RECOVER is to continue refining the definition of long COVID and to understand the biological causes of the condition.  As a major hub for RECOVER enrollment and given the richly diverse communities that we serve—along with our grit—we are uniquely positioned to do just that,” says Dr. Charney.

However, most AI models for ECG analysis use a particular deep learning method called convolutional neural networks (CNNs). CNNs require large training datasets to make diagnoses, which spell limitations when it comes to rare heart diseases that do not have a wealth of data.

Researchers at the Icahn School of Medicine at Mount Sinai have developed an AI model, called HeartBEiT, for ECG analysis, which works by interpreting ECGs as language.

The model uses a transformer-based neural network, a class of network that is unlike conventional networks but does serve as a basis for popular generative language models, such as ChatGPT.

Here’s how HeartBEiT works as an artificial intelligence deep-learning model, and how it compares to CNNs.

In this Q&A, Dr. Vaid discusses the impact of this new AI model on reading ECGs.

What was the motivation for your study?

Deep learning as applied to ECGs has had much success, but most deep learning studies for ECGs use convolutional neural networks, which have limitations.

Recently, the transformer class of models has assumed a position of importance. These models function by establishing relationships between parts of the data they see. Generative transformer models such as the popular ChatGPT utilize this understanding to generate plain-language text.

By using another generative image model, HeartBEiT creates representations of the ECG that may be considered “words,” and the whole ECG may be considered a single “document.” HeartBEiT understands the relationship between these words within the context of the document, and uses this understanding to perform diagnostic tasks better.

Deployment of this model and its derivatives into clinical practice can greatly enhance the manner in which clinicians interact with ECGs. We are no longer limited to models for commonly seen conditions, since the paradigm can be extended to nearly any pathology.

What is your plan for following up on this study?

We intend to scale up the model so that it can capture even more detail. We also intend to validate this approach externally, in places outside Mount Sinai.

Learn more about how Mount Sinai researchers and clinicians are leveraging machine learning to improve patient lives

Girish Nadkarni, MD, MPH, and Faris Gulamali

Artificial intelligence (AI) and machine learning (ML) have seen increasing use in health care, from guiding clinicians in diagnosis to helping them decide the best course of treatment. However, AI still has much unrealized potential in various health care settings.

Mount Sinai researchers are exploring bringing AI into intensive care, and developed Spatial Resolved Temporal Networks (SpaRTeN), a model to assess high-frequency patient data and generate representations of their state in real time.

The work was presented at the Time Series Representation Learning for Health workshop on Friday, May 5, hosted by the International Conference for Learned Representations, a premier gathering dedicated to machine learning.

Hear from Girish Nadkarni, MD, MPH, Irene and Dr. Arthur Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai and the leader of the SpaRTeN research, and Faris Gulamali, medical student at Icahn Mount Sinai and member of the Augmented Intelligence in Medicine and Science lab, on what lay behind creating the model and what it could achieve for patients.

What was the motivation for your study?

A growing amount of research is indicating the need to redefine critical illness by biological state rather than a non-specific illness syndrome. Advances in genomics, data science, and machine learning have generated evidence of different underlying etiologies for common ICU syndromes. As a result, patients with the exact same diagnosis can have entirely different outcomes.

What are the implications?

In the ICU, representations of a patient can be used to guide personalized treatments based on personalized diagnoses rather than generic treatments with empirical diagnoses.

What are the limitations of the study?

In this study, we only looked at using one type of data at a time in real time. For example, we looked primarily at measures of intracranial pressure. However, the ICU has many types of data being output simultaneously. Future work hopes to integrate all the different types of data such as electrocardiograms, blood pressure, and imaging to improve patient representations.

How might these findings be put to use?

These patient representations are being combined with data on medications and procedures to determine how to optimize patient treatment based on underlying state rather than common illness syndromes.

What is your plan for following up on this study?

In this study, we focused primarily on creating the algorithm and showing that it works for the case of intracranial hypertension. In future studies, we would like to integrate multiple data modalities such as imaging, electrocardiograms, and blood pressure as well as intervention-based data such as medications and procedures to determine precise empirical interventions that lead to improvements in short-term and long-term patient outcomes.

Learn more about how Mount Sinai researchers and clinicians are leveraging machine learning to improve patient lives