Mount Sinai COVID Informatics Center

In the Face of a Crisis We Stand Together and Fight
The Mount Sinai COVID Informatics Center is a collection of highly skilled, motivated, and collaborative “Doers” from all corners of the Mount Sinai Health System.

Who We Are
The Mount Sinai COVID Informatics Center is a diverse group of the best data scientists, engineers, clinical physicians, and researchers. We are standing together to fight the deadliest pandemic of our lifetime.

What We Do
Our team is undertaking the most vital tasks as we combat COVID-19.
We harmonize all electronic data across the Mount Sinai Health System and use our data science expertise to empower clinicians and researchers within Mount Sinai.
We implement community-wide surveys and digital health tools to track disease and exposure patterns.
We develop and deploy algorithms to predict COVID-19 outcomes.
We translate data science discovery into clinical care and research.
We enable rapid enrollment of COVID-19 patients into biobanks and/or clinical trials (pending regulatory approval).

Featured Research

Digital Health Applications

STOP COVID NYC

In the wake of this COVID-19 pandemic and state of emergency, our team of data scientists, clinicians and patient- centered investigators will work together with Mount Sinai Health System (MSHS) stakeholders to provide the general population, MSHS patients, and potentially MSHS clinicians simple tools/questionnaires to check symptoms and determine whether individuals experiencing symptoms should seek care or remain home.

We will utilize IRB-approved phone/web-based apps to track daily symptoms. This will provide critical insights into the spread of COVID-19 throughout the region. The web app will consist of a survey that covers demographic information, COVID-19-related health information, contact information, and a daily symptom checker. The data collected may be combined and used with data collected by other hospitals for the same purpose of fighting COVID-19.

We will use existing data from MSHS patients who have been tested in the MSHS for COVID-19, along with the data pulled from phone/web-based tools, to build predictive models that will help clinicians to better risk-stratify individuals with respect to COVID-19. We will also use patient data collected in real-time related to COVID-19 clinical care for the duration of this pandemic.

Tracking Healthcare Workers with Apple Watches

COVID-19 has already infected hundreds of thousands of people globally, and healthcare workers (HCW) disproportionately. There is a limited testing capability in the United States with no readily available means to identify infected HCWs during the latent period of infection.

Within the latent period of infection, asymptomatic HCWs can shed the virus contributing to the significant transmission of the virus within the healthcare facilities. Wearable biosensors provide a unique opportunity to monitor physiological parameters accurately. The Apple Watch representing the most commonly used wearable device, is able to assess physiological metrics accurately, continuously and precisely. Primarily among this, is heart rate variability (HRV). HRV provides insight into the interplay between the parasympathetic and sympathetic nervous system. Continuous HRV analyses demonstrate significant alterations prior to clinical diagnosis of infection, which we will explore in the context of this pandemic.

The goals of the project are:

  1. To identify people who are infected but preclinical or asymptomatic in the infection process using Apple watch, HRV outputs.
  2. First would do this in our healthcare workers as we have a reliable registry of who is infected.
  3. If this is successful the next step would be perform this in a larger population of patients

Gender Differences in Covid-19 Patients In The Mount Sinai Experience

Based on a survey of Mount Sinai Healthcare System electronic medical records (EMR), it appears that men have higher rates of COVID-19 admissions and higher rates of diagnoses of acute respiratory distress syndrome and end organ damage (elevated lactate). In this study, we seek to validate these data through de-identified datasets that allow for multivariable analysis of gender in assessing disease severity.

The Mount Sinai Health Systems serves a large number of COVID-19-infected patients. This study aims to:

  • Compare the risk of men and women for development of severe COVID-19 infection, including admission risk, risk of respiratory failure, risk of end organ damage, and risk of cytokine release syndrome
  • Determine demographic and comorbid risk factors that predispose to severe COVID-19
  • Determine the prevalence of high-risk features of COVID-19 in men vs. women
  • Characterize COVID-19 disease course and related covariates for potential linkage to translational data (cell-free DNA) from a consented biospecimen cohort.
  • We plan to use Mount Sinai Data Warehouse (MSDW) data from the Mount Sinai Healthcare System to address these aims using data from patients presenting for care.

Mental Health in COVID-19

Understanding the mental health impacts of COVID-19 on:

  • The general community
  • Individuals with or recovering from COVID-19
  • Healthcare workers treating patients with COVID-19

We will be open and welcoming to input on these research goals and will work with diverse mental health experts within the Mount Sinai Healthcare System and the psychiatric genomics community.

Translational Research

COVID-19 Subtyping

Stratification analysis of all the patients diagnosed with COVID to identify disease subtypes that could inform personalized treatments.

COVID-19 Host Genetics in the Community

  • We propose to obtain genotypes and biospecimens, if possible, from individuals completing the STOP COVID NYC survey. We will test for genetic correlation with disease trajectory and morbidity. If possible, we will test for associations between genetics, antibody tests, and multi-omic phenotypes.
  • We will work collaboratively with international efforts to probe COVID-19 host genetics.
  • Our efforts target broad community involvement, not existing patients.
  • Additionally, we will coordinate with existing Mount Sinai patient-based biobanking efforts, e.g. through BioMe, IPM, and NYCIT efforts.

Epidemiology of COVID-19 in The Mount Sinai Healthcare System

We will perform a banner project to describe the clinical and phenotype landscape of all MSHS patients in the vein of the March 30, 2020 publication in New England Journal of Medicine (case series, Washington State).

Characteristics of Critically Ill Patients

We will identify patients with ICU admission during their hospital course to:

  • Determine predictors of disease severity
  • Describe disease course of ICU patients

Immunologic Predictors of COVID-19 Disease Severity

  • As a joint team of immunologists, infectious disease experts, and bioinformaticians, we propose to build a predictive tool based on retrospective analysis of patient data to identify patients likely to rapidly decompensate and develop respiratory distress, as well as cytokine release syndrome
  • We propose to create a dynamic model of predictive biomarkers for decompensation (ARDS, respiratory distress, intubation) based on fixed measurements and trended measurements over time
  • This work will be critical for informing clinical care and to identify the optimal time for therapeutic intervention

Predictive Modeling

Profiling and Prognostic Impact of Cardiac Involvement in COVID-19 Infections

Reports from China and Italy have shown patients with preexisting cardiovascular disease are more at risk for COVID-19; furthermore, myocardial injury is common and if detected, portends a worse prognosis.

Hypotheses

  • There may be phenotypic profiles and circulatory biomarkers that predispose and predict to cardiovascular complications during a COVID-19 infection
  • Myocardial involvement in COVID-19 is associated with poor clinical outcomes.
  • Myocardial involvement in COVID-19 can be due to
    • Direct myocardial injury as a result of infection and ensuing inflammation
    • Increased hemodynamic demand/stress
    • COVID-19 treatment

Objectives

  1. To describe the prevalence of myocardial involvement and cardiovascular complications associated with COVID-19
  2. To determine the phenotypic profiles and circulating biomarkers related to cardiovascular complications
  3. To assess the prognostic impact of myocardial involvement in patients who test positive for COVID-19

There is an unmet need to better understand which of these hypotheses predominantly underlies myocardial injury in COVID-19, with far reaching implications. Further, how patients will be impacted as a result of acquiring this illness and recovering is unknown. This can be further studied with comprehensive cardiovascular imaging wider community.