# Automated detection and prediction of atrial fibrillation during sepsis

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $535,600

## Abstract

7. ABSTRACT / PROJECT SUMMARY
We propose the “Automated detection and prediction of atrial fibrillation during sepsis” study to develop
automated technologies capable of accurate atrial fibrillation (AF) detection and prediction during sepsis.
Sepsis is a life-threatening, dysregulated response to infection and the most common illness leading to
hospitalization in the United States, affecting ~1 million Americans yearly, and is associated with 50% of all
hospital deaths. With the exception early antibiotic and fluid use, few therapies improve outcomes among
septic patients; new treatment strategies are greatly needed to improve survival. New-onset AF is a common
dysrhythmia among critically ill patients with sepsis, affecting up to 1 in 3 septic patients and conferring
increased short- and long-term risks stroke, heart failure, and death. Prevention of AF or its complications may
improve sepsis outcomes by reducing AF-related morbidity and mortality. Although several evidence-based
treatments have shown efficacy in treating and preventing AF in certain high-risk subgroups (e.g., AF
prevention following cardiac surgery), studying application of these therapies among critically ill patients with
sepsis has been hampered by two major factors: 1) we lack validated automated mechanisms to detect AF and
facilitate real-world AF research in large clinical databases, and 2) we cannot presently predict which patients
with sepsis will develop AF. Our project will leverage the unique resources of the recently released
Multiparameter Intelligent Monitoring in Intensive Care (MIMIC III) database. MIMIC III links continuous ECG
and pulse plethysmographic waveforms to a wealth of time-varying clinical and hemodynamic data. Our project
will develop and validate state-of-the art automated AF detection algorithms using waveform data from critically
ill patients. Automated AF detection would enable expedited clinical treatment of AF, identification of subclinical
AF, and will catalyze the study of AF in emerging electronic health record waveform databases. We will
develop innovative automated AF prediction capabilities using state-of-the-art waveform analysis algorithms
and machine learning methods in critically ill patients. Automated algorithms that identify patients at high risk
for developing AF in the near-term would enable targeting of preventative therapies and potentially usher in a
new era of AF prevention for critically ill patients. AF prevention and treatment facilitated through our project
will allow targeting of novel, AF-based mechanisms of poor outcomes during and following sepsis.

## Key facts

- **NIH application ID:** 9910440
- **Project number:** 5R01HL136660-04
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Allan J. Walkey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $535,600
- **Award type:** 5
- **Project period:** 2017-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9910440

## Citation

> US National Institutes of Health, RePORTER application 9910440, Automated detection and prediction of atrial fibrillation during sepsis (5R01HL136660-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9910440. Licensed CC0.

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