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Real-Time Diagnosis of Life-Threatening Necrotizing Soft Tissue Infections (NSTI) Using Indocyanine Green (ICG) Kinetic Modeling

Not yet recruiting
Conditions
Necrotizing Fascitis
Registration Number
NCT06877793
Lead Sponsor
Eric R. Henderson
Brief Summary

Necrotizing soft-tissue infections (NSTIs, a.k.a. "necrotizing fasciitis" or "flesh-eating bacteria") are aggressive infections that can progress rapidly from mild symptoms to sepsis, multi-organ failure, and death. NSTI cases present with non-specific clinical, imaging, and laboratory findings, and standard-of-care techniques for NSTI diagnosis lack sensitivity and specificity, resulting in frequent misdiagnosis and delayed care, which is the single most important predictor of survival. Consequently, the cumulative mortality rate for patients with NSTIs is 20- 30%; a dire need exists for more accurate and rapid detection of NSTIs. Fluorescence-guided surgery is a nascent technology seeking to improve the recognition of anatomical structures and disease processes using fluorescent probes (fluorophores). Indocyanine green (ICG) is an FDA-approved, near-infrared fluorophore with a \>60-year safety record for vascular perfusion assessment. A distinguishing histological feature of NSTIs is prominent blood vessel thrombosis in affected tissues. Leveraging these pro-thrombotic effects, our study group has demonstrated in a first-in-human study (NCT04839302) that intravenous administration of ICG and immediate fluorescence imaging reveals prominent signal deficits in NSTI-positive tissues that differentiate significantly with increased signal seen with more common-and less virulent-infections such as cellulitis. We seek now to evaluate this imaging technique on a broader scale and determine if our findings are consistent for patients affected by NSTI-causing pathogens that are not endemic to our region. This prospective, observational, multicenter clinical study will involve video-rate ICG fluorescence imaging of patients suspected of having NSTIs who present to eight tertiary, Level 1 medical centers across the United States (Aim 1). Using dynamic contrast-enhanced fluorescence imaging (DCE-FI), time profiles of ICG fluorescence intensity from different tissue pixels/regions will be extracted and parameterized to extract first-pass kinetic features. These DCE-FI features, which characterize tissue perfusion, will be evaluated alone and in combination with anonymized electronic medical record data to create a DCE-FI-based clinical decision tool and a machine- learning-based fusion (DCE FI+lab/imaging data) tool; these will be compared to identify the most accurate means of diagnosing NSTIs (Aim 2). The best-performing tool will then be evaluated-compared to current diagnostic tests-in a prospective observational clinical study of patients presenting to tertiary emergency departments with findings concerning for NSTIs (Aim 3). Based on our human study, fluorescence imaging will not delay current standard of care. To ensure data fidelity, all sites will use similar: 1) commercial fluorescence imaging systems and accessories; and 2) validated commercial fluorescence reference phantoms. Based on our early results, we have strong confidence that following rigorous testing, ICG DCE-FI will lead to an entirely new methodology for rapid identification of patients with NSTIs, which will ultimately reduce patient morbidity and improve survival.

Detailed Description

Not available

Recruitment & Eligibility

Status
NOT_YET_RECRUITING
Sex
All
Target Recruitment
420
Inclusion Criteria
  • Age ≥18 years.
  • Clinical suspicion of NSTI based on the local standard of care warranting:
  • Hospital admission for observation due to suspected NSTI; and/or
  • Soft tissue biopsy to rule in/out suspected NSTI; and/or
  • Surgical debridement for suspected NSTI; and/or
  • Specific institutional threshold criteria for triggering NSTI work-up; and
  • Ability to give written informed consent.
Exclusion Criteria
  • History of allergy to ICG and/or iodine.
  • Pregnant women or nursing mothers.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Determine if tissue perfusion, determined by first-pass ICG fluorescence kinetics, is reliably reduced in the setting of a necrotizing infection compared to a non-necrotizing infection.From enrollment to the end of fluorescence imaging (about 1 day)

We will evaluate first-pass fluorescence signal intensity changes from indocyanine green (ICG), a vascular perfusion fluorophore, in the setting of severe soft-tissue infections to determine if ICG fluorescence may be an accurate and reliable diagnostic test for differentiating life-threatening necrotizing infections from non-life-threatening non-necrotizing infections.

Secondary Outcome Measures
NameTimeMethod
Determine if first-pass ICG fluorescence kinetics in patients with necrotizing infections vary based on the causative bacterial species.From enrollment to the end of imaging and lab results (about 30 days)

In patients who had a documented necrotizing soft-tissue infection, we will compare first-pass fluorescence signal intensity changes from ICG based on the predominant causative bacterial species isolated from a patient's infection to determine if ICG signal changes vary by species.

Trial Locations

Locations (8)

University of California, Los Angeles

🇺🇸

Los Angeles, California, United States

Stanford University

🇺🇸

Stanford, California, United States

Emory University/Grady Memorial Hospital

🇺🇸

Atlanta, Georgia, United States

University of Michigan

🇺🇸

Ann Arbor, Michigan, United States

Dartmouth-Hitchcock Medical Center

🇺🇸

Lebanon, New Hampshire, United States

University of Pennsylvania

🇺🇸

Philadelphia, Pennsylvania, United States

University of Pittsburgh Medical Center

🇺🇸

Pittsburgh, Pennsylvania, United States

Vanderbilt University

🇺🇸

Nashville, Tennessee, United States

University of California, Los Angeles
🇺🇸Los Angeles, California, United States
Christopher Lee, MD
Principal Investigator
Nicholas Bernthal, MD
Sub Investigator

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