A New Point-of-Care Auto�uorescence Imaging Device for Real-time Detection and

Tracking of Pathogenic Bacteria in Chronic Wounds: First-in-Human Results

Ralph S. DaCosta1, Liis Lindvere-Teene1, Iris Kulbatski1, Danielle Starr1, Kristina Blackmore2, Jason I Silver1, Julie Opoku4, Charlie Wu1, Wei Xu 5,6, Lizhen Xu6,

Christine Massey6, Brian C. Wilson1, Cheryl Rosen3, Ludwik Fedorko4, Ron Linden4

1- Princess Margaret Cancer Center, Toronto, ON2 - Mount Sinai Hospital, Toronto, ON

3 - Toronto Western Hospital, Toronto, ON4 - Judy Dan Research and Treatment Centre, Toronto, ON

5 - University Health Network, Toronto, ON6 - University of Toronto, Toronto, ON

INTRODUCTION• Chronic wounds present an enormous burden to patients and the health care systems worldwide.• Traditional clinical diagnosis of chronic wound infection by pathogenic bacteria, using clinical signs and symptoms, is based on visual inspection under white light and microbiological sampling (e.g. swabbing and/or biopsy), which are, respectively, subjective and suboptimal.• Bacteria are invisible to the unaided eye, so wound sampling is largely blind and can lead to signi�cant delay in diagnosis and treatment decisions at the bedside.• An accurate (image-based) method that detects bacteria within wounds addresses the unmet need for new ways to provide rapid wound care diagnostics at the point-of-care.• We have developed a handheld fluorescence imaging device platform, which rapidly detects bacteria (and tissue components) in wounds, based on endogenous (auto)fluorescence (AF) signals. Fluorescence images are displayed in real-time to the user on an LCD touch screen.

METHODS• 58 male and female patients (median age 56) were imaged at the Judy Dan Research and Treatment Centre (JDRTC) in Toronto, Canada in an on-going UHN REB-approved clinical trial (REB #09-0015-A) and in accordance with Good Clinical Practice. Patients presented with diabetic foot ulcers. Sequential white light and corresponding AF images of each wound were acquired with the fluorescence imaging device to longitudinally track wound progression over time.• A total of 490 microbial swabs were obtained from wounds guided by either white light or fluorescence imaging and sent for independent (blinded) correlative microbiological laboratory analysis. Areas in and around wounds that were suspected of being infected by either white light (e.g. using standard clinical signs and symptoms) or AF light (e.g. detected red or green fluorescent signal) were swabbed by clinical staff and sent for microbiological analysis. • White light (WL) and AF imaging data were statistically analyzed and compared to correlative microbiological lab results to determine the diagnostic sensitivity of each method to accurately detect relative bacterial load.• Swab culture results were classified as None, Light, Moderate, or Heavy growth of bacterial. Clinical diagnosis protocols considered a level of ‘Heavy’ as clinical infection when combined with standard clinical signs and symptoms (CSS).• Our study evaluated swabs for the ‘top ten’ most clinically relevant and prevalent pathogenic bacteria in wounds, including Staphyllococcus aureus, Staphyllococcus Epidermidis, Pseudomonas Aeruginosa, and Serratia Marscecens. • We developed proprietary image analysis algorithms to quantify changes in wound bacterial load over time based on the AF image intensities for each patient.

RESULTS

Figure 2. Auto�uorescence detects clinically signi�cant bacterial load in wound peripheries and surrounding off-site areas missed by conventional methods. (A), (B) WL shows unremarkable areas between toes, while corresponding AF images detected bacterial bio�lm, con�rmed by microbiology. This could pose a recontamination risk to primary wound if unmanaged. (C), (D) Heavy growth S. aureus was detected in wound periphery only by AF imaging.

Figure 1. The handheld prototype imaging device. (A) Fluorescence images are displayed in real-time on the LCD screen. (B) Dual broad band white light and violet LED arrays provide illumination of the wound, while the �uorescence mission �lter is placed in front of the CCD sensor. (C) The device is aimed at a diabetic foot ulcer to visualize the auto�uorescence image on the viewing screen. (D) Illustration of the excitation LED illumination geometry.

A B C

D

Capture button

LCD screen

ON/OFF

Power cable

LED Array+

Exc. �lter

CCD Sensor+

Emiss. �lter

A B

C D

Figure 3. Auto�uorescence imaging di�erentiates P. aeruginosa from other species without contrast agents. (A) WL image shows chronic wounds, while (B) corresponding AF image di�erentiates between S. aureus (red color) and P. aeruginosa (green color) based on endogeous red �uorescent porphyrins in S. aureus and green �uorescent siderophores (e.g. pyoverdin) in P. aruginosa.

S. aureus

P. aeruginosa A

Figure 5. (Right) Schematic illustrates location-based �uorescence detection of bacterial load. (Left) Accuracy comparison between standard (WL) and (AF) exam for detecting clinically signi�cant bioburden (CSB) in wound bed, periphery and o�-site areas. In wound bed, AF correctly detected 74.5% CSB compared with 52.5% for WL; in periphery and o�-site areas, standard practice would not have assessed these areas during wound examination, while AF accurately detected CSB 82.4% in periphery and 67.1% in other areas. WL examination was correct 17.6% in peripheries and 32.9% in other areas e.g. by not assessing these areas at all, standard practice with WL failed to detect 82.4% and 67.1% of clinically signi�cant bacterial load in the periphery and o�-site regions.

Figure 4. Semi-quanti�cation of bacterial load correlates with average red �uorescence intensity (n=68). Average red �uorescence pixel intensity (scale 0-255) is greater when bacte-rial load is scored higher. Error bars are standard errors. ANOVA performed using Newman-Keuls. Legend: *= 0.05>p>0.01, **= 0.01>p>0.001, ***= p<0.001. Semi-quantitative growth scale: 0 – Normal flora, 1 – Light, 2 – Moderate, 3 – Heavy

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RESULTS

ACKNOWLEDGEMENTS This work was funded by:

CLINICAL HIGHLIGHTS• Current standard of care is correct about half the time (52.5%) in

determining whether a wound was ‘infected’ (105 CFU).

• AF correctly identified ‘infected’ wounds 74.5 % of the time.

• WL missed about half of the ’infected’ wounds which were caught by AF.

• As level of ‘infection’ decreases, the value of AF vs. WL increases significantly (i.e. WL ‘miss rate’ decreased from about half to about 20% going from very light to heavy bacterial burden).

• 82.4% of the wounds had ‘infection’ in periphery regions of the wound only caught by AF and not WL.

• On per-swab basis, when WL said to take a swab, it showed ‘infection’ 95% of the time while when AF said to take a swab, it showed ‘infection’ 75% of the time. However, WL has a sensitivity of approximately 14%.

SUMMARYReal-time autofluorescence imaging of wounds : • Provides an instant and quantifiable image of bacteria presence, distribution and load.

• Enables image-guidance for:

• improving wound cleaning and debridement.

• targeted microbiological sampling.

• quantitative treatment response monitoring over time.

• Potentially differentiates viable from non-viable tissues, for fluorescence-guided debridement.

• Could provide evidence-based optimization of antimicrobial treatment selection.

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