Australian Donation and Transplantation Biobank: A Research Biobank Integrated Within a Deceased Organ and Tissue Donation Program

Sharma, Varun; Starkey, Graham; D’Costa, Rohit; James, Fiona; Mouhtouris, Effie; Davis, Lisa; Wang, Boa Zhong; Vago, Angela; Raman, Jaishankar; Mackay, Laura K.; Opdam, Helen; Jones, Robert M.; Grayson, M. L.; Martin, Dominique; Gordon, Claire L.

doi:10.1097/txd.0000000000001422

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…Physiological control samples of liver were prospectively obtained from consecutive deceased organ donors by the Australian Donation and Transplantation Biobank (ADTB) 38 . These were acquired intra‐operatively at the time of organ retrieval in organ donors with consent for donation to the ADTB.…”

Section: Methodsmentioning

confidence: 99%

“…Physiological control samples of liver were prospectively obtained from consecutive deceased organ donors by the Australian Donation and Transplantation Biobank (ADTB). 38 These were acquired intra‐operatively at the time of organ retrieval in organ donors with consent for donation to the ADTB. Samples were transported on ice and immediately stored into 2 mL Eppendorf tubes, snap‐frozen in liquid nitrogen and stored in cryogenic tanks at −80°C.…”

Section: Methodsmentioning

confidence: 99%

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Point‐of‐care detection of fibrosis in liver transplant surgery using near‐infrared spectroscopy and machine learning

Sharma,

Adegoke,

Fasulakis

et al. 2023

Health Science Reports

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IntroductionVisual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3‐s scans using a handheld near‐infrared‐spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples.MethodsWe undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors.ResultsLiver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20–60) and mature fibrosis of 30% (10%–50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%–15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial‐least square regression machine learning, this study predicted the percentage of both immature (R2 = 0.842) and mature (R2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively).ConclusionThis study demonstrates that a point‐of‐care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Point‐of‐care detection of fibrosis in liver transplant surgery using near‐infrared spectroscopy and machine learning

Sharma,

Adegoke,

Fasulakis

et al. 2023

Health Science Reports

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“…Ethics approval for ADTB has been described previously. 3 Approval for this project was obtained on June 21, 2021 (HREC/73660/Austin-2021).…”

Section: Ethics and Consentmentioning

confidence: 99%

“…The donation coordination agency obtains informed consent for donation to the ADTB from the senior available next of kin. 3 If the heart is not suitable for transplantation ( Figure 1 , A ), the heart is excised immediately after all organs for transplantation have been retrieved to minimize heart ischemic time. Access is via a median sternotomy, after which the pericardium is opened and aorta transected in the descending aorta with 2 to 3 cm of the arch vessels.…”

Section: Heart Procurementmentioning

confidence: 99%

“… 1 Only 30% of deceased organ donors have hearts suitable for organ transplantation, 2 providing an opportunity for donation of the heart for use in research. The Australian Donation and Transplantation Biobank (ADTB) facilitates the donation of tissue samples for use in research using a centralized system integrated into the deceased organ donation program (Reference Transplant Direct paper 3 ). We describe the technical aspects of collecting, processing, and storing fresh human cardiac tissue for use in future research as part of ADTB.…”

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confidence: 99%

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Fresh human cardiac tissue for translational research: A novel method of sampling deceased organ donors

Sharma¹,

Grant²,

Gangahanumiah³

et al. 2023

JTCVS Techniques

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Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue

Sharma,

Green,

McLean

et al. 2023

Heart Vessels

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To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a “multimodal spectroscopic scan” (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49–52) for IHD, 47 (IQR 45–50) for DCM and 36 (IQR 33–52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm−1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts.Table 1 Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined “stack” (LR + SGD + SVM) AUC Precision Recall Specificity (a) NIR model Logistic regression 0.980 0.944 0.933 0.967 SGD 0.550 0.281 0.400 0.700 SVM 0.840 0.806 0.800 0.900 Stack 0.933 0.794 0.800 0.900 (b) Raman model Logistic regression 0.985 0.940 0.929 0.960 SGD 0.892 0.869 0.857 0.932 SVM 0.992 0.940 0.929 0.960 Stack 0.954 0.869 0.857 0.932 (c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients Logistic regression 0.975 0.841 0.828 0.917 SGD 0.847 0.803 0.793 0.899 SVM 0.971 0.853 0.828 0.917 Stack 0.961 0.853 0.828 0.917 (d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients Logistic regression 0.961 0.969 0.966 0.984 SGD 0.944 0.967 0.966 0.923 SVM 1.000 1.000 1.000 1.000 Stack 1.000 0.944 0.931 0.969 Bold values indicate values obtained from the stack algorithm and used for analyses

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Australian Donation and Transplantation Biobank: A Research Biobank Integrated Within a Deceased Organ and Tissue Donation Program

Cited by 5 publications

References 39 publications

Point‐of‐care detection of fibrosis in liver transplant surgery using near‐infrared spectroscopy and machine learning

Point‐of‐care detection of fibrosis in liver transplant surgery using near‐infrared spectroscopy and machine learning

Fresh human cardiac tissue for translational research: A novel method of sampling deceased organ donors

Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue

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