Blood Biomarker Discovery by Raman Spectroscopy in Acute Aortic Dissection

Study Overview
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Brief Summary
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This study intends to collect about 500 cases of blood samples from acute aortic dissection (AAD) and other acute chest pain diseases (eg, AMI, PE, or angina).The investigators tend to detect Raman metabolic profile of blood samples collected from AAD and other control groups using the Raman spectroscopy. The data collected will be analyzed and used to create a Raman database able to provide a classification model based on machine learning. The investigators then compared them with healthy participants to evaluate the diagnostic performance of different levels of Raman metabolic profile at discriminating AAD from other diagnoses and assess whether Raman metabolic profile is a potential novel biomarker for AAD under different circumstances.

Detailed Description
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Sample Collection: Blood (serum and plasma) collection from all the selected subjects at admission will be performed following the collection tube manufacturer's instructions. Then the collected samples will be stored at -80° C. Data Collection: For the Raman analysis, 5 μL of each sample will be casted on an aluminium foil in order to achieve the Surface Enhanced Raman Scattering (SERS). Raman and SERS spectra will be acquired using a WITec Alpha500 confocal micro-Raman spectroscopy system (WITec, Germany) with a 532 nm He-Ne laser (Research Electro-Optics, Inc., USA) as the excitation source, which offered a resolution of approximately 3 cm-1. The instrument will be calibrated before each analysis using the reference band of silicon at 520.7 cm-1. A 20× microscope objective (NA = 1.25, Zeiss, Germany) was used for spectral excitation and measurements. All spectral information were recorded by a back-illuminated deep-depletion charge coupled device camera (ardorTechnology, UK) working at -60°C with a 1.5 s acquisition time for a single spectrum with a spectral range of 300-4000 cm-1. The software package WITec Project spectral analysis (WITec GmbH, Germany) will be used for map design and the acquisition of spectra. Data Processing: All the acquired spectra will be fit with a nine-order polynomial baseline and normalized by unit vector using the dedicated WITec Project spectral analysis software. The statistical analysis to validate the method, will be performed using a multivariate analysis approach. Principal Component analysis (PCA) will be performed in order to reduce data dimensions and to evidence major trends. Mann-Whitney will be performed on PCs scores to verify the differences statistically relevant between the analysed groups. Correlation and partial correlation analysis will be performed using the Spearman's test, assuming as valid correlation only the coefficients with a p-value lower than 0.05. Subsequently, principal components analysis (PCA) combined with linear discriminant analysis (LDA), PCA combined with support vector machine (SVM), partial least squares-discriminant analysis (PLS-DA) and etc were tested for evaluation of diagnostic power. The statistical analysis will be performed using Origin2018 (OriginLab, USA) and MATLAB R2018b software (Mathworks. Inc., Natica, MA, USA).

Official Title
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Characterization of the Blood Signature as a Potential Tool for the Fast Diagnosis of of Acute Aortic Dissection

Conditions
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Acute Aortic Dissection

Intervention / Treatment
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* Other: Raman spectroscopy


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Clinical diagnosis of AAD; Clinical diagnosis of AMI; Clinical diagnosis of PE; Clinical diagnosis of angina; Healthy participants; Provided written consent for the blood samples analysis; Age between 18 and 90 years. Exclusion Criteria: Transfusion history (packed red blood cells, whole blood, or platelets) 10 days before the blood sample was taken; Aortic trauma; Pseudo aneurysm; History of heart failure; Renal dysfunction; Severe pulmonary diseases; Active cancer; Checkup after surgery.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 90 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| healthy participants<br>healthy participants | Other: Raman spectroscopy<br>* Blood (serum or plasma) will be collected, processed and analysed through Raman spectroscopy. Data acquired will be normalized and treated for the creation of the classification model.<br>|
| acute aortic dissection (AAD)<br>acute aortic dissection (AAD) | Other: Raman spectroscopy<br>* Blood (serum or plasma) will be collected, processed and analysed through Raman spectroscopy. Data acquired will be normalized and treated for the creation of the classification model.<br>|
| acute myocardial infarction (AMI)<br>acute myocardial infarction (AMI) | Other: Raman spectroscopy<br>* Blood (serum or plasma) will be collected, processed and analysed through Raman spectroscopy. Data acquired will be normalized and treated for the creation of the classification model.<br>|
| pulmonary embolism (PE)<br>pulmonary embolism (PE) | Other: Raman spectroscopy<br>* Blood (serum or plasma) will be collected, processed and analysed through Raman spectroscopy. Data acquired will be normalized and treated for the creation of the classification model.<br>|
| angina<br>angina | Other: Raman spectroscopy<br>* Blood (serum or plasma) will be collected, processed and analysed through Raman spectroscopy. Data acquired will be normalized and treated for the creation of the classification model.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification and characterization of a new AAD blood signature through Raman spectroscopy | The Raman analysis of blood samples collected from AAD patients and other control groups, will be used to characterize a AAD signature able to discriminate subjects from other chest pain individuals. | One day | 
| Correlation with the clinical characteristics | Raman data related to subjects with AAD will be correlated with the clinical data, validating in this way our methodology. The principal correlation will be carried out between the D-Dimer and B type urine natriuretic peptide（BNP） concentrations and Raman signature. | One Day | 
| Portable Raman as point of care | The characterized and implemented classification model will be translated to a portable Raman equipped with a laser emitting at 532 nm and with a spectral resolution comparable with the one of the bench Raman. This station will be firstly tested with patients coming to the hospital and then applied continuously implementing the classification model with new Raman spectra and clinical data. In this way the investigators will highly implement the accuracy, sensitivity, precision and specificity of the model. | One Year |