In vivo Raman Spectroscopy of Skin in Predicting Mortality in Patients with Heart Failure

Heart failure (HF) is an age-related condition that complicates heart disease. Currently, the number of patients with HF continues to increase, representing a significant burden on the healthcare system. Prognostication of patients with HF may be performed with modern spectroscopic approaches.

This study proposes to utilize conventional spontaneous Raman spectroscopy and autofluorescence for the in vivo analysis of skin tissues to create a prognosis for patients with HF. We collected skin spectral data from 160 HF patients. Twenty-nine patients died during the 1-year observation period. After the preprocessing of spectral data, we proposed a classification model for the prediction of mortality. This model utilizes projection on latent structures combined with discriminant analysis. Stability of the model was demonstrated during division of the data into training and test.

Analysis of full spectral data provided only 55% accuracy in a 1-year mortality prediction, while analysis of autofluorescence and Raman spectral data provides 66% and 70% accuracy, respectively. The combination of autofluorescence and Raman spectroscopy provides an accuracy of 74% (68% sensitivity and 80% specificity) and an receiver operating characteristic-area under the curve of 0.80 for the prediction of 1-year mortality in patients with HF. The most important Raman bands for the prediction of 1-year mortality appeared at 1,086-1,180, 1,320, 1,465, and 1,770 cm-1.

Raman spectroscopy could be a powerful tool for the prognosis of patients with HF; however, further studies on a larger cohort are required to demonstrate the applicability of the proposed spectral in vivo analysis.