Introduction: 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. Methods: 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. 29 patients died during the one-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 (PLS-DA). Stability of the model was demonstrated during division of the data into training and test. Results: Analysis of full spectral data provided only 55% accuracy in a one-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 ROC AUC (Receiver Operating Characteristic — Area Under Curve) of 0.80 for the prediction of 1-year mortality in patients with HF. The most important Raman bands for the prediction of one-year mortality appeared at 1086-1180, 1320, 1465, and 1770 cm-1. Conclusion: 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.
Bratchenko et al. (Thu,) studied this question.