PDF(Pubmed)
Abstract：
Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.
摘要：
谵妄提出了重大的临床挑战，主要是由于其对患者预后的深远影响以及当前诊断方法的局限性，这在很大程度上是主观的。在COVID-19大流行期间，随着重症监护病房(ICU)谵妄评估频率的下降，这一挑战加剧了，即使在危重患者中谵妄的患病率增加。本研究评估了血清分子指纹图谱，通过傅里叶变换红外光谱(FTIR)获得，可以开发谵妄的预测模型。血清FTIR光谱的初步单变量分析表明，26例ICU谵妄患者和26例无谵妄患者之间的条带显着不同，所有这些人都被确诊为COVID-19。然而,这些带导致了表现不佳的朴素贝叶斯预测模型。考虑到使用基于快速相关的滤波器进行特征选择，有可能定义一组新的光谱带，具有更广泛的分子官能团覆盖范围。这些波段确保了一个优秀的朴素贝叶斯预测模型，AUC,一种敏感性，特异性均超过0.92。这些光谱带，通过微创分析获得并快速获得，经济上,在高吞吐量模式下，因此,为治疗危重患者谵妄提供了巨大的潜力.
