Perfusion index for early identification of regional anesthesia effectiveness: a narrative review.

Regional anesthesia (RA) is a common and irreplaceable technique in clinical, which can be used in different surgery sites and control of acute and chronic pain, especially for outpatients, pediatrics and the elderly. RA demands are increasing during COVID-19 pandemic because many surgeries could be performed under RA to reduce the risk of cross-infection between patients and health care workers. Early and accurate identification of the effects of RA can help physicians make timely decisions about whether to supplement analgesics or switch to general anesthesia, which will save time and improve patient satisfaction in a busy operating room. Perfusion index (PI) is a parameter derived from photoplethysmography (PPG) and represents the ratio of pulsatile and non-pulsatile blood flow at monitoring sites. It reflects local perfusion and is mainly affected by stroke volume and vascular tone. With characteristics of non-invasive, rapid, simple, and objective, PI is widely used in clinical practice, such as fluid responsiveness prediction, nociceptive assessment, etc. Recently, many studies have assessed the accuracy of PI in early prediction of RA success, including brachial plexus block, sciatic nerve block, neuraxial anesthesia, paravertebral block, caudal block and stellate ganglion block. Successful RA often parallels increased PI. In this narrative review, we describe the principles and influencing factors of PI, and introduce the effects of PI on early identification of RA effectiveness.

Prediction of mortality in COVID-19 through combing CT severity score with NEWS, qSOFA, or peripheral perfusion index.

INTRODUCTION: The assessment of disease severity and the prediction of clinical outcomes at early disease stages can contribute to decreased mortality in patients with Coronavirus disease 2019 (COVID-19). This study was conducted to develop and validate a multivariable risk prediction model for mortality with using a combination of computed tomography severity score (CT-SS), national early warning score (NEWS), and quick sequential (sepsis-related) organ failure assessment (qSOFA) in COVID-19 patients. METHODS: We retrospectively collected medical data from 655 adult COVID-19 patients admitted to our hospital between July and November 2020. Data on demographics, clinical characteristics, and laboratory and radiological findings measured as part of standard care at admission were used to calculate NEWS, qSOFA score, CT-SS, peripheral perfusion index (PPI) and shock index (SI). Logistic regression and Cox proportional hazard models were used to predict mortality, which was our primary outcome. The predictive accuracy of distinct scoring systems was evaluated by the receiver-operating characteristic (ROC) curve analysis. RESULTS: The median age was 50.0 years [333 males (50.8%), 322 females (49.2%)]. Higher NEWS and SI was associated with time-to-death within 90-days, whereas higher age, CT-SS and lower PPI were significantly associated with time-to-death within both 14 days and 90 days in the adjusted Cox regression model. The CT-SS predicted different mortality risk levels within each stratum of NEWS and qSOFA and improved the discrimination of mortality prediction models. Combining CT-SS with NEWS score yielded more accurate 14 days (DBA: -0.048, p = 0.002) and 90 days (DBA: -0.066, p < 0.001) mortality prediction. CONCLUSION: Combining severity tools such as CT-SS, NEWS and qSOFA improves the accuracy of predicting mortality in patients with COVID-19. Inclusion of these tools in decision strategies might provide early detection of high-risk groups, avoid delayed medical attention, and improve patient outcomes.