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Background: Phantom limb pain (PLP) is highly prevalent after amputation. However, the influence of non-painful sensations (PLS) remains unclear. This study examines the PLP-PLS index as a novel tool to differentiate PLP from PLS and explores the association of clinical factors with the index. Methods: We conducted a cross-sectional analysis of baseline data from 112 participants in a previous factorial trial in patients with unilateral traumatic lower limb amputation. Linear regression models were used to examine the associations between the index and various demographic, psychological and clinical factors. Logistic and Poisson regression, and e-value calculation were utilized for sensitivity analyses. Results: Adjusted multivariable linear regression models demonstrated significant associations of phantom movement sensation (β: −1.532; 95% CI: −2.615 to −0.449; p = 0.006) and time since amputation (β: 0.005; 95% CI: 0.0006 to 0.0101; p = 0.026) with the PLP-PLS index. These findings were confirmed by multivariable logistic regression (phantom movement sensation OR: 0.469; 95% CI: 0.200 to 1.099, p = 0.082; time since amputation OR: 1.003; 95% CI: 1.00003 to 1.007; p = 0.048) and sensitivity analyses. Conclusions: Time since amputation and phantom movement sensation likely reflect distinct phenotypes and potential mechanisms for PLP and PLS. The PLP-PLS index is a promising clinical tool for selecting therapies to prevent/treat PLP and for measuring treatment effects to modulate phantom pain. These findings emphasize the importance of understanding the mechanisms underlying PLP and PLS for improving clinical management and guiding future research. [ABSTRACT FROM AUTHOR]

Background: Pain management plays an essential role in postoperative recovery after lung surgeries. The Erector Spinae Plane Block (ESPB) is a widely used regional anaesthesia technique; however, few clinical trials have compared this block to active control in thoracic surgeries. This study evaluated the non‐inferiority of the analgesia provided by ESPB when compared to paravertebral block (PVB) in lung surgeries. Methods: Randomised, active‐controlled, blinded for patients and assessors, non‐inferiority trial. Patients who underwent unilateral lung surgeries were divided into two groups according to the regional anaesthesia technique—continuous ESPB or PVB at the T5 level. The primary outcome was to assess pain using a numerical rating scale (NRS) with a test of the interaction of three measures over 24 h postoperatively. An NRS score ≥ 7 was considered analgesia failure, and the prespecified non‐inferiority margin was 10%. Results: In the interim analysis that terminated this study, 120 participants were enrolled. ESPB patients reported higher mean NRS general values over 24 h, 4.6 ± 3.2 in the ESPB group versus 3.9 ± 2.9 in the PVB group, with a difference of −0.67 (−15.2%) and 95%CI: −1.29 to −0.05 (p =.02), demonstrating not non‐inferiority. In addition, the ESPB group presented higher NRS failure of analgesia over 24 h (p <.01) and required more postoperative opioids (p =.01 over 24 h). There was no difference in patient satisfaction between groups. Conclusion: This trial demonstrated that a continuous erector spinae plane block was not non‐inferior to a continuous paravertebral block for analgesia after lung surgery but resulted in higher levels of postoperative pain and opioid consumption. [ABSTRACT FROM AUTHOR]

Introduction: Neuromonitoring (electroencephalogram [EEG] and cerebral oximetry) is essential for appropriate anesthesia and neuroprotection assessment during pediatric cardiac surgery. Methods: We describe the intraoperative pediatric multimodal and multiparametric neuromonitoring pattern of the software system Neuron-Spectrum (Kandel®) that consists of continuous electroencephalogram (cEEG), spectral analysis, amplitude-integrated electroencephalogram (aEEG), depth of anesthesia monitor (NINDEX), and regional cerebral and somatic oximetry (near-infrared spectroscopy-INVOS™). A physiological algorithm for management using neuromonitoring and physiological data is also described. Results: Visual data examples are presented for interpretation of the cerebral perfusion and oxygenation, neurophysiological state, anesthesia depth, possible neurologic predictions, and identification of cerebral drug effects (EEG signature). Conclusion: The neuromonitoring model can be an effective tool for anesthesia control and to provide adequate cerebral oxygenation during surgery. [ABSTRACT FROM AUTHOR]