Postoperative pain treatment after spinal fusion surgery : A systematic review with meta-analyses and trial sequential analyses

Patients undergoing spinal surgery are at high risk of acute and persistent postoperative pain. Therefore, adequate pain relief is crucial. This systematic review aimed to provide answers about best-proven postoperative analgesic treatment for patients undergoing lumbar 1- or 2-level fusions for degenerative spine diseases. We performed a search in PubMed, Embase, and The Cochrane Library for randomized controlled trials. The primary outcome was opioid consumption after 24 hours postoperatively. We performed meta-analyses, trial sequential analyses, and Grading of Recommendations assessment to accommodate systematic errors. Forty-four randomized controlled trials were included with 2983 participants. Five subgroups emerged: nonsteroidal anti-inflammatory drugs (NSAIDs), epidural, ketamine, local infiltration analgesia, and intrathecal morphine. The results showed a significant reduction in opioid consumption for treatment with NSAID (P < 0.0008) and epidural (P < 0.0006) (predefined minimal clinical relevance of 10 mg). Concerning secondary outcomes, significant reductions in pain scores were detected after 6 hours at rest (NSAID [P < 0.0001] and intrathecal morphine [P < 0.0001]), 6 hours during mobilization (intrathecal morphine [P = 0.003]), 24 hours at rest (epidural [P < 0.00001] and ketamine [P < 0.00001]), and 24 hours during mobilization (intrathecal morphine [P = 0.03]). The effect of wound infiltration was nonsignificant. The quality of evidence was low to very low for most trials. The results from this systematic review showed that some analgesic interventions have the capability to reduce opioid consumption compared with control groups. However, because of the high risk of bias and low evidence, it was impossible to recommend a "gold standard" for the analgesic treatment after 1- or 2-level spinal fusion surgery

Photoacoustic imaging for three-dimensional visualization and delineation of basal cell carcinoma in patients

Background: Photoacoustic (PA) imaging is an emerging non-invasive biomedical imaging modality that could potentially be used to determine the borders of basal cell carcinomas (BCC) preoperatively in order to reduce the need for repeated surgery.Methods: Two- and three-dimensional PA images were obtained by scanning BCCs using 59 wavelengths in the range 680-970 nm. Spectral unmixing was performed to visualize the tumor tissue distribution. Spectral signatures from 38 BCCs and healthy tissue were compared ex vivo.Results and discussion: The PA spectra could be used to differentiate between BCC and healthy tissue ex vivo (p < 0.05). Spectral unmixing provided visualization of the overall architecture of the lesion and its border.Conclusion: PA imaging can be used to differentiate between BCC and healthy tissue and can potentially be used to delineate tumors prior to surgical excision

Unique spectral signature of human cutaneous squamous cell carcinoma by photoacoustic imaging

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer with metastatic potential. To reduce reoperations due to nonradical excision, there is a need to develop a technique for identification of tumor margins preoperatively. Photoacoustic (PA) imaging is a novel imaging technology that combines the strengths of laser optics and ultrasound. Our aim was to determine the spectral signature of cSCC using PA imaging and to use this signature to visualize tumor architecture and borders. Two-dimensional PA images of 33 cSCCs and surrounding healthy skin were acquired ex vivo, using 59 excitation wavelengths from 680 to 970 nm. The spectral response of the cSCCs was compared to healthy tissue, and the difference was found to be greatest at wavelengths in the range 765 to 960 nm (P <.05). Three-dimensional PA images were constructed from spectra obtained in the y-z plane using a linear stepper motor moving along the x-plane. Spectral unmixing was then performed which provided a clear three-dimensional view of the distribution of tumor masses and their borders