narcotic drugs

  • 文章类型: Journal Article
    背景:术后阿片类药物的使用存在显著差异。在一端,事实证明,适当的疼痛控制是患者管理的关键方面;另一方面,过去几十年与阿片类药物相关的药物过量和成瘾治疗大幅增加有关.我们假设可以确定影响长期阿片类药物使用的几个术前和术后风险因素。
    目的:评估成人脊柱畸形手术后至少5年使用阿片类药物的相关因素。
    方法:前瞻性随访研究组数据库。
    方法:纳入2009年至2016年接受择期脊柱手术的成年脊柱畸形患者。
    方法:阿片类药物的使用或至少5年的随访。使用非阿片类镇痛药,方法:回顾性分析接受择期脊柱畸形手术的患者。共有37个因素包括患者特征,射线照相测量,操作细节,术前和术后早期使用阿片类药物,并对机械并发症和修订进行分析。提供了有关已确定因素的详细信息。
    结果:共265例患者(215F,包括来自五个站点的50M)。平均随访时间为68.4±11.7(60~102)个月。平均而言,融合10.6±3.5水平。术前,64例(24.2%)患者使用阿片类药物。阿片类药物使用者的比率在6周时增加到33.6%,在6个月时减少到21.5%。随访期间,有患者停用了阿片类药物,而其他人已经开始和/或重新开始使用阿片类药物。因此,在最新的随访中,有59例(22.3%)患者仍在使用阿片类药物。多变量分析表明,在术后平均68个月,独立影响阿片类药物使用的因素,按照重要性的顺序,在第六周使用阿片类药物,术前使用阿片类药物和第6个月使用阿片类药物的比值比分别为2.88,2.51和2.38.在这些时间点,年龄等因素,合并症的数量,烟草使用,上一次脊柱手术前的时间和术后矢状面对齐影响阿片类药物的使用率.
    结论:发现与术前使用相比,6周时使用阿片类药物对长期使用阿片类药物的预测能力更强。在考虑成人脊柱畸形手术时,患者应充分了解有关阿片类药物使用的现实期望。
    There remains significant variability in the use of postoperative opioids. On one end, it is proven that appropriate pain control is a critical aspect of patient management; on the other end, past few decades have been associated with major increases in opioid-related overdoses and addiction treatment. We hypothesized that several pre- and postoperative risk factors affecting long-term opioid use could be identified.
    Evaluation of factors associated with minimum 5-year postoperative opioid use following adult spinal deformity surgery.
    Prospectively followed study group database.
    Adult spinal deformity patients who underwent elective spine surgery between 2009 and 2016 were included.
    Opioid usage or otherwise at minimum 5 years follow-up. Use of nonopioid analgesics, weak and strong opioids METHODS: Retrospective analysis of patients undergoing elective spinal deformity surgery. A total of 37 factors comprising patient characteristics, radiographic measurements, operative details, preoperative and early postoperative opioid use, and mechanical complications and revisions were analyzed. Details on identified factors were provided.
    A total of 265 patients (215F, 50M) from five sites were included. The mean follow-up duration was 68.4±11.7 (60-102) months. On average, 10.6±3.5 levels were fused. Preoperatively, 64 (24.2%) patients were using opioids. The rate of opioid users increased to 33.6% at 6 weeks and decreased to 21.5% at 6 months. During follow-up, there were patients who discontinued opioids, while others have started and/or restarted using opioids. As a result, 59 (22.3%) patients were still on opioids at the latest follow-up. Multivariate analyses showed that factors independently affecting opioid use at an average of 68 months postoperatively, in order of significance, were opioid use at sixth weeks, preoperative opioid use and opioid use at sixth months with the odds ratios of 2.88, 2.51, and 2.38 respectively. At these time points, factors such as age, number of comorbidities, tobacco use, the time of the last prior spine surgery and postoperative sagittal plane alignment affected opioid usage rates.
    Opioid usage at 6 weeks was found to be more predictive of long-term opioid use compared to preoperative use. Patients should be well informed to have realistic expectations regarding opioid use when considering adult spinal deformity surgery.
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  • 文章类型: Journal Article
    未经授权:为了研究RBPs在宫颈鳞状细胞癌(CESC)中的差异表达,分析麻醉药品对RBPs的调节作用,建立CESC患者的预后风险模型。
    UNASSIGNED:从癌症基因组图谱(TCGA)数据库和基因型-组织表达(GTEx)数据库获得来自CESC患者的癌症和正常样品的RNA-SEQ数据和临床病例数据。通过R语言筛选并富集差异表达的RBP。CMAP数据库用于预测调节RBP差异表达的麻醉药物。采用COX回归分析构建预后风险评分模型。计算每位CESC患者的风险评分,并根据中位风险评分分为高危组和低危组。采用Kaplan-Meier(KM)分析和受试者工作特征(ROC)曲线评价预后风险模型的预测效率,并分析预后风险模型与临床特征的相关性。免疫组化法检测组织中RNASEH2A和HENMT1的表达。
    未经证实:在CESC中有65个差异表达的RBPs。五种麻醉药,包括苯佐卡因,普鲁卡因,喷托维林,获得丁卡因来调节RBPs。生存分析显示7个基因与患者预后相关,通过COX回归构建CESC风险评分模型。风险评分可作为独立的预后因素。RNASEH2A和HENMT1在肿瘤中上调,能有效区分正常组织和肿瘤组织。
    UNASSIGNED:发现不同的麻醉药物对RBPs的差异表达具有不同的调节作用。基于差异表达的RBP,建立CESC患者预后风险评分模型。为制定个体化精准麻醉方案和癌痛镇痛方案提供思路,有助于提高癌症患者的围手术期生存率。
    UNASSIGNED: To investigate the differential expression of RBPs in cervical squamous cell carcinoma (CESC), analyze the regulatory effect of narcotic drugs on RBPs, and establish the prognostic risk model of CESC patients.
    UNASSIGNED: RNA-SEQ data and clinical case data of cancer and normal samples from CESC patients were obtained from the Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) database. Differentially expressed RBPs were screened by R language and enriched. The CMAP database is used to predict the anesthetic drugs that regulate the differential expression of RBPs. The prognostic risk score model was constructed by COX regression analysis. Risk score of each CESC patient was calculated and divided into high-risk group and low-risk group according to the median risk score. The prediction efficiency of prognostic risk model was evaluated by Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve, and the correlation between prognostic risk model and clinical characteristics was analyzed. Immunohistochemistry was used to detect the expression of RNASEH2A and HENMT1 in tissues.
    UNASSIGNED: There were 65 differentially expressed RBPs in CESC. Five anesthetics, including benzocaine, procaine, pentoxyverine, and tetracaine were obtained to regulate RBPs. Survival analysis showed that seven genes were related to the prognosis of patients, and the CESC risk score model was constructed by COX regression. The risk score can be used as an independent prognostic factor. RNASEH2A and HENMT1 are up-regulated in tumors, which can effectively distinguish normal tissues from tumor tissues.
    UNASSIGNED: It is found that different anesthetic drugs have different regulatory effects on the differential expression of RBPs. Based on the differentially expressed RBPs, the prognostic risk score model of CESC patients was constructed. To provide ideas for the formulation of individualized precise anesthesia scheme and cancer pain analgesia scheme, which is helpful to improve the perioperative survival rate of cancer patients.
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