Skin regeneration,repair, and reconstruction: present and future

European Surgery - Large skin defects caused by trauma (e.g., burns) or due to other reasons (e.g., tumor-related skin resections) require sufficient skin replacement. The constant improvement of...     

Praxis der perioperativen Prävention von Phantomschmerz: eine deutschlandweite Umfrage

Die Anaesthesiologie - Phantomschmerzen haben eine hohe Prävalenz nach Majoramputationen und sind mit einer zusätzlichen Einschränkung der Lebensqualität verbunden....     

Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.     

Higher Preimplantation Opioid Doses Associated With Long‐Term Spinal Cord Stimulation Failure in 211 Patients With Failed Back Surgery Syndrome

ObjectiveSpinal cord stimulation (SCS) is an effective treatment in failed back surgery syndrome (FBSS). We studied the effect of preimplantation opioid use on SCS outcome and the effect of SCS on opioid use during a two-year follow-up period.Materials and methodsThe study cohort included 211 consecutive FBSS patients who underwent an SCS trial from January 1997 to March 2014. Participants were divided into groups, which were as follows: 1) SCS trial only (n = 47), 2) successful SCS (implanted and in use throughout the two-year follow-up period, n = 131), and 3) unsuccessful SCS (implanted but later explanted or revised due to inadequate pain relief, n = 29). Patients who underwent explantation for other reasons (n = 4) were excluded. Opioid purchase data from January 1995 to March 2016 were retrieved from national registries.ResultsHigher preimplantation opioid doses associated with unsuccessful SCS (ROC: AUC = 0.66, p = 0.009), with 35 morphine milligram equivalents (MME)/day as the optimal cutoff value. All opioids were discontinued in 23% of patients with successful SCS, but in none of the patients with unsuccessful SCS (p = 0.004). Strong opioids were discontinued in 39% of patients with successful SCS, but in none of the patients with unsuccessful SCS (p = 0.04). Mean opioid dose escalated from 18 ± 4 MME/day to 36 ± 6 MME/day with successful SCS and from 22 ± 8 MME/day to 82 ± 21 MME/day with unsuccessful SCS (p < 0.001).ConclusionsHigher preimplantation opioid doses were associated with SCS failure, suggesting the need for opioid tapering before implantation. With continuous SCS therapy and no explantation or revision due to inadequate pain relief, 39% of FBSS patients discontinued strong opioids, and 23% discontinued all opioids. This indicates that SCS should be considered before detrimental dose escalation.     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.