Intensive care in an older population: Influence of age, comorbidities and sex in 304 plastic and reconstructive surgical patients older than 75 years.

AIM: An aging population will lead to an increasing demand for critical care resources. Hence, we evaluated the potential influence of age, comorbidities and sex in plastic and reconstructive patients ≥75 years that were admitted to the intensive care unit (ICU). METHODS: We included 304 patients who required intensive care between 2000 and 2019. Besides patient demographics, medical case characteristics were statistically evaluated. RESULTS: In this study, 184 patients were female (61%) (120 male), the median age was 81.8 years (25th and 75th percentiles: 77.4-87.2) with a range of 75.0-98.9 years. The median length of stay in the ICU was 12 days (25th and 75th percentiles: 3-28) with a range of 0-382 days. The reasons for admission were burn injury (n = 230, 76%), necrotizing fasciitis (n = 34, 11%), non-combustion-related traumas (n = 22, 7%) and postoperative observation after plastic surgery procedures (n = 18, 6%). In total, 108 patients (36%), who were significantly older (P = 0.005) and had a significantly shorter stay (P < 0.001) compared with the surviving cohort, died during their stay in the ICU. Our multivariable logistic regression model revealed that age (odds ratio: 1.05 [1.01, 1.09]; P = 0.017) and number of operations (odds ratio: 0.75 [0.60, 0.96]; P = 0.023) were significant predictors for death in the ICU. DISCUSSION: Age plays a critical role in determining fatal outcome of old patients requiring intensive care. In contrast, sex and number of comorbidities shows no significant influence. Geriatr Gerontol Int 2022; 22: 597-602.

Reproducibility of an Automated Quantitative MRI Assessment of Low-Grade Knee Articular Cartilage Lesions.

OBJECTIVE: The goal of this study was to assess the reproducibility of an automated knee cartilage segmentation of 21 cartilage regions with a model-based algorithm and to compare the results with manual segmentation. DESIGN: Thirteen patients with low-grade femoral cartilage defects were included in the study and were scanned twice on a 7-T magnetic resonance imaging (MRI) scanner 8 days apart. A 3-dimensional double-echo steady-state (3D-DESS) sequence was used to acquire MR images for automated cartilage segmentation, and T2-mapping was performed using a 3D triple-echo steady-state (3D-TESS) sequence. Cartilage volume, thickness, and T2 and texture features were automatically extracted from each knee for each of the 21 subregions. DESS was used for manual cartilage segmentation and compared with automated segmentation using the Dice coefficient. The reproducibility of each variable was expressed using standard error of measurement (SEM) and smallest detectable change (SDC). RESULTS: The Dice coefficient for the similarity between manual and automated segmentation ranged from 0.83 to 0.88 in different cartilage regions. Test-retest analysis of automated cartilage segmentation and automated quantitative parameter extraction revealed excellent reproducibility for volume measurement (mean SDC for all subregions of 85.6 mm3), for thickness detection (SDC = 0.16 mm) and also for T2 values (SDC = 2.38 ms) and most gray-level co-occurrence matrix features (SDC = 0.1 a.u.). CONCLUSIONS: The proposed technique of automated knee cartilage evaluation based on the segmentation of 3D MR images and correlation with T2 mapping provides highly reproducible results and significantly reduces the segmentation effort required for the analysis of knee articular cartilage in longitudinal studies.

Characterization of transcriptional changes in ERG rearrangement-positive prostate cancer identifies the regulation of metabolic sensors such as neuropeptide Y.

ERG gene rearrangements are found in about one half of all prostate cancers. Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. To identify transcriptional changes in prostate cancer, including tumors with ERG gene rearrangements, we performed a meta-analysis on published gene expression data followed by validations on mRNA and protein levels as well as first functional investigations. Eight expression studies (n = 561) on human prostate tissues were included in the meta-analysis. Transcriptional changes between prostate cancer and non-cancerous prostate, as well as ERG rearrangement-positive (ERG+) and ERG rearrangement-negative (ERG-) prostate cancer, were analyzed. Detailed results can be accessed through an online database. We validated our meta-analysis using data from our own independent microarray study (n = 57). 84% and 49% (fold-change>2 and >1.5, respectively) of all transcriptional changes between ERG+ and ERG- prostate cancer determined by meta-analysis were verified in the validation study. Selected targets were confirmed by immunohistochemistry: NPY and PLA2G7 (up-regulated in ERG+ cancers), and AZGP1 and TFF3 (down-regulated in ERG+ cancers). First functional investigations for one of the most prominent ERG rearrangement-associated genes - neuropeptide Y (NPY) - revealed increased glucose uptake in vitro indicating the potential role of NPY in regulating cellular metabolism. In summary, we found robust population-independent transcriptional changes in prostate cancer and first signs of ERG rearrangements inducing metabolic changes in cancer cells by activating major metabolic signaling molecules like NPY. Our study indicates that metabolic changes possibly contribute to the selective pressure favoring ERG rearrangements in prostate cancer.