Search

Objectives Carbapenem-resistant Enterobacteriaceae (CRE) are a major public-health threat. The most important mechanism of carbapenem resistance in CRE is carbapenemase production. Early identification of carbapenemase-producing Enterobacteriaceae (CPE) leads to improved clinical outcomes. This systematic review aimed to assess the accuracy and applicability of the modified Hodge test (MHT), the carbapenemase Nordmann–Poirel (Carba NP) test, the modified carbapenem inactivation method (mCIM) and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS) for CPE detection. Methods The meta-analysis included pooled sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC) curve and area under the curve (AUC). Results A total of 67 studies were included in the analysis. Pooled effect sizes (95% confidence interval) of the MHT, Carba NP, mCIM and MALDI-TOF/MS, respectively, were as follows: sensitivity, 92% (87–95%), 97% (94–98%), 99% (99–100%) and 99% (96–100%); specificity, 93% (86–97%), 100% (99–100%), 99% (96–100%) and 99% (96–100%); diagnostic odds ratio, 98.156 (48.175–199.995), 1277.710 (751.391–2172.692), 3597.352 (1287.575–10000) and 1781.360 (651.827–4868.228); and AUC, 0.97, 1, 1 and 1. Conclusion Carba NP, mCIM and MALDI-TOF/MS all demonstrated high accuracy in CPE detection, whereas the MHT is not recommended owing to some clear drawbacks. We recommend the selection of carbapenemase detection tests in the order of mCIM, Carba NP and MALDI-TOF/MS according to their simplicity, cost, and equipment and skills involved.

Purpose This observational study aimed to identify the independent risk factors for both the acquisition and mortality of carbapenemase-producing carbapenem-resistant Enterobacteriaceae (CP-CRE) bacteremia and further assess the in vitro antimicrobial activities of ceftazidime–avibactam (CAZ/AVI) and aztreonam–avibactam (ATM/AVI) against recent CRE bacteremic isolates. Patients and methods This observational study was conducted to reveal the risk factors and mortality rate for CP-CRE bacteremia between 2012 and 2018 and also evaluate the in vitro antimicrobial activities of CAZ/AVI and ATM/AVI against recent CRE bacteremic isolates from 2016 to 2018. Results A total of 81 non-repetitive isolates were collected from 2012 to 2018, with 67.90% (55/81) being CP-CRE. Old age (P = 0.01), transfusion [odds ratio (OR): 17.19; 95% CI: 3.15–93.72; P = 0.001], longer ICU stay (P = 0.02), cancer (OR: 15.91; 95% CI: 3.56–71.37; P < 0.001), and previous carbapenem exposure (OR: 27.86; 95% CI: 5.03–154.19; P = 0.001) were identified as independent risk factors for the acquisition of CP-CRE bacteremia compared with the ESBL bacteremia. The in vitro antimicrobial activities of CAZ/AVI and ATM/AVI against the CRE bacteremic isolates from 2016 to 2018 showed a respective susceptibility rate of 70.68% (41/58) and 100.00% (58/58). Conclusion The findings indicated that both CP-CRE/non-CP-CRE stratification and CRE resistance mechanism determination were necessary for better guiding the clinical management of CRE bacteremia: ATM/AVI probably works with both non-CP-CRE and CP-CRE bacteremia, even the most notorious double-carbapenemase producer with porin loss/deficiency, whereas CAZ/AVI works with most of the non-CP-CRE and KPC-producers in the region.

Background and Purpose: Hematoma expansion is closely associated with adverse functional outcomes in patients with intracerebral hemorrhage (ICH). Prediction of hematoma expansion would therefore be of great clinical significance. We therefore attempted to predict hematoma expansion using a dual-modal machine learning (ML) strategy which combines information from non-contrast computed tomography (NCCT) images and multiple clinical variables. Method: We retrospectively identified 140 ICH patients (57 with hematoma expansion) with 5,616 NCCT images of hematoma (2,635 with hematoma expansion) and 10 clinical variables. The dual-modal ML strategy consists of two steps. The first step is to derive a mono-modal predictor from a deep convolutional neural network (DCNN) using solely NCCT images. The second step is to achieve a dual-modal predictor by combining the mono-modal predictor with 10 clinical variables to predict hematoma growth using a multi-layer perception (MLP) network. Result: For the mono-modal predictor, the best performance was merely 69.5% in accuracy with solely the NCCT images, whereas the dual-modal predictor could boost the accuracy greatly to be 86.5% by combining clinical variables. Conclusion: To our knowledge, this is the best performance from using ML to predict hematoma expansion. It could be potentially useful as a screening tool for high-risk patients with ICH, though further clinical tests would be necessary to show its performance on a larger cohort of patients.