Introduction: Postoperative acute kidney injury (AKI) is a frequently observed complication after on-pump cardiac surgery (CS) and is associated with adverse patient outcomes. Early identification of patients at risk is essential for the prevention of AKI after CS. In this study, we analysed whether urinary tissue inhibitor of metalloproteinase 2 (TIMP-2) combined with urine insulin-like growth factor binding protein 7 (IGFBP-7) ([TIMP-2] × [IGFBP-7]) is an adequate diagnostic test to identify early AKI after on-pump CS. Methods: In 42 patients undergoing coronary artery bypass graft surgery, we surveyed individual risk factors for AKI and defined AKI by applying the Kidney Disease: Improving Global Outcomes (KDIGO) classification during the day of surgery and the following 2 days after surgery. Concentrations of urinary TIMP-2 multiplied by IGFBP-7 were recorded at four time points: at baseline pre-surgery, at the end of surgery, 4 hours after cardiopulmonary bypass (CPB) and at 8:00 am on the first postoperative day. Results: In total, 38% of the patients experienced AKI. The results showed a median baseline [TIMP-2] × [IGFBP-7] concentration of 0.3 (ng/ml)²/1,000, decreasing at the end of surgery and then increasing at the next measurement point 4 hours after CPB and further on the first postoperative day. On the first postoperative day, patients with AKI had significantly higher concentrations of [TIMP-2] × [IGFBP-7]. On the day of surgery, the concentration did not significantly differ between patients classified as KDIGO 0 or KDIGO 1 or 2. Previously published cutoff points of 0.3 and 2 were not confirmed in our study cohort. Conclusion: [TIMP-2] × [IGFBP-7] concentration can be used as a diagnostic test to identify patients at increased risk of AKI after CS on the first postoperative day. At earlier time points, no significant difference in [TIMP-2] × [IGFBP-7] concentration was found between patients classified as KDIGO 0 or KDIGO 1 or 2. Trial registration: German Clinical Trials Register (DRKS) DRKS00005457. Registered 26 November 2013.

Headspace analyses over microbial cultures using multi-capillary column-ion mobility spectrometry (MCC-IMS) could lead to a faster, safe and cost-effective method for the identification of pathogens. Recent studies have shown that MCC-IMS allows identification of bacteria and fungi, but no information is available from when on during their growth a differentiation between bacteria is possible. Therefore, we analysed the headspace over human pathogenic reference strains of Escherichia coli and Pseudomonas aeruginosa at four time points during their growth in a complex fluid medium. In order to validate our findings and to answer the question if the results of one bacterial strain can be transferred to other strains of the same species, we also analysed the headspace over cultures from isolates of random clinical origin. We detected 19 different volatile organic compounds (VOCs) that appeared or changed their signal intensity during bacterial growth. These included six VOCs exclusively changing over E. coli cultures and seven exclusively changing over P. aeruginosa cultures. Most changes occurred in the late logarithmic or static growth phases. We did not find differences in timing or trends in signal intensity between VOC patterns of different strains of one species. Our results show that differentiation of human pathogenic bacteria by headspace analyses using MCC-IMS technology is best possible during the late phases of bacterial growth. Our findings also show that VOC patterns of a bacterial strain can be transferred to other strains of the same species.

The early beginning of an adequate antibiotic therapy is crucial in hospital-acquired pneumonia (HAP), but depends on the results of conventional microbiological diagnostics (cMD). It was the aim of this study to evaluate the performance and turnaround times of a new point-of-care multiplex polymerase chain reaction (mPCR) system for rapid identification of pathogens and antibiotic resistance markers. We assessed the applicability of the system under real-life conditions in critical ill patients with HAP. We enrolled forty critical ill patients with clinical signs for HAP into an observational study. Two samples of respiratory secretions were collected during one course of aspiration and cMD and mPCR testing (Unyvero, Curetis AG, Holzgerlingen, Germany) were performed immediately. The mPCR device was operated as a point-of-care system at the intensive care unit. We compared turnaround times, results of pathogen identification and results of antibiotic resistance testing of both methods. Mean turnaround times (min-max) were 6.5 h (4.7–18.3 h) for multiplex PCR and 71 h (37.2–217.8 h) for conventional microbiology (final cMD results, incomplete results neglected). 60 % (n = 24) of the mPCR tests were completely valid. Complete test failure occurred in 10 % (n = 4) and partial test failure occurred in 30 % (n = 12). We found concordant results in 45 % (n = 18) and non-concordant results in 45 % (n = 18) of all patients. 55 % (n = 16) of the results were concordant in patients with a clinical pulmonary infection score (CPIS) > 5 (n = 29). Concordant results included three cases of multidrug resistant bacteria. MPCR frequently detected antibiotic resistance markers that were not found by cMD. Unyvero allowed point-of-care microbial testing with short turnaround times. The performance of the system was poor. However, an improved system with a more reliable performance and an extended microbial panel could be a useful addition to cMD in intensive care medicine. ClinicalTrials.gov NCT01858974 (registered 16 May 2013)

Diagnosis of bacterial infections until today mostly relies on conventional microbiological methods. The resulting long turnaround times can lead to delayed initiation of adequate antibiotic therapy and prolonged periods of empiric antibiotic therapy (e.g., in intensive care medicine). Therewith, they contribute to the mortality of bacterial infections and the induction of multidrug resistances. The detection of species specific volatile organic compounds (VOCs) emitted by bacteria has been proposed as a possible diagnostic approach with the potential to serve as an innovative point-of-care diagnostic tool with very short turnaround times. A range of spectrometric methods are available which allow the detection and quantification of bacterial VOCs down to a range of part per trillion. This narrative review introduces the application of spectrometric analytical methods for the purpose of detecting VOCs of bacterial origin and their clinical use for diagnosing different infectious conditions over the last decade. Key Points: • Detection of VOCs enables bacterial differentiation in various medical conditions. • Spectrometric methods may function as point-of-care diagnostics in near future.