Urinary tract infection (UTI) is a common disease with significant morbidity and economic burden, accounting for a significant part of the workload in clinical microbiology laboratories. Current clinical chemisty point-of-care diagnostics rely on imperfect dipstick analysis which only provides indirect and insensitive evidence of urinary bacterial pathogens. An electronic nose (eNose) is a handheld device mimicking mammalian olfaction that potentially offers affordable and rapid analysis of samples without preparation at athmospheric pressure. In this study we demonstrate the applicability of ion mobility spectrometry (IMS) –based eNose to discriminate the most common UTI pathogens from gaseous headspace of culture plates rapidly and without sample preparation. We gathered a total of 101 culture samples containing four most common UTI bacteries: E. coli, S. saprophyticus, E. faecalis, Klebsiella spp and sterile culture plates. The samples were analyzed using ChemPro 100i device, consisting of IMS cell and six semiconductor sensors. Data analysis was conducted by linear discriminant analysis (LDA) and logistic regression (LR). The results were validated by leave-one-out and 5-fold cross validation analysis. In discrimination of sterile and bacterial samples sensitivity of 95% and specificity of 97% were achieved. The bacterial species were identified with sensitivity of 95% and specificity of 96% using eNose as compared to urine bacterial cultures. In conclusion: These findings strongly demonstrate the ability of our eNose to discriminate bacterial cultures and provides a proof of principle to use this method in urinanalysis of UTI.

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Background: Soft tissue infections, including postoperative wound infections, result in a significant burden for modern society. Rapid diagnosis of wound infections is based on bacterial stains, cultures, and polymerase chain reaction assays, and the results are available earliest after several hours, but more often not until days after. Therefore, antibiotic treatment is often administered empirically without a specific diagnosis. Methods: We employed our electronic nose (eNose) system for this proof-of-concept study, aiming to differentiate the most relevant bacteria causing wound infections utilizing a set of clinical bacterial cultures on identical blood culture dishes, and established bacterial lines from the gaseous headspace. Results: Our eNose system was capable of differentiating both methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, Escherichia coli, Pseudomonas aeruginosa, and Clostridium perfringens with an accuracy of 78% within minutes without prior sample preparation. Most importantly, the system was capable of differentiating MRSA from MSSA with a sensitivity of 83%, a specificity of 100%, and an overall accuracy of 91%. Conclusions: Our results support the concept of rapid detection of the most relevant bacteria causing wound infections and ultimately differentiating MRSA from MSSA utilizing gaseous headspace sampling with an eNose.

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Objective Emergency department (ED) crowding is a global problem associated with negative patient outcomes such as mortality and prolonged length of stay. Forecasting overcrowding would enable pre-emptive strategical maneuvers and is a subject of constant academic interest. However, most studies focus on forecasting arrivals in United States ED setting. We propose a novel and intuitive crowding metric called daily peak occupancy and assess forecasting ED crowding in a Nordic Combined ED using both established and novel predictive algorithms.Methods All episodes of care in Tampere University Hospital ED were acquired from December 1, 2014 to June 19, 2019, amounting to 488 167 individual events. Predictability of two target variables was investigated: total daily arrivals (TDA) and daily peak occupancy (DPO) with forecast horizon of one day. Three models were investigated: Seasonal Autoregressive Moving Average (SARIMA), Facebook’s Prophet algorithm, and General Linear Model (GLM). Calendar variables were used as independent variables.Results SARIMA outperformed other models in predicting both total daily arrivals and daily peak occupancy with mean absolute percentage errors of 6.6% (±5.3) and 12.4% (±10.7) respectively. Next day overcrowding can be predicted using SARIMA with an AUC of 0.74 and accuracy of 79 %.Conclusion Predictive models can be utilized in a Nordic emergency medicine setting with similar or better accuracy as previously reported. Predicting future occupancy is possible but more challenging than predicting arrivals. Predicting peaks in demand remains a significant challenge. Future work should focus on investigating the value of exogenous variables in increasing model sensitivity.