Use of artificial intelligence for tailored routine urine analyses.

OBJECTIVES: Urine is the most common material tested in clinical microbiology laboratories. Automated analysis is already performed, permitting quicker results and decreasing the laboratory technologist's (LT) workload. These automatic systems have introduced digital imaging concepts. PhenoMATRIX (PHM) is an artificial intelligence software that merges picture algorithms and user rules to provide presumptive results. This study aimed at designing a tailored workflow using PHM, performing its validation and checking its performance in routine practice. METHODS: Two data collections including 96 and 135 urine samples from nephrostomy/ureterostomy and artificial bladder (US), 948 and 1257 urine samples from catheter (UC) and 3251 and 2027 midstream urine (MSU) were used to compare LT results with those obtained using two versions of PHM. Another 19 US, 102 UC and 508 MSU were used to monitor performance level 3 months after routine implementation. RESULTS: Before and after revisions, agreement between the first version of PHM and LT results were 83% (95% confidence interval [CI], 74.3-90.2) and 83% (95% CI, 75.3-90.9) (US), 66.7% (95% CI, 63.5-69.5) and 71.7% (95% CI, 68.8-74.4) (UC) and 65.4% (95% CI, 63.8-67.1) and 76% (95% CI, 74.1-77.1) (MSU). The second version improved results, demonstrating 96.2% (95% CI, 91.6-98.8) and 97% (95% CI, 92.6-99.2) (US), 87.5% (95% CI, 85.5-89.2) and 88.9% (95% CI, 87.0-90.5) (UC) and 91% (95% CI, 89.7-92.1) and 92% (95% CI, 91.1-93.4) (MSU) of agreement with LT results before and after revisions. The reliability of PHM results was confirmed by a routine study demonstrating 92% (95% CI, 90.0-94.2) overall agreement. CONCLUSIONS: PHM showed high performance, with >90% of results in agreement with LT. PHM could help standardize and secure results, prioritize positive plates during analytical workflow and likely save LT time.

Metapopulation ecology links antibiotic resistance, consumption, and patient transfers in a network of hospital wards.

Antimicrobial resistance (AMR) is a global threat. A better understanding of how antibiotic use and between-ward patient transfers (or connectivity) impact population-level AMR in hospital networks can help optimize antibiotic stewardship and infection control strategies. Here, we used a metapopulation framework to explain variations in the incidence of infections caused by seven major bacterial species and their drug-resistant variants in a network of 357 hospital wards. We found that ward-level antibiotic consumption volume had a stronger influence on the incidence of the more resistant pathogens, while connectivity had the most influence on hospital-endemic species and carbapenem-resistant pathogens. Piperacillin-tazobactam consumption was the strongest predictor of the cumulative incidence of infections resistant to empirical sepsis therapy. Our data provide evidence that both antibiotic use and connectivity measurably influence hospital AMR. Finally, we provide a ranking of key antibiotics by their estimated population-level impact on AMR that might help inform antimicrobial stewardship strategies.