PurposeComplicated urinary tract infections (cUTIs) are among the most frequent health-care-associated infections. In patients with cUTI, Pseudomonas aeruginosa deserves special attention, since it can affect patients with serious underlying conditions. Our aim was to gain insight into the risk factors and prognosis of P. aeruginosa cUTIs in a scenario of increasing multidrug resistance (MDR).MethodsThis was a multinational, retrospective, observational study at 20 hospitals in south and southeastern Europe, Turkey, and Israel including consecutive patients with cUTI hospitalized between January 2013 and December 2014. A mixed-effect logistic regression model was performed to assess risk factors for P. aeruginosa and MDR P. aeruginosa cUTI.ResultsOf 1,007 episodes of cUTI, 97 (9.6%) were due to P. aeruginosa. Resistance rates of P. aeruginosa were: antipseudomonal cephalosporins 35 of 97 (36.1%), aminoglycosides 30 of 97 (30.9%), piperacillin–tazobactam 21 of 97 (21.6%), fluoroquinolones 43 of 97 (44.3%), and carbapenems 28 of 97 (28.8%). The MDR rate was 28 of 97 (28.8%). Independent risk factors for P. aeruginosa cUTI were male sex (OR 2.61, 95% CI 1.60–4.27), steroid therapy (OR 2.40, 95% CI 1.10–5.27), bedridden functional status (OR 1.79, 95% CI 0.99–3.25), antibiotic treatment within the previous 30 days (OR 2.34, 95% CI 1.38–3.94), indwelling urinary catheter (OR 2.41, 95% CI 1.43–4.08), and procedures that anatomically modified the urinary tract (OR 2.01, 95% CI 1.04–3.87). Independent risk factors for MDR P. aeruginosa cUTI were age (OR 0.96, 95% CI 0.93–0.99) and anatomical urinary tract modification (OR 4.75, 95% CI 1.06–21.26). Readmission was higher in P. aeruginosa cUTI patients than in other etiologies (23 of 97 [23.7%] vs 144 of 910 [15.8%], P=0.04), while 30-day mortality was not significantly different (seven of 97 [7.2%] vs 77 of 910 [8.5%], P=0.6).ConclusionPatients with P. aeruginosa cUTI had characteristically a serious baseline condition and manipulation of the urinary tract, although their mortality was not higher than that of patients with cUTI caused by other etiologies.

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Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers. Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. Results: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. Conclusion: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.