The Significance of Polymicrobial Growth in Urine: Contamination or True Infection

Bacterial prostatitis affects 1% of men, with increased incidence in the elderly. It is defined by the frequency and urgency to urinate, localized pain, and positive bacterial cultures in expressed seminal fluids. Acute bacterial prostatitis frequently progresses to chronicity, which is marked by recurrent acute episodes interspersed with asymptomatic periods of variable duration. Up to 80% of bacterial prostatitis cases are caused by Gram-negative uropathogenic E. coli (UPEC) or Gram-positive E. faecalis. Antibiotic treatment is standard of care, however, global dissemination of antimicrobial resistant uropathogens threatens efficacy of therapy. Thus, development of non-antibiotic-based approaches to treat bacterial prostatitis is a priority. One challenge is that the immune response to infection in the prostate is incompletely understood. We used a mouse model of transurethral bacterial instillation to study the immune response to UPEC or E. faecalis prostate infection. Both uropathogens exhibited tropism for the prostate over the bladder early post-infection. UPEC infection induced greater proinflammatory cytokine expression and neutrophil and monocyte infiltration compared to E. faecalis infection. Following challenge infection, cytokine responses and myeloid cell infiltration were largely comparable to primary infection.Characteristic of memory responses, more lymphoid cells infiltrating the prostate in the second infection compared to the primary infection. Unexpectedly, however, bacterial burden in prostates challenged with either UPEC or E. faecalis was equal or greater than in primary infection, despite that an adaptive response to UPEC infection was evident in the bladder of the same animals. Thus, an immune response to primary infection is initiated, however it does not protect against reinfection. Our findings support the idea that chronic or recurrent prostatitis develops in the absence of efficacious immunity to infection. A greater understanding of the mechanisms underlying this observation may point to actionable targets for immunotherapy.

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Can the application of machine learning to electronic health records guide antibiotic prescribing decisions for suspected urinary tract infection in the Emergency Department?

Rockenschaub

Gill

McNulty

et al. 2022

Preprint

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Background: Urinary tract infections (UTIs) are a major cause of emergency hospital admissions, but it remains challenging to diagnose them reliably. Application of machine learning (ML) to routine patient data could support clinical decision-making. We developed a ML model for bacteriuria in the ED and evaluated its performance in key patient groups to determine scope for its future use in clinical practice. Methods: We used retrospective electronic health records from a large UK hospital (2011-2019). Non-pregnant adults who attended the ED and had a urine sample cultured were eligible for inclusion. The primary outcome was predominant bacterial growth ≥104 cfu/mL in urine. Predictors included demography, medical history, ED diagnoses, blood tests, and urine flow cytometry. Linear and tree-based models were trained via repeated cross-validation, re-calibrated, and validated on data from 2018/19. Changes in performance were investigated by age, sex, ethnicity, and suspected ED diagnosis, and compared to clinical judgement. Results: Among 12,680 included samples, 4,677 (36.9%) showed bacterial growth. Relying primarily on flow cytometry parameters, our best model achieved an area under the ROC curve (AUC) of 0.813 (95% CI 0.792-0.834) in the test data, and achieved both higher sensitivity and specificity compared to proxies of clinician's judgement. Performance remained stable for white and non-white patients but was lower during a period of laboratory procedure change in 2015, in patients ≥65 years (AUC 0.783, 95% CI 0.752-0.815), in men (AUC 0.758, 95% CI 0.717-0.798). Performance was also slightly reduced in patients with recorded suspicion of UTI (AUC 0.797, 95% CI 0.765-0.828). Conclusions: Our results suggest scope for use of ML in ED decision making for suspected UTI but performance varied with patient characteristics. Clinical utility of predictive models for UTI is therefore likely to differ for important patient subgroups including women <65 years, women ≥65 years, and men. Tailored models and decision thresholds may be required that account for differences in achievable performance, background incidence, and risks of infectious complications in these groups.

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The Significance of Polymicrobial Growth in Urine: Contamination or True Infection

Cited by 8 publications

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Polymicrobial and monomicrobial bacteraemic urinary tract infection

Polymicrobial and monomicrobial bacteraemic urinary tract infection

The immune system fails to mount a protective response to Gram-positive or Gram-negative bacterial prostatitis

Can the application of machine learning to electronic health records guide antibiotic prescribing decisions for suspected urinary tract infection in the Emergency Department?

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