Association of Warmer Weather and Infectious Complications Following Transrectal Ultrasound-Guided Prostate Biopsy.
Yu-Chen ChenHao-Wei ChenShu-Pin HuangSzu-Huai LinTing-Yin ChuChing-Chia LiYung-Shun JuanWen-Jeng WuPublished in: Journal of personalized medicine (2022)
The seasonal and meteorological factors in predicting infections after urological interventions have not been systematically evaluated. This study aimed to determine the seasonality and the effects of the weather on the risk and severity of infectious complications (IC) after a transrectal ultrasound-guided prostate biopsy (TRUS-Bx). Using retrospectively collected data at the tertiary care hospital in Taiwan, we investigated the seasonal and meteorological differences in IC after TRUS-Bx. The IC included urinary tract infection (UTI), sepsis, and a positive culture finding (PCF). The severity was assessed on the basis of the Common Terminology Criteria for Adverse Events grading system. The prevalences of the infectious complications (UTI, sepsis, PCF and grade ≥ 3 IC) were significantly higher in the summer than in the winter. Monthly temperature and average humidity were significant factors for IC. After adjusting the demographic factors, multivariate regression revealed that UTI, sepsis, PCF, and grade ≥ 3 IC increased by 12.1%, 16.2%, 21.3%, and 18.6% for every 1 °C increase in the monthly average temperature, respectively (UTI: p = 0.010; sepsis: p = 0.046; PCF: p = 0.037; grade ≥ 3 IC: p = 0.021). In conclusion, the development and severity of IC after TRUS-Bx had significant seasonality. These were dose-dependently associated with warmer weather. Infectious signs after TRUS-Bx should be monitored more closely and actively during warm weather.
Keyphrases
- urinary tract infection
- ultrasound guided
- acute kidney injury
- intensive care unit
- septic shock
- fine needle aspiration
- prostate cancer
- tertiary care
- risk factors
- air pollution
- healthcare
- emergency department
- big data
- artificial intelligence
- benign prostatic hyperplasia
- electronic health record
- single cell
- deep learning
- high speed