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Assessment of the optimal age for a preventive ultrasonographic screening of the uterine health in bitches.

Monica MelandriGabriele BarellaSalvatore Alonge
Published in: Reproduction in domestic animals = Zuchthygiene (2019)
Studies about prevalence of uterine pathologies in bitches are scarce. Although correlation between age and uterine disorders was documented, the most suitable age for a preventive sonographic screening has not been proposed yet. Present study aimed to estimate the eligible age for an ultrasonographic screening of uterine abnormalities in dogs. Data regarding ultrasound examination and clinical records of non-pregnant intact females were retrospectively analysed. The age of each bitch was expressed as age ratio (actual/maximum age expected for the respective breed). The cut-off age ratio was determined by a ROC curve for overall uterine abnormalities. Frequencies of different abnormalities below and over the cut-off derived from the ROC curve were calculated and statistically analysed by chi-Square and OR. Prevalence of three categories of ultrasonographic findings was as follows: cystic endometrial hyperplasia (CEH) 18%; uterine collections (UC) 10.5%; masses (M) 1.3%. By the cut-off age ratio (0.325), derived by ROC curve (AUC = 0.91; SP 84.23%; SE 79.2%; PPV 83.4%; NPV 80%), 228 cases were divided into two subgroups: bitches over (exposed group: n.83) and below cut-off (control group: n.145). All abnormalities resulted more frequent in exposed group: OR was 24.96 (p < 0.0001: 71.1% over vs. 9% below cut-off) for overall abnormalities; 13.68 (p < 0.0001: 40.9% vs. 4.8%) for CEH; 6.13 (p < 0.002: 21.7% vs. 4.1%) for UC; 12.65 (p = 0.09: 3.6% vs. 0%) for M. Cystic endometrial hyperplasia represents the most common finding in adult bitches, followed by UC. A preventive sonographic screening for uterine abnormalities should start from 33% of expected longevity to preventively select animals requiring further evaluations.
Keyphrases
  • healthcare
  • magnetic resonance imaging
  • public health
  • risk factors
  • risk assessment
  • ultrasound guided
  • big data
  • climate change