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用于检测马子宫内膜炎的子宫内膜拭子样本细胞学和培养的贝叶斯准确性估计和适合目的阈值。

Bayesian accuracy estimates and fit for purpose thresholds of cytology and culture of endometrial swab samples for detecting endometritis in mares.

机构信息

Rossdales Veterinary Surgeons, Beaufort Cottage Stables, Newmarket, Suffolk, United Kingdom.

Department of Comparative Biomedical Sciences, The Royal Veterinary College, University of London, Hertfordshire, United Kingdom.

出版信息

Prev Vet Med. 2022 Dec;209:105783. doi: 10.1016/j.prevetmed.2022.105783. Epub 2022 Oct 20.

Abstract

The overall aim of this work was to identify the potential impact of misclassification errors associated with routine screening and diagnostic testing for endometritis in mares. Using Bayesian latent class models (BLCM), specific objectives were to: 1) estimate the diagnostic accuracy of cytology and culture of endometrial swab samples to detect endometritis in mares; 2) assess the impact of different cytology thresholds on test accuracy and misclassification costs; and 3) assess the sensitivity (Se) and specificity (Sp) of a diagnostic strategy including both tests interpreted in series and parallel. Diagnostic and pre-breeding endometrial swab samples collected from 3448 mares based at breeding premises located in the South East of England between 2014 and 2020 were retrospectively analysed. Culture results were classified as positive according to three different case definitions: (A) > 90% of the growth colonies were a monoculture; (B) pathogenic or pathogenic and non-pathogenic bacteria were identified; and (C) any growth was observed. Endometrial smears were graded based on the percent of polymorphonuclear cells (PMN) per high power field (HPF). A hierarchical BLCM was fitted using the cross-tabulated results of the three culture case definitions with a cytology threshold fixed at > 0.5% PMN. Fit for purpose cytology thresholds were proposed using a misclassification cost analysis in the context of good antimicrobial stewardship and for varying endometritis prevalence estimates. Median [95% Bayesian credible intervals (BCI)] cytology Se estimates were 6.5% (2.2-11.6), 6.4% (2.2-10.8) and 6.3% (2.2-10.8) for scenario A, B and C, respectively. Median (95% BCI) cytology Sp estimates were 88.8% (83.1-94.8), 88.9% (83.9-93.8) and 88.8% (84.0-93.8) for scenarios A, B and C, respectively. Median (95% BCI) culture Se estimates were 37.5% (29.9-46.0), 42.3% (33.8-51.1) and 46.4% (35.7-55.9) for scenarios A, B and C, respectively. Median (95% BCI) culture Sp estimates were 92.8% (84.3-99.0), 91.5% (82.5-98.0) and 90.8% (80.1-97.4) for scenarios A, B and C, respectively. Regardless of the culture case definition, Se and Sp of cytology (> 0.5% PMN) was lower than previously reported for swab samples in studies using histology as the reference standard test. The misclassification cost term decreased as the cytology threshold increased for all scenarios and all prevalence contexts, suggesting that, regardless of the endometritis prevalence in the population, increasing the cytology threshold would reduce the misclassification costs associated with false positive mares contributing to good antimicrobial stewardship.

摘要

本研究旨在明确常规筛查和诊断性检测在马属动物子宫内膜炎中的潜在误诊误差。采用贝叶斯潜在类别模型(BLCM),具体目标为:1)评估子宫内膜拭子样本细胞学和培养检测马属动物子宫内膜炎的诊断准确性;2)评估不同细胞学阈值对检测准确性和分类错误成本的影响;3)评估包括串联和并联两种检测的诊断策略的灵敏度(Se)和特异性(Sp)。本研究回顾性分析了 2014 年至 2020 年期间,在英格兰东南部繁殖场采集的 3448 匹母马的诊断和繁殖前子宫内膜拭子样本。根据三种不同的病例定义对培养结果进行分类:(A)>90%的生长菌落为纯培养物;(B)鉴定出病原体或病原体和非病原体细菌;(C)观察到任何生长。子宫内膜涂片根据高倍镜视野(HPF)中多形核细胞(PMN)的百分比进行分级。采用交叉列联表结果的分层 BLCM 拟合方法,将细胞学阈值固定在>0.5%PMN。在良好的抗菌药物管理和不同子宫内膜炎流行率估计的背景下,采用分类错误成本分析提出了适合目的的细胞学阈值。方案 A、B 和 C 中,中位数(95%贝叶斯可信区间(BCI))细胞学 Se 估计值分别为 6.5%(2.2-11.6)、6.4%(2.2-10.8)和 6.3%(2.2-10.8)。方案 A、B 和 C 中,中位数(95%BCI)细胞学 Sp 估计值分别为 88.8%(83.1-94.8)、88.9%(83.9-93.8)和 88.8%(84.0-93.8)。方案 A、B 和 C 中,中位数(95%BCI)培养 Se 估计值分别为 37.5%(29.9-46.0)、42.3%(33.8-51.1)和 46.4%(35.7-55.9)。方案 A、B 和 C 中,中位数(95%BCI)培养 Sp 估计值分别为 92.8%(84.3-99.0)、91.5%(82.5-98.0)和 90.8%(80.1-97.4)。无论采用哪种培养病例定义,与以前使用组织学作为参考标准检测的研究相比,PMN>0.5%的细胞学(Se 和 Sp)都较低。在所有情况下,所有流行率背景下,随着细胞学阈值的增加,分类错误成本都呈下降趋势,这表明,无论人群中的子宫内膜炎流行率如何,增加细胞学阈值都将减少与假阳性马相关的分类错误成本,从而有助于良好的抗菌药物管理。

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