The clinical diagnosis of equine sarcoids — Part 1: Assessment of sensitivity and specificity using a multicentre case-based online examination

•An online examination to assess diagnostic accuracy measures for the clinical diagnosis of equine sarcoids was developed.•One set of 40 clinical cases was assessed by 181 respondents.•Sarcoid diagnosis was accurate in 82.0% of the cases, while sensitivity and specificity were 83.3% and 79.6%, respectively.•The reported accuracy measures may improve the interpretation of existing and future studies on equine sarcoids. Equine clinicians and researchers often make the diagnosis of equine sarcoids (ES) on clinical assessment alone, without histopathological confirmation. However, the accuracy of the clinical diagnosis of ES has not been critically assessed. To assess sensitivity, specificity, positive and negative predictive values of the clinical diagnosis of ES, 40 clinical cases with histologically confirmed equine skin lesions (26 ES and 14 non-ES) were compiled in a representative online examination. For each case and lesion, at least one photograph and all relevant information were presented in a standardised format. This included the horse’s signalment, lesion localisation, lesion progression, presence of other skin lesions, earlier treatments and response to treatment. No information relevant for the assessment of the lesion was intentionally withheld. Fourteen ES experts, 39 board-certified equine specialists, 103 equine practitioners and 25 novices categorised the cases into ES or non-ES and graded their level of diagnostic confidence on a scale from 1 to 6 for each case. The overall success rate was 82.0% while sensitivity and specificity were 83.3% and 79.6%, respectively. The positive and negative predictive values were 88.4% and 72.0%, respectively, in the tested population with a 66% prevalence of ES. However, less experienced veterinarians were frequently wrong in their clinical judgement despite a high level of diagnostic confidence. Therefore, the authors propose to develop a diagnostic tool to help clinicians optimise their selection of lesions requiring a biopsy. Ultimately, this will help reduce costs and morbidity generated by unnecessary diagnostic and therapeutic efforts.