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Clinical reasoning in canine spinal disease: what combination of clinical information is useful?

机译:犬脊柱疾病的临床推理:哪些临床信息组合有用?

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Spinal disease in dogs is commonly encountered in veterinary practice. Numerous diseases may cause similar clinical signs and presenting histories. The study objective was to use statistical models to identify combinations of discrete parameters from the patient signalment, history and neurological examination that could suggest the most likely diagnoses with statistical significance. A retrospective study of 500 dogs referred to the Queen Mother Hospital for Animals before June 2012 for the investigation of spinal disease was performed. Details regarding signalment, history, physical and neurological examinations, neuroanatomical localisation and imaging data were obtained. Univariate analyses of variables (breed, age, weight, onset, deterioration, pain, asymmetry, neuroanatomical localisation) were performed, and variables were retained in a multivariate logistic regression model if P<0.05. Leading diagnoses were intervertebral disc extrusion (IVDE, n=149), intervertebral disc protrusion (n=149), ischaemic myelopathy (IM, n=48) and neoplasms (n=44). Multivariate logistic regression characterised IM and acute non-compressive nucleus pulposus extrusions as the only peracute onset, non-progressive, non-painful and asymmetrical T3-L3 myelopathies. IVDE was most commonly characterised as acute onset, often deteriorating, painful and largely symmetrical T3-L3 myelopathy. This study suggests that most spinal diseases cause distinctive combinations of presenting clinical parameters (signalment, onset, deterioration, pain, asymmetry, neuroanatomical localisation). Taking particular account of these parameters may aid decision making in a clinical setting.
机译:狗的脊椎疾病在兽医实践中很常见。许多疾病可能导致相似的临床体征和病史。该研究的目的是使用统计模型从患者的信号,病史和神经系统检查中识别出离散参数的组合,这些组合可能暗示最可能的诊断具有统计学意义。回顾性研究了500只狗,并于2012年6月之前将其转交给了女王母亲动物医院进行脊髓疾病调查。获得了有关信号传导,病史,身体和神经系统检查,神经解剖定位和成像数据的详细信息。对变量(品种,年龄,体重,发作,恶化,疼痛,不对称,神经解剖学定位)进行单变量分析,如果P <0.05,则将变量保留在多元逻辑回归模型中。领先的诊断是椎间盘突出(IVDE,n = 149),椎间盘突出(n = 149),缺血性脊髓病(IM,n = 48)和肿瘤(n = 44)。多元logistic回归将IM和急性非压迫性髓核突出症作为唯一的急性发作,非进行性,非疼痛性和不对称性T3-L3骨髓病。 IVDE最常见的特征是急性发作,经常恶化,疼痛且很大程度上对称的T3-L3脊髓病。这项研究表明,大多数脊柱疾病会引起临床参数的独特组合(信号,发病,恶化,疼痛,不对称,神经解剖学定位)。特别考虑这些参数可能有助于临床环境中的决策。

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