Fecteau G, Paré J, Van Metre D C, Smith B P, Holmberg C A, Guterbock W, Jang S
Département de sciences cliniques, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, Québec.
Can Vet J. 1997 Feb;38(2):101-4.
In human, equine, and bovine neonates, early diagnosis of bacteremia remains a challenge for the internist. The objective of this study was to develop a predictive model for risk of bacteremia, based on a clinical evaluation system called the clinical sepsis score. Blood from 90 ill calves, 1- to 14-days-old from a calf-raising farm in the San Joaquin Valley of California was cultured. The calves were also scored according to a clinical score for hydration status, fecal appearance, general attitude, appearance of scleral vessels, and umbilical abnormality. Age, rectal temperature, heart rate, respiratory rate, and presence or absence of a focal site of infection were recorded. Prevalence of bacteremia was 31% (28/90). A logistic regression model indicated that high clinical score, presence of a focal infection, and increased age were associated with an increased risk of bacteremia in ill calves (P < 0.06). Calves for which the model predicted bacteremia with a probability > or = 40.8% were considered bacteremic, yielding acceptable sensitivity (75%) and specificity (71%) estimates. The predictive model was validated through a 2nd sampling of 100 calves (79 ill calves and 21 controls), of which 17 calves were bacteremic. The classification was 75% correct using the model, with an estimated sensitivity of 76% and specificity of 75%. Overall, results indicated that the model could be a useful tool for predicting bacteremia in ill calves in a clinical setting.
对于人类、马和牛的新生儿,内科医生要早期诊断菌血症仍然是一项挑战。本研究的目的是基于一种名为临床脓毒症评分的临床评估系统,开发一种菌血症风险预测模型。对来自加利福尼亚州圣华金谷一个养牛场的90头1至14日龄患病小牛的血液进行培养。还根据包括水合状态、粪便外观、总体态度、巩膜血管外观和脐部异常的临床评分对小牛进行评分。记录年龄、直肠温度、心率、呼吸频率以及是否存在感染灶。菌血症患病率为31%(28/90)。逻辑回归模型表明,临床评分高、存在局灶性感染以及年龄增加与患病小牛菌血症风险增加相关(P<0.06)。模型预测菌血症概率≥40.8%的小牛被视为菌血症,其敏感性(75%)和特异性(71%)估计值可以接受。通过对100头小牛(79头患病小牛和21头对照)的第二次采样对预测模型进行验证,其中17头小牛患有菌血症。使用该模型分类的正确率为75%,估计敏感性为76%,特异性为75%。总体而言,结果表明该模型可能是临床环境中预测患病小牛菌血症的有用工具。