Abstract
The difficulty in distinguishing between septicemic and nonsepticemic diarrheic calves prompted a study of variables to predict septicemia in diarrheic calves <28 days old that were presented to a referral institution. The prevalence of septicemia in the study population was 31%. Variables whose values were significantly different (P < .10) between septicemic and nonsepticemic diarrheic calves were selected using stepwise, forward, and backward logistic regression. Variables identified as potentially useful predictors were used in the final model-building process. Two final models were selected: 1 based on all possible types of predictors (laboratory model) and 1 based only on demographic data and physical examination results (clinical model). In the laboratory model, 5 variables retained significance: serum creatinine > 5.66 mg/dL (>500 micromol/L) (odds ratio [OR] = 8.63, P = .021), toxic changes in neutrophils > or = 2+ (OR = 2.88, P = .026), failure of passive transfer (OR = 2.72, P = .023), presence of focal infection (OR = 2.68. P = .024), and poor suckle reflex (OR = 4.10, P = .019). Four variables retained significance in the clinical model: age < 5 days (OR = 2.58, P = .006), presence of focal infection (OR = 2.45, P = .006), recumbency (OR = 2.98, P = .011), and absence of a suckling reflex (OR = 3.03, P = .031). The Hosmer-Lemeshow goodness-of-fit chi-square statistics for the laboratory and clinical models had P-values of .72 and .37, respectively, indicating that the models fit the observed data reasonably well. The laboratory model outperformed the clinical model by a small margin at a predictabilty cutoff of 0.5, however, the predictive abilities of the 2 models were quite similar. The low sensitivities (39% and 40%) of both models at a predicted probability cutoff of 0.5 meant many septicemic calves were not being detected by the models. The specificity of both models at a predicted probability cutoff of 0.5 was >90%, indicating that >90% of nonsepticemic calves would be predicted to be nonsepticemic by the 2 models. The positive and negative predictive values of the models were 66-82%, which indicated the proportion of cases for which a predictive result would be correct in a population with a prevalence of septicemia of 31%.