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 μmol/L) (odds ratio [OR] = 8.63, P = .021), toxic changes in neutrophils ≥ 21 (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%.

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