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Our objective was to evaluate the accuracy of predictive models for metritis spontaneous cure (SC) and cure among ceftiofur-treated cows using farm-collected data only, and with the addition of hemogram variables and circulating concentration of metabolites, minerals, and biomarkers (BM) of inflammation measured at time of diagnosis. Data related to parity, calving-related issues, BCS, rectal temperature, and DIM at metritis diagnosis were collected from a randomized clinical trial that included 422 metritic cows from 4 herds in Texas, California, and Florida. Metritis was defined as the presence of red-brownish, watery, and fetid vaginal discharge, and cure was defined as the absence of metritis 14 d after initial diagnosis. Cows were randomly allocated to receive systemic ceftiofur therapy (2 subcutaneous doses of 6.6 mg/kg of ceftiofur crystalline-free acid on the day of diagnosis and 3 d later; CEF) or to remain untreated (control). At enrollment (day of metritis diagnosis), blood samples were collected and submitted to complete blood count (CBC) and processed for the measurement of 13 minerals and BM of metabolism and inflammation. Univariable analysis to evaluate the association of farm-collected data and blood-assessed variables with metritis cure were performed, and variables with P ≤ 0.20 were offered to multivariable logistic regression models and retained if P ≤ 0.15. The areas under the curve for models predicting SC using farm data only and farm + BM were 0.70 and 0.76, respectively. Complete blood count variables were not retained in the models for SC. For models predicting cure among CEF cows, the area under the curve was 0.75, 0.77, 0.80, and 0.80 for models using farm data only, farm + CBC, farm + BM, and farm + CBC + BM, respectively. Predictive models of metritis cure had fair accuracy, with SC models being less accurate than models predictive of cure among CEF cows. Additionally, adding BM variables marginally improved the accuracy of models using farm collected data, and CBC data did not improve the accuracy of predictive models. [ABSTRACT FROM AUTHOR]