Effect of a non-steroidal anti-inflammatory drug on subclinical endometritis in dairy cows and the identification of at-risk cows

Abstract

Subclinical endometritis (SCE) is a uterine pathology characterised by an increased proportion of polymorphonuclear cells (PMN) in the uterus after calving, and it is known that SCE has negative effects on dairy cow reproductive performance. However, the mechanism by which SCE affects reproductive performance in New Zealand dairy cows appears to be different from that reported in international literature. This provided the basis for the research reported herein, which sought to investigate the mechanism by which SCE reduces reproductive performance of New Zealand dairy cows. Furthermore, the need for a practical method to detect or diagnose SCE was identified. The objective of the first experiment was to determine if the inflammation associated with SCE, both uterine and systemic, is a part of the mechanism by which reproductive performance is reduced in cows with this disease. The hypothesis was that reducing this inflammation with a non-steroidal anti-inflammatory drug (NSAID) would reduce the severity of uterine inflammation (average PMN %), and improve reproductive performance. Dairy cows (n = 213) were paired by calving date and day 14 uterine PMN %, and randomly assigned to either the NSAID treatment (administered 21 - 31 days postpartum) or control group. Cows with ≥ 14% PMN in the cytological sample collected at day 14 postpartum were defined as having SCE. Treatment with a NSAID increased pregnancy rate in SCE cows and reduced metabolic indicators of systemic inflammation. There was, however, no effect of NSAID treatment on day 42 PMN %, postpartum anovulatory interval, or milk production. Further research is required to determine the effect of NSAID on SCE, and evaluate the influence of timing of drug application on treatment effectiveness. The objective of the second experiment was to determine whether a model could be developed, based on known associations between SCE and serum metabolites and cow body condition score, to both predict cows at risk of SCE and reduce the number of cows to be submitted for cytological examination to a manageable level. Models were developed based on either a single week’s data (week relative to calving; -4, -3, -1, +1, +2), or the herd’s planned start of calving date (27th June, and a week later; 4th July). The optimum PMN % threshold was determined for the model with the highest predictive value (R²; week +1). The optimum PMN % threshold for the week +1 model’s fitted values was 10.7% (sensitivity = 58%, specificity = 81%). This threshold, and all other thresholds investigated, however, resulted in combinations of sensitivity and specificity where either too few SCE cows were identified, or too many cows would be submitted for cytological examination. These results indicate that although a model was generated that could predict all SCE cows, the dual aim of predicting cows at-risk and enabling only a subset of cows to be submitted for cytological examination was unable to be achieved with the serum and physical parameters evaluated. Further research into the aetiology of SCE may provide better biological markers to use for prediction of SCE.