Integration of statistical inferences and machine learning algorithms for prediction of metritis cure in dairy cows

Abstract

The study's objectives were to identify cow-level and environmental factors associated with metritis cure to predict metritis cure using traditional statistics and machine learning algorithms. The data set used was from a previous study comparing the efficacy of different therapies and self-cure for metritis. Metritis was defined as fetid, watery, reddish-brownish discharge, with or without fever. Cure was defined as an absence of metritis signs 12 d after diagnosis. Cows were randomly allocated to receive a subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid (Excede, Zoetis) at the day of diagnosis and 3 d later (n = 275); and no treatment at the time of metritis diagnosis (n = 275). The variables days in milk (DIM) at metritis diagnosis, treatment, season of the metritis diagnosis, month of metritis diagnostic, number of lactation, parity, calving score, dystocia, retained fetal membranes, body condition score at d 5 postpartum, vulvovaginal laceration score, the rectal temperature at the metritis diagnosis, fever at diagnosis, milk production from the day before to metritis diagnosis, and milk production slope up to 5, 7, and 9 DIM were offered to univariate logistic regression. Variables included in the multivariable logistic regression model were selected from the univariate analysis according to P-value. Variables were offered to the model to assess the association between these factors and metritis cure. Additionally, the univariate logistic regression variables were offered to a recursive feature elimination to find the optimal subset of features for a machine learning algorithms analysis. Cows without vulvovaginal laceration had 1.91 higher odds of curing of metritis than cows with vulvovaginal laceration. Cows that developed metritis at >7 DIM had 2.09 higher odds of being cured than cows that developed metritis at ≤7 DIM. For rectal temperature, each degree Celsius above 39.4°C led to lower odds to be cured than cows with rectal temperature ≤39.4°C. Furthermore, milk production slope and milk production difference from the day before to the metritis diagnosis were essential variables to predict metritis cure. Cows that had reduced milk production from the day before to the metritis diagnosis had lower odds to be cured than cows with moderate milk production increase. The results from the multivariable logistic regression and receiver operating characteristic analysis indicated that cows developing metritis at >7 DIM, with increase in milk production, and with a rectal temperature ≤39.40°C had increased likelihood of cure of metritis with an accuracy of 75%. The machine learning analysis showed that in addition to these variables, calving-related disorders, season, and month of metritis event were needed to predict whether the cow will cure or not from metritis with an accuracy ≥70% and F1 score (harmonic mean between precision and recall) ≥0.78. Although machine learning algorithms are acknowledged as powerful tools for predictive classification, the current study was unable to repli...

Changes in Uterine Metabolome Associated with Metritis Development and Cure in Lactating Holstein Cows

“…On the other hand, studies evaluating metabolomic changes in the uterus and related reproductive tract components during the first weeks postpartum and their association with metritis are only now emerging and need further exploration [16]. Our approach in the current study was supported by a premise that vaginal discharge, a sample that is relatively simpler and less invasive to collect than uterine fluid [16], could still be satisfactory to identify biomarkers for metritis development and its cure that could serve as data entry points to advance current predictive models for a metritis cure [18,20] and shed light on potential mechanisms of disease pathogenesis.…”

“…Studies have reported the associations of minerals or metabolites with immune function and metritis development [17,18]. Also, studies revealed that predictive models using cows' sensor data [19] or machine learning algorithms [20] to predict a metritis cure might help improve the judicious use of antibiotics. However, studies characterizing the vaginal-uterine metabolome and potential biomarkers associated with the risk of metritis development and its cure remain scarce.…”

Shifts in uterine microbiome associated with pregnancy outcomes at first insemination and clinical cure in dairy cows with metritis

“…Considering the similarity in the uterine microbiome of cows with metritis that cured and those with clinical cure failure on day 5, it is possible that other mechanisms related to immune response play a major role in clinical cure. Studies have reported that cows diagnosed with metritis and pyrexia (rectal temperature ≥ 39.5 °C) were less likely to cure compared with cows with metritis without pyrexia 5,27 . Furthermore, concurrent injuries and inflammation were associated with reduced a risk of achieving clinical cure.…”

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

“…Furthermore, to optimize animal reproduction, it is crucial to associate management strategies that can promote better efficiency in reproductive animal management. In this regard, there are already several studies proposing the use of AAM systems and exploring machine learning algorithms to enhance the prediction of health and fertility disorders in dairy cows [ 51 , 52 , 53 ]. A previous study has also shown that routinely collected farm data and milk production records on the test day are valuable for predicting the success of insemination in dairy cows [ 54 ].…”