Characteristics and prediction of subclinical hypocalcemia in dairy cows during the transition period using blood analytes

This study aimed to present the characteristics of and to predict subclinical hypocalcemia in dairy cows during the transition period using blood analytes. We examined fluctuations in plasma calcium (Ca), phosphorus (P), bone metabolic markers carboxy-terminal telopeptide of type I collagen (CTX), fibroblast growth factor (FGF23), 1,25(OH)2D3, parathyroid hormone, and other blood biochemical analytes from prepartum week 2 to postpartum day 14 in 116 multiparous high-producing Holstein cows from a free-stall barn dairy farm. With a plasma concentration of Ca <2.0 mmol/L as a criterion for the diagnosis of subclinical hypocalcemia, 64 cows were classified as normocalcemic, and 52 cows as subclinically hypocalcemic. Among the 52 hypocalcemic cows, 50 were detected on postpartum days 1 or 3, and 2 on postpartum day. The subclinically hypocalcemic cows were in a state of low bone turnover in the prepartum period, with low plasma concentrations of Ca and CTX. The subclinically hypocalcemic cows showed signs of a P regulation disorder in the prepartum period. This was marked by high plasma concentrations of P and low concentrations of 1,25(OH)2D3 and FGF23, which is also considered to be the cause of the low bone turnover. The results of a multiple logistic regression model showed that prepartum plasma concentrations of FGF23, CTX, and Ca were ideal predictors of postpartum subclinical hypocalcemia in dairy cows, using the model equation 38.8-0.052*FGF23-0.492*CTX-10.645*Ca, with a score of > 0 considered as an indication of increased risk of subclinical hypocalcemia after calving. The scoring rule had an accuracy of 79.3%, sensitivity of 76.9%, and specificity of 81.3%. The plasma concentrations of FGF23, CTX, and Ca were ideal predictors of postpartum subclinical hypocalcemia in dairy cows.

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validate using a Machine-Learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Early Prediction of In-Hospital Death of COVID-19 Patients: A Machine-Learning Model Based on Age, Blood Analyses, and Chest X-Ray Score

Background: To develop and validate an early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED).<br /> Methods: In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. Results: The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively.<br />Conclusions: The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

79 Clinical health markers in dogs fed raw meat based or commercial extruded kibble diets

Anecdotal reports of health benefits of raw meat-based diets (RMBD) for dogs include cleaner teeth, improved integument, and general health. However, little to no evidence is present in the scientific literature to support assertions of improved clinical outcomes in RMBD-fed dogs. We hypothesized that healthy dogs fed diets which have undergone less processing will show a clinical benefit and improved general health markers compared to dogs fed a highly processed diet. Enrollment criteria included healthy, client-owned adult dogs fed either RMBD or extruded kibble (EK) for &gt; 1 year (RMBD n = 28; EK n = 27). Management history, clinical examination, hematology, urinalysis and serum biochemistry measures were collected for each dog. Dental, ear and coat scores were assigned by a blinded veterinary observer and a clinical composite score (CCS) calculated. Dogs fed RMBD showed an improved CCS and coat score compared to EK dogs (CCS: Mann Whitney test, P = 0.03; Coat: Fisher Exact Test, P = 0.04). Differences in blood analytes between feeding group were observed (Table 1). For each significant difference found by univariate analysis, forward stepwise linear regression was performed with age, breed, gender, and body condition score as independent factors. Serum alkaline phosphatase activity was 50% lower in dogs fed RMBD than those fed EK (P = 0.001). BUN was higher (P &lt; 0.01) in RMBD dogs, while glucose concentration (P &lt; 0.05) was lower in RMBD dogs compared to EK dogs. Platelet count was greater in RMBD dogs relative to EK group (P &lt; 0.001). Owner management differed, with greater likelihood of management interventions and sporting activities in the RMBD group. RMBD may have mild clinical benefits and significant effects on blood analytes compared to EK, even when management was considered. Further work is needed to determine the impact of owner practices, diet processing, and nutrient content on health outcomes.

A composite biomarker using multiparametric magnetic resonance imaging and blood analytes accurately identifies patients with non-alcoholic steatohepatitis and significant fibrosis

Non-alcoholic steatohepatitis (NASH) is major health burden lacking effective pharmacological therapies. Clinical trials enrol patients with histologically-defined NAFLD (non-alcoholic fatty liver disease) activity score (NAS) ≥ 4 and Kleiner-Brunt fibrosis stage (F) ≥ 2; however, screen failure rates are often high following biopsy. This study evaluated a non-invasive MRI biomarker, iron-corrected T1 mapping (cT1), as a diagnostic pre-screening biomarker for NASH. In a retrospective analysis of 86 biopsy confirmed NAFLD patients we explored the potential of blood and imaging biomarkers, both in isolation and in combination, to discriminate those who have NAS ≥ 4 and F ≥ 2 from those without. Stepwise logistic regression was performed to select the optimal combination of biomarkers, diagnostic accuracy was determined using area under the receiver operator curve and model validated confirmed with and fivefold cross-validation. Results showed that levels of cT1, AST, GGT and fasting glucose were all good predictors of NAS ≥ 4 and F ≥ 2, and the model identified the combination of cT1-AST-fasting glucose (cTAG) as far superior to any individual biomarker (AUC 0.90 [0.84–0.97]). This highlights the potential utility of the composite cTAG score for screening patients prior to biopsy to identify those suitable for NASH clinical trial enrolment.