Objective—To determine the effect of PCV on veterinary point-of-care (POC) glucometer measurements in canine blood samples and develop a formula to correct the glucose concentration as measured by a point-of-care glucometer (POCgluc) given a known PCV. Design—Experimental and prospective study. Samples—Blood samples from 6 healthy dogs and from 30 hospitalized dogs. Procedures—60 mL of heparinized blood was obtained from each of 6 healthy dogs. Samples were processed into packed RBCs and plasma. Packed RBCs were resuspended with plasma to achieve a range of PCVs from 0% to 94%. Duplicate POCgluc and PCV measurements were obtained for each dilution; following POCgluc measurements, plasma samples were analyzed for glucose concentration by a clinical laboratory biochemical analyzer (LABgluc). A correction formula for POCgluc was developed. Measurements of POCgluc, PCV, and LABgluc were also determined from blood samples of 30 dogs admitted to the veterinary teaching hospital. Results—Values of LABgluc for each sample were similar at any PCV. As PCV decreased, POCgluc was falsely increased; as PCV increased, POCgluc was falsely decreased, compared with LABgluc. The absolute difference between POCgluc and LABgluc increased as the PCV changed from 50%. Compared with POCgluc, the corrected POCgluc had a significantly improved correlation with LABgluc, which was also reflected in improvements in Clarke and consensus error grid analyses. Conclusions and Clinical Relevance—Results indicated that in dogs with hemodilution or hemoconcentration, POCgluc did not reflect actual patient glucose concentrations. Use of a correction formula reduced this error. Corrected POCgluc data had strong, significant correlations with LABgluc data.

Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient’s survival once the relevant data are available.