Abstract:The present study reports a snakebite in a horse in the state of Pará, Brazil. At initial evaluation the animal was reluctant to walk and had tachycardia, tachypnea, severe lameness, bleeding on the pastern and swelling around the left hind leg. Blood samples from the bleeding sites, took on the first day, showed leukocytosis and neutrophilia, whereas biochemical values of urea and creatinine were significantly increased. The chosen treatment was snake antivenom, fluid therapy, antibiotics, anti-inflammatory agents and diuretic drugs. On the fourth day of therapy, the hematological values were within normal parameters. There was improvement related to the clinical lameness and swelling of the limb. However, a decrease in water intake and oliguria were observed. On the seventh day the animal died. Necropsy revealed areas of hemorrhagic edema in the left hind limb and ventral abdomen; the kidneys presented equimosis in the capsule, and when cut they were wet. Moreover, the cortex was pale, slightly yellow and the medullary striae had the same aspect. Based on these data, we concluded that the snakebite in the present study was caused by Bothrops spp. and that renal failure contributed to death.
Objectives: Monte Carlo simulation is widely used in diabetes models principally due to the ease with which conditional time-dependent logic and risk factor and health state interactions can be handled. Stochastic variability (1st order uncertainty) is minimised by increasing the size of the simulated cohort and the sampling of input parameters is typically restricted to conducting probability sensitivity analysis (PSA). The objective of this study was to illustrate why input parameters should be sampled regardless of whether PSA output is required. MethOds: We used the IMS-CORE Diabetes Model to illustrate the effect of replacing sampled input parameters with their means when estimating time to therapy escalation (TTE). The following published input parameters [means (standard deviation)] were used: HbA1c at diagnosis 7.8% (1.9%); baseline HbA1c 7.3% (1.2%); treatment effect 0.67% (0.14%). The UKPDS HbA1c progression equation was also used with/without sampled regression coefficients. Therapy escalation was assumed to occur at a threshold of 7.5%. Results: Holding all input parameters at their mean TTE was 2 years. Sampling all input parameters resulting in mean TTE of 2.7 years (SD= 3.4); minimum 0 years, maximum 29 years. The majority of observed variability in TTE was attributable to HbA1c at diagnosis which, when sampled in isolation, resulted in mean TTE of 2.8 years (SD= 3.4); minimum 0 years, maximum 29 years. Sampling only the UKPDS HbA1c progression equation coefficients resulted in mean TTE of 2.7 years (SD= 0.1); minimum 1 year, maximum 3 years; while sampling only the treatment effect resulted in mean TTE of 1.8 years (SD= 1.2); minimum 0 years, maximum 5 years. cOnclusiOns: Replacing input parameter probability distributions by their means can result in biased model output, particularly when their effects within the model are non-linear and used in conjunction with structural parameters; for example, therapy escalation thresholds.
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