F(2)-isoprostanes are useful markers for assessing oxidant injury; however, the validity of measuring urinary 15-F(2t)-isoprostane concentration by enzyme-linked immunosorbent assay (ELISA) has not been evaluated in veterinary species. The current study assesses the agreement between 2 commercially available urinary isoprostane kits and gas chromatography and negative ion chemical ionization-mass spectrometry (GC/NICI-MS). The results indicate that only feline urinary isoprostane measurement by glucuronidase (GL)-ELISA has acceptable agreement with GC/NICI-MS. Urinary isoprostane concentration was highly variable in critically ill animals, but there were too many variations between healthy and critically ill animals to draw meaningful conclusions. Currently, GC/NICI-MS is the only method that can be recommended for the assessment of urinary isoprostanes in dogs, cattle, and horses. Feline urinary isoprostanes can be assessed by GL-ELISA, but caution is still warranted when comparing data from manuscripts using different methods given the relatively low Spearman rank correlation coefficient. Future studies may require large sample sizes or focused inclusion criteria to account for variability in isoprostane concentration.

Smoking and the presence of metastatic disease are major risk factors for the development of anastomotic leak following low anterior resection.

Dyspneic dogs with severe pulmonary contusions may require and benefit from positive-pressure ventilation Prognosis is better for dogs that weigh > 25 kg (55 lb).

Data accuracy was favourable compared with other renal registry validation studies. Data accuracy may be improved by education and training of collectors. A larger audit is necessary to validate ANZDATA.

Results suggest that administration of hydromorphone to healthy dogs undergoing elective ovariohysterectomy or castration may result in transient increases in PaCO2 postoperatively and that administration of hydromorphone or butorphanol may result in transient decreases in PaO2. However, increases in PaCO2 and decreases in PaO2 were mild, and mean PaCO2 and PaO2 remained within reference limits.

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Background Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. Objective This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. Methods An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. Results A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. Conclusions Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.