Prevalence of Disorders Recorded in Dogs Attending Primary-Care Veterinary Practices in England

Purebred dog health is thought to be compromised by an increasing occurence of inherited diseases but inadequate prevalence data on common disorders have hampered efforts to prioritise health reforms. Analysis of primary veterinary practice clinical data has been proposed for reliable estimation of disorder prevalence in dogs. Electronic patient record (EPR) data were collected on 148,741 dogs attending 93 clinics across central and south-eastern England. Analysis in detail of a random sample of EPRs relating to 3,884 dogs from 89 clinics identified the most frequently recorded disorders as otitis externa (prevalence 10.2%, 95% CI: 9.1-11.3), periodontal disease (9.3%, 95% CI: 8.3-10.3) and anal sac impaction (7.1%, 95% CI: 6.1-8.1). Using syndromic classification, the most prevalent body location affected was the head-and-neck (32.8%, 95% CI: 30.7-34.9), the most prevalent organ system affected was the integument (36.3%, 95% CI: 33.9-38.6) and the most prevalent pathophysiologic process diagnosed was inflammation (32.1%, 95% CI: 29.8-34.3). Among the twenty most-frequently recorded disorders, purebred dogs had a significantly higher prevalence compared with crossbreds for three: otitis externa (P = 0.001), obesity (P = 0.006) and skin mass lesion (P = 0.033), and popular breeds differed significantly from each other in their prevalence for five: periodontal disease (P = 0.002), overgrown nails (P = 0.004), degenerative joint disease (P = 0.005), obesity (P = 0.001) and lipoma (P = 0.003). These results fill a crucial data gap in disorder prevalence information and assist with disorder prioritisation. The results suggest that, for maximal impact, breeding reforms should target commonly-diagnosed complex disorders that are amenable to genetic improvement and should place special focus on at-risk breeds. Future studies evaluating disorder severity and duration will augment the usefulness of the disorder prevalence information reported herein

Nitrogen and Carbon Isotopic Dynamics of Subarctic Soils and Plants in Southern Yukon Territory and its Implications for Paleoecological and Paleodietary Studies

We examine here the carbon and nitrogen isotopic compositions of bulk soils (8 topsoil and 7 subsoils, including two soil profiles) and five different plant parts of 79 C3 plants from two main functional groups: herbs and shrubs/subshrubs, from 18 different locations in grasslands of southern Yukon Territory, Canada (eastern shoreline of Kluane Lake and Whitehorse area). The Kluane Lake region in particular has been identified previously as an analogue for Late Pleistocene eastern Beringia. All topsoils have higher average total nitrogen δ15N and organic carbon δ13C than plants from the same sites with a positive shift occurring with depth in two soil profiles analyzed. All plants analyzed have an average whole plant δ13C of −27.5 ± 1.2 ‰ and foliar δ13C of ±28.0 ± 1.3 ‰, and average whole plant δ15N of −0.3 ± 2.2 ‰ and foliar δ15N of ±0.6 ± 2.7 ‰. Plants analyzed here showed relatively smaller variability in δ13C than δ15N. Their average δ13C after suitable corrections for the Suess effect should be suitable as baseline for interpreting diets of Late Pleistocene herbivores that lived in eastern Beringia. Water availability, nitrogen availability, spacial differences and intra-plant variability are important controls on δ15N of herbaceous plants in the study area. The wider range of δ15N, the more numerous factors that affect nitrogen isotopic composition and their likely differences in the past, however, limit use of the modern N isotopic baseline for vegetation in paleodietary models for such ecosystems. That said, the positive correlation between foliar δ15N and N content shown for the modern plants could support use of plant δ15N as an index for plant N content and therefore forage quality. The modern N isotopic baseline cannot be applied directly to the past, but it is prerequisite to future efforts to detect shifts in N cycling and forage quality since the Late Pleistocene through comparison with fossil plants from the same region

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability