No abstract

Background: Objective risk stratification models are used routinely in human critical care medicine. Applications include quantitative and objective delineation of illness severity for patients enrolled in clinical research, performance benchmarking, and protocol development for triage and therapeutic management.Objective: To develop an accurate, validated, and user-friendly model to stratify illness severity by mortality risk in hospitalized dogs.Animals: Eight hundred and ten consecutive intensive care unit (ICU) admissions of dogs at a veterinary teaching hospital. Methods: Prospective census cohort study. Data on 55 management, physiological, and biochemical variables were collected within 24 hours of admission. Data were randomly divided, with 598 patient records used for logistic regression model construction and 212 for model validation.Results: Patient mortality was 18.4%. Ten-variable and 5-variable models were developed to provide both a high-performance model and model maximizing accessibility, while maintaining good performance. The 10-variable model contained creatinine, WBC count, albumin, SpO 2 , total bilirubin, mentation score, respiratory rate, age, lactate, and presence of free fluid in a body cavity. Area under the receiver operator characteristic (AUROC) on the construction data set was 0.93, and on the validation data set was 0.91. The 5-variable model contained glucose, albumin, mentation score, platelet count, and lactate. AUROC on the construction data set was 0.87, and on the validation data set was 0.85.Conclusions and Clinical Importance: Two models are presented that enable allocation of an accurate and user-friendly illness severity index for dogs admitted to an ICU. These models operate independent of primary diagnosis, and have been independently validated.

BackgroundOverweight, obesity, and related chronic diseases are becoming serious public health concerns in rural areas of India. Compounded with the existing issue of underweight, such concerns expose the double burden of disease and may put stress on rural healthcare. The purpose of this article was to present the prevalence and factors associated with underweight, overweight, and obesity in an area of rural south India.MethodsDuring 2013 and 2014, a random sample of adults aged 20–80 years were selected for participation in a cross-sectional study that collected information on diet (using a food frequency questionnaire), physical activity (using the Global Physical Activity Questionnaire), socioeconomic position (using a wealth index), rurality (using the MSU rurality index), education, and a variety of descriptive factors. BMI was measured using standard techniques. Using a multivariate linear regression analysis and multivariate logistic regression analyses, we examined associations between BMI, overweight, obesity, and underweight, and all potential risk factors included in the survey.ResultsAge and sex-adjusted prevalence of overweight, obesity class I, and obesity class II were 14.9, 16.1, and 3.3 % respectively. Prevalence of underweight was 22.7 %. The following variables were associated with higher BMI and/or increased odds of overweight, obesity class I, and/or obesity class II: Low physical activity, high wealth index, no livestock, low animal fat consumption, high n-6 polyunsaturated fat consumption, television ownership, time spent watching television, low rurality index, and high caste. The following variables were associated with increased odds of underweight: low wealth index, high rurality index, and low intake of n-6 PUFAs.ConclusionUnderweight, overweight, and obesity are prevalent in rural regions of southern India, indicating a village-level dual burden. A variety of variables are associated with these conditions, including physical activity, socioeconomic position, rurality, television use, and diet. To address the both underweight and obesity, policymakers must simultaneously focus on encouraging positive behaviour through education and addressing society-level risk factors that inhibit individuals from achieving optimal health.

The conduct of randomized controlled trials in livestock with production, health, and foodsafety outcomes presents unique challenges that may not be adequately reported in trial reports. The objective of this project was to modify the CONSORT (Consolidated Standards of Reporting Trials) statement to reflect the unique aspects of reporting these livestock trials. A two-day consensus meeting was held on November [18][19] 2008 in Chicago, IL, United States of America, to achieve the objective. Prior to the meeting, a Web-based survey was conducted to identify issues for discussion. The 24 attendees were biostatisticians, epidemiologists, foodsafety researchers, livestock-production specialists, journal editors, assistant editors, and associate editors. Prior to the meeting, the attendees completed a Web-based survey indicating which CONSORT statement items may need to be modified to address unique issues for livestock trials. The consensus meeting resulted in the production of the REFLECT (Reporting Guidelines For Randomized Control Trials) statement for livestock and food safety (LFS) and 22-item checklist. Fourteen items were modified from the CONSORT checklist, and an additional sub-item was proposed to address challenge trials. The REFLECT statement proposes new terminology, more consistent with common usage in livestock production, to describe study subjects. Evidence was not always available to support modification to or inclusion of an item. The use of the REFLECT statement, which addresses issues unique to livestock trials, should improve the quality of reporting and design for trials reporting production, health, and food-safety outcomes.

In both human and veterinary medicine, diagnosing and staging renal disease can be difficult. Measurement of glomerular filtration rate is considered the gold standard for assessing renal function but methods for its assessment can be technically challenging and impractical. The main parameters used to diagnose acute and chronic kidney disease include circulating creatinine and urea concentrations, and urine‐specific gravity. However, these parameters can be insensitive. Therefore, there is a need for better methods to diagnose and monitor patients with renal disease. The use of renal biomarkers is increasing in human and veterinary medicine for the diagnosis and monitoring of acute and chronic kidney diseases. An ideal biomarker would identify site and severity of injury, and correlate with renal function, among other qualities. This article will review the advantages and limitations of renal biomarkers that have been used in dogs and cats, as well as some markers used in humans that may be adapted for veterinary use. In the future, measuring a combination of biomarkers will likely be a useful approach in the diagnosis of kidney disorders.

One of the most complex aspects of the veterinarian-client-patient interaction is the clinical decision-making process. Research suggests that the approach to communication used by veterinarians can impact veterinary clients’ involvement in the decision-making process and their ultimate satisfaction. Using different approaches to the decision-making process may affect how information is exchanged and consequently how decisions are made. The objective of this study was to determine pet owners’ expectations with respect to information exchange and decision-making during veterinarian-client-patient interactions and to compare veterinarians’ perceptions of those expectations and the challenges they face in meeting them. Five pet owner focus groups (27 owners) and three veterinarian focus groups (24 veterinarians) were conducted with standardized open-ended questions and follow-up probes. Thematic analysis of the transcribed data was conducted to identify trends and patterns that emerged during the focus groups. Three pet owner-based themes were identified: 1) understanding the client; 2) providing information suitable for the client; and 3) decision-making. In addition, three barriers for veterinarians affecting information exchange and decision-making were identified: 1) time constraints; 2) involvement of multiple clients; and 3) language barriers. Results suggest that pet owners expect to be supported by their veterinarian to make informed decisions by understanding the client’s current knowledge, tailoring information and educating clients about their options. Breakdowns in the information exchange process can impact pet owners’ perceptions of veterinarians’ motivations. Pet owners’ emphasis on partnership suggests that a collaborative approach between veterinarians and clients may improve client satisfaction.

Background: Scores allowing objective stratification of illness severity are available for dogs and horses, but not cats. Validated illness severity scores facilitate the risk-adjusted analysis of results in clinical research, and also have applications in triage and therapeutic protocols.Objective: To develop and validate an accurate, user-friendly score to stratify illness severity in hospitalized cats. Animals: Six hundred cats admitted consecutively to a teaching hospital intensive care unit.Methods: This observational cohort study enrolled all cats admitted over a 32-month period. Data on interventional, physiological, and biochemical variables were collected over 24 hours after admission. Patient mortality outcome at hospital discharge was recorded. After random division, 450 cats were used for logistic regression model construction, and data from 150 cats for validation.Results: Patient mortality was 25.8%. Five-and 8-variable scores were developed. The 8-variable score contained mentation score, temperature, mean arterial pressure (MAP), lactate, PCV, urea, chloride, and body cavity fluid score. Area under the receiver operator characteristic curve (AUROC) on the construction cohort was 0.91 (95% CI, 0.87-0.94), and 0.88 (95% CI, 0.84-0.96) on the validation cohort. The 5-variable score contained mentation score, temperature, MAP, lactate, and PCV. AUROC on the construction cohort was 0.83 (95% CI, 0.79-0.86), and 0.76 (95% CI, 0.72-0.84) on the validation cohort.Conclusions and Clinical Importance: Two scores are presented enabling allocation of an accurate and user-friendly illness severity measure to hospitalized cats. Scores are calculated from data obtained over the 1st 24 hours after admission, and are diagnosis-independent. The 8-variable score predicts outcome significantly better than does the 5-variable score.

No abstract