Diagnostic decision rule for support in clinical assessment of the need for surgical intervention in horses with acute abdominal pain.

A prospective survey of horses with colic referred to a university hospital was undertaken to elaborate on a simple clinical decision support system capable of predicting whether or not horses require surgical intervention. Cases were classified as requiring surgical intervention or not on the basis of intraoperative findings or necropsy reports. Logistic regression analysis was applied to identify predictors with the strongest association with treatment needed.

Risk factors for the hazard of lameness in Danish Standardbred trotters.

A follow-up study focusing on health problems interfering with optimal training of Danish Standardbred trotters was conducted with the participation of seven professional trainers. Our aim was to estimate the incidence of health problems that cause interruptions of optimal training, and to identify associations between the hazard of lameness and selected risk factors. The study population was dynamic and contained data of 265 Standardbred trotters monitored during 5 months in 1997 and 1998.

Prognostic indicators in a Danish hospital-based population of colic horses.

A prospective survey of 528 colic horses, referred to the Large Animal Hospital at the Royal Veterinary and Agricultural University of Copenhagen, Denmark, during the period August 1994 to December 1998, was undertaken to develop a predictive model for application in the clinical assessment of prognosis. In the multivariate logistic regression analysis, 357 colic cases were used in the elaboration of a simple clinical-practical model consisting of degree of pain, packed cell volume, capillary refill time and rectal temperature. The relationship between rectal temperature and outcome (survival/death) has been regarded as linear.

Factor analysis of the interrelationships between clinical variables in horses with colic.

A prospective survey of horses with colic referred to the Large Animal Hospital at the Royal Veterinary and Agricultural University of Copenhagen, Denmark, was undertaken between August 1994 and December 1997. The interrelationships between 17 clinical variables were analysed using factor analysis. Factor analysis uncovers the structure of the variability in data and therefore detects multicollinearity.