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Development and validation of a new standardised data collection tool to aid in the diagnosis of canine skin allergies.

ABSTRACT: Canine atopic dermatitis (cAD) is a common hereditary clinical syndrome in domestic dogs with no definitive diagnostic tests, which causes marked morbidity and has a high economic impact internationally. We created a novel questionnaire for Labrador (LR) and Golden retriever (GR) owners to evaluate canine skin health with respect to clinical signs of cAD. 4,111 dogs had fully completed questionnaires (2,803 LR; 1,308 GR). 'Cases' (793) had a reported veterinary diagnosis of cAD, and 'controls' (1652) had no current or past clinical signs of cAD and were aged >3 years. Remaining dogs (1666) were initially categorised as 'Other'. Simulated annealing was used comparing 'Cases' and 'Others' to select a novel set of features able to classify a known case. Two feature sets are proposed, one for use on first evaluation and one for dogs with a history of skin problems. A sum for each list when applied to the whole population (including controls) was able to classify 'Cases' with a sensitivity of 89% to 94% and specificity of 71% to 69%, respectively, and identify potentially undiagnosed cases. Our findings demonstrate for the first time that owner questionnaire data can be reliably used to aid in the diagnostic process of cAD.

SUBMITTER: Harvey ND

PROVIDER: S-EPMC6395597 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Development and validation of a new standardised data collection tool to aid in the diagnosis of canine skin allergies.

Harvey N D ND Shaw S C SC Blott S C SC Vàzquez-Diosdado J A JA England G C W GCW

Scientific reports 20190228 1

Canine atopic dermatitis (cAD) is a common hereditary clinical syndrome in domestic dogs with no definitive diagnostic tests, which causes marked morbidity and has a high economic impact internationally. We created a novel questionnaire for Labrador (LR) and Golden retriever (GR) owners to evaluate canine skin health with respect to clinical signs of cAD. 4,111 dogs had fully completed questionnaires (2,803 LR; 1,308 GR). 'Cases' (793) had a reported veterinary diagnosis of cAD, and 'controls' ( ...[more]

PMID: 30816238

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Project description:BackgroundThe greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer(TM) , has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods.Methods752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer(TM) ) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R(2) statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm.ResultsThe Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS = 75.0% (95% CI 72.3%-77.6%), Stroke Riskometer(TM) = 74.0(95% CI 71.3%-76.7%) and females [FSRS = 70.3% (95% CI 67.9%-72.8%, Stroke Riskometer(TM) = 71.5% (95% CI 69.0%-73.9%)], and better than QStroke [males - 59.7% (95% CI 57.3%-62.0%) and comparable to females = 71.1% (95% CI 69.0%-73.1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0.51-0.56, D-statistic ranging from 0.01-0.12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0.006).ConclusionsThe Stroke Riskometer(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.

Dataset Information

Development and validation of a new standardised data collection tool to aid in the diagnosis of canine skin allergies.

Publications

Development and validation of a new standardised data collection tool to aid in the diagnosis of canine skin allergies.

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