Validation of a predictive model for asthma admission in children: how accurate is it for predicting admissions?
We studied 364 index presentations to the Emergency Department of a children's hospital with a diagnosis of asthma. The admission rate for this group of children was about 31%. We developed a parsimonious multiple logistic regression model to predict asthma hospital admission based on asthma severity indicators. We then evaluated the model's predictive ability using two methods of cross-validation, using the same sample that was used for the predictive model, and using data from a split sample. The logistic regression model had a predictive accuracy of 90% (95% confidence interval 85-95%). The sensitivity and specificity were 86% and 88%, respectively. Cross-validation models confirmed that the predictive ability of the model was stable. In studies with limited sample sizes, it is possible to validate a model without setting aside a split sample for cross-validation.
Chey
T
T
Epidemiology Unit, Southwestern Sydney Area Health Service, Australia. t.chey@unsw.edu.au
Jalaludin
B
B
Hanson
R
R
Leeder
S
S
D000208:Acute Disease; D000293:Adolescent; D001249:Asthma / Q000175:diagnosis* / Q000628:therapy; D001315:Australia; D002648:Child; D002675:Child, Preschool; D003955:Diagnostic Tests, Routine; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D010343:Patient Admission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D011795:Surveys and Questionnaires