Validation of a predictive model for asthma admission in children: how accurate is it for predicting admissions?

J Clin Epidemiol. 1999 Dec;52(12):1157-63. doi: 10.1016/s0895-4356(99)00111-0.

Abstract

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.

Publication types

  • Comparative Study

MeSH terms

  • Acute Disease
  • Adolescent
  • Asthma / diagnosis*
  • Asthma / therapy
  • Australia
  • Child
  • Child, Preschool
  • Diagnostic Tests, Routine
  • Female
  • Humans
  • Logistic Models*
  • Male
  • Patient Admission / statistics & numerical data*
  • Predictive Value of Tests
  • ROC Curve
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity
  • Severity of Illness Index
  • Surveys and Questionnaires