Prediction of asthma episodes in children using peak expiratory flow rates, medication compliance, and exercise data

Asthmatic patients demonstrate considerable variation in the extent to which their subjective evaluations and objective measures of airways obstruction correspond. The detection and prediction of asthma episodes have been improved through the use of objective pulmonary measures, such as peak expiratory flow rate (PEFR). Furthermore, information regarding adherence to medication regimens and specific asthma triggers (ie, exercise) may also improve the predictability of asthma in children. The utility of using PEFR values, medication compliance information, and exercise data for the prediction of asthma episodes in individual children was investigated in the present study. Individual logistic regression equations were calculated for ten asthmatic children. In predicting asthma episodes, PEFR was present in the model for nine out of ten subjects; exercise was included for two out of five subjects providing such data; whether an attack had occurred in the preceding 12-hour period was predictive for one out of ten participants; and whether an attack had been experienced in the previous 24 hours was present for four out of ten subjects. Hosmer-Lemeshow values indicated that the models fit the data well for nine subjects (alpha greater than .05). The mean model prediction rate (80.85%) was higher than the mean base rate (75.72%). The relative risk ratio values ranged from 0 to 5.72, with a mean of 3.50. These results suggest that the prediction of episodes may be enhanced by using PEFR, exercise, and time of day.