Chest
Clinical InvestigationsPredicting the Need for Hospitalization in Children with Acute Asthma
Section snippets
Methods
Our study population was comprised of 200 children who presented to the ER of the Hospital for Sick Children, Toronto, between September and December 1988, for the treatment of acute asthma when one of the investigators was available. Children with acute bronchiolitis or with complicating pulmonary or cardiac disorders were excluded from the study.
On arrival to the ER, the following historic data were obtained from the parents of each subject: age, sex, duration of present episode, number of
Results
Of the 200 patients in the study, 66 (33 percent) were hospitalized, and 134 (67 percent) were discharged home. Five of the discharged patients returned to the ER within seven days, and one was subsequently hospitalized. All patients were treated with nebulized albuterol, and in addition, 8 percent received intravenous theophylline, and 8 percent received systemic corticosteroids. The mean age of all patients was 5.6±3.1 years (range 0.4 to 16 years). No statistically significant age difference
Discussion
The objective of the present study was to determine the value of clinical measurements and common, noninvasive laboratory variables both alone and in various combinations in predicting physicians’ decisions to admit children with acute asthma to the hospital. Although the hospitalized and the discharged groups of patients had statistically significant differences in most of the variables measured, the clinical score, based on standard physical examination, was found to be the most effective in
ACKNOWLEDGMENT
The authors thank J. Chay for secretarial assistance.
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Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning
2021, International Journal of Medical InformaticsCitation Excerpt :As hospitalization contributes the largest proportion (∼65 %) of asthma care costs, early identification of the need for hospitalization is important [8,9], as it may reduce ED boarding time, facilitate more effective use of ED resources, or enable provision of life-threatening care to patients with a high risk for hospitalization. Most of the previous studies on developing hospitalization decision models have focused on the prediction of safe discharge from hospitals, for which they have usually used clinical scores or a conventional statistical model, such as logistic regression [10–18]. Despite the ease of calculation and implementation in real-world setting, these conventional approaches (e.g., scoring model, conventional statistical model, etc.) typically lack the strength to provide statistical accuracy [19,20].
Repeated dyspnea score and percent FEV<inf>1</inf> are modest predictors of hospitalization/relapse in patients with acute asthma exacerbation
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Presented in part at the 55th Annual Scientific Assembly, American College of Chest Physicians, Boston, October 29–November 2, 1989.
Manuscript received February 12; revision accepted May 15.