OBJECTIVES: To develop and validate predictive models to determine the need for hospitalization in children treated for acute asthma in the emergency department (ED). METHODS: Prospective cohort study of children aged 2 years and older treated at 2 pediatric EDs for acute asthma. The primary outcome was successful ED discharge, defined as actual discharge from the ED and no readmission for asthma within 7 days, versus need for extended care. Among those defined as requiring extended care, a secondary outcome of inpatient care (>24 hours) or short-stay care (<24 hours) was defined. Logistic regression and recursive partitioning were used to create predictive models based on historical and clinical data from the ED visit. Models were developed with data from 1 ED and validated in the other. RESULTS: There were 852 subjects in the derivation group and 369 in the validation group. A model including clinical score (Pediatric Asthma Severity Score) and number of albuterol treatments in the ED distinguished successful discharge from need for extended care with an area under the receiver-operator characteristic curve of 0.89 (95% confidence interval [CI], 0.87-0.92) in the derivation group and 0.92 (95% CI, 0.89-0.95) in the validation group. Using a score of 5 or more as a cutoff, the likelihood ratio positive was 5.2 (95% CI, 4.2-6.5), and the likelihood ratio negative was 0.22 (95% CI, 0.17-0.28). Among those predicted to need extended care, a classification tree using number of treatments in the ED, clinical score at end of ED treatment, and initial pulse oximetry correctly classified 63% (95% CI, 56-70) of the derivation group as short stay or inpatient, and 62% (95% CI, 55-68) of the validation group. CONCLUSIONS: Successful discharge from the ED for children with acute asthma can be predicted accurately using a simple clinical model, potentially improving disposition decisions. However, predicting correct placement of patients requiring extended care is problematic.
OBJECTIVES: To develop and validate predictive models to determine the need for hospitalization in children treated for acute asthma in the emergency department (ED). METHODS: Prospective cohort study of children aged 2 years and older treated at 2 pediatric EDs for acute asthma. The primary outcome was successful ED discharge, defined as actual discharge from the ED and no readmission for asthma within 7 days, versus need for extended care. Among those defined as requiring extended care, a secondary outcome of inpatient care (>24 hours) or short-stay care (<24 hours) was defined. Logistic regression and recursive partitioning were used to create predictive models based on historical and clinical data from the ED visit. Models were developed with data from 1 ED and validated in the other. RESULTS: There were 852 subjects in the derivation group and 369 in the validation group. A model including clinical score (Pediatric Asthma Severity Score) and number of albuterol treatments in the ED distinguished successful discharge from need for extended care with an area under the receiver-operator characteristic curve of 0.89 (95% confidence interval [CI], 0.87-0.92) in the derivation group and 0.92 (95% CI, 0.89-0.95) in the validation group. Using a score of 5 or more as a cutoff, the likelihood ratio positive was 5.2 (95% CI, 4.2-6.5), and the likelihood ratio negative was 0.22 (95% CI, 0.17-0.28). Among those predicted to need extended care, a classification tree using number of treatments in the ED, clinical score at end of ED treatment, and initial pulse oximetry correctly classified 63% (95% CI, 56-70) of the derivation group as short stay or inpatient, and 62% (95% CI, 55-68) of the validation group. CONCLUSIONS: Successful discharge from the ED for children with acute asthma can be predicted accurately using a simple clinical model, potentially improving disposition decisions. However, predicting correct placement of patients requiring extended care is problematic.
Authors: Jeremiah D Schuur; Christopher W Baugh; Erik P Hess; Joshua A Hilton; Jesse M Pines; Brent R Asplin Journal: Acad Emerg Med Date: 2011-06 Impact factor: 3.451
Authors: Robert Chen; Hang Su; Mohammed Khalilia; Sizhe Lin; Yue Peng; Tod Davis; Daniel A Hirsh; Elizabeth Searles; Javier Tejedor-Sojo; Michael Thompson; Jimeng Sun Journal: AMIA Annu Symp Proc Date: 2015-11-05
Authors: Heather G Zook; Nathaniel R Payne; Susan E Puumala; Katherine M Ziegler; Anupam B Kharbanda Journal: Pediatr Emerg Care Date: 2019-03 Impact factor: 1.454
Authors: Daniel Z Louis; Clara A Callahan; Mary Robeson; Mengdan Liu; Jacquelyn McRae; Joseph S Gonnella; Marco Lombardi; Vittorio Maio Journal: BMJ Open Date: 2018-05-05 Impact factor: 2.692