Predicting Need for Hospitalization in Acute Pediatric Asthma

Gorelick, Marc H.; Scribano, Philip V.; Stevens, Martha W.; Schultz, Theresa Ryan; Shults, Justine

doi:10.1097/pec.0b013e31818c268f

Cited by 38 publications

(27 citation statements)

References 28 publications

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“…Predictive models for paediatric asthma in particular have also been developed. Some models have used Cox regressions [9] or logistic regressions [10][11][12][13][14] with demographic or clinical attributes as predictors of readmissions and are therefore important for use in case management. These models predict population at risk but do not have the capacity to predict readmission frequencies or their timing, and are therefore of little use for hospital management resource planning.…”

Section: Introductionmentioning

confidence: 99%

Modelling and predicting low count child asthma hospital readmissions using General Additive Models

Vicendese¹,

Olenko²,

Dharmage³

et al. 2013

OJEpi

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Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends. We sought to model and predict daily trends of childhood asthma readmissions over time in Victoria, Australia. Methods: We used a database of 75,000 childhood asthma admissions from the Department of Health, Victoria, Australia in 1997-2009. Daily admissions over time were modeled using a semi parametric Generalized Additive Model (GAM) and by sex and age group. Predictions were also estimated by using these models. Results: N = 2401 asthma readmissions within 28 days occurred during study period. Of these, n = 1358 (57%) were boys. Overall, seasonal peaks occurred in winter (30.5%) followed by autumn (28.6%) and then spring (24.6%) (p < 0.0005). Day of the week and month were significantly associated with trends in readmission. Smooth function of time was significant (p < 0.0005) and indicated declining trends in readmissions in 2001-2002 and then increasing, returning to roughly initial levels. Predictions suggested readmissions would continue to increase by 5% per year with boys in the 2 to 5 years age group experiencing the largest increase. Conclusions: GAMs are reliable methods for low count time series such as repeat admissions. Our model implied: health services may need to be revised to accommodate for seasonal peaks in readmission especially for younger age groups.

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Section: Introductionmentioning

confidence: 99%

Modelling and predicting low count child asthma hospital readmissions using General Additive Models

Vicendese¹,

Olenko²,

Dharmage³

et al. 2013

OJEpi

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show abstract

“…22,23 In patients with COPD exacerbations, peak flow rate measurement and spirometry are not recommended, because they may not correlate as well with exacerbation severity. 24,25 Pulse oximetry may be useful for asthmatic children who are unable to perform peak flow measurement 26 and may predict respiratory failure in COPD. 27 What clinical features can help to differentiate causes of wheezing?…”

Section: Evaluation and Diagnosismentioning

confidence: 99%

Wheezing

Bardowell¹,

Mahoney²

2015

Hospital Medicine Clinics

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“…Although PEFR appears to provide little predictive value in isolation, it may be a useful parameter when used in conjunction with other indicators of severity such as heart rate, respiratory rate or oxygen saturations 20. Much work has gone into creating indices to predict poor outcomes in acute severe asthma, and to identify those patients who are considered low risk who can be safely discharged.…”

Section: Clinical Questionsmentioning

confidence: 99%