Clinical predictors of admission in infants with acute bronchiolitis

Marlais, Matko; Evans, James B.; Abrahamson, E

doi:10.1136/adc.2010.201079

Cited by 59 publications

(81 citation statements)

References 21 publications

(24 reference statements)

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“…Much existing work on predictive modeling for bronchiolitis, such as Parker et al [2,21,[29][30][31]42], starts from univariate logistic regression to identify useful predictors. This approach may miss an opportunity to identify combinations of predictors that together have good predictive power, when each individual predictor has little or no predictive power by itself [263].…”

Section: Using Machine Learning Methodsmentioning

confidence: 99%

“…Marlais et al [31] used a scoring system to predict whether a bronchiolitis patient in the ED will be admitted, with an Area…”

Section: Predicting Hospital Admission From the Edmentioning

confidence: 99%

“…With rare exceptions such as Marlais et al [31,40,42], existing predictive models for bronchiolitis use no lab test data. As is the case with clinical predictive modeling in general, we would expect that using lab test data can help improve prediction accuracy.…”

Section: Using Lab Test Datamentioning

confidence: 99%

“…Another way to standardize care for bronchiolitis is to develop predictive models [21,[29][30][31][32][33][34][35][36][37] and use them to help direct an optimal disease management plan. By using a data-driven approach to summarize useful information accumulated in clinical and administrative data sets, predictive models [38] can manage the level of individualized detail inherent in a clinical setting, complement clinical practice guidelines, and overcome their limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Predictive models are often integrated into computerized decision support tools [273]. These tools can support clinicians' provisional judgment, or lead clinicians to question and reconsider that judgment [31]. This is particularly useful for inexperienced junior physicians and physicians who see children relatively infrequently.…”

Section: Introductionmentioning

confidence: 99%

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A systematic review of predictive modeling for bronchiolitis

Luo

Nkoy

Gesteland

et al. 2014

International Journal of Medical Informatics

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Purpose: Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases.Methods: A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process.Search results were limited to human subjects, the English language, and children (birth-18 years). Results:The literature search returned 2,312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. Conclusions:Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.

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Section: Using Machine Learning Methodsmentioning

confidence: 99%

“…Marlais et al [31] used a scoring system to predict whether a bronchiolitis patient in the ED will be admitted, with an Area…”

Section: Predicting Hospital Admission From the Edmentioning

confidence: 99%

Section: Using Lab Test Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A systematic review of predictive modeling for bronchiolitis

Luo

Nkoy

Gesteland

et al. 2014

International Journal of Medical Informatics

View full text Add to dashboard Cite

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Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department

2022

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Objectives This study aims to develop and internally validate a low‐dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post‐triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low‐dimensional model with fewer variables may be easier to implement. Methods This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtained from the model with the highest area under the curve (AUC) and used to create a low‐dimensional model. Results Eligible attendances totaled 72,229 (15% admission rate). The AUC was 0.853 (95% confidence interval [CI], 0.846–0.859) for GBM, 0.845 (95% CI, 0.838–0.852) for logistic regression and 0.813 (95% CI, 0.806–0.821) for naïve Bayes. Important predictors in the GBM model used to create a low‐dimensional model were presenting complaint, triage category, referral source, registration month, location type (resuscitation/other), distance traveled, admission history, and weekday (AUC 0.835 [95% CI, 0.829‐0.842]). Conclusions Admission and discharge probability can be predicted early in a pediatric ED using 8 variables. Future work could analyze the false positives and false negatives to gain an understanding of the implementation of these predictions.

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