Evidence map›Paper›PMID 29105238›Full record

ArticleAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2018

Comparing Statewide and Single-center Data to Predict High-frequency Emergency Department Utilization Among Patients With Asthma Exacerbation.

Margaret E Samuels-Kalow, Mohammad K Faridi, Janice A Espinola, Jean E Klig, Carlos A Camargo

Abstract readComparative Study
In one paragraph

Article in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Margaret E Samuels-KalowDepartment of Emergency Medicine, Massachusetts General Hospital, Boston, MA.
Mohammad K FaridiDepartment of Emergency Medicine, Massachusetts General Hospital, Boston, MA.
Janice A EspinolaDepartment of Emergency Medicine, Massachusetts General Hospital, Boston, MA.
Jean E KligDepartment of Emergency Medicine, Massachusetts General Hospital, Boston, MA.
Carlos A Camargo

Funding

Enabling Medical Research Growth in Emergency MedicineK12HL109009 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI KIMMEL, STEPHEN E. · 2011 to 2015
$3.4M
NHLBI NIH HHS K12 HL109009
6 · The paper itself

Abstract

backgroundPrevious studies examining high-frequency emergency department (ED) utilization have primarily used single-center data, potentially leading to ascertainment bias if patients visit multiple centers. The goals of this study were 1) to create a predictive model to prospectively identify patients at risk of high-frequency ED utilization for asthma and 2) to examine how that model differed using statewide versus single-center data.

methodsTo track ED visits within a state, we analyzed 2011 to 2013 data from the New York State Healthcare Cost and Utilization Project State Emergency Department Databases. The first year of data (2011) was used to determine prior utilization, 2012 was used to identify index ED visits for asthma and for demographics, and 2013 was used for outcome ascertainment. High-frequency utilization was defined as 4+ ED visits for asthma within 1 year after the index visit. We performed analyses separately for children (age < 21 years) and adults and constructed two models: one included all statewide (multicenter) visits and the other was restricted to index hospital (single-center) visits. Multivariable logistic regression models were developed from potential predictors selected a priori. The final model was chosen by evaluating model performance using Akaike's Information Criterion scores, 10-fold cross-validation, and receiver operating characteristic curves.

resultsAmong children, high-frequency ED utilization for asthma was observed in 2,417 of 94,258 (2.56%) using all statewide visits, compared to 1,853 of 94,258 (1.97%) for index hospital visits only. Among adults, the corresponding results were 7,779 of 159,874 (4.87%) and 5,053 of 159,874 (3.16%), respectively. In the multicenter visit model, the area under the curve (AUC) from 10-fold cross-validation for children was 0.70 (95% confidence interval [CI] = 0.69-0.72), compared to 0.71 (95% CI = 0.69-0.72) in the single-center visit model. The corresponding AUC results for adults were 0.76 (95% CI = 0.76-0.77) and 0.76 (95% CI = 0.75-0.77), respectively.

conclusionData available at the index ED visit can predict subsequent high-frequency utilization for asthma with AUC ranging from 0.70 to 0.76. Model accuracy was similar regardless of whether outcome ascertainment included all statewide visits (multicenter) or was limited to the index hospital (single-center).

Indexed as

AdolescentAdultAsthmaChildChild, PreschoolDatabases, FactualData CollectionDisease ProgressionEmergency Service, HospitalFemaleHumansLogistic ModelsMaleMiddle AgedYoung Adult

Identifiers

PMID29105238
PMCPMC6051421

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.