Identifying asthma exacerbations in a pediatric emergency department: A feasibility study

Sanders, David; Gregg, William M.; Aronsky, Dominik

doi:10.1016/j.ijmedinf.2006.03.003

Cited by 39 publications

(52 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study was conducted at an urban, pediatric ED that provides care for 55,000 patient visits annually with approximately 7–10% of patients presenting with an asthma exacerbation [9]. The ED has 68 attending and resident physicians, 95 nurses, and 16 respiratory therapists.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the computerized patient status board integrates and displays information relevant to the current visit from the previous three systems. Prior to the start of the study, an 8-page, paper-based guideline including a validated asthma severity metric [2] has been available for guiding asthma care including reassessment and treatment suggestions; however, the guideline was used in only 7–10% of asthma cases [9]. …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

Dexheimer

Abramo

Arnold

et al. 2014

International Journal of Medical Informatics

View full text Add to dashboard Cite

Objective The use of evidence-based guidelines can improve the care for asthma patients. We implemented a computerized asthma management system in a pediatric emergency department (ED) to integrate national guidelines. Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system (Bayesian network) combined with an asthma management system embedded in the workflow decreases time to disposition decision. Methods We performed a prospective, randomized controlled trial in an urban, tertiary care pediatric ED. All patients 2–18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system. Patients identified to have an asthma exacerbation were randomized to intervention or control. For intervention patients, asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage, and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline-compliant order sets. Control patients received standard asthma management. The primary outcome measure was the time from triage to disposition decision. Results The Bayesian network identified 1,339 patients with asthma exacerbations, of which 788 had an asthma diagnosis determined by an ED physician-established reference standard (positive predictive value 69.9%). The median time to disposition decision did not differ among the intervention (228 minutes; IQR=(141, 326)) and control group (223 minutes; IQR= (129, 316));(p=0.362). The hospital admission rate was unchanged between intervention (25%) and control groups (26%); (p=0.867). ED length of stay did not differ among intervention (262 minutes; IQR=(165, 410)) and control group (247 minutes; IQR=(163, 379));(p=0.818). Conclusions The control and intervention groups were similar in regards to time to disposition; the computerized management system did not add additional wait time. The time to disposition decision did not change; however the management system integrated several different information systems to support clinicians’ communication.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

Dexheimer

Abramo

Arnold

et al. 2014

International Journal of Medical Informatics

View full text Add to dashboard Cite

show abstract

“…of studiesOverall accuracy (%)ApplicationANNSVMBNFLAsthma1882.44 ± 23.71To classify exacerbations [10–14]1121To classify severity [6, 15, 16]––12To classify pathologic vs control [4, 17–19]21–1To classify asthma control level [8, 20]1––1To classify how manage their pathology [21]1–––To predict the clinical effect of salbutamol [22, 23]2–––To classify health state[24]––1–ADR694.5 ± 2.12To predict the posterior probability of a drug (BARDI tool) [28–30]––3–To predict hypersensitivity reaction (AERS database) [25–27]––3–Allergic rhinitis188.31To classify pathologic vs control [31]1…”

Section: Resultsmentioning

confidence: 99%

The soft computing-based approach to investigate allergic diseases: a systematic review

et al. 2017

View full text Add to dashboard Cite

BackgroundEarly recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.ObjectiveThe aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases.MethodsThe review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016.ResultsThe review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause–effect relationships, which make it difficult to assess the evolution of disease.ConclusionsAlthough most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.

show abstract

“…Inclusion criteria are age 5e17 years, doctor-diagnosed asthma, signs or symptoms of an asthma exacerbation (cough, dyspnoea, wheezing and/or chest pain), 29 need for treatment with systemic corticosteroid (CCS) and inhaled bronchodilator as determined by the attending physician, and availability for phone follow-up if discharged to home. We exclude participants with chronic lung disease other than asthma, foreign body aspiration or other reason for pertinent signs and symptoms.…”

Section: Materials and Methods Setting Study Population And Participamentioning

confidence: 99%

The Acute Asthma Severity Assessment Protocol (AASAP) study: objectives and methods of a study to develop an acute asthma clinical prediction rule

et al. 2011

View full text Add to dashboard Cite

Acute asthma exacerbations are one of the most common reasons for pediatric emergency department (PED) visits and hospitalizations, and relapse frequently necessitates repeat urgent care. While care plans exist, there are no acute asthma prediction rules (APR)to assess severity and predict outcome. The primary objective of the Acute Asthma Severity Assessment Protocol (AASAP) study is to develop a multivariable APR for acute asthma exacerbations in the pediatric patient. We are enrolling a prospective, convenience sample aged 5 to 17 years with acute asthma exacerbations who present to an urban, academic, tertiary PED. The study protocol and data analysis plan conform to accepted biostatistical and clinical standards for clinical prediction rule development. Modeling of the APR will be performed once the entire sample size of 1,500 has accrued. We anticipate that the APR will improve resource utilization in the emergency department, aid in standardization of disease assessment, and allow physician and non-physician providers to participate in earlier objective decision making. The objective of this report is to describe the study objectives and detailed methodology of the AASAP study.

show abstract

Identifying asthma exacerbations in a pediatric emergency department: A feasibility study

Cited by 39 publications

References 36 publications

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

The soft computing-based approach to investigate allergic diseases: a systematic review

The Acute Asthma Severity Assessment Protocol (AASAP) study: objectives and methods of a study to develop an acute asthma clinical prediction rule

Contact Info

Product

Resources

About