Developing a random forest algorithm to identify patent foramen ovale and atrial septal defects in Ontario administrative databases.
Purpose: Routinely collected administrative data is widely used for population-based research. However, although clinically very different, atrial septal defects (ASD) and patent foramen ovale (PFO) share a single diagnostic code (ICD-9: 745.5, ICD-10: Q21.1). Using machine-learning based approaches...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/6/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156635964&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156635964 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/6/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 156635964 156635964 NLM35387650 156635964 10.1186/s12911-022-01837-2 NLM35387650 156635964 ppf: 1 ppct: 9 formats: tig: atl: Developing a random forest algorithm to identify patent foramen ovale and atrial septal defects in Ontario administrative databases. aug: au: Oliva, Laura Horlick, Eric Wang, Bo Huszti, Ella Hall, Ruth Abrahamyan, Lusine affil: Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada sug: subj: Heart Septal Defects, Atrial Diagnosis Heart Septal Defects, Atrial Epidemiology Heart Catheterization Methods Treatment Outcomes Ontario Algorithms Scales ab: Purpose: Routinely collected administrative data is widely used for population-based research. However, although clinically very different, atrial septal defects (ASD) and patent foramen ovale (PFO) share a single diagnostic code (ICD-9: 745.5, ICD-10: Q21.1). Using machine-learning based approaches, we developed and validated an algorithm to differentiate between PFO and ASD patient populations within healthcare administrative data.Methods: Using data housed at ICES, we identified patients who underwent transcatheter closure in Ontario between October 2002 and December 2017 using a Canadian Classification of Interventions code (1HN80GPFL, N = 4680). A novel random forest model was developed using demographic and clinical information to differentiate those who underwent transcatheter closure for PFO or ASD. Those patients who had undergone transcatheter closure and had records in the CorHealth Ontario cardiac procedure registry (N = 1482) were used as the reference standard. Several algorithms were tested and evaluated for accuracy, sensitivity, and specificity. Variable importance was examined via mean decrease in Gini index.Results: We tested 7 models in total. The final model included 24 variables, including demographic, comorbidity, and procedural information. After hyperparameter tuning, the final model achieved 0.76 accuracy, 0.76 sensitivity, and 0.75 specificity. Patient age group had the greatest influence on node impurity, and thus ranked highest in variable importance.Conclusions: Our random forest classification method achieved reasonable accuracy in identifying PFO and ASD closure in administrative data. The algorithm can now be applied to evaluate long term PFO and ASD closure outcomes in Ontario, pending future external validation studies to further test the algorithm. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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