Vaccine · 2013
A systematic review of validated methods for identifying Bell's palsy using administrative or claims data.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Lee Christopher D, Carnahan Ryan M, McPheeters Melissa L
Аннотация
PURPOSE: To identify and assess billing, procedural, or diagnosis code, or pharmacy claims-based algorithms used to identify Bell's palsy in administrative and claims databases.
METHODS: We searched the MEDLINE database via PubMed from 1991 to September 2012 using controlled vocabulary and key terms related to Bell's palsy. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria. Two reviewers independently extracted data regarding participant and algorithm characteristics and assessed a study's methodologic rigor.
RESULTS: One study identified Bell's palsy using an algorithm that included ICD-9 code 351.x and H-ICDA code 350.x, and two other studies analyzed a dataset for ICD-9 code 351.0. The positive predictive values of these studies were 0.81 and 0.88, based on case adjudication of ICD-9 matches. Two further studies calculated incidence rates without validation of their methods, also including ICD-9 code 351.0. No study reported the sensitivity of algorithms to identify Bell's palsy.
CONCLUSIONS: Few publications used rigorous methods to identify a validated algorithm that could identify cases of Bell's palsy from an administrative database. The best evidence from two different datasets in the literature addressed in this review used ICD-9 code 351.0 or a collection of ICD-9 codes 351.x for facial nerve disorders including Bell's palsy, along with other ICD-9 and H-ICDA codes for facial weakness. Each study had acceptable PPV, suggesting that ICD-9 based-algorithms have some utility in detecting Bell's palsy cases.
METHODS: We searched the MEDLINE database via PubMed from 1991 to September 2012 using controlled vocabulary and key terms related to Bell's palsy. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria. Two reviewers independently extracted data regarding participant and algorithm characteristics and assessed a study's methodologic rigor.
RESULTS: One study identified Bell's palsy using an algorithm that included ICD-9 code 351.x and H-ICDA code 350.x, and two other studies analyzed a dataset for ICD-9 code 351.0. The positive predictive values of these studies were 0.81 and 0.88, based on case adjudication of ICD-9 matches. Two further studies calculated incidence rates without validation of their methods, also including ICD-9 code 351.0. No study reported the sensitivity of algorithms to identify Bell's palsy.
CONCLUSIONS: Few publications used rigorous methods to identify a validated algorithm that could identify cases of Bell's palsy from an administrative database. The best evidence from two different datasets in the literature addressed in this review used ICD-9 code 351.0 or a collection of ICD-9 codes 351.x for facial nerve disorders including Bell's palsy, along with other ICD-9 and H-ICDA codes for facial weakness. Each study had acceptable PPV, suggesting that ICD-9 based-algorithms have some utility in detecting Bell's palsy cases.