Search

Purpose: To identify and assess billing, procedural, or diagnosis code, or pharmacy claim-based algorithms used to identify the following health outcomes in administrative and claims databases: acute disseminated encephalomyelitis (ADEM), optic neuritis, tics, and Henoch Schönlein purpura (HSP)., Methods: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to the conditions. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria and extracted case validation data from those studies meeting inclusion criteria., Results: Two eligible studies addressed ADEM, two addressed optic neuritis, and four studies addressed tics. Only one study addressed HSP. Among these, one study of ADEM reported a positive predictive value of 66%, however the identification algorithm contained a combination of International Classification of Diseases (ICD) codes and other identification methods and the performance of the ICD-9 codes alone was not reported. No other studies reported validation data., Conclusions: The lack of data on the validity of algorithms to identify these conditions may hamper our ability to determine incidence patterns with respect to infection and vaccination exposures. Further epidemiologic research to define validated methods of identifying cases could improve surveillance using large linked healthcare databases., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: To identify and assess diagnosis, procedure and pharmacy dispensing codes used to identify stillbirths and spontaneous abortion in administrative and claims databases from the United States or Canada., Methods: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to stillbirth or spontaneous abortion. 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 each study's methodological rigor using a pre-defined approach., Results: Ten publications addressing stillbirth and four addressing spontaneous abortion met our inclusion criteria. The International Classification of Diseases, Ninth Revision (ICD-9) codes most commonly used in algorithms for stillbirth were those for intrauterine death (656.4) and stillborn outcomes of delivery (V27.1, V27.3-V27.4, and V27.6-V27.7). Papers identifying spontaneous abortion used codes for missed abortion and spontaneous abortion: 632, 634.x, as well as V27.0-V27.7. Only two studies identifying stillbirth reported validation of algorithms. The overall positive predictive value of the algorithms was high (99%-100%), and one study reported an algorithm with 86% sensitivity. However, the predictive value of individual codes was not assessed and study populations were limited to specific geographic areas., Conclusions: Additional validation studies with a nationally representative sample are needed to confirm the optimal algorithm to identify stillbirths or spontaneous abortion in administrative and claims databases.', (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: To identify and assess billing, procedural, or diagnosis code, or pharmacy claim-based algorithms used to identify acute bronchospasm in administrative and claims databases., Methods: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to bronchospasm, wheeze and acute asthma. 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., Results: Our searches identified 677 citations of which 38 met our inclusion criteria. In these 38 studies, the most commonly used ICD-9 code was 493.x. Only 3 studies reported any validation methods for the identification of bronchospasm, wheeze or acute asthma in administrative and claims databases; all were among pediatric populations and only 2 offered any validation statistics. Some of the outcome definitions utilized were heterogeneous and included other disease based diagnoses, such as bronchiolitis and pneumonia, which are typically of an infectious etiology. One study offered the validation of algorithms utilizing Emergency Department triage chief complaint codes to diagnose acute asthma exacerbations with ICD-9 786.07 (wheezing) revealing the highest sensitivity (56%), specificity (97%), PPV (93.5%) and NPV (76%)., Conclusions: There is a paucity of studies reporting rigorous methods to validate algorithms for the identification of bronchospasm in administrative data. The scant validated data available are limited in their generalizability to broad-based populations., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: To identify algorithms that can capture incident cases of myocarditis and pericarditis in administrative and claims databases; these algorithms can eventually be used to identify cardiac inflammatory adverse events following vaccine administration., Methods: We searched MEDLINE from 1991 to September 2012 using controlled vocabulary and key terms related to myocarditis. 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 as well as study conduct., Results: Nine publications (including one study reported in two publications) met criteria for inclusion. Two studies performed medical record review in order to confirm that these coding algorithms actually captured patients with the disease of interest. One of these studies identified five potential cases, none of which were confirmed as acute myocarditis upon review. The other study, which employed a search algorithm based on diagnostic surveillance (using ICD-9 codes 420.90, 420.99, 422.90, 422.91 and 429.0) and sentinel reporting, identified 59 clinically confirmed cases of myopericarditis among 492,671 United States military service personnel who received smallpox vaccine between 2002 and 2003. Neither study provided algorithm validation statistics (positive predictive value, sensitivity, or specificity)., Conclusions: A validated search algorithm is currently unavailable for identifying incident cases of pericarditis or myocarditis. Several authors have published unvalidated ICD-9-based search algorithms that appear to capture myocarditis events occurring in the context of other underlying cardiac or autoimmune conditions., (Copyright © 2013. Published by Elsevier Ltd.)

Purpose: To review the evidence supporting the validity of billing, procedural, or diagnosis code, or pharmacy claim-based algorithms used to identify patients with rheumatoid arthritis (RA) in administrative and claim databases., Methods: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to RA and reference lists of included studies were searched. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria and extracted the data. Data collected included participant and algorithm characteristics., Results: Nine studies reported validation of computer algorithms based on International Classification of Diseases (ICD) codes with or without free-text, medication use, laboratory data and the need for a diagnosis by a rheumatologist. These studies yielded positive predictive values (PPV) ranging from 34 to 97% to identify patients with RA. Higher PPVs were obtained with the use of at least two ICD and/or procedure codes (ICD-9 code 714 and others), the requirement of a prescription of a medication used to treat RA, or requirement of participation of a rheumatologist in patient care. For example, the PPV increased from 66 to 97% when the use of disease-modifying antirheumatic drugs and the presence of a positive rheumatoid factor were required., Conclusions: There have been substantial efforts to propose and validate algorithms to identify patients with RA in automated databases. Algorithms that include more than one code and incorporate medications or laboratory data and/or required a diagnosis by a rheumatologist may increase the PPV., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: To review algorithms used to identify uveitis in administrative and claims databases., Methods: We searched the MEDLINE database via PubMed from 1991 to September 2012 using vocabulary and key terms related to uveitis. We also searched the reference lists of included studies. Two investigators independently assessed studies against pre-determined inclusion criteria. The same two investigators independently extracted data regarding participant and algorithm characteristics and assessed a study's methodological rigor using a pre-defined approach., Results: Seven studies met inclusion criteria. Variability exists among algorithms employed in these studies for finding cases of uveitis and related conditions as well as in use and implementation of validation methods. Of the seven included studies, three involved case validation. One used a narrow algorithm in addition to text mining of electronic medical records to identify incident cases and found a positive predictive value of 52.1%. The other two, which used broader uveitis definitions and included both incident and prevalent cases, found positive predictive values of 24.8% and 52.6%., Conclusions: Further research, with case as well as individual code validation, is needed to determine appropriate uveitis algorithms for purposes of active surveillance in administrative data. Decisions about which algorithm to use will depend on the desired balance of sensitivity and specificity., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

This report provides an overview of methods used to conduct systematic reviews for the US Food and Drug Administration (FDA) Mini-Sentinel project, which is designed to inform the development of safety monitoring tools for FDA-regulated products including vaccines. The objective of these reviews was to summarize the literature describing algorithms (e.g., diagnosis or procedure codes) to identify health outcomes in administrative and claims data. A particular focus was the validity of the algorithms when compared to reference standards such as diagnoses in medical records. The overarching goal was to identify algorithms that can accurately identify the health outcomes for safety surveillance. We searched the MEDLINE database via PubMed and required dual review of full text articles and of data extracted from studies. We also extracted data on each study's methods for case validation. We reviewed over 5600 abstracts/full text studies across 15 health outcomes of interest. Nearly 260 studies met our initial criteria (conducted in the US or Canada, used an administrative database, reported case-finding algorithm). Few studies (N=45), however, reported validation of case-finding algorithms (sensitivity, specificity, positive or negative predictive value). Among these, the most common approach to validation was to calculate positive predictive values, based on a review of medical records as the reference standard. Of the studies reporting validation, the ease with which a given clinical condition could be identified in administrative records varied substantially, both by the clinical condition and by other factors such as the clinical setting, which relates to the disease prevalence., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: To identify and assess algorithms used to identify Kawasaki syndrome/Kawasaki disease in administrative and claims databases., Methods: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to Kawasaki disease. 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., Results: Our searches identified 177 citations of which 22 met our inclusion criteria. All studies used algorithms including International Classification of Diseases, Ninth Revision (ICD-9) code 446.1 either alone, or with evidence of intravenous immunoglobulin (IVIG) administration, or with ICD-10 code M30.3. Six studies confirmed diagnoses by medical chart review. Three of these six studies reported validation statistics, with positive predictive values of 74%, 84%, and 86%, respectively., Conclusions: All studies that reported algorithms used either the ICD-9 code 446.1 either alone, with evidence of IVIG administration or with ICD-10 code M30.3. The ICD-9 code 446.1 alone produced positive predictive values of 74%, 84%, and 86% in separate studies in Georgia and California. The sensitivity of these codes to detect Kawasaki disease is unknown, as no sampling of medical records for missed true cases of Kawasaki disease was done. Further research would be helpful to determine whether the relatively high positive predictive values found in southern California are seen elsewhere and also to evaluate the performance of other codes to identify cases of Kawasaki disease and the sensitivity of the narrow algorithms that have been used to date., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: To identify and assess billing, procedural, or diagnostic code algorithms used to identify transverse myelitis in administrative databases., Methods: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to transverse myelitis. 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., Results: Three studies met criteria for inclusion in this review. The only algorithm based solely on administrative claims data with a reported positive predictive value included five ICD-9 codes (codes 341.20, 341.21, 341.22, 323.8, 323.9). The positive predictive value for physician-diagnosed acute transverse myelitis was 62%., Conclusions: More research is needed to establish an accurate algorithm to identify transverse myelitis in large administrative databases using diagnosis and/or procedure codes. Use of standardized consensus definitions, clear description for algorithm selection, and reporting of validation procedure and results would be most beneficial., (Copyright © 2013 Elsevier Ltd. All rights reserved.)