Document Detail


Improving automated case finding for ectopic pregnancy using a classification algorithm.
MedLine Citation:
PMID:  21911435     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Research and surveillance work addressing ectopic pregnancy often rely on diagnosis and procedure codes available from automated data sources. However, the use of these codes may result in misclassification of cases. Our aims were to evaluate the accuracy of standard ectopic pregnancy codes; and, through the use of additional automated data, to develop and validate a classification algorithm that could potentially improve the accuracy of ectopic pregnancy case identification.
METHODS: Using automated databases from two US managed-care plans, Group Health Cooperative (GH) and Kaiser Permanente Colorado (KPCO), we sampled women aged 15-44 with an ectopic pregnancy diagnosis or procedure code from 2001 to 2007 and verified their true case status through medical record review. We calculated positive predictive values (PPV) for code-selected cases compared with true cases at both sites. Using additional variables from the automated databases and classification and regression tree (CART) analysis, we developed a case-finding algorithm at GH (n = 280), which was validated at KPCO (n = 500).
RESULTS: Compared with true cases, the PPV of code-selected cases was 68 and 81% at GH and KPCO, respectively. The case-finding algorithm identified three predictors: ≥ 2 visits with an ectopic pregnancy code within 180 days; International Classification of Diseases, 9th Revision, Clinical Modification codes for tubal pregnancy; and methotrexate treatment. Relative to true cases, performance measures for the development and validation sets, respectively, were: 93 and 95% sensitivity; 81 and 81% specificity; 91 and 96% PPV; 84 and 79% negative predictive value. Misclassification proportions were 32% in the development set and 19% in the validation set when using standard codes; they were 11 and 8%, respectively, when using the algorithm.
CONCLUSIONS: The ectopic pregnancy algorithm improved case-finding accuracy over use of standard codes alone and generalized well to a second site. When using administrative data to select potential ectopic pregnancy cases, additional widely available automated health plan data offer the potential to improve case identification.
Authors:
D Scholes; O Yu; M A Raebel; B Trabert; V L Holt
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2011-09-12
Journal Detail:
Title:  Human reproduction (Oxford, England)     Volume:  26     ISSN:  1460-2350     ISO Abbreviation:  Hum. Reprod.     Publication Date:  2011 Nov 
Date Detail:
Created Date:  2011-10-19     Completed Date:  2012-03-25     Revised Date:  2013-06-27    
Medline Journal Info:
Nlm Unique ID:  8701199     Medline TA:  Hum Reprod     Country:  England    
Other Details:
Languages:  eng     Pagination:  3163-8     Citation Subset:  IM    
Affiliation:
Group Health Research Institute, Group Health Cooperative, 1730 Minor Ave., 16th floor, Seattle, WA 98101, USA. scholes.d@ghc.org
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MeSH Terms
Descriptor/Qualifier:
Adolescent
Adult
Algorithms
Automatic Data Processing
Databases, Factual
Diagnosis, Computer-Assisted
Female
Humans
Medical Records Systems, Computerized
Obstetrics / methods,  standards*
Predictive Value of Tests
Pregnancy
Pregnancy, Ectopic / diagnosis*
Reproducibility of Results
Grant Support
ID/Acronym/Agency:
R03 HD052687/HD/NICHD NIH HHS; T32 HD052462/HD/NICHD NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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