ArticleHuman reproduction (Oxford, England)2011
Improving automated case finding for ectopic pregnancy using a classification algorithm.
Article in Human reproduction (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The risk of opioid use disorder among women undergoing obstetric-related procedures: Results from the Cerner Real-World Database.Drug and alcohol dependence reports · 2024Article
- Disparities in the Incidence of Ectopic Pregnancy in a Large Health Care System in California, 2010-2019.The Permanente journal · 2022Article
- Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.PloS one · 2021Article
- Antidepressant Use around Conception, Prepregnancy Depression, and Risk of Ectopic Pregnancy.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2020Article
- Identifying Ectopic Pregnancy in a Large Integrated Health Care Delivery System: Algorithm Validation.JMIR medical informatics · 2020Article
- Opioid Prescription and Persistent Opioid Use After Ectopic Pregnancy.Obstetrics and gynecology · 2020Article
- Benzodiazepine use before conception and risk of ectopic pregnancy.Human reproduction (Oxford, England) · 2020Article
- Observational
- Inferring pregnancy episodes and outcomes within a network of observational databases.PloS one · 2018Article
- The utility of ICD9-CM codes in identifying induction of labor.American journal of perinatology · 2015Article
- Sustaining Research Networks: the Twenty-Year Experience of the HMO Research Network.EGEMS (Washington, DC) · 2014Article
- Detection of pelvic inflammatory disease: development of an automated case-finding algorithm using administrative data.Infectious diseases in obstetrics and gynecology · 2011Article
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5 authors.
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Abstract
backgroundResearch 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.
methodsUsing 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).
resultsCompared 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.
conclusionsThe 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.
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