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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Mar 4, 2020
Date Accepted: Oct 30, 2020
Date Submitted to PubMed: Nov 3, 2020

The final, peer-reviewed published version of this preprint can be found here:

Identifying Ectopic Pregnancy in a Large Integrated Health Care Delivery System: Algorithm Validation

Getahun D, Shi JM, Chandra M, Fassett MJ, Alexeeff S, Im TM, Chiu VY, Armstrong MA, Xie F, Stern J, Takhar HS, Asiimwe A, Raine-Bennett T

Identifying Ectopic Pregnancy in a Large Integrated Health Care Delivery System: Algorithm Validation

JMIR Med Inform 2020;8(11):e18559

DOI: 10.2196/18559

PMID: 33141678

PMCID: 7735905

Validation of an Algorithm to Identify Ectopic Pregnancy in a Large Integrated Health Care Delivery System

  • Darios Getahun; 
  • Jiaxiao M. Shi; 
  • Malini Chandra; 
  • Michael J. Fassett; 
  • Stacey Alexeeff; 
  • Theresa M. Im; 
  • Vicki Y. Chiu; 
  • Mary Anne Armstrong; 
  • Fagen Xie; 
  • Julie Stern; 
  • Harpreet S. Takhar; 
  • Alex Asiimwe; 
  • Tina Raine-Bennett

ABSTRACT

Background:

Surveillance of ectopic pregnancy (EP) using electronic databases is important. There is no study that assessed the validity of EP case ascertainment using electronic health records.

Objective:

We aimed to assess the validity of an enhanced version of a previously validated algorithm, which used a combination of encounters with EP related diagnostic/procedure codes and methotrexate injections. The enhanced algorithm included ICD-10 diagnostic/procedure codes, used telephone appointment visits, and excluded cases with only abdominal EP diagnosis codes.

Methods:

Medical records of 500 women, aged 15-44 years with membership at Kaiser Permanente Southern and Northern California between 2009-2018 with a potential EP were randomly selected for chart review and true cases were identified. The sensitivity, specificity, positive (PPV) and negative (NPV) predictive values and the overall performance (Youden’s index and F-score) of the algorithm were evaluated and compared to the validated algorithm.

Results:

There were 334 true positive and 166 false positive EP cases with available records. True positive and true negative EP cases did not differ significantly according to maternal age, race/ethnicity and smoking status. EP cases with only one encounter and non-tubal EP were more likely to be misclassified. The sensitivity, specificity, PPV, and NPV of the enhanced algorithm for EP were 97.6%, 84.9%, 92.9%, and 94.6%, respectively. The Youden index and F-score were 82.5% and 95.2%. The sensitivity and NPV were lower at 94.3% and 88.1%, respectively, for the previously published algorithm. The sensitivity of surgical procedure codes from electronic chart abstraction to correctly identify surgical management was 91.9%. The overall accuracy, defined as the percentage of EP cases with correct management (surgical, medical, and unclassified) identified by electronic chart abstraction, was 92.3%.

Conclusions:

The performance of the enhanced algorithm for EP case ascertainment in the integrated healthcare databases is adequate to allow for use in future epidemiological studies. Use of this algorithm will likely result in better capture of true EP cases than the previously validated algorithm.


 Citation

Please cite as:

Getahun D, Shi JM, Chandra M, Fassett MJ, Alexeeff S, Im TM, Chiu VY, Armstrong MA, Xie F, Stern J, Takhar HS, Asiimwe A, Raine-Bennett T

Identifying Ectopic Pregnancy in a Large Integrated Health Care Delivery System: Algorithm Validation

JMIR Med Inform 2020;8(11):e18559

DOI: 10.2196/18559

PMID: 33141678

PMCID: 7735905

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