Format

Send to

Choose Destination
EGEMS (Wash DC). 2019 Mar 29;7(1):5. doi: 10.5334/egems.264.

Identification of Incident Uterine Fibroids Using Electronic Medical Record Data.

Author information

1
Kaiser Permanente Washington, US.
2
University of Washington, US.
3
Bayer Pharma AG, DE.

Abstract

Introduction:

Uterine fibroids are the most common benign tumors of the uterus and are associated with considerable morbidity. Diagnosis codes have been used to identify fibroid cases, but their accuracy, especially for incident cases, is uncertain.

Methods:

We performed medical record review on a random sample of 617 women who received a fibroid diagnosis during 2012-2014 to assess diagnostic accuracy for incident fibroids. We developed 2 algorithms aimed at improving incident case-finding using classification and regression tree analysis that incorporated additional electronic health care data on demographics, symptoms, treatment, imaging, health care utilization, comorbidities and medication. Algorithm performance was assessed using medical record as gold standard.

Results:

Medical record review confirmed 482 fibroid cases as incident, resulting a 78 percent positive predictive value (PPV) for incident cases based on diagnosis codes alone. Incorporating additional electronic data, the first algorithm classified 395 women with a pelvic ultrasound on diagnosis date but none before as incident cases. Of these, 344 were correctly classified, yielding an 87 percent PPV, 71 percent sensitivity, and 62 percent specificity. A second algorithm built on the first algorithm and further classified women based on a fibroid diagnosis code of 218.9 in 2 years after incident diagnosis and lower body mass index; yielded 93 percent PPV, 53 percent sensitivity, and 85 percent specificity.

Conclusions:

Compared to diagnosis codes alone, our algorithms using fibroid diagnosis codes and additional electronic data improved identification of incident cases with higher PPV, and high sensitivity or specificity to meet different aims of future studies seeking to identify incident fibroids from electronic data.

KEYWORDS:

algorithms; classification and regression tree; diagnosis codes; electronic health records; positive predictive value; uterine fibroids; uterine leiomyomata

Conflict of interest statement

This work was supported by a research grant from Bayer Pharma AG. Dr. Schulze-Rath is an employee of Bayer Pharma AG. All other authors have no conflict of interest to declare.

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center