Development and Validation of ICD-10-CM-based Algorithms for Date of Last Menstrual Period, Pregnancy Outcomes, and Infant Outcomes.

INTRODUCTION AND OBJECTIVE: Validation studies of algorithms for pregnancy outcomes based on International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes are important for conducting drug safety research using administrative claims databases. To facilitate the conduct of pregnancy safety studies, this exploratory study aimed to develop and validate ICD-10-CM-based claims algorithms for date of last menstrual period (LMP) and pregnancy outcomes using medical records. METHODS: Using a mother-infant-linked claims database, the study included women with a pregnancy between 2016-2017 and their infants. Claims-based algorithms for LMP date utilized codes for gestational age (Z3A codes). The primary outcomes were major congenital malformations (MCMs) and spontaneous abortion; additional secondary outcomes were also evaluated. Each pregnancy outcome was identified using a claims-based simple algorithm, defined as presence of ≥ 1 claim for the outcome. Positive predictive values (PPV) and 95% confidence intervals (CI) were calculated. RESULTS: Overall, 586 medical records were sought and 365 (62.3%) were adjudicated, including 125 records each for MCMs and spontaneous abortion. Last menstrual period date was validated among maternal charts procured for pregnancy outcomes and fewer charts were adjudicated for the secondary outcomes. The median difference in days between LMP date based on Z3A codes and adjudicated LMP date was 4.0 (interquartile range: 2.0-10.0). The PPV of the simple algorithm for spontaneous abortion was 84.7% (95% CI 78.3, 91.2). The PPV for the MCM algorithm was < 70%. The algorithms for the secondary outcomes pre-eclampsia, premature delivery, and low birthweight performed well, with PPVs > 70%. CONCLUSIONS: The ICD-10-CM claims-based algorithm for spontaneous abortion performed well and may be used in pregnancy studies. Further algorithm refinement for MCMs is needed. The algorithms for LMP date and the secondary outcomes would benefit from additional validation in a larger sample.

Antidepressant Use and Risk of Colorectal Cancer in the Women's Health Initiative.

Background: Some prior studies have reported reduced colorectal cancer risk among individuals using antidepressant medications, especially selective serotonin reuptake inhibitors (SSRIs). Yet most studies have not considered the potential role of depression or other confounders in their analyses.Methods: We utilized prospectively collected data from 145,190 participants in the Women's Health Initiative, among whom 2,580 incident colorectal cancer cases were diagnosed. Antidepressant use and depressive symptoms were assessed at baseline and follow-up study visits. Cox proportional hazards regression models with adjustment for depressive symptoms and other covariates were utilized to estimate HRs and 95% confidence intervals (CIs) for associations between antidepressant use and colorectal cancer.Results: Antidepressant use was reported by 6.9% of participants at baseline, with SSRIs the most common class of antidepressant used. In multivariable analyses, including adjustment for depressive symptomology, we observed no statistically significant association between antidepressant use overall (HR = 0.90; 95% CI, 0.75-1.09) or with SSRIs specifically (HR = 1.08; 95% CI, 0.85-1.37) and colorectal cancer risk. A borderline significant reduction in colorectal cancer risk was observed for use of tricyclic antidepressants (HR = 0.76; 95% CI, 0.56-1.04). Severe depressive symptoms were independently associated with a 20% increased risk of colorectal cancer (HR = 1.21; 95% CI, 1.09-1.48). Results were similar for separate evaluations of colon and rectal cancer.Conclusions: We observed no evidence of an association between antidepressant use, overall or by therapeutic class, and colorectal cancer risk.Impact: These results suggest that antidepressants may not be useful as chemopreventive agents for colorectal cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 892-8. ©2018 AACR.