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Abstract: FR-PO044

Characterizing Urinary Tract Infections and Their Treatment Pathways EHR

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Shang, Ning, Columbia University, New York, New York, United States
  • Xu, Katherine, Columbia University, New York, New York, United States
  • Kiryluk, Krzysztof, Columbia University, New York, New York, United States
Background

UTIs are one of the most common infections worldwide. UTI epidemiology studies show a 27% recurrent rate in women within 6 months after the first UTI. Hence UTI early diagnosis and systematic treatment are very important. This project studies antibiotics treatment pathways (medication usage sequence) using big data.

Methods

We developed an electronic UTI phenotype identifying UTI by having at least 1 diagnosis/positive urine culture. It can further subgroup UTI as having a single episode of UTI or recurrent UTIs using 1-week time window to define each episode. UTI treatment pathways were analyzed for those who had at least one antibiotic in EHR. The patient had to have at least a 1-year of data in the database before the first UTI occurrence to be included. The medications were defined by ingredients using RxNorm and were grouped to drug class using ATC. The medications were extracted, and then ordered by first exposure. The sunburst plots were generated to visualize the pathways.

Results

Analyzing ~5M CUIMC EHR spanning back to 1980s, we identified 176,533 UTI patients (71% single vs 29% recurrent), consistent with prior reports. Among them, 112,126 patients qualified for this analysis. Recurrent UTIs patients were younger than single UTI patients (48 vs 53 years). The prevalence of female UTI was significantly higher than male UTI (female in overall 83%, in single UTI 82%, in recurrent UTI 87%). The five most commonly used antibiotics were nitrofurantoin monohydrate (24%), cephalexin (23%), nitrofurantoin/tetracaine (22%), ceftriaxone (18%), sulfamethoxazole/trimethoprim (17%) (Fig 1).

Conclusion

We designed a new electronic phenotype to detect single and recurrent UTI events. This algorithm can be useful for big data approaches to studies of UTI epidemiology and treatment patterns.

4 level treatment pathways for UTI. The inner circle shows the first relevant medication that the patient took, and so forth. N/A means no drug.

Funding

  • Other NIH Support