Performance characteristics of code-based algorithms to identify urinary tract infections in large United States administrative claims databases.
Pharmacoepidemiol Drug Saf
; 31(9): 953-962, 2022 09.
Article
en En
| MEDLINE
| ID: mdl-35790044
ABSTRACT
BACKGROUND:
In real-world evidence research, reliability of coding in healthcare databases dictates the accuracy of code-based algorithms in identifying conditions such as urinary tract infection (UTI). This study evaluates the performance characteristics of code-based algorithms to identify UTI.METHODS:
Retrospective observational study of adults contained within three large U.S. administrative claims databases on or after January 1, 2010. A targeted literature review was performed to inform the development of 10 code-based algorithms to identify UTIs consisting of combinations of diagnosis codes, antibiotic exposure for the treatment of UTIs, and/or ordering of a urinalysis or urine culture. For each database, a probabilistic gold standard was developed using PheValuator. The performance characteristics of each code-based algorithm were assessed compared with the probabilistic gold standard.RESULTS:
A total of 2 950 641, 1 831 405, and 2 294 929 patients meeting study criteria were identified in each database. Overall, the code-based algorithm requiring a primary UTI diagnosis code achieved the highest positive predictive values (PPV; >93.8%) but the lowest sensitivities (<12.9%). Algorithms requiring three UTI diagnosis codes achieved similar PPV (>0.899%) and improved sensitivity (<41.6%). Algorithms requiring a single UTI diagnosis code in any position achieved the highest sensitivities (>72.1%) alongside a slight reduction in PPVs (<78.3%). All-time prevalence estimates of UTI ranged from 21.6% to 48.6%.CONCLUSIONS:
Based on these findings, we recommend use of algorithms requiring a single UTI diagnosis code, which achieved high sensitivity and PPV. In studies where PPV is critical, we recommend code-based algorithms requiring three UTI diagnosis codes rather than a single primary UTI diagnosis code.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Infecciones Urinarias
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Pharmacoepidemiol Drug Saf
Asunto de la revista:
EPIDEMIOLOGIA
/
TERAPIA POR MEDICAMENTOS
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos