Performance characteristics of code-based algorithms to identify urinary tract infections in large United States administrative claims databases.

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.

Development and performance characteristics of novel code-based algorithms to identify invasive Escherichia coli disease.

PURPOSE: Evaluation of novel code-based algorithms to identify invasive Escherichia coli disease (IED) among patients in healthcare databases. METHODS: Inpatient visits with microbiological evidence of invasive bacterial disease were extracted from the Optum© electronic health record database between January 1, 2016 and June 30, 2020. Six algorithms, derived from diagnosis and drug exposure codes associated to infectious diseases and Escherichia coli, were developed to identify IED. The performance characteristics of algorithms were assessed using a reference standard derived from microbiology data. RESULTS: Among 97 194 eligible records, 25 310 (26.0%) were classified as IED. Algorithm 1 (diagnosis code for infectious invasive disease due to E. coli) had the highest positive predictive value (PPV; 96.0%) and lowest sensitivity (60.4%). Algorithm 2, which additionally included patients with diagnosis codes for infectious invasive disease due to an unspecified organism, had the highest sensitivity (95.5%) and lowest PPV (27.8%). Algorithm 4, which required patients with a diagnosis code for infectious invasive disease due to unspecified organism to have no diagnosis code for non-E. coli infections, achieved the most balanced performance characteristics (PPV, 93.6%; sensitivity, 78.1%; F1 score, 85.1%). Finally, adding exposure to antibiotics in the treatment of E. coli had limited impact on performance algorithms 5 and 6. CONCLUSION: Algorithm 4, which achieved the most balanced performance characteristics, offers a useful tool to identify patients with IED and assess the burden of IED in healthcare databases.

Evaluating the Effect of Standard of Care Treatment on Burden of Chronic Hepatitis B: A Retrospective Analysis of the United States Veterans Population.

INTRODUCTION: This study aimed to characterize chronic hepatitis B (CHB)-infected patients and estimate the association between nucleos(t)ide analogue (NA) persistence and economic outcomes using data from the Veterans Health Administration (VHA) database. METHODS: Patients (at least 18 years of age) with two or more claims for CHB and at least one pharmacy claim for NA were identified using VHA data from 1 April 2013 to 31 March 2018. The index date was the first NA prescription fill date during 1 October 2014 to 31 March 2017. Persistence and non-persistence to NA treatment were assessed during the first 2 years post index date. Non-persistence was defined as at least one failure to refill medication within 30 days from the run-out date. Generalized linear models were used to compare health care utilization and costs between persistent and non-persistent patients. RESULTS: Among patients treated with NAs (N = 2368), 1428 (60%) were CHB mono-infected and 748 (32%) were HIV co-infected. Total costs per patient per year (PPPY) were $39,240, $29,957, and $55,220 PPPY for NA-treated, mono-infected, and HIV co-infected patients, respectively. An inception cohort of 564 patients (24%), without a NA prescription in the 6 months pre-index period and at least 2 years of follow-up, was created. Persistence among the inception cohort was 29% for first year and 14% for first 2 years. After adjustment for baseline differences, persistent patients had lower cumulative overall health care costs compared to non-persistent patients, with a net cost saving of $851 (p > 0.05) in the first 2 years. CONCLUSION: CHB is associated with considerable economic burden. We observed suboptimal persistence to NAs which decreased over time. Short-term savings could be generated for CHB-infected patients when they remain persistent to NAs.