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

Pharmacoepidemiology and Drug Safety, vol. 31, pp. 983–991

Abstract

Abstract 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.

Authors 7

1. Stephen Patrick Fortin corresponding

   Janssen Research & Development (United States)

   Affiliation as printed

   Janssen Research & Development Observational Health Data Analytics Raritan New Jersey USA

   Janssen Research & Development, Observational Health Data Analytics, Raritan, New Jersey, USA

2. Luis Hernandez Pastor

   Janssen (Belgium)

   Affiliation as printed

   Janssen Research & Development Infectious Diseases and Vaccines Beerse Belgium

   Janssen Research & Development Infectious Diseases and Vaccines Beerse Belgium

3. Joachim Doua

   Janssen (Belgium)

   Affiliation as printed

   Janssen Research & Development Infectious Diseases and Vaccines Beerse Belgium

   Janssen Research & Development Infectious Diseases and Vaccines Beerse Belgium

4. Michal Sarnecki

   Cilag (Switzerland)

   Affiliation as printed

   Janssen Vaccines Branch of Cilag GmbH International Bern Switzerland

   Janssen Vaccines, Branch of Cilag GmbH International, Bern, Switzerland

5. Joel N. Swerdel

   Janssen Research & Development (United States)

   Affiliation as printed

   Janssen Research & Development Observational Health Data Analytics Raritan New Jersey USA

   Janssen Research & Development, Observational Health Data Analytics, Raritan, New Jersey, USA

6. Jamie Colasurdo

   Janssen Research & Development (United States)

   Affiliation as printed

   Janssen Research & Development Epidemiology Raritan New Jersey USA

   Janssen Research & Development, Epidemiology, Raritan, New Jersey, USA

7. Jeroen Geurtsen Aachen

   Janssen (Netherlands)

   Affiliation as printed

   Janssen Vaccines & Prevention Bacterial Vaccines Research & Early Development CN Leiden Netherlands

   Janssen Vaccines & Prevention Bacterial Vaccines Research & Early Development CN Leiden Netherlands