• Luis Hernandez-Pastor, Jeroen Geurtsen, Antoine C El Khoury, Stephen P Fortin, Marjolaine Gauthier-Loiselle, Louise H Yu, and Martin Cloutier.
• Janssen Pharmaceutica NV, Beerse, Belgium.
• Curr Med Res Opin. 2023 Oct 1; 39 (10): 130313121303-1312.

ObjectiveTo assess the predictive accuracy of code-based algorithms for identifying invasive Escherichia coli (E. coli) disease (IED) among inpatient encounters in US hospitals.MethodsThe PINC AI Healthcare Database (10/01/2015-03/31/2020) was used to assess the performance of six published code-based algorithms to identify IED cases among inpatient encounters. Case-confirmed IEDs were identified based on microbiological confirmation of E. coli in a normally sterile body site (Group 1) or in urine with signs of sepsis (Group 2). Code-based algorithm performance was assessed overall, and separately for Group 1 and Group 2 based on sensitivity, specificity, positive and negative predictive value (PPV and NPV) and F1 score. The improvement in performance of refinements to the best-performing algorithm was also assessed.ResultsAmong 2,595,983 encounters, 97,453 (3.8%) were case-confirmed IED (Group 1: 60.9%; Group 2: 39.1%). Across algorithms, specificity and NPV were excellent (>97%) for all but one algorithm, but there was a trade-off between sensitivity and PPV. The algorithm with the most balanced performance characteristics included diagnosis codes for: (1) infectious disease due to E. coli OR (2) sepsis/bacteremia/organ dysfunction combined with unspecified E. coli infection and no other concomitant non-E. coli invasive disease (sensitivity: 56.9%; PPV: 56.4%). Across subgroups, the algorithms achieved lower algorithm performance for Group 2 (sensitivity: 9.9%-61.1%; PPV: 3.8%-16.0%).ConclusionsThis study assessed code-based algorithms to identify IED during inpatient encounters in a large US hospital database. Such algorithms could be useful to identify IED in healthcare databases that lack information on microbiology data.

13 keywords...

hide…