Enhancement and external validation of algorithms using diagnosis codes to identify invasive Escherichia coli disease
OBJECTIVE: To assess the predictive accuracy of code-based algorithms for identifying invasive Escherichia coli (E. coli) disease (IED) among inpatient encounters in US hospitals.
METHODS: The 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.
RESULTS: Among 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%).
CONCLUSIONS: This 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:39 |
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| Enthalten in: |
Current medical research and opinion - 39(2023), 10 vom: 22. Okt., Seite 1303-1312 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hernandez-Pastor, Luis [VerfasserIn] |
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| Links: |
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| Themen: |
Diagnosis codes |
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| Anmerkungen: |
Date Completed 11.10.2023 Date Revised 15.10.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/03007995.2023.2247968 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM361071582 |
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| 245 | 1 | 0 | |a Enhancement and external validation of algorithms using diagnosis codes to identify invasive Escherichia coli disease |
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| 500 | |a Date Revised 15.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: To assess the predictive accuracy of code-based algorithms for identifying invasive Escherichia coli (E. coli) disease (IED) among inpatient encounters in US hospitals | ||
| 520 | |a METHODS: The 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 | ||
| 520 | |a RESULTS: Among 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%) | ||
| 520 | |a CONCLUSIONS: This 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Escherichia coli | |
| 650 | 4 | |a diagnosis codes | |
| 650 | 4 | |a electronic health record database | |
| 650 | 4 | |a invasive E. coli disease | |
| 650 | 4 | |a sepsis | |
| 700 | 1 | |a Geurtsen, Jeroen |e verfasserin |4 aut | |
| 700 | 1 | |a El Khoury, Antoine C |e verfasserin |4 aut | |
| 700 | 1 | |a Fortin, Stephen P |e verfasserin |4 aut | |
| 700 | 1 | |a Gauthier-Loiselle, Marjolaine |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Louise H |e verfasserin |4 aut | |
| 700 | 1 | |a Cloutier, Martin |e verfasserin |4 aut | |
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