Validation for using electronic health records to identify community acquired pneumonia hospitalization among people with and without HIV
© The Author(s) 2020..
BACKGROUND: Cohort studies identifying the incidence, complications and co-morbidities associated with community acquired pneumonia (CAP) are largely based on administrative datasets and rely on International Classification of Diseases (ICD) codes; however, the reliability of ICD codes for hospital admissions for CAP in people with HIV (PWH) has not been systematically assessed.
METHODS: We used data from the Veterans Aging Cohort Study survey sample (N = 6824; 3410 PWH and 3414 uninfected) to validate the use of electronic health records (EHR) data to identify CAP hospitalizations when compared to chart review and to compare the performance in PWH vs. uninfected patients. We used different EHR algorithms that included a broad set of CAP ICD-9 codes, a set restricted to bacterial and viral CAP codes, and algorithms that included pharmacy data and/or other ICD-9 diagnoses frequently associated with CAP. We also compared microbiologic workup and etiologic diagnosis by HIV status among those with CAP.
RESULTS: Five hundred forty-nine patients were identified as having an ICD-9 code compatible with a CAP diagnosis (13% of PWH and 4% of the uninfected, p < 0.01). The EHR algorithm with the best overall positive predictive value (82%) was obtained by using the restricted set of ICD-9 codes (480-487) in primary position or secondary only to selected codes as primary (HIV disease, respiratory failure, sepsis or bacteremia) with the addition of EHR pharmacy data; this algorithm yielded PPVs of 83% in PWH and 73% in uninfected (P = 0.1) groups. Adding aspiration pneumonia (ICD-9 code 507) to any of the ICD-9 code/pharmacy combinations increased the number of cases but decreased the overall PPV. Allowing COPD exacerbation in the primary position improved the PPV among the uninfected group only (to 76%). More PWH than uninfected patients underwent microbiologic evaluation or had respiratory samples submitted.
CONCLUSIONS: ICD-9 code-based algorithms perform similarly to identify CAP in PLWH and uninfected individuals. Adding antimicrobial use data and allowing as primary diagnoses ICD-9 codes frequently used in patients with CAP improved the performance of the algorithms in both groups of patients. The algorithms consistently performed better among PWH.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Pneumonia (Nathan Qld.) - 12(2020) vom: 23., Seite 6 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rodriguez-Barradas, Maria C [VerfasserIn] |
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| Links: |
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| Themen: |
Community-acquired pneumonia |
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| Anmerkungen: |
Date Revised 15.04.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1186/s41479-020-00068-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM313004641 |
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| 100 | 1 | |a Rodriguez-Barradas, Maria C |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Validation for using electronic health records to identify community acquired pneumonia hospitalization among people with and without HIV |
| 264 | 1 | |c 2020 | |
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| 500 | |a Date Revised 15.04.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Author(s) 2020. | ||
| 520 | |a BACKGROUND: Cohort studies identifying the incidence, complications and co-morbidities associated with community acquired pneumonia (CAP) are largely based on administrative datasets and rely on International Classification of Diseases (ICD) codes; however, the reliability of ICD codes for hospital admissions for CAP in people with HIV (PWH) has not been systematically assessed | ||
| 520 | |a METHODS: We used data from the Veterans Aging Cohort Study survey sample (N = 6824; 3410 PWH and 3414 uninfected) to validate the use of electronic health records (EHR) data to identify CAP hospitalizations when compared to chart review and to compare the performance in PWH vs. uninfected patients. We used different EHR algorithms that included a broad set of CAP ICD-9 codes, a set restricted to bacterial and viral CAP codes, and algorithms that included pharmacy data and/or other ICD-9 diagnoses frequently associated with CAP. We also compared microbiologic workup and etiologic diagnosis by HIV status among those with CAP | ||
| 520 | |a RESULTS: Five hundred forty-nine patients were identified as having an ICD-9 code compatible with a CAP diagnosis (13% of PWH and 4% of the uninfected, p < 0.01). The EHR algorithm with the best overall positive predictive value (82%) was obtained by using the restricted set of ICD-9 codes (480-487) in primary position or secondary only to selected codes as primary (HIV disease, respiratory failure, sepsis or bacteremia) with the addition of EHR pharmacy data; this algorithm yielded PPVs of 83% in PWH and 73% in uninfected (P = 0.1) groups. Adding aspiration pneumonia (ICD-9 code 507) to any of the ICD-9 code/pharmacy combinations increased the number of cases but decreased the overall PPV. Allowing COPD exacerbation in the primary position improved the PPV among the uninfected group only (to 76%). More PWH than uninfected patients underwent microbiologic evaluation or had respiratory samples submitted | ||
| 520 | |a CONCLUSIONS: ICD-9 code-based algorithms perform similarly to identify CAP in PLWH and uninfected individuals. Adding antimicrobial use data and allowing as primary diagnoses ICD-9 codes frequently used in patients with CAP improved the performance of the algorithms in both groups of patients. The algorithms consistently performed better among PWH | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Community-acquired pneumonia | |
| 650 | 4 | |a Electronic health records | |
| 650 | 4 | |a HIV | |
| 700 | 1 | |a McGinnis, Kathleen A |e verfasserin |4 aut | |
| 700 | 1 | |a Akgün, Kathleen |e verfasserin |4 aut | |
| 700 | 1 | |a Tate, Janet P |e verfasserin |4 aut | |
| 700 | 1 | |a Brown, Sheldon T |e verfasserin |4 aut | |
| 700 | 1 | |a Butt, Adeel A |e verfasserin |4 aut | |
| 700 | 1 | |a Fine, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Goetz, Matthew Bidwell |e verfasserin |4 aut | |
| 700 | 1 | |a Graber, Christopher J |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Laurence |e verfasserin |4 aut | |
| 700 | 1 | |a Rimland, David |e verfasserin |4 aut | |
| 700 | 1 | |a Justice, Amy |e verfasserin |4 aut | |
| 700 | 1 | |a Crothers, Kristina |e verfasserin |4 aut | |
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