Evidence map›Paper›PMID 38498108›Full record

ReviewInfectious diseases and therapy2024

Best Practices for Identifying Hospitalized Lower Respiratory Tract Infections Using Administrative Data: A Systematic Literature Review of Validation Studies.

Germaine Hanquet, Christian Theilacker, Jeffrey Vietri, Ingrid Sepúlveda-Pachón, Sonia Menon, Bradford Gessner, Elizabeth Begier

Open access · goldAbstract readReview
In one paragraph

Review in Infectious diseases and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
13.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 3 countries.

Germaine HanquetP95 Epidemiology and Pharmacovigilance, Koning Leopold III Laan 1, 3001, Louvain, Belgium.
Christian TheilackerPfizer Inc., Linkstrasse 10, 10785, Berlin, Germany.
Jeffrey VietriPfizer Inc., 500 Arcola Rd, Collegeville, PA, 19426, USA.
Ingrid Sepúlveda-PachónP95 Epidemiology and Pharmacovigilance, Koning Leopold III Laan 1, 3001, Louvain, Belgium.
Sonia MenonP95 Epidemiology and Pharmacovigilance, Koning Leopold III Laan 1, 3001, Louvain, Belgium.
Bradford GessnerPfizer Inc., 500 Arcola Rd, Collegeville, PA, 19426, USA.
Elizabeth BegierScientific Affairs, Older Adult RSV Vaccine Program, Global Medical Development Scientific and Clinical Affairs, Pfizer Vaccines, Pfizer Inc., 9 Riverwalk, Citywest Business Campus, Dublin 24, Dublin, Ireland. Elizabeth.Begier@pfizer.com.ORCID http://orcid.org/0000-0002-1287-5416
Pfizer (United States) · USPfizer (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEstimating the burden of lower respiratory tract infections (LRTIs) increasingly relies on administrative databases using International Classification of Diseases (ICD) codes, but no standard methodology exists. We defined best practices for ICD-based algorithms that estimate LRTI incidence in adults.

methodsWe conducted a systematic review of validation studies assessing the use of ICD code-based algorithms to identify hospitalized LRTIs in adults, published in Medline, EMBASE, and LILACS between January 1996 and January 2022, according to PRISMA guidelines. We assessed sensitivity, specificity, and other accuracy measures of different algorithms.

resultsWe included 26 publications that used a variety of ICD code-based algorithms and gold standard criteria, and 18 reported sensitivity and/or specificity. Sensitivity was below 80% in 72% (38/53) of algorithms and specificity exceeded 90% in 77% (37/48). Algorithms for all-cause LRTI (n = 18) that included only pneumonia codes in primary position (n = 3) had specificity greater than 90% but low sensitivity (55-72%). Sensitivity increased by 5-15%, with minimal loss in specificity, with the addition of primary codes for severe pneumonia (e.g. sepsis) while pneumonia codes were in secondary position, and by 13% with codes from LRTI-related infections (e.g. viral) or other respiratory diseases (e.g. empyema). Sensitivity increased by 8% when pneumonia codes were in any position, but specificity was not reported. In hospital-acquired pneumonia and pneumococcal-specific pneumonia, algorithms containing only nosocomial- or pathogen-specific ICD codes had poor sensitivity, which improved when broader pneumonia codes were added, in particular codes for unspecified organisms.

conclusionOur systematic review highlights that most ICD code-based algorithms are relatively specific, but miss a substantial number of hospitalized LRTI adult cases. Best practices to estimate LRTI incidence in this population include the use of all pneumonia ICD codes for any LRTI outcome and, to a lesser extent, those for other LRTI-related infections or respiratory diseases.

Indexed as

Administrative dataICDIncidenceLower respiratory tract infectionPneumoniaSensitivitySpecificitySystematic reviewValidation

Identifiers

PMID38498108
PMCPMC11058181
OpenAlexW4392914510

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.