Evidence map›Paper›PMID 36879198›Full record

ArticleBMC geriatrics2023

Clinical prediction rule for identifying older patients with toxigenic clostridioides difficile at the time of hospital admission.

Ki-Byung Lee, Mina Lee, Jin Woong Suh, Kyung-Sook Yang, Youseung Chung, Jeong Yeon Kim, Sun Bean Kim, Jang Wook Sohn, Young Kyung Yoon

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 31% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Derivation and internal validation of the multivariate toxigenicAntimicrobial stewardship & healthcare epidemiology : ASHE · 2024
    Article
  2. Review
  3. Review
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

9 authors at 1 institution in 1 country.

Ki-Byung LeeDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea.
Mina LeeInfection Control Unit, Korea University Anam Hospital, Seoul, Republic of Korea.
Jin Woong SuhDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea.
Kyung-Sook YangDepartment of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
Youseung ChungDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea.
Jeong Yeon KimDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea.
Sun Bean KimDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea.
Jang Wook SohnDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea.
Young Kyung YoonDivision of Infectious Diseases, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 73, Inchon-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea. young7912@korea.ac.kr.
Korea University · KR

Funding

National Research Foundation of Korea (NRF) grant, funded by the Korean Government (Ministry of Science and ICT) NRF-2022R1A2C1010808
6 · The paper itself

Abstract

backgroundThis study aimed to develop and validate a clinical prediction rule to screen older patients at risk of being toxigenic Clostridioides difficile carriers at the time of hospital admission.

methodsThis retrospective case-control study was performed at a university-affiliated hospital. Active surveillance using a real-time polymerase chain reaction (PCR) assay for the toxin genes of C. difficile was conducted among older patients (≥ 65 years) upon admission to the Division of Infectious Diseases of our institution. This rule was drawn from a derivative cohort between October 2019 and April 2021 using a multivariable logistic regression model. Clinical predictability was evaluated in the validation cohort between May 2021 and October 2021.

resultsOf 628 PCR screenings for toxigenic C. difficile carriage, 101 (16.1%) yielded positive findings. To establish clinical prediction rules in the derivation cohort, the formula was derived using significant predictors for toxigenic C. difficile carriage at admission, such as septic shock, connective tissue diseases, anemia, recent use of antibiotics, and recent use of proton-pump inhibitors. In the validation cohort, the sensitivity, specificity, and positive and negative predictive values of the prediction rule, based on a cut-off value of ≥ 0.45, were 78.3%, 70.8%, 29.5%, and 95.4%, respectively.

conclusionThis clinical prediction rule for identifying toxigenic C. difficile carriage at admission may facilitate the selective screening of high-risk groups. To implement it in a clinical setting, more patients from other medical institutions need to be prospectively examined.

Indexed as

Clinical Decision RulesClostridioides difficileCase-Control StudiesHospitals, UniversityHumansRetrospective StudiesActive surveillanceClinical prediction ruleClostridioides difficileInfection controlRisk factors

Identifiers

PMID36879198
PMCPMC9990199
OpenAlexW4323310686

What OpenQuestion holds

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LicenceCC BY
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.