Evidence map›Paper›PMID 38127118›Full record

ArticleInfection2024

Clinical evaluation of a multiplex droplet digital PCR for pathogen detection in critically ill COVID-19 patients with bloodstream infections.

Yanbing Li, Kangkang Huang, Jun Yin, Zheren Tan, Manli Zhou, Jiaoyang Dai, Bin Yi

Open access · hybridAbstract read
In one paragraph

Article in Infection, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

6 citing papers in PubMed, 12 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 1 country.

Yanbing LiDepartment of Laboratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Kangkang HuangDepartment of Laboratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Jun YinDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Zheren TanIntensive Care Unit, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Manli ZhouDepartment of Laboratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Jiaoyang DaiDepartment of Laboratory Medicine, Xiangya Medical School, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Bin YiDepartment of Laboratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China. xyyibin@163.com.
Central South University · CNXiangya Hospital Central South University · CN

Funding

Natural Science Foundation of Hunan Province 2023JJ30905
6 · The paper itself

Abstract

backgroundNosocomial bloodstream infections (nBSI) have emerged as a clinical concern for physicians treating COVID-19 patients. In this study, we aimed to evaluate the effectiveness of a multiplex ddPCR in detecting bacterial pathogens in the blood of COVID-19 critically ill patients.

methodsThis prospective diagnostic study included RT-PCR-confirmed COVID-19 patients admitted to our hospital from December 2022 to February 2023. A multiplex ddPCR assay was used to detect common bacterial pathogens and AMR genes in blood samples of the patients, along with antimicrobial susceptibility testing (AST). The diagnostic performance of the ddPCR assay was evaluated by comparing the results with those obtained through blood culture and clinical diagnosis. Additionally, the ability of ddPCR in detecting bacterial resistance was compared with the AST results.

resultsOf the 200 blood samples collected from 184 patients, 45 (22.5%) were positive using blood culture, while 113 (56.5%) were positive for bacterial targets using the ddPCR assay. The ddPCR assay outperformed blood culture in pathogen detection rate, mixed infection detection rate, and fungal detection rate. Acinetobacter baumannii and Klebsiella pneumoniae were the most commonly detected pathogens in COVID-19 critically ill patients, followed by Enterococcus and Streptococcus. Compared to blood culture, ddPCR achieved a sensitivity of 75.5%, specificity of 51.0%, PPV of 30.9%, and NPV of 87.8%, respectively. However, there were significant differences in sensitivity among different bacterial species, where Gram-negative bacteria have the highest sensitivity of 90.3%. When evaluated on the ground of clinical diagnosis, the sensitivity, specificity, PPV and NPV of ddPCR were 78.1%, 90.5%, 94.7%, and 65.5%, respectively. In addition, the ddPCR assay detected 23 cases of bla

conclusionThe multiplex ddPCR assay had a significantly higher pathogen detection positivity than the blood culture, which could be an effective diagnostic tool for BSIs in COVID-19 patients and to improve patient outcomes and reduce the burden of sepsis on the healthcare system, though there is room for optimization of the panels used.- Adjusting the targets to include E. faecalis and E. faecium as well as Candida albicans and Candida glabrata could improve the ddPCR' s effectiveness. However, further research is needed to explore the potential of ddPCR in predicting bacterial resistance through AMR gene detection.

Indexed as

COVID-19Critical IllnessMultiplex Polymerase Chain ReactionAdultAgedAged, 80 and overBacteremiaBacteriaCross InfectionFemaleHumansMaleMiddle AgedProspective StudiesSARS-CoV-2Sensitivity and SpecificityAntimicrobial resistanceBloodstream infectionsMultiplex droplet digital PCRPathogen detectionSevere Acute Respiratory Syndrome Coronavirus 2

Identifiers

PMID38127118
PMCPMC11143000
OpenAlexW4390059021

What OpenQuestion holds

Textmetadata
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.