Evidence map›Paper›PMID 37732245›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Universal digital high resolution melt analysis for the diagnosis of bacteremia.

April Aralar, Tyler Goshia, Nanda Ramchandar, Shelley M Lawrence, Aparajita Karmakar, Ankit Sharma, Mridu Sinha, David T Pride, Peiting Kuo, Khrissa Lecrone and 8 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 4 institutions in 1 country.

April Aralar
Tyler Goshia
Nanda Ramchandar
Shelley M Lawrence
Aparajita Karmakar
Ankit Sharma
Mridu Sinha
David T Pride
Peiting Kuo
Khrissa Lecrone
Megan Chiu
Karen Mestan
Eniko Sajti
Michelle Vanderpool
Sarah Lazar
Melanie Crabtree
Yordanos Tesfai
Stephanie I FraleyORCID 0000-0003-1548-4442
University of California San Diego · USNaval Medical Center San Diego · USRady Children's Hospital-San Diego · USUniversity of Utah · US

Funding

NeoChip for specific and rapid identification of congenital CMV and neonatal HSV infections on minimal sample volumeR01HD099250 · NICHD · UNIVERSITY OF UTAH · PI LAWRENCE, SHELLEY M · 2020 to 2024
$2.9M
NICHD NIH HHS R01 HD099250
6 · The paper itself

Abstract

Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires ∼15 hours to detect the presence of a pathogen. Here, we assess the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 21 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 90.5% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1 mL sample input and sample-to-answer time of 6 hrs. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection. Universal digital high resolution melt analysis for the diagnosis of bacteremia: April Aralar, Tyler Goshia, Nanda Ramchandar, Shelley M. Lawrence, Aparajita Karmakar, Ankit Sharma, Mridu Sinha, David Pride, Peiting Kuo, Khrissa Lecrone, Megan Chiu, Karen Mestan, Eniko Sajti, Michelle Vanderpool, Sarah Lazar, Melanie Crabtree, Yordanos Tesfai, Stephanie I. Fraley.

Identifiers

PMID37732245
PMCPMC10508820
OpenAlexW4386532423

What OpenQuestion holds

Textmetadata
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.