Evidence map›Paper›PMID 35873565›Full record

ArticleFrontiers in pharmacology2022

Detection of Synergistic Interaction on an Additive Scale Between Two Drugs on Abnormal Elevation of Serum Alanine Aminotransferase Using Machine-Learning Algorithms.

Hayato Akimoto, Takuya Nagashima, Kimino Minagawa, Takashi Hayakawa, Yasuo Takahashi, Satoshi Asai

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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

6 authors at 1 institution in 1 country.

Hayato AkimotoDivision of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Tokyo, Japan.
Takuya NagashimaDivision of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Tokyo, Japan.
Kimino MinagawaDivision of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan.
Takashi HayakawaDivision of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Tokyo, Japan.
Yasuo TakahashiDivision of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan.
Satoshi AsaiDivision of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, Tokyo, Japan.
Nihon University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-induced liver injury (DILI) is a common adverse drug reaction, with abnormal elevation of serum alanine aminotransferase (ALT). Several clinical studies have investigated whether a combination of two drugs alters the reporting frequency of DILI using traditional statistical methods such as multiple logistic regression (MLR), but this model may over-fit the data. This study aimed to detect a synergistic interaction between two drugs on the risk of abnormal elevation of serum ALT in Japanese adult patients using three machine-learning algorithms: MLR, logistic least absolute shrinkage and selection operator (LASSO) regression, and extreme gradient boosting (XGBoost) algorithms. A total of 58,413 patients were extracted from Nihon University School of Medicine's Clinical Data Warehouse and assigned to case (

Indexed as

alanine aminotransferasedrug induced liver injury (DILI)drug interactionliver functionmachine-learning (ML) algorithmsrelative excess risk due to interaction (RERI)synergistic effect

Identifiers

PMID35873565
PMCPMC9298751
OpenAlexW4286632943

What OpenQuestion holds

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LicenceCC BY
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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.