Evidence map›Paper›PMID 36369020›Full record

ArticleBMC infectious diseases2022

Risk factors for multidrug resistance in tuberculosis patients with diabetes mellitus.

Shuangping Li, Yalin Liang, Xinjun Hu

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 17% 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

8 citing papers in PubMed, 14 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

3 authors at 1 institution in 1 country.

Shuangping LiInfectious Diseases Department, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China.
Yalin LiangInfectious Diseases Department, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China.
Xinjun HuInfectious Diseases Department, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan, China. hxj5129@163.com.
First Affiliated Hospital of Henan University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo study the risk factors and prediction models of multidrug resistance in patients with tuberculosis and diabetes and those with a history of tuberculosis treatment.

methodsA total of 256 tuberculosis patients with diabetes who were registered in Luoyang city, Henan Province, from January 2018 to December 2021. Logistic regression analysis was performed to analyse the risk factors for multidrug resistance. ROC curves were used to analyse the predictive model for multidrug resistance.

resultsAge < 65 years old, HbA1c, and a history of tuberculosis treatment were independent risk factors for multidrug resistance in patients with tuberculosis and diabetes (P < 0.05). The area under the ROC curve of predictive model for MDR was 0.878 (95% CI (0.824, 0.932)). Age < 65 years old and HbA1c were independent risk factors for MDR in patients with TB and diabetes with a history of TB treatment. The area under the ROC curve of predictive model for MDR was 0.920 [95% CI (0.831, 0.999)].

conclusionThe predictive model had certain prediction value for the risk of multidrug resistance in patients with tuberculosis and diabetes.

Indexed as

Diabetes MellitusMycobacterium tuberculosisTuberculosisTuberculosis, Multidrug-ResistantAgedAntitubercular AgentsDrug Resistance, MultipleGlycated HemoglobinHumansRisk FactorsAntitubercular AgentsGlycated HemoglobinDiabetesMultidrug resistantPrediction modelRisk factorsTuberculosis

Identifiers

PMID36369020
PMCPMC9650799
OpenAlexW4308786521

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.