Evidence map›Paper›PMID 36175101›Full record

ArticleBMJ open2022

Clinical prediction models for multidrug-resistant organism colonisation or infection in critically ill patients: a systematic review protocol.

Yi Wang, Yanyan Xiao, Qidi Yang, Fang Wang, Ying Wang, Cui Yuan

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 51% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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.

Yi WangIntensive Care Unit, Peking University First Hospital, Beijing, China.
Yanyan XiaoIntensive Care Unit, Peking University First Hospital, Beijing, China.
Qidi YangIntensive Care Unit, Peking University First Hospital, Beijing, China.
Fang WangIntensive Care Unit, Peking University First Hospital, Beijing, China.
Ying WangIntensive Care Unit, Peking University First Hospital, Beijing, China.
Cui YuanIntensive Care Unit, Peking University First Hospital, Beijing, China yuancui2013@sina.com.ORCID 0000-0002-7075-7688
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMultidrug-resistant organisms (MDROs) are pathogenic bacteria that are the leading cause of hospital-acquired infection which is associated with high morbidity and mortality rates in intensive care units, increasing hospitalisation duration and cost. Predicting the risk of MDRO colonisation or infection for critically ill patients supports clinical decision-making. Several models predicting MDRO colonisation or infection have been developed; however, owing to different disease scenarios, bacterial species and few externally validated cohorts in different prediction models; the stability and applicability of these models for MDRO colonisation or infection in critically ill patients are controversial. In addition, there are currently no standardised risk scoring systems to predict MDRO colonisation or infection in critically ill patients. The aim of this systematic review is to summarise and assess models predicting MDRO colonisation or infection in critically ill patients and to compare their predictive performance. METHODS AND ANALYSIS: We will perform a systematic search of PubMed, Cochrane Library, CINAHL, Embase, Web of science, China National Knowledge Infrastructure and Wanfang databases to identify all studies describing the development and/or external validation of models predicting MDRO colonisation or infection in critically ill patients. Two reviewers will independently extract and review the data using the Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist; they will also assess the risk of bias using the Prediction Model Risk of Bias Assessment Tool. Quantitative data on model predictive performance will be synthesised in meta-analyses, as applicable. ETHICS AND DISSEMINATION: Ethical permissions will not be required because all data will be extracted from published studies. We intend to publish our results in peer-reviewed scientific journals and to present them at international conferences on critical care. PROSPERO REGISTRATION NUMBER: CRD42022274175.

Indexed as

Critical IllnessDrug Resistance, Multiple, BacterialHumansModels, StatisticalPlant ExtractsPrognosisSystematic Reviews as TopicPlant ExtractsAdult intensive & critical careInfection controlProtocols & guidelinesRisk management

Identifiers

PMID36175101
PMCPMC9528596
OpenAlexW4298008597

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.