Evidence map›Paper›PMID 37590196›Full record

SynthesisPloS one2023

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.

Urszula A Snigurska, Yiyang Liu, Sarah E Ser, Tamara G R Macieira, Margaret Ansell, David Lindberg, Mattia Prosperi, Ragnhildur I Bjarnadottir, Robert J Lucero

Abstract readSystematic Review
In one paragraph

Synthesis in PloS one, 2023. 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
–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

2 citing papers in PubMed.

  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

9 authors.

Urszula A SnigurskaDepartment of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States of America.ORCID 0000-0001-9806-8553
Yiyang LiuDepartment of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States of America.
Sarah E SerDepartment of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States of America.
Tamara G R MacieiraDepartment of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States of America.
Margaret AnsellHealth Science Center Libraries, George A. Smathers Libraries, University of Florida, Gainesville, FL, United States of America.ORCID 0000-0003-1653-3816
David LindbergDepartment of Statistics, College of Liberal Arts and Sciences, University of Florida, Gainesville, FL, United States of America.
Mattia ProsperiDepartment of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States of America.
Ragnhildur I BjarnadottirDepartment of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States of America.
Robert J LuceroDepartment of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States of America.

Funding

Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE)R33AG062884 · NIA · UNIVERSITY OF FLORIDA · PI INGIBJARGARDOTTIR BJARNADOTTIR, RAGNHILDUR, LUCERO, ROBERT J · 2021 to 2023
$2.1M
NIA NIH HHS R33 AG062884
6 · The paper itself

Abstract

purposeThe purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

methodsAPA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis.

resultsThirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability.

conclusionsOur findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Indexed as

DeliriumHospitalsAdultBiasHumansPrognosis

Identifiers

PMID37590196
PMCPMC10434879

What Socratic holds

Textmetadata
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 Socratic graph.