Evidence map›Paper›PMID 37704994›Full record

ReviewBMC medical informatics and decision making2023

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

Urszula A Snigurska, Sarah E Ser, Laurence M Solberg, Mattia Prosperi, Tanja Magoc, Zhaoyi Chen, Jiang Bian, Ragnhildur I Bjarnadottir, Robert J Lucero

Open access · goldAbstract readReview
In one paragraph

Review in BMC medical informatics and decision making, 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
0.2field-weighted citation impact, top 46% 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, 1 citations in OpenAlex.

  1. Observational
  2. Identifying Candidate Predictors of Hospital-Acquired Pressure Injuries among Adult, Medical-Surgical Patients Using Clinical Expert Knowledge: A Delphi Study.Journal of wound, ostomy, and continence nursing : official publication of The Wound, Ostomy and Continence Nurses Society
    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 at 3 institutions in 1 country.

Urszula A SnigurskaCollege of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America. usnigurska@ufl.edu.
Sarah E SerCollege of Public Health and Health Professions & College of Medicine, Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.
Laurence M SolbergCollege of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America.
Mattia ProsperiCollege of Public Health and Health Professions & College of Medicine, Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.
Tanja MagocClinical and Translational Science Institute (CTSI), Integrated Data Repository Research Services, University of Florida, 3300 SW Williston Rd, Gainesville, FL, 32608, United States of America.
Zhaoyi ChenCollege of Medicine, Department of Health Outcomes & Biomedical Informatics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.
Jiang BianCollege of Medicine, Department of Health Outcomes & Biomedical Informatics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.
Ragnhildur I BjarnadottirCollege of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America.
Robert J LuceroCollege of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America.
University of Florida · USUniversity of California, Los Angeles · USUniversity of Central Florida · US

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

backgroundPrognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.

methodsThis study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.

resultsIn the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.

conclusionsHeterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

Indexed as

Advisory CommitteesDeliriumAgedAlcohol DrinkingHospitalsHumansRetrospective StudiesCandidate predictorDeliriumExpert judgmentPractice-based approachPrediction modelVariable selection

Identifiers

PMID37704994
PMCPMC10500854
OpenAlexW4386708664

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.