Evidence map›Paper›PMID 35396283›Full record

ArticleBMJ open2022

Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study.

Chung Kwan Wong, Barbara C van Munster, Athanasios Hatseras, Else Huis In 't Veld, Barbara L van Leeuwen, Sophia E de Rooij, Rick G Pleijhuis

Registry-linked trialOpen 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. It is linked to trial NCT05992506 (Electroencephalographic Biomarker to Predict the Development of Postoperative Delirium), which is not on this map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
1.9field-weighted citation impact, top 15% 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.

NCT05992506 active not recruitingnot on this mapstarted 2023, after this paper: background citation

Electroencephalographic Biomarker to Predict the Development of Postoperative Delirium: a Protocol of an Observational Study in a Cohort of Patients From Five Centers

Typeobservational_patient_registrySponsorUniversity of ChileRan2023 to 2026Enrolled264ConditionsPostoperative DeliriumArmsPOD risk estimation using PEUMA
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Postoperative delirium: A tragedy for elderly cancer patients.World journal of gastrointestinal oncology · 2024
    Article
  8. 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

7 authors at 1 institution in 1 country.

Chung Kwan WongDepartment of Geriatrics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.ORCID 0000-0001-5746-2888
Barbara C van MunsterDepartment of Geriatrics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Athanasios HatserasDepartment of Geriatrics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Else Huis In 't VeldDepartment of Geriatrics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Barbara L van LeeuwenDepartment of Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Sophia E de RooijDepartment of Geriatrics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Rick G PleijhuisDepartment of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands r.g.pleijhuis@umcg.nl.
University Medical Center Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDelirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.

settingSingle-site university hospital.

designSecondary analysis of prospective cohort study. PARTICIPANTS AND INCLUSION: CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients. PRIMARY OUTCOME MEASURES: Discrimination, calibration and clinical usefulness.

results14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai

conclusionOur head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

Indexed as

DeliriumAgedHumansProspective Studiesdelirium & cognitive disordersgeriatric medicineinternal medicinerisk managementsurgery

Identifiers

PMID35396283
PMCPMC8996014
OpenAlexW4223479830

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.