Evidence map›Paper›PMID 34064001›Full record

ArticleJournal of personalized medicine2021

Profiling Delirium Progression in Elderly Patients via Continuous-Time Markov Multi-State Transition Models.

Honoria Ocagli, Danila Azzolina, Rozita Soltanmohammadi, Roqaye Aliyari, Daniele Bottigliengo, Aslihan Senturk Acar, Lucia Stivanello, Mario Degan, Ileana Baldi, Giulia Lorenzoni and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome.International journal of environmental research and public health · 2021
    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

11 authors at 3 institutions in 2 countries.

Honoria OcagliUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.ORCID 0000-0002-5823-1659
Danila AzzolinaUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.
Rozita SoltanmohammadiUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.ORCID 0000-0001-8904-463X
Roqaye AliyariUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.
Daniele BottigliengoUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.
Aslihan Senturk AcarDepartment of Actuarial Sciences, Hacettepe University, Ankara 06800, Turkey.ORCID 0000-0002-1708-2028
Lucia StivanelloHealth professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy.
Mario DeganHealth professional Management Service (DPS) of the University Hospital of Padova, 35128 Padova, Italy.
Ileana BaldiUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.ORCID 0000-0002-8578-9164
Giulia LorenzoniUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.ORCID 0000-0003-1771-4686
Dario GregoriUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35122 Padova, Italy.ORCID 0000-0001-7906-0580
University of Padua · ITHacettepe University · TRUniversità degli Studi del Piemonte Orientale “Amedeo Avogadro” · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.

Indexed as

4AT scalecontinuous-time Markov multi-state transition modelCox modeldelirium

Identifiers

PMID34064001
PMCPMC8223967
OpenAlexW3165199710

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.