Evidence map›Paper›PMID 36981783›Full record

ArticleInternational journal of environmental research and public health2023

Applying Machine Learning to Construct a Model of Risk of Depression in Patients Following Cardiac Surgery with the Use of the SF-12 Survey.

Katarzyna Nowicka-Sauer, Krzysztof Jarmoszewicz, Andrzej Molisz, Krzysztof Sobczak, Marta Sauer, Mariusz Topolski

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

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 47% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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 3 institutions in 1 country.

Katarzyna Nowicka-SauerDepartment of Family Medicine, Faculty of Medicine, Medical University of Gdańsk, Dębinki 2 Str., 80-211 Gdańsk, Poland.ORCID 0000-0002-7510-5478
Krzysztof JarmoszewiczDepartment of Cardiac Surgery, Kashubian Centre for Cardiac and Vascular Diseases, Ceynowa Specialist Hospital, Jagalskiego 10 Str., 84-200 Wejherowo, Poland.ORCID 0000-0001-7979-0962
Andrzej MoliszDepartment of Otolaryngology, University Clinical Centre, Medical University of Gdańsk, Smoluchowskiego 17 Str., 80-214 Gdansk, Poland.ORCID 0000-0003-3662-1581
Krzysztof SobczakDivision of Medical Sociology and Social Pathology, Faculty of Health Sciences, Medical University of Gdańsk, Tuwima 15 Str., 80-210 Gdańsk, Poland.ORCID 0000-0002-8354-2299
Marta SauerRadiation Protection Office, University Clinical Centre, Medical University of Gdańsk, Smoluchowskiego 17 Str., 80-214 Gdańsk, Poland.ORCID 0009-0004-6571-8854
Mariusz TopolskiDepartment of Systems and Computer Networks, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Janiszewskiego 11/17 Str., 50-372 Wroclaw, Poland.ORCID 0000-0002-5213-6845
Gdańsk Medical University · PLUniversity Clinical Centre · PLWrocław University of Science and Technology · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression is a common problem in patients with cardiovascular diseases. Identifying a risk factor model of depression has been postulated. A model of the risk of depression would provide a better understanding of this disorder in this population. We sought to construct a model of the risk factors of depression in patients following cardiac surgery, with the use of machine learning. METHODS AND MEASURES: Two hundred and seventeen patients (65.4% men; mean age 65.14 years) were asked to complete the short form health survey-12 (SF-12v.2), three months after hospital discharge. Those at risk of depression were identified based on the SF-12 mental component summary (MCS). Centroid class principal component analysis (CCPCA) and the classification and regression tree (CART) were used to design a model.

resultsA risk of depression was identified in 29.03% of patients. The following variables explained 82.53% of the variance in depression risk: vitality, limitation of activities due to emotional problems (role-emotional, RE), New York Heart Association (NYHA) class, and heart failure. Additionally, CART revealed that decreased vitality increased the risk of depression to 45.44% and an RE score > 68.75 increased it to 63.11%. In the group with an RE score < 68.75, the NYHA class increased the risk to 41.85%, and heart failure further increased it to 44.75%.

conclusionAssessing fatigue and vitality can help health professionals with identifying patients at risk of depression. In addition, assessing functional status and dimensions of fatigue, as well as the impact of emotional state on daily functioning, can help determine effective intervention options.

Indexed as

Cardiac Surgical ProceduresHeart FailureAgedDepressionEmotionsFatigueFemaleHumansMaleQuality of Lifecardiac surgerydepressionmachine learningpatients reported outcomes (PROs)risk factors

Identifiers

PMID36981783
PMCPMC10048966
OpenAlexW4323847899

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read29
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.