PMID- 36981783
OWN - NLM
STAT- MEDLINE
DCOM- 20230330
LR  - 20230419
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 20
IP  - 6
DP  - 2023 Mar 10
TI  - Applying Machine Learning to Construct a Model of Risk of Depression in Patients 
      Following Cardiac Surgery with the Use of the SF-12 Survey.
LID - 10.3390/ijerph20064876 [doi]
LID - 4876
AB  - BACKGROUND: Depression 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. RESULTS: A 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%. CONCLUSION: Assessing 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.
FAU - Nowicka-Sauer, Katarzyna
AU  - Nowicka-Sauer K
AUID- ORCID: 0000-0002-7510-5478
AD  - Department of Family Medicine, Faculty of Medicine, Medical University of Gdansk, 
      Debinki 2 Str., 80-211 Gdansk, Poland.
AD  - Department of Cardiac Surgery, Kashubian Centre for Cardiac and Vascular 
      Diseases, Ceynowa Specialist Hospital, Jagalskiego 10 Str., 84-200 Wejherowo, 
      Poland.
FAU - Jarmoszewicz, Krzysztof
AU  - Jarmoszewicz K
AUID- ORCID: 0000-0001-7979-0962
AD  - Department of Cardiac Surgery, Kashubian Centre for Cardiac and Vascular 
      Diseases, Ceynowa Specialist Hospital, Jagalskiego 10 Str., 84-200 Wejherowo, 
      Poland.
FAU - Molisz, Andrzej
AU  - Molisz A
AUID- ORCID: 0000-0003-3662-1581
AD  - Department of Otolaryngology, University Clinical Centre, Medical University of 
      Gdansk, Smoluchowskiego 17 Str., 80-214 Gdansk, Poland.
FAU - Sobczak, Krzysztof
AU  - Sobczak K
AUID- ORCID: 0000-0002-8354-2299
AD  - Division of Medical Sociology and Social Pathology, Faculty of Health Sciences, 
      Medical University of Gdansk, Tuwima 15 Str., 80-210 Gdansk, Poland.
FAU - Sauer, Marta
AU  - Sauer M
AUID- ORCID: 0009-0004-6571-8854
AD  - Radiation Protection Office, University Clinical Centre, Medical University of 
      Gdansk, Smoluchowskiego 17 Str., 80-214 Gdansk, Poland.
FAU - Topolski, Mariusz
AU  - Topolski M
AUID- ORCID: 0000-0002-5213-6845
AD  - Department 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.
LA  - eng
PT  - Journal Article
DEP - 20230310
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Male
MH  - Humans
MH  - Aged
MH  - Female
MH  - Depression/epidemiology
MH  - Emotions
MH  - *Cardiac Surgical Procedures/adverse effects
MH  - *Heart Failure/epidemiology
MH  - Fatigue
MH  - Quality of Life/psychology
PMC - PMC10048966
OTO - NOTNLM
OT  - cardiac surgery
OT  - depression
OT  - machine learning
OT  - patients reported outcomes (PROs)
OT  - risk factors
COIS- The authors declare no conflict of interest.
EDAT- 2023/03/30 06:00
MHDA- 2023/03/30 06:11
PMCR- 2023/03/10
CRDT- 2023/03/29 01:32
PHST- 2023/01/13 00:00 [received]
PHST- 2023/02/28 00:00 [revised]
PHST- 2023/03/07 00:00 [accepted]
PHST- 2023/03/30 06:11 [medline]
PHST- 2023/03/29 01:32 [entrez]
PHST- 2023/03/30 06:00 [pubmed]
PHST- 2023/03/10 00:00 [pmc-release]
AID - ijerph20064876 [pii]
AID - ijerph-20-04876 [pii]
AID - 10.3390/ijerph20064876 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2023 Mar 10;20(6):4876. doi: 
      10.3390/ijerph20064876.