Evidence map›Paper›PMID 30095631›Full record

ArticleMedicine2018

Predictive markers of depression in hypertension.

Xiuli Song, Zhong Zhang, Rui Zhang, Miye Wang, Dongtao Lin, Tao Li, Junming Shao, Xiaohong Ma

Open access · goldAbstract read
In one paragraph

Article in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Feasibility and acceptability of an integrated mind-body intervention for depression: whole-body hyperthermia (WBH) and cognitive behavioral therapy (CBT).International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group · 2024
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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

8 authors at 4 institutions in 2 countries.

Xiuli SongPsychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University Web Sciences Center Big Data Research Center, University of Electronic Science and Technology of China Information Center, West China Hospital, Sichuan University College of Foreign Languages and Cultures, Sichuan University, Chengdu, PR China.
Zhong Zhang
Rui Zhang
Miye Wang
Dongtao Lin
Tao Li
Junming Shao
Xiaohong Ma
Sichuan University · CNNational Student Clearinghouse Research Center · USUniversity of Electronic Science and Technology of China · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension and depression, as 2 major public health issues, are closely related. For patients having hypertension, in particular, depression is a risk factor for mortality and jeopardizes their wellbeing. The aim of the study is to apply support vector machine (SVM) learning to blood tests and vital signs to classify patients having hypertension complicated by depression and patients having hypertension alone for the identification of novel markers.Data on patients having both hypertension and depression (n = 147) and patients having hypertension alone (n = 147) were obtained from electronic medical records of admissions containing the records on blood tests and vital signs. Using SVM, we distinguished patients having both hypertension and depression from gender- and age-matched patients having hypertension alone.SVM-based classification achieved 73.5% accuracy by 10-fold cross-validation between patients having both hypertension and depression and those having hypertension alone. Twelve features were selected to compose the optimal feature sets, including body temperature (T), glucose (GLU), creatine kinase (CK), albumin (ALB), hydroxybutyrate dehydrogenase (HBDH), blood urea nitrogen (BUN), uric Acid (UA), creatinine (Crea), cholesterol (TC), total protein (TP), pulse (P), and respiration (R).SVM can be used to distinguish patients having both hypertension and depression from those having hypertension alone. A significant association was identified between depression and blood tests and vital signs. This approach can be helpful for clinical diagnosis of depression, but further studies are needed to verify the role of these candidate markers for depression diagnosis.

Indexed as

Support Vector MachineAgedAge FactorsBiomarkersBlood GlucoseBlood Urea NitrogenBody TemperatureCase-Control StudiesCholesterolCreatine KinaseDepressionFemaleHumansHypertensionLipidsMaleBiomarkersBlood GlucoseCholesterolCreatine KinaseLipidsSerum AlbuminUric Acid

Identifiers

PMID30095631
PMCPMC6133604
OpenAlexW2886521751

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.