Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy

Jialu Qiao,1 Rubo Sui,1 Lei Zhang,2 Jiannan Wang1 1Department of Neurology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of China; 2School of Nursing, Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of ChinaCorresp...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Qiao J, Sui R, Zhang L, Wang J
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	psd 1h-mrs imaging prediction model nomogram; Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/2d3f81584e654b8fa2a8664e6d16fc9b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2d3f81584e654b8fa2a8664e6d16fc9b
record_format	dspace
spelling	oai:doaj.org-article:2d3f81584e654b8fa2a8664e6d16fc9b2021-12-02T09:07:09ZConstruction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy1178-2021https://doaj.org/article/2d3f81584e654b8fa2a8664e6d16fc9b2020-05-01T00:00:00Zhttps://www.dovepress.com/construction-of-a-risk-model-associated-with-prognosis-of-post-stroke--peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Jialu Qiao,1 Rubo Sui,1 Lei Zhang,2 Jiannan Wang1 1Department of Neurology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of China; 2School of Nursing, Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of ChinaCorrespondence: Rubo SuiDepartment of Neurology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of ChinaTel +8618640605108Email 907052401@qq.comPurpose: This study aimed to develop a risk prediction model for post-stroke depression (PSD) based on magnetic resonance (MR) spectroscopy.Patients and Methods: Data of 61 patients hospitalized with stroke (November 2017–March 2019) were retrospectively analyzed. After 61 patients had been admitted to hospital for routine clinical information collection, when the patients were in stable condition, proton MR spectroscopy (1H-MRS) examinations were performed to measure the ratio of choline to creatine (Cho/Cr) and N-acetylaspartate to creatine (NAA/Cr) in brain regions related to emotion. From the second month to the sixth month after the onset, these 61 patients were assessed by the Hamilton Depression Scale once a month. Based on the scores, patients were divided into PSD and post-stroke non-depression (N-PSD) groups. Twenty-two characteristics were extracted from clinical data and the 1H-MRS imaging indexes. The least absolute shrinkage and selection operator (LASSO) regression was used for optimal feature selection and the nomogram prediction model was established. The model’s predictive ability was validated by a calibration plot and the area under the curve (AUC) of the receiver operating characteristic curve.Results: Two demographic characteristics (activities of daily living and initial National Institutes of Health Stroke Scale scores) and three 1H-MRS imaging characteristics (frontal-lobe Cho/Cr, temporal-lobe Cho/Cr, and anterior cingulated-cortex Cho/Cr) were screened out by LASSO regression. The consistency test through the calibration plot found that the predicted probability of the nomogram for PSD correlates well with the actual probability. The AUCs for internal validation and external validation were 0.8635 and 0.8851, respectively.Conclusion: The PSD risk model based on 1H-MRS may help guide early treatment of stroke and prevent progression to PSD.Keywords: PSD, 1H-MRS imaging, prediction model, nomogramQiao JSui RZhang LWang JDove Medical Pressarticlepsd1h-mrs imagingprediction modelnomogram;Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 16, Pp 1171-1180 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	psd 1h-mrs imaging prediction model nomogram; Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	psd 1h-mrs imaging prediction model nomogram; Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Qiao J Sui R Zhang L Wang J Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy
description	Jialu Qiao,1 Rubo Sui,1 Lei Zhang,2 Jiannan Wang1 1Department of Neurology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of China; 2School of Nursing, Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of ChinaCorrespondence: Rubo SuiDepartment of Neurology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, People’s Republic of ChinaTel +8618640605108Email 907052401@qq.comPurpose: This study aimed to develop a risk prediction model for post-stroke depression (PSD) based on magnetic resonance (MR) spectroscopy.Patients and Methods: Data of 61 patients hospitalized with stroke (November 2017–March 2019) were retrospectively analyzed. After 61 patients had been admitted to hospital for routine clinical information collection, when the patients were in stable condition, proton MR spectroscopy (1H-MRS) examinations were performed to measure the ratio of choline to creatine (Cho/Cr) and N-acetylaspartate to creatine (NAA/Cr) in brain regions related to emotion. From the second month to the sixth month after the onset, these 61 patients were assessed by the Hamilton Depression Scale once a month. Based on the scores, patients were divided into PSD and post-stroke non-depression (N-PSD) groups. Twenty-two characteristics were extracted from clinical data and the 1H-MRS imaging indexes. The least absolute shrinkage and selection operator (LASSO) regression was used for optimal feature selection and the nomogram prediction model was established. The model’s predictive ability was validated by a calibration plot and the area under the curve (AUC) of the receiver operating characteristic curve.Results: Two demographic characteristics (activities of daily living and initial National Institutes of Health Stroke Scale scores) and three 1H-MRS imaging characteristics (frontal-lobe Cho/Cr, temporal-lobe Cho/Cr, and anterior cingulated-cortex Cho/Cr) were screened out by LASSO regression. The consistency test through the calibration plot found that the predicted probability of the nomogram for PSD correlates well with the actual probability. The AUCs for internal validation and external validation were 0.8635 and 0.8851, respectively.Conclusion: The PSD risk model based on 1H-MRS may help guide early treatment of stroke and prevent progression to PSD.Keywords: PSD, 1H-MRS imaging, prediction model, nomogram
format	article
author	Qiao J Sui R Zhang L Wang J
author_facet	Qiao J Sui R Zhang L Wang J
author_sort	Qiao J
title	Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy
title_short	Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy
title_full	Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy
title_fullStr	Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy
title_full_unstemmed	Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy
title_sort	construction of a risk model associated with prognosis of post-stroke depression based on magnetic resonance spectroscopy
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/2d3f81584e654b8fa2a8664e6d16fc9b
work_keys_str_mv	AT qiaoj constructionofariskmodelassociatedwithprognosisofpoststrokedepressionbasedonmagneticresonancespectroscopy AT suir constructionofariskmodelassociatedwithprognosisofpoststrokedepressionbasedonmagneticresonancespectroscopy AT zhangl constructionofariskmodelassociatedwithprognosisofpoststrokedepressionbasedonmagneticresonancespectroscopy AT wangj constructionofariskmodelassociatedwithprognosisofpoststrokedepressionbasedonmagneticresonancespectroscopy
_version_	1718398234995458048

Construction of a Risk Model Associated with Prognosis of Post-Stroke Depression Based on Magnetic Resonance Spectroscopy

Ejemplares similares