Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients
Background and ObjectivesPatients with primary malignant brain tumors may experience mental health disturbances that can significantly affect their daily life. This study aims to identify risk factors and generate predictive models for postoperative mental health disturbances (PMHDs) in adult glioma...
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| Format: | Article |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1153455/full |
| _version_ | 1797843266183364608 |
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| author | Yi Wang Jie Zhang Jie Zhang Jie Zhang Jie Zhang Chen Luo Chen Luo Chen Luo Chen Luo Ye Yao Ye Yao Ye Yao Guoyou Qin Guoyou Qin Jinsong Wu Jinsong Wu Jinsong Wu Jinsong Wu |
| author_facet | Yi Wang Jie Zhang Jie Zhang Jie Zhang Jie Zhang Chen Luo Chen Luo Chen Luo Chen Luo Ye Yao Ye Yao Ye Yao Guoyou Qin Guoyou Qin Jinsong Wu Jinsong Wu Jinsong Wu Jinsong Wu |
| author_sort | Yi Wang |
| collection | DOAJ |
| description | Background and ObjectivesPatients with primary malignant brain tumors may experience mental health disturbances that can significantly affect their daily life. This study aims to identify risk factors and generate predictive models for postoperative mental health disturbances (PMHDs) in adult glioma patients in accordance with different clinical periods; additionally, survival analyses will be performed.MethodsThis longitudinal cohort study included 2,243 adult patients (age at diagnosis ≥ 18 years) with nonrecurrent glioma who were pathologically diagnosed and had undergone initial surgical resection. Six indicators of distress, sadness, fear, irritability, mood and enjoyment of life, ranging from 0-10, were selected to assess PMHDs in glioma patients in the third month after surgery, mainly referring to the M.D. Anderson Symptom Inventory Brain Tumor Module (MDASI-BT). Factor analysis (FA) was applied on these indicators to divide participants into PMHD and control groups based on composite factor scores. Survival analyses were performed, and separate logistic regression models were formulated for preoperative and postoperative factors predicting PMHDs.ResultsA total of 2,243 adult glioma patients were included in this study. Based on factor analysis results, 300 glioma patients had PMHDs in the third postoperative month, and the remaining 1,943 were controls. Candidate predictors for PMHDs in the preoperative model were associated with age, clinical symptoms (intracranial space-occupying lesion, muscle weakness and memory deterioration), and tumor location (corpus callosum, basal ganglia and brainstem), whereas age, clinical symptoms (nausea and memory deterioration), tumor location (basal ganglia and brainstem), hospitalization days, WHO grade 4, postoperative chemotherapy or radiotherapy and postoperative Karnofsky Performance Scale (KPS) served as important factors in the postoperative model. In addition, the median overall survival (OS) time for glioma patients with PMHDs was 19 months, compared to 13 months for glioblastoma, IDH-wild type (GBM) patients with PMHDs.ConclusionThe risk factors for PMHDs were identified. These findings may provide new insights into predicting the probability of PMHD occurrence in glioma patients in addition to aiding effective early intervention and improving prognosis based on different clinical stages. |
| first_indexed | 2024-04-09T17:02:23Z |
| format | Article |
| id | doaj.art-c47ece1d02b64b30ae48f89cd6b3485c |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T17:02:23Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-c47ece1d02b64b30ae48f89cd6b3485c2023-04-21T04:39:28ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-04-011310.3389/fonc.2023.11534551153455Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patientsYi Wang0Jie Zhang1Jie Zhang2Jie Zhang3Jie Zhang4Chen Luo5Chen Luo6Chen Luo7Chen Luo8Ye Yao9Ye Yao10Ye Yao11Guoyou Qin12Guoyou Qin13Jinsong Wu14Jinsong Wu15Jinsong Wu16Jinsong Wu17Department of Biostatistics, School of Public Health, Fudan University, Shanghai, ChinaDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, ChinaNeurosurgical Institute, Fudan University, Shanghai, ChinaShanghai Clinical Medical Center of Neurosurgery, Shanghai Municipal Health Commission, Shanghai, ChinaShanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Science and Technology Commission of Shanghai Municipality, Shanghai, ChinaDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, ChinaNeurosurgical Institute, Fudan University, Shanghai, ChinaShanghai Clinical Medical Center of Neurosurgery, Shanghai Municipal Health Commission, Shanghai, ChinaShanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Science and Technology Commission of Shanghai Municipality, Shanghai, ChinaDepartment of Biostatistics, School of Public Health, Fudan University, Shanghai, ChinaNational Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, ChinaKey Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, ChinaDepartment of Biostatistics, School of Public Health, Fudan University, Shanghai, ChinaKey Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, ChinaDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, ChinaNeurosurgical Institute, Fudan University, Shanghai, ChinaShanghai Clinical Medical Center of Neurosurgery, Shanghai Municipal Health Commission, Shanghai, ChinaShanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Science and Technology Commission of Shanghai Municipality, Shanghai, ChinaBackground and ObjectivesPatients with primary malignant brain tumors may experience mental health disturbances that can significantly affect their daily life. This study aims to identify risk factors and generate predictive models for postoperative mental health disturbances (PMHDs) in adult glioma patients in accordance with different clinical periods; additionally, survival analyses will be performed.MethodsThis longitudinal cohort study included 2,243 adult patients (age at diagnosis ≥ 18 years) with nonrecurrent glioma who were pathologically diagnosed and had undergone initial surgical resection. Six indicators of distress, sadness, fear, irritability, mood and enjoyment of life, ranging from 0-10, were selected to assess PMHDs in glioma patients in the third month after surgery, mainly referring to the M.D. Anderson Symptom Inventory Brain Tumor Module (MDASI-BT). Factor analysis (FA) was applied on these indicators to divide participants into PMHD and control groups based on composite factor scores. Survival analyses were performed, and separate logistic regression models were formulated for preoperative and postoperative factors predicting PMHDs.ResultsA total of 2,243 adult glioma patients were included in this study. Based on factor analysis results, 300 glioma patients had PMHDs in the third postoperative month, and the remaining 1,943 were controls. Candidate predictors for PMHDs in the preoperative model were associated with age, clinical symptoms (intracranial space-occupying lesion, muscle weakness and memory deterioration), and tumor location (corpus callosum, basal ganglia and brainstem), whereas age, clinical symptoms (nausea and memory deterioration), tumor location (basal ganglia and brainstem), hospitalization days, WHO grade 4, postoperative chemotherapy or radiotherapy and postoperative Karnofsky Performance Scale (KPS) served as important factors in the postoperative model. In addition, the median overall survival (OS) time for glioma patients with PMHDs was 19 months, compared to 13 months for glioblastoma, IDH-wild type (GBM) patients with PMHDs.ConclusionThe risk factors for PMHDs were identified. These findings may provide new insights into predicting the probability of PMHD occurrence in glioma patients in addition to aiding effective early intervention and improving prognosis based on different clinical stages.https://www.frontiersin.org/articles/10.3389/fonc.2023.1153455/fullpostoperative mental health disturbancesfactor analysisrisk factorsgliomapredictive models |
| spellingShingle | Yi Wang Jie Zhang Jie Zhang Jie Zhang Jie Zhang Chen Luo Chen Luo Chen Luo Chen Luo Ye Yao Ye Yao Ye Yao Guoyou Qin Guoyou Qin Jinsong Wu Jinsong Wu Jinsong Wu Jinsong Wu Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients Frontiers in Oncology postoperative mental health disturbances factor analysis risk factors glioma predictive models |
| title | Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients |
| title_full | Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients |
| title_fullStr | Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients |
| title_full_unstemmed | Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients |
| title_short | Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients |
| title_sort | predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients |
| topic | postoperative mental health disturbances factor analysis risk factors glioma predictive models |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1153455/full |
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