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. 2023 Apr 21:13:1153455.
doi: 10.3389/fonc.2023.1153455. eCollection 2023.

Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients

Yi Wang  1 , Jie Zhang  2   3   4   5 , Chen Luo  2   3   4   5 , Ye Yao  1   6   7 , Guoyou Qin  1   7 , Jinsong Wu  2   3   4   5
Affiliations

Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients

Yi Wang et al. Front Oncol. .

Abstract

Background and objectives: Patients 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.

Methods: This 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.

Results: A 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.

Conclusion: The 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.

Keywords: factor analysis; glioma; postoperative mental health disturbances; predictive models; risk factors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Details of study recruitment. PMHDs, postoperative mental health disturbances.
Figure 2
Figure 2
Radar plot of six indicators of postoperative mental health disturbances in the PMHD and control groups. The figure shows the mean values of the six indicators after standardization (patients with PMHDs in red, controls in blue).
Figure 3
Figure 3
ROC curves generated for PMHD prediction with different clinical periods and subgroups. DA, diffuse astrocytoma, IDH-mutant; GBM, glioblastoma, IDH-wildtype.
Figure 4
Figure 4
Kaplan−Meier curves of overall survival. Kaplan-Meier plots of overall survival for all glioma patients (A) and glioblastoma, IDH-wildtype (B) patients (PMHD group in red, control group in blue).
See this image and copyright information in PMC

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