The prevalence and clinical correlation factors of cognitive impairment in patients with major depressive disorder hospitalized during the acute phase
Bibliographic record
Abstract
Objective This study aimed to investigate the prevalence of cognitive impairment among patients with major depressive disorder (MDD) hospitalized during the acute phase and to analyze the in-depth association between this cognitive impairment and clinical correlation factors. Methods In this cross-sectional study, we recruited 126 patients aged between 18 and 65 years who were diagnosed with MDD. All these patients were inpatients from the Department of Psychiatry at the Second People’s Hospital of Hunan Province. We employed a series of assessment tools, including the Pittsburgh Sleep Quality Index (PSQI), the 16-item Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16), the Pre-sleep Arousal Scale (PSAS), the Morningness-Eveningness Questionnaire (MEQ), the Hamilton Anxiety Rating Scale (HAMA), and the 17-item Hamilton Depression Rating Scale (HAMD-17). The patients were divided into a cognitive impairment group and a non-cognitive impairment group based on their scores on the Montreal Cognitive Assessment Scale (MoCA). Through Spearman’s correlation analysis, we explored the correlation between the total MoCA score and the score of each factor. Additionally, we utilized binary logistic regression analysis to investigate the relationship between cognitive impairment and clinically relevant factors in MDD patients hospitalized during the acute phase and plotted ROC curves to evaluate their clinical efficacy. Results In this study, we found that the prevalence of cognitive impairment among MDD patients hospitalized during the acute phase was as high as 63.49%. Through statistical analysis, we observed significant differences between the cognitive impairment group and the non-cognitive impairment group in terms of age, place of residence, education level, and HAMD-17 scores. In the Spearman correlation analysis, we noted the following trends: visuospatial and executive abilities were negatively correlated with the HAMD-17 score (P < 0.05); naming ability was positively correlated with the PSAS score but negatively correlated with the MEQ score (P < 0.05); memory was also negatively correlated with the MEQ score (P < 0.05); attention was negatively correlated with the HAMA score; and abstract cognitive ability was negatively correlated with the MEQ score (P < 0.05). Through binary logistic regression analysis, we further revealed the relationship between cognitive impairment and factors such as living in a rural area (OR = 2.7, 95% CI = 1.083-6.731, P < 0.05), increased age (OR = 1.049, 95% CI = 1.013-1.087, P < 0.01), and the HAMD-17 score (OR = 1.10295, 95% CI = 1.031-11.79, P < 0.01). Additionally, ROC curve analysis demonstrated a significant correlation between the HAMD-17 score and the prediction of cognitive function in MDD patients hospitalized during the acute phase (P < 0.001). Specifically, the AUC for the HAMD-17 score was 0.73, with an optimal cut-off value of 19.5, sensitivity of 70.0%, and specificity of 63.0%. Furthermore, the AUC for age was 0.71, with an optimal cut-off value of 33.5, sensitivity of 59.0%, and specificity of 80.0%. Conclusions This study indicates that MDD patients hospitalized during the acute phase have a higher prevalence of cognitive impairment. This phenomenon reflects a significant correlation between clinical factors such as age, sleep-related characteristics, and the severity of depression with cognitive impairment. Therefore, regular assessment of cognitive function in MDD patients and early intervention may be crucial for the treatment and prognosis of the disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".