Risk Factor Analysis and a Predictive Model of Postoperative Depressive Symptoms in Elderly Patients Undergoing Video-Assisted Thoracoscopic Surgery

Abstract

Among the elderly, depression is one of the most common mental disorders, which seriously affects their physical and mental health and quality of life, and their suicide rate is particularly high. Depression in the elderly is strongly associated with surgery. In this study, we aimed to explore the risk factors and establish a predictive model of depressive symptoms 1 month after video-assisted thoracoscopic surgery (VATS) in elderly patients. The study participants included 272 elderly patients (age > 65 years) undergoing VATS from April 2020 to May 2021 at 1 of 18 medical centers in China. The patients were divided into a depression group and a nondepression group according to the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9). The patients’ pre- and postoperative characteristics and questionnaires were collected and compared. Then, binary logistic regression was used to determine the risk factors that affect postoperative depressive symptoms, and the predictive model was constructed. The prediction efficiency of the model was evaluated by drawing the receiver operating characteristic curve (ROC), and the area under the curve (AUC) was calculated to evaluate the value of the predictive model. Among all of the included patients, 16.54% (45/272) suffered from depressive symptoms after VATS. The results of the univariate analysis showed that body mass index (BMI), chronic pain, leukocyte count, fibrinogen levels, prothrombin time, ASA physical status, infusion volume, anxiety, sleep quality, and postoperative pain were related to postoperative depressive symptoms (all p < 0.05). The results of multivariate logistic regression analysis showed that a high fibrinogen level (OR = 2.42), postoperative anxiety (OR = 12.05), poor sleep quality (OR = 0.61), and pain (OR = 2.85) were risk factors of postoperative depressive symptoms. A predictive model was constructed according to the regression coefficient of each variable, the ROC curve was drawn, and the AUC value was calculated to be 0.889. The prediction model may help medical personnel identify older patients at risk of developing depressive disorders associated with VATS and may be useful for clinical purposes.

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

“…Differences between groups were evaluated using the Chi-squared test or Fisher’s exact test for categorical variables, and Student’s t-test or Mann-Whitney U test for continuous variables. Univariate analysis was performed to screen for possible risk factors [ 24 ]. Second, collinearity was diagnosed for possible risk factors using variance inflation factors (VIF), with VIF <5 and tolerance >0.1 considered to indicate no significant collinearity [ 25 ].…”

A Dynamic Probabilistic Model for Heterogeneous Data Fusion: A Pilot Case Study from Computer-Aided Detection of Depression

“…shows at once that system (11) is redundant, and that the third and the fourth of Equation ( 11) are reproductions of the first and second ones respectively. Now, as a system of two equations with four unknowns, system (12) does not allow us to determine all the required transition probabilities: two of them must be assigned as parameters. Therefore, setting (11) becomes…”

“…More precisely, in this case, for any subject, the experimental procedure involves the collection of speech data obtained from the reading of a narrative text and the free description of daily activities performed during a week, and the answers provided by the participants to data collection to the questions of BDI-II [ 4 ]. Further data can be collected on this line of conduct, other than speech, including facial expressions, handwriting and drawing, body movements, hands’ gestures, and any other activity useful for behavior analysis ([ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], among others). Though all the outcomes of these experiments can be (and actually are) given a quantitative expression, by means of suitably defined measures or scores, nevertheless, this raises a new problem, regarding the comparison of heterogeneous measures.…”