A personality trait contributes to the occurrence of postoperative delirium: a prospective study

Abstract

Background: Although various physical risk factors for delirium have been identified, the effect of psychological aspects is currently unknown. This study aimed to examine psychological risk factors for postoperative delirium and to identify hidden subgroups of delirium in clinical and psychological feature space.

Methods: Among 200 patients with hip fracture, 78 elderly patients were prospectively evaluated for clinical and psychological assessments before surgery. As delirium was assessed from the next day to the 7th day after surgery, postoperative delirium was found in 40 patients, but not in 38 patients. Univariate and multivariate logistic regression analyses were used to explore risk factors for postoperative delirium. Phenotypic subgroups of delirium were assessed using Topological Data Analysis, in which the significant risk factors were used for evaluating filter and distance metrics.

Results: Mini-Mental State Examination, neuroticism, conscientiousness, and regional anesthesia were identified as a predictive risk factor for postoperative delirium. The filter metric showed significant negative correlations with nutrition-related factors such as total protein and albumin. When filter metric and Euclidean distances were entered, delirious patients were bifurcated as a function of personality traits and anesthesia method in the patient-patient network.

Conclusions: A personality trait of neuroticism and conscientiousness may predispose elderly patients to postoperative delirium and this influence may be amplified by regional anesthesia. This study verifies the contribution of psychological risk factors to delirium and provides new insight for complex etiologies of delirium by mapping various clinical variables in the topological space.

Keywords: Delirium; Logistic regression; Personality; Risk factor; Topological data analysis.

Fig. 1

Flowchart for participant enrollment (a) and analysis procedures (b)

Fig. 2

Distribution of filter metric (a), average filter metric for general anesthesia (GA) and regional anesthesia (RA) groups (b), scatter plot for filter metric and mini mental state examination (MMSE) scores (c), scatter plot for filter metric and neuroticism (d), and scatter plot for filter metric and conscientiousness (e)

Fig. 3

Output of the topological data analysis: a Topology of patient-patient networks. Filter metric was subdivided into 8 intervals with 80 % overlap. Several nodes were disconnected from the main graph. An inset graph in bottom right represents a lower resolution topology with 4 intervals and 60 % overlap. A subgroup 1, G ₁, includes seven delirious patients with a low Mini-Mental State Examination (MMSE) score and regional anesthesia and G ₂ includes four delirious patients with medium MMSE score, high neuroticism, and low conscientiousness scores. G ₀ includes six patients with high MMSE, low neuroticism, and high conscientiousness scores; b Mapping clinical information on topology. Clinical variables, which showed significant correlations with filter metric, were visualized. The numbers within parenthesis right after the variable name indicate ranges of the node color. Abbreviations: HAS, Hamilton Anxiety Scale; HRDS, Hamilton Rating Scale for Depression