Medical illness, past depression, and present depression: a predictive triad for in-hospital mortality.

OBJECTIVE: The authors' objectives were to determine 1) whether major depressive disorder diagnosed according to DSM-IV criteria modified for the medically ill predicted in-hospital mortality better than major depressive disorder diagnosed according to inclusive DSM-IV criteria and 2) whether a history of depression and current depression predicted mortality independent of severity of physical illness. METHOD: Of 392 consecutive medical inpatients, 241 were interviewed within the first 3 days of admission and 151 were excluded from the study. Chart review and a clinical interview that included the Schedule for Affective Disorders and Schizophrenia were used to determine demographic variables, past psychiatric history, psychiatric diagnoses, and illness measures. Diagnoses included major depressive disorder and minor depression diagnosed according to DSM-IV criteria that included all symptoms regardless of etiology and according to criteria modified for the medically ill (hopelessness, depression, or anhedonia were used as the qualifying affective symptoms; depressive symptoms were eliminated if easily explained by medical illness, treatments, or hospitalization). The Charlson combined age-comorbidity index was used to measure severity of illness. RESULTS: A diagnosis of major depressive disorder based on criteria modified for patients with medical illness better predicted mortality than a diagnosis based on inclusive criteria. A past history of depression and the Charlson combined age-comorbidity index predicted in-hospital mortality, but demographic variables, pain, discomfort, length of stay, medical diagnoses, and minor depression did not. In the final multivariate logistic regression model, the Charlson combined age-comorbidity index, a modified diagnosis of major depressive disorder, and a history of depression were independent predictors of in-hospital death. CONCLUSIONS: Severity of medical illness, a diagnosis of major depressive disorder based on modified criteria, and a past history of depression independently predicted in-hospital mortality in medical inpatients.

Association between depressive symptoms and mortality in medical inpatients.

The authors interviewed a consecutive series of medical inpatients (N = 241) using the Schedule for Affective Disorders and Schizophrenia to determine which depressive symptoms are associated with in-hospital mortality. Fifteen depressive symptoms, pain, and physical discomfort were assessed along with medical comorbidity. Twenty patients died in-hospital (8.3%). Logistic regression showed that anhedonia, hopelessness, worthlessness, indecisiveness, and insomnia predicted in-hospital death after adjusting for physical comorbidity and age. Clinicians should be aware that these depressive symptoms may predict mortality in medical inpatients. Future studies should address which treatment modalities lead to better outcomes.

Sociodemographic predictors of temperament and character.

The Unified Biosocial Theory of Personality postulates that human personality is organized around four temperaments - Novelty Seeking, Harm Avoidance, Reward Dependence, and Persistence - and three characters - Self-Directedness, Cooperativeness, and Self-Transcendence. The objective of the present study was to investigate the influence of sociodemographic factors on temperament and character without the confounding influence of mental disorders. Volunteers (n=94) did not meet criteria for any Axis I and Axis II diagnosis, had no first-degree relatives with mental disorders, and were medically healthy. After giving written informed consent, volunteers completed the Temperament and Character Inventory. Analyses were conducted to determine the degree of association of each sociodemographic factor (i.e., age, gender, ethnicity, marital status, educational attainment, and occupational status) to personality dimension, while controlling for possible interactions with other sociodemographic factors. Partial correlation analysis showed a significant association between gender and Reward Dependence, and occupational status was significantly related to Reward Dependence, Cooperativeness, and Self-Transcendence. Stepwise regression analysis indicated that gender and occupational status were significant predictors of Reward Dependence. Occupational status was the only predictor of Cooperativeness and Self-Transcendence. These data suggest that sociodemographic factors should be considered in studies investigating temperaments and characters as defined by the Unified Biosocial Theory of Personality.