Depression Treatment: Do Sleep Problems Mediate the Relationship Between Traumatic Brain Injury and Development of Mental Health Symptoms After Deployment?
Do Sleep Problems Mediate the Relationship between Traumatic Brain Injury and Development of Mental Health Symptoms after Deployment?
Filed under: Depression Treatment
Sleep. 2013; 36(1): 83-90
Macera CA, Aralis HJ, Rauh MJ, Macgregor AJ
Military members screening positive for blast-related traumatic brain injury (TBI) may subsequently screen positive for posttraumatic stress disorder (PTSD) or depression. The role of sleep as a mediating factor in the development of mental health symptoms was explored.Prospective study with symptoms evaluated at two time points.Postdeployment service in Iraq, Afghanistan, or Kuwait during 2008 and 2009.There were 29,640 US Navy and Marine Corps men (29,019 who did not screen positive for PTSD at baseline, 27,702 who did not screen positive for depression at baseline, and 27,320 who did not screen positive at baseline for either condition).After controlling for sleep problems, the adjusted odds of receiving a positive PTSD screening at follow-up decreased from 1.61 (95% confidence interval [CI] 1.21-2.14) to 1.32 (95% CI 0.99-1.77) for a subject screening positive for TBI relative to a subject screening negative, suggesting that sleep problems mediated 26% of TBI’s effect on development of PTSD. Likewise, after controlling for sleep problems, the adjusted odds of receiving a positive depression screening decreased from 1.41 (95% CI 1.11-1.80) to 1.15 (95% CI 0.90-1.47), suggesting that sleep problems mediated 41% of TBI’s effect on development of depression. Results were similar for those with either PTSD or depression (37% mediated).These results suggest that sleep problems mediate the effect of a positive TBI screening on the development of mental health disorders, and sleep problems may be an early indicator of risk for PTSD or depression. CITATION: Macera CA; Aralis HJ; Rauh MJ; MacGregor AJ. Do sleep problems mediate the relationship between traumatic brain injury and development of mental health symptoms after deployment? SLEEP 2013;36(1):83-90.
HubMed – depression
Results Following Operative Treatment of Tibial Plateau Fractures.
Filed under: Depression Treatment
J Knee Surg. 2012 Sep 10;
Urruela AM, Davidovitch R, Karia R, Khurana S, Egol KA
A total of 96 displaced tibial plateau fractures in 94 patients (average age, 48 years) were treated with open reduction and internal fixation. At 12 months postoperatively, mean range of knee motion was 126 degrees and 10 (10%) of the patients had required a secondary surgery. Using a binary regression model, no demographic variable such as age, gender, smoking history, diabetes, BMI; or fracture characteristic such as mechanism of injury, initial plateau depression, Schatzker classification was identified that correlated with short-term functional outcome such as a complication, range of motion, healing time, or residual depression. We determined that radiographic fracture reduction was not superior following fractures with less initial displacement, and increased plateau collapse at 12 months postoperatively was not indicative of decreased function. However, an increased postoperative articular step-off was found to be associated with an increased risk of plateau collapse (p?HubMed – depression
PREDICTIVE SOCIOECONOMIC AND CLINICAL PROFILES OF ANTIDEPRESSANT RESPONSE AND REMISSION.
Filed under: Depression Treatment
Depress Anxiety. 2013 Jan 3;
Jain FA, Hunter AM, Brooks JO, Leuchter AF
BACKGROUND: There are many prognostic factors for treatment outcome in major depressive disorder (MDD). The predictive power of any single factor, however, is limited. We aimed to develop profiles of antidepressant response and remission based upon hierarchical combinations of baseline clinical and demographic factors. METHODS: Using data from Level 1 of the Sequenced Treatment Alternatives to Relieve Depression trial (STAR*D), in which 2,876 participants with MDD were treated with citalopram, a signal-detection analysis was performed to identify hierarchical predictive profiles for patients with different treatment outcome. An automated algorithm was used to determine the optimal predictive variables by evaluating sensitivity, specificity, positive and negative predictive value, and test efficiency. RESULTS: Hierarchical combinations of baseline clinical and demographic factors yielded profiles that significantly predicted treatment outcome. In contrast to an overall 47% response rate in STAR*D Level 1, response rates of profiled patient subgroups ranged from 31 to 63%. In contrast to an overall remission rate of 28%, identified subsets of patients had a 12 to 55% probability of remission. The predictors of antidepressant treatment outcome most commonly incorporated into profiles were related to socioeconomic status (e.g., income, education), whereas indicators of depressive symptom type and severity, as well as comorbid clinical conditions, were useful but less powerful predictors. CONCLUSIONS: Hierarchical profiles of demographic and clinical baseline variables categorized patients according to the likelihood they would benefit from a single antidepressant trial. Socioeconomic factors had greater predictive power than symptoms or other clinical factors, and profiles combining multiple factors were stronger predictors than individual factors alone.
HubMed – depression
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