Browsing by Subject "early mortality"

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    Lifetime Duration of Depressive Disorders in Patients With Type 2 Diabetes
    (American Diabetes Association, 2016-12) de Groot, Mary; Crick, Kent A.; Long, Molly; Saha, Chandan; Shubrook, Jay H.; Medicine, School of Medicine
    OBJECTIVE Depression in patients with type 2 diabetes (T2D) is associated with long-term complications, disability, and early mortality. No studies have systematically examined the length of episodes and remission in adults with major depressive disorder (MDD) and T2D. This study examined the course of depressive disorders in patients with T2D and MDD. RESEARCH DESIGN AND METHODS Participants (N = 50) enrolled in a behavioral intervention for adults with T2D and MDD were interviewed using the Structured Clinical Interview for DSM-IV-TR to assess history of depressive disorders at baseline (lifetime history), postintervention, and 3-month follow-up. Onset and remission dates were recorded for all Axis I depressive disorders from birth to final interview. RESULTS Average number of MDD episodes was 1.8 with a mean duration of 23.4 months (SD 31.9; range 0.5–231.3). Over the life course, mean exposure to MDD was 43.1 months (SD 46.5; range 0.5–231.3). Kaplan-Meier survival curve analysis indicated median episode duration decreased with subsequent episodes (14 months, first episode; 9 months, second episode; P < 0.002). In patients with multiple depressive episodes, recovery time was shorter with each subsequent episode (P = 0.002). No differences in length of episode or remission were observed based on chronology of T2D diagnosis. CONCLUSIONS The overall exposure to depression in this sample of adults with T2D represents a substantial period of time that can contribute to negative medical and psychiatric outcomes. Recurrent episodes decrease in duration as do recovery periods, resulting in a waxing and waning pattern. Findings from this study underscore the need to effectively diagnose and treat depression in patients with T2D to minimize risk of future depressive episodes.
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    Recognition of early mortality in multiple myeloma by a prediction matrix
    (Wiley, 2017) Terebelo, Howard; Srinivasan, Shankar; Narang, Mohit; Abonour, Rafat; Gasparetto, Cristina; Toomey, Kathleen; Hardin, James W.; Larkins, Gail; Kitali, Amani; Rifkin, Robert M.; Shah, Jatin J.; Department of Medicine, IU School of Medicine
    Early mortality (EM; death ≤ 6 months from diagnosis) has been reported in several newly diagnosed multiple myeloma (NDMM) trials. Before the era of novel agents, the incidence was 10%-14%. Causes of death included infections/pneumonia, renal failure, refractory disease, and cardiac events. Staging systems, such as the revised International Staging System (r-ISS), and prognostic factors including cytogenetics, lactate dehydrogenase levels, and myeloma-specific factors, are useful to assess overall prognosis; however, they cannot predict EM. We evaluated patients treated with novel agents in the Connect MM® Registry and identified risk factors of the EM cohort. Eligible patients were enrolled in the registry within 60 days of diagnosis. Univariate and multivariate analyses were conducted to evaluate associations between baseline characteristics and EM. Prediction matrices for EM were constructed from a logistic model. Between September 2009 and December 2011, 1493 patients were enrolled in the registry and had adequate follow-up. Of these patients, 102 (6.8%) had EM and 1391 (93.2%) survived for > 180 days. Baseline factors significantly associated with increased EM risk included age > 75 years, higher Eastern Cooperative Oncology Group performance status, lower EQ-5D mobility score, higher ISS stage, lower platelet count, and prior hypertension. Renal insufficiency trended toward increased EM risk. These risk factors were incorporated into a prediction matrix for EM. The EM prediction matrix uses differential weighting of risk factors to calculate EM risk in patients with NDMM. Identifying patients at risk for EM may provide new opportunities to implement patient-specific treatment strategies to improve outcomes.