Type 1 Diabetes Mellitus and Cognitive Impairments: A Systematic Review

Type 1 diabetes mellitus (T1DM) is a major subtype of diabetes and is usually diagnosed at a young age with insulin deficiency. The life expectancy of T1DM patients has increased substantially in comparison with that three decades ago due to the availability of exogenous insulin, though it is still shorter than that of healthy people. However, the relation remains unclear between T1DM and dementia as an aging-related disease. We conducted a systematic review of existing literature on T1DM and cognition impairments by carrying out searches in electronic databases Medline, EMBASE, and Google Scholar. We restricted our review to studies involving only human subjects and excluded studies on type 2 diabetes mellitus or non-classified diabetes. A meta-analysis was first performed on the relationship between T1DM and cognitive changes in youths and adults respectively. Then the review focused on the cognitive complications of T1DM and their relation with the characteristics of T1DM, glycemic control, diabetic complications, comorbidities, and others. First, age at onset, disease duration, and glycemic dysregulation were delineated for their association with cognitive changes. Then diabetic ketoacidosis, angiopathy, and neuropathy were examined as diabetic complications for their involvement in cognitive impairments. Lastly, body mass index and blood pressure were discussed for their relations with the cognitive changes. Future studies are needed to elucidate the pathogenesis of T1DM-related cognitive impairments or dementia

Prediction of Coronary Artery Disease Risk Based on Multiple Longitudinal Biomarkers

In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well-documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort

A penalized Cox proportional hazards model with multiple time-varying exposures

In recent pharmacoepidemiology research, the increasing use of electronic medication dispensing data provides an unprecedented opportunity to examine various health outcomes associated with long-term medication usage. Often, patients may take multiple types of medications intended for the same medical condition and the medication exposure status and intensity may vary over time, posing challenges to the statistical modeling of such data. In this article, we propose a penalized Cox proportional hazards (PH) model with multiple functional covariates and potential interaction effects. We also consider constrained coefficient functions to ensure a diminishing medication effect over time. Hypothesis testing of interaction effect and main effect was discussed under the penalized Cox PH model setting. Our simulation studies demonstrate the adequate performance of the proposed methods for both parameter estimation and hypothesis testing. Application to a primary care depression cohort study was also illustrated to examine the effects of two common types of antidepressants on the risk of coronary artery disease

Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome

Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease

Post-Intensive Care Unit Psychiatric Comorbidity and Quality of Life

The prevalence of psychiatric symptoms ranges from 17% to 44% in intensive care unit (ICU) survivors. The relationship between the comorbidity of psychiatric symptoms and quality of life (QoL) in ICU survivors has not been carefully examined. This study examined the relationship between psychiatric comorbidities and QoL in 58 survivors of ICU delirium. Patients completed 3 psychiatric screens at 3 months after discharge from the hospital, including the Patient Health Questionnaire-9 (PHQ-9) for depression, the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for anxiety, and the Post-Traumatic Stress Syndrome (PTSS-10) questionnaire for posttraumatic stress disorder. Patients with 3 positive screens (PHQ-9 ≥ 10; GAD-7 ≥ 10; and PTSS-10 > 35) comprised the high psychiatric comorbidity group. Patients with 1 to 2 positive screens were labeled the low to moderate (low-moderate) psychiatric comorbidity group. Patients with 3 negative screens were labeled the no psychiatric morbidity group. Thirty-one percent of patients met the criteria for high psychiatric comorbidity. After adjusting for age, gender, Charlson Comorbidity Index, discharge status, and prior history of depression and anxiety, patients who had high psychiatric comorbidity were more likely to have a poorer QoL compared with the low-moderate comorbidity and no morbidity groups, as measured by a lower EuroQol 5 dimensions questionnaire 3-level Index (no, 0.69 ± 0.25; low-moderate, 0.70 ± 0.19; high, 0.48 ± 0.24; P = 0.017). Future studies should confirm these findings and examine whether survivors of ICU delirium with high psychiatric comorbidity have different treatment needs from survivors with lower psychiatric comorbidity

Antidepressant Use and Depressive Symptoms in Intensive Care Unit Survivors

Nearly 30% of intensive care unit (ICU) survivors have depressive symptoms 2-12 months after hospital discharge. We examined the prevalence of depressive symptoms and risk factors for depressive symptoms in 204 patients at their initial evaluation in the Critical Care Recovery Center (CCRC), an ICU survivor clinic based at Eskenazi Hospital in Indianapolis, Indiana. Thirty-two percent (N = 65) of patients had depressive symptoms on initial CCRC visit. For patients who are not on an antidepressant at their initial CCRC visit (N = 135), younger age and lower education level were associated with a higher likelihood of having depressive symptoms. For patients on an antidepressant at their initial CCRC visit (N = 69), younger age and being African American race were associated with a higher likelihood of having depressive symptoms. Future studies will need to confirm these findings and examine new approaches to increase access to depression treatment and test new antidepressant regimens for post-ICU depression

Perioperative Risk Factors for Postoperative Delirium in Patients Undergoing Esophagectomy

Background Postoperative delirium affects up to 50% of patients undergoing esophagectomy and is associated with negative outcomes. The perioperative risk factors for delirium in this population are not well understood. We conducted this study to assess perioperative risk factors for postoperative delirium among esophagectomy patients. Methods We performed a secondary data analysis of patients enrolled in a randomized controlled trial evaluating the efficacy of haloperidol prophylaxis postoperatively in reducing delirium among esophagectomy patients. Postoperative delirium was assessed twice daily using the Confusion Assessment Method for the ICU. Univariate and logistic regression analyses were performed to examine the association between perioperative variables and development of postoperative delirium. Results Of 84 consecutive esophagectomy patients, 27 (32%) developed postoperative delirium. Patients who developed postoperative delirium had higher APACHE II scores [22.1 (6.5) versus 17.4 (6.8); p=0.003], longer mechanical ventilation days [1.7 (1.4) versus 1.0 (1.1); p=0.001], and longer ICU days [5.1 (2.6) versus 2.6 (1.6); p<0.001]. In a logistic regression model, only ICU length of stay was found to have significant association with postoperative delirium [OR 1.65; 95% CI 1.21-2.25]. Conclusions ICU length of stay was significantly associated with postoperative delirium. Other perioperative factors including duration of surgery, blood loss, and hemoglobin levels were not significantly associated with postoperative delirium