Bacteremia in kidney transplant recipients: Burden, causes, and consequences.

Bacteremia is an important complication after kidney transplantation. We examined bacteremia and its outcomes in a large cohort of kidney transplant recipients. Kidney transplants from 1-Jul-2004 to 1-Dec-2014 at the Toronto General Hospital were eligible for study inclusion. Bacteremia was defined as two blood culture positives for common skin contaminants or one blood culture positive for other organisms. The cumulative incidence of first bacteremia was estimated using the Kaplan-Meier method, and risk factors were examined in a Cox proportional hazards model. The risk of graft failure or death was assessed in a time-dependent Cox model. Over follow-up, 154 of 1333 patients had at least one bacteremia episode. The cumulative incidence of first bacteremia was 6.8% (6 months) and 11.9% (5 years). Risk factors included recipient diabetes mellitus, time on dialysis, dialysis modality, delayed graft function, donor age, and donor eGFR. Bacteremia increased the risk of total graft failure (hazard ratio 2.11 [95% CI: 1.50, 2.96]), death-censored graft failure (1.73 [0.99, 3.02]), and death with graft function (2.52 [1.63, 3.89]). In conclusion, bacteremia is common after kidney transplantation and impacts both graft and patient survival. Identifying high-risk patients for targeted preventive strategies may reduce the burden and adverse consequences of this important complication.

Whole Person Modeling: a transdisciplinary approach to mental health research.

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Whole Person Modeling: a transdisciplinary approach to mental health research.

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.