PCEN Research Overview



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Learn more about research projects underway within the Pediatric Center of Excellence in Nephrology.

PI: Julie Fitzgerald, MD, PhD, MSCE, FCCM

Acute kidney injury (AKI) is a risk factor for adverse outcomes in critically ill children and is particularly significant for children with sepsis. Children with sepsis-induced AKI and acute kidney disease (AKD) may also be at risk for chronic kidney disease (CKD). 

As an ancillary study to the PRagMatic Pediatric Trial of Balanced vs. nOrmaL saline flUid in Sepsis (PRoMPT BOLUS) randomized clinical trial, this study will use the PCEN Learning Health System Core to link clinical trial data to electronic health record data available in PEDSnet to augment trial outcome analyses. The primary objectives of this study are to: 1) assess whether resuscitation with balanced fluids (BF) instead of normal saline (NS) will decrease AKI and translate to a decrease in CKD in trial participants, and 2) measure the real-world impact of crystalloid fluid choice on sepsis-induced AKI and CKD in the larger PEDSnet source population. 

The team will utilize electronic health record data in PEDSnet to measure AKI and CKD in 2,450 children enrolled in the PRoMPT BOLUS study at 7 trial sites and determine the impact of crystalloid fluid exposure on AKI and CKD in a broader cohort of over 16,600 children in PEDSnet presenting to an ED for treatment of septic shock using a quasi-experimental observational study design. They will also determine other risk factors for progression from AKI to AKD to CKD in children with sepsis. 

PI: Jarcy Zee, PhD  

Pragmatic trials and comparative effectiveness research often require causal inference methods to establish cause-effect relationships between interventions and outcomes. Statistical methods to account for time-dependent confounders have not been developed for recurrent time-to-event outcomes, although such outcomes are often observed in children with chronic kidney disease. 

This project aims to develop a novel class of statistical methods to estimate the effects of time-varying treatments on recurrent event outcomes. A marginal structural model approach will be applied to the proportional rates model, conditional gap time model, and conditional frailty model to estimate both recurrent event rates and risks of subsequent outcomes after the first outcome event. 

This project will develop theoretical properties of each model and test its performance using Monte Carlo simulation studies. The new models will then be applied to real data from observational cohort studies and electronic health record databases. Specifically, this project will enable estimation of (1) the effect of time-varying renin-angiotensin-aldosterone system inhibitor dose on the rate of proteinuria remissions among children enrolled in the Chronic Kidney Disease in Children (CKiD) study; (2) the effect of time-varying corticosteroid use on time from one infection-related acute care event to the next event among patients with glomerular disease in the Cure Glomerulonephropathy (CureGN) study; and (3) the effect of time-varying calcium-based and non-calcium-based phosphate binder use on the time from one skeletal fracture to the next fracture among a heterogeneous population of children with chronic kidney disease in PEDSnet. User-friendly statistical software and associated documentation for implementation of the models will be developed to facilitate their use in a wide range of applications.