Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

BACKGROUND: Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting. MATERIALS AND METHODS: For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy. RESULTS: The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ≥65 years old. After screening for multicollinearity, the best predictive model of mREE (R2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability (R2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation. CONCLUSIONS: This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.

Opening (and Swallowing) A Can of Worms to Treat My Crohn's Disease.

Editor's Note: This article discusses the experience, ingenuity, and determination of Sean Ahrens, a young patient with Crohn's disease who took it upon himself to treat his longstanding, symptomatic Crohn's disease with pig whipworm eggs. Reading this story will make some of you uncomfortable. You might question whether this work belongs in a medical journal or sends the wrong message to readers. However, we recognize that this topic is controversial and that N=1 reports cannot and should not change practice. The purpose of this story is not to encourage the use of pig whipworm or to demonstrate its efficacy (or lack thereof). We firmly believe that patients are uniquely qualified to provide insights into how they view their illnesses, weigh risks and benefits, and ultimately achieve self-efficacy. Stories like this are important for us to acknowledge and understand, even if they do not change our practice.

Harnessing person-generated health data to accelerate patient-centered outcomes research: the Crohn's and Colitis Foundation of America PCORnet Patient Powered Research Network (CCFA Partners).

The Crohn's and Colitis Foundation of America Partners Patient-Powered Research Network (PPRN) seeks to advance and accelerate comparative effectiveness and translational research in inflammatory bowel diseases (IBDs). Our IBD-focused PCORnet PPRN has been designed to overcome the major obstacles that have limited patient-centered outcomes research in IBD by providing the technical infrastructure, patient governance, and patient-driven functionality needed to: 1) identify, prioritize, and undertake a patient-centered research agenda through sharing person-generated health data; 2) develop and test patient and provider-focused tools that utilize individual patient data to improve health behaviors and inform health care decisions and, ultimately, outcomes; and 3) rapidly disseminate new knowledge to patients, enabling them to improve their health. The Crohn's and Colitis Foundation of America Partners PPRN has fostered the development of a community of citizen scientists in IBD; created a portal that will recruit, retain, and engage members and encourage partnerships with external scientists; and produced an efficient infrastructure for identifying, screening, and contacting network members for participation in research.