On-line products of substitution fluid permits virtually unlimited fluid volume exchange during continuous hemodiafiltration (CHDF) to critical care. In on-line hemodiafiltration (HDF), endotoxin free dialysate obtained using pyrogen cut filters is infused into the blood circuit, and HDF is automatically performed using the closed-loop balancing system of the dialysis machine. On-line CHDF is the application of this on-line HDF to continuous renal replacement therapy in the critical care field. We performed on-line CHDF on 376 acute renal failure patients during a 5 year period, and the mean survival rate was 62.5%. We concluded that the on-line CHDF system is safe and effective at maintaining acute renal failure patients.
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http://dx.doi.org/10.1046/j.1526-0968.2002.00432.x | DOI Listing |
Am J Manag Care
January 2025
RAND, 1776 Main St, Santa Monica, CA 90401. Email:
Objectives: Patient experience surveys are essential to measuring patient-centered care, a key component of health care quality. Low response rates in underserved groups may limit their representation in overall measure performance and hamper efforts to assess health equity. Telephone follow-up improves response rates in many health care settings, yet little recent work has examined this for surveys of Medicare enrollees, including those with Medicare Advantage.
View Article and Find Full Text PDFAm J Respir Crit Care Med
January 2025
National and Kapodistrian University of Athens, Athens, Greece;
Am J Respir Crit Care Med
January 2025
Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Respiratory and Critical Care Medicine, Shanghai, China;
Am J Respir Crit Care Med
January 2025
University of Michigan, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ann Arbor, Michigan, United States.
Shock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
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