Regional citrate anticoagulation with a substitute containing calcium for continuous hemofiltration in children.

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ID: 56738
2019
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Abstract
Regional citrate anticoagulation (RCA) was recommended as the first treatment option for adults by the Kidney Disease Improving Global Outcomes Kidney Foundation in 2012, for the characteristic of sufficient anticoagulation in vitro, but almost no anticoagulation in vivo. Traditionally, the substitute for RCA is calcium-free. This study investigated a simplified protocol of RCA for continuous hemofiltration (CHF) in children using a commercially available substitute containing calcium.An analytical, observational, retrospective study assessed 59 pediatric patients with 106 sessions and 3580 hours of CHF. Values before and after treatment were compared, including Na, ionic calcium (iCa) and HCO3 concentrations, pH, and the ratio of total calcium to iCa (T/iCa). In addition, in vivo and in vitro iCa, treatment time, sessions with continuous transmembrane pressure >200 mm Hg, and sessions with clotting and bleeding were recorded.The average treatment time was 33.8 ± 10.1 hours. In vitro, 88.5% of iCa achieved the target (0.25-0.35 mmol/L), and in vivo, 95.4% of iCa achieved the target (1.0-1.35 mmol/L). There were 8 sessions with a transmembrane pressure >200 mm Hg and 3 sessions with filters clotted. After treatment, there were 2, 1, and 2 sessions with T/iCa > 2.5 (implying citrate accumulation), iCa < 0.9 mmol/L, and iCa > 1.35 mmol/L. No sodium disorders were recorded. There were fewer cases of acidemia and more cases of alkalemia after treatment compared to before.RCA-CHF with a substitute containing calcium and close monitoring could be a safe and effective treatment for children. In addition, the calcium test site in vitro and the adjustment of citrate should be given strict attention.
Reference Key
bai2019regionalmedicine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bai, Ke;Liu, Chengjun;Zhou, Fang;Xu, Feng;Dang, Hongxing;
Journal Medicine
Year 2019
DOI
10.1097/MD.0000000000017421
URL
Keywords
Machine learning earth fissure geohazard hazard prediction

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