Impact of Cumulative Fluid Balance During Continuous Renal Replacement Therapy on Mortality in Patients With Septic Acute Kidney Injury: A Retrospective Cohort Study

Lin, Jinxi; Zhuang, Hai; Zhi, De Yuan; Qi, Zhili; Bai, Jing; Dong, Lei; Liu, Shuai; Duan, Meili

doi:10.3389/fmed.2021.762112

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…In case of diuretic resistance, one strategy to maintain the natriuretic effect is sequential blockade of the nephron with inhibition of the Na/Cl co-transporter of the distal tubule [12]. Acetazolamide may also be useful for combination therapy by increasing urinary sodium excretion [13].…”

Section: Introductionmentioning

confidence: 99%

The Science of Extracorporeal Ultrafiltration: Introducing a Novel Miniaturized Device

et al. 2023

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Introduction. Fluid overload has been associated with untoward outcomes in a variety of clinical settings. Isolated extracorporeal ultrafiltration (UF) allows for mechanical extraction of excess fluid and optimization of volume status without the established risks associated with use of high dose diuretics. Conventional machines for renal replacement therapy can be used to perform isolated UF. However, they typically need high blood flow rates with high circuit volumes and the therapy has to be performed by trained nurses. Herein, we describe a novel device, the Artificial Diuresis-1, or AD 1 (Medica S.p.A., Medolla, Italy), which is a portable technology designed to perform extracorporeal UF at bedside. Materials and Methods. The AD 1 uses a polysulfone mini-filter to generate ultrafiltrate with the help of two forces: blood flow (Qb) and gravity (based on the height at which the ultrafiltrate collection bag is placed). In vitro experiments were performed using human blood to evaluate vascular access pressures and ultrafiltrate volumes using various central venous catheters (12 Fr bilume, 10 Fr with 2 separate lumens, pediatric catheter 7 Fr). A variety of combinations were tested with Qb of 20, 35, 50 mL/min and collection bag height at 20, 40, 60 cm, measuring the UF rate per minute each while monitoring the pressures in the venous and arterial lines and filtration fraction. Results. The device’s performance was as expected. Regarding the pediatric CVC, it was possible to perform measurements only with a Qb of 20 mL/min due to increased venous pressure. Ultrafiltration rates when lines were directly connected to the blood container as well as for CVC Tesio ranged from 3.7 to 11 mL/min, for the CVC Niagara™ from 4.5 to 12.5 mL/min and for the CVC 7 Fr from 8.5 to 10 mL/min. The pressures of the vascular accesses were kept within a range of -5/-40 mmHg for the artery and +10/+70 mmHg for the vein. The highest venous pressure values were found with the CVC 7 Fr (+80/+100 mmHg). Conclusions. This novel device allows to treat patients with fluid overload in a variety of settings, from low-intensity department such as long-term care facilities to the intensive care unit. The device is small and portable, has a simple design, and is user-friendly. Future studies will be needed to evaluate whether gentle ultrafiltration and treatment of volume overload will translate into improvement in clinical outcomes such as a reduction in congestion-related hospital admission.

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Section: Introductionmentioning

confidence: 99%

The Science of Extracorporeal Ultrafiltration: Introducing a Novel Miniaturized Device

et al. 2023

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“…A recent meta-analysis showed that older age and sepsis were risk factors for mortality in AKI patients undergoing CRRT (6). Some other studies have indicated that cumulative fluid balance, mechanical ventilation duration, fluid overload, and disease severity are risk factors for mortality in AKI patients with CRRT (4,7,8). In addition, several scoring systems, including the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE-II), have been proposed for predicting the mortality in AKI patients receiving CRRT (9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

Zha¹,

Yang²,

Hai-fen³

et al. 2022

Ann Transl Med

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Background: Currently, the prediction values of models for the prognosis of acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT) were ordinary and establishing a better prediction model is necessary. Nursing notes are an important predictor of in-hospital mortality in intensive care unit (ICU) patients. This study established prognostic prediction models for AKI patients receiving CRRT especially using nursing notes.Methods: Totally, 682 AKI patients undergoing CRRT were included. AKI was diagnosed based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Four hundred and twelve patients lacking nursing notes data were excluded. Finally, 270 patients were included and randomly divided into a training set (n=189) and a testing set (n=81) at a ratio of 7:3. Univariate analysis explored the possible predictors of mortality in AKI patients receiving CRRT. Random forest models and broad learning system (BLS) models (with or without sentiment scores) were respectively constructed in the training set and verified in the testing set. The performances of the models were assessed by the sensitivity, specificity, and area under the curve (AUC).Results: For the random forest model including the sentiment scores, the AUC was 0.86 (95% CI: 0.81-0.91), the sensitivity was 0.72 (95% CI: 0.63-0.80), and the specificity was 0.87 (95% CI: 0.80-0.94) in the training set and the AUC was 0.78 (95% CI: 0.68-0.88), the sensitivity was 0.65 (95% CI: 0.49-0.80), and the specificity was 0.75 (95% CI: 0.62-0.88) in the testing set. For the BLS model including the sentiment scores, the AUC was 0.87 (95% CI: 0.82-0.92), the sensitivity was 0.95 (95% CI: 0.91-0.99) and the specificity was 0.48 (95% CI: 0.38-0.59) in the training set and the AUC was 0.82 (95% CI: 0.73-0.91), the sensitivity was 0.41 (95% CI: 0.25-0.56) and the specificity was 0.98 (95% CI: 0.93-1.00) in the testing set. Conclusions:The BLS models including the sentiment scores might offer a tool for quickly identifying patients AKI patients receiving CRRT with high risk of mortality and providing timely interventions to them for improving their prognosis.

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Transfer learning-enabled outcome prediction for guiding CRRT treatment of the pediatric patients with sepsis

Li,

Wang,

et al. 2024

BMC Med Inform Decis Mak

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Impact of Cumulative Fluid Balance During Continuous Renal Replacement Therapy on Mortality in Patients With Septic Acute Kidney Injury: A Retrospective Cohort Study

Cited by 3 publications

References 33 publications

The Science of Extracorporeal Ultrafiltration: Introducing a Novel Miniaturized Device

The Science of Extracorporeal Ultrafiltration: Introducing a Novel Miniaturized Device

Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

Transfer learning-enabled outcome prediction for guiding CRRT treatment of the pediatric patients with sepsis

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