A 2018 overview of diuretic resistance in heart failure

Jardim, Sofia; Santos, Luís Ramos dos; Araújo, Inês; Marquês, Filipa; Branco, Patrícia; Gaspar, Augusta; Fonseca, Cândida

doi:10.1016/j.repc.2018.03.014

Cited by 21 publications

(12 citation statements)

References 54 publications

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“…Diuretic resistance is defined as the failure to achieve effective decongestion with low urine sodium concentration despite the use of appropriate or escalating diuretic doses (10). When diuretic resistance develops, the response to diretics is reduced before the achieving the goal of treatment, leading to poor Abbreviations: CHF, congestive heart failure; HF, heart failure; ANS, autonomic nervous system; NKCC, sodium-potassium-chloride symporter; RASS, reninangiotensin-aldosterone system; CKD, chronic kidney disease; OAT, organic anion transporter; SNS, sympathetic nervous system; ADHF, acute decompensated heart failure; PA, pulmonary artery; QALY, quality-adjusted life years; SCN, suprachiasmatic nucleus; CLOCK, circadian locomotor output cycle kaput; BMAL1, brain and muscle ARNT-like 1; Per, period; Cry, cryptochrome; BP, blood pressure; HR, heart rate; L/D, light/dark; HFrEF.…”

Section: Definition Of Diuretic Resistancementioning

confidence: 99%

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Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

Kenig

Kolben

Asleh

et al. 2021

Front. Cardiovasc. Med.

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Heart failure is a major public health problem, which is associated with significant mortality, morbidity, and healthcare expenditures. A substantial amount of the morbidity is attributed to volume overload, for which loop diuretics are a mandatory treatment. However, the variability in response to diuretics and development of diuretic resistance adversely affect the clinical outcomes. Morevoer, there exists a marked intra- and inter-patient variability in response to diuretics that affects the clinical course and related adverse outcomes. In the present article, we review the mechanisms underlying the development of diuretic resistance. The role of the autonomic nervous system and chronobiology in the pathogenesis of congestive heart failure and response to therapy are also discussed. Establishing a novel model for overcoming diuretic resistance is presented based on a patient-tailored variability and chronotherapy-guided machine learning algorithm that comprises clinical, laboratory, and sensor-derived inputs, including inputs from pulmonary artery measurements. Inter- and intra-patient signatures of variabilities, alterations of biological clock, and autonomic nervous system responses are embedded into the algorithm; thus, it may enable a tailored dose regimen in a continuous manner that accommodates the highly dynamic complex system.

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Section: Definition Of Diuretic Resistancementioning

confidence: 99%

“…It may be prevented by reestablishing diuretic-induced loss of volume. Long-term tolerance is observed following administration of a loop diuretic for prolonged periods of time, which is associated with sodium reabsorption at the distal sites (10).…”

Section: Definition Of Diuretic Resistancementioning

confidence: 99%

Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

Kenig

Kolben

Asleh

et al. 2021

Front. Cardiovasc. Med.

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“…Jardim et al (20) describe in details the pathophysiology of diuretic resistance, in particular the neuro-hormonal activation. Hyponatraemia and activation of the Renin Angiotensin Aldosterone System (RAAS) could have led to the observed dose-dependent aldosterone response.…”

Section: Resistance To Natriuresis Induced By Loop-diureticsmentioning

confidence: 99%

Population Pharmacokinetics and Pharmacodynamics Modeling of Torasemide and Furosemide After Oral Repeated Administration in Healthy Dogs

et al. 2020

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Torasemide is a loop diuretic licensed in dogs for cardiogenic pulmonary oedema. The aim of this pharmacokinetic-pharmacodynamic (PK/PD) study was to define an optimally effective dosage regimen based on preclinical data. In a first study, 5 dogs received once-daily oral torasemide (0, 0.1, 0.2, 0.4, 0.8 mg/kg/day) for 14 days. A second study compared once-daily oral torasemide (0, 0.1, 0.2, 0.3, 0.4 mg/kg/day) to twice-daily furosemide (1, 2, 4, 8 mg/kg/day). For all doses of the second study, 11 dogs received a first day of treatment, followed by a 3 day washout and resumed daily treatment for 10 days (until Day 14). Blood and urine were collected to measure urinary torasemide excretion and plasma torasemide concentrations and daily diuresis and natriuresis. Torasemide PK was linear. After rapid absorption (T max 0.5-1 h), 61% of the bioavailable torasemide was eliminated unchanged in urine. Diuresis and natriuresis observed with torasemide were similar to the ones obtained after furosemide (daily dose-ratios: 1/20 to 1/10). The average diuresis increased from baseline (220 ± 53 mL/day for 10 kg dogs) to 730 ±120 mL after the first torasemide administration and up to 1150 ± 252 mL after 10 administrations at the highest dose. At higher doses (≥0.3 mg/kg/day), daily diureses after 10 diuretic treatment-days were higher than Day 1 and variable between dogs; in contrast, diureses remained constant over time and less variable for doses up to 0.2 mg/kg/day. Natriuresis peaked after the first day and decreased dramatically after the 2nd treatment-day then stabilized to a value close to baseline, except for 0.4 mg/kg/day. Urinary torasemide excretion predicted pharmacodynamics better than plasma concentrations. The decrease in natriuresis observed was successfully modeled using a resistance mechanism; this is likely due to a reabsorption of sodium which did not seem however to affect the volume of urine excreted. For a daily target diuresis of 460 mL/dog/day in severe pulmonary oedema (net fluid loss 240 mL/dog/day), a computed dose of 0.26 mg/kg/day (3.5 mg/kg/day furosemide-equivalent) was selected for clinical studies. Due to high inter-individual variability in diureses at doses ≥0.3 mg/kg, higher doses should be limited to 3-5 days to avoid supra-clinical effects in high responders.

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“…It is a major cause of recurrent hospitalizations in patients with chronic heart failure and predicts death but is difficult to diagnose unequivocally [ 10 ]. Despite the fact that diuretics themselves are not linked to increased survival, diuretic efficacy has been shown to prolong event-free survival, regardless of glomerular filtration rate [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Diuretic Resistance Prediction and Risk Factor Analysis of Patients with Heart Failure During Hospitalization

Xin

Zhu

et al. 2022

Global Heart

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Objectives: This study performed a prediction and risk factor analysis of diuretic resistance (DR) in patients with decompensated heart failure during hospitalization. Methods: The data of patients with decompensated heart failure treated in 2010–2018 with DR (n = 3,383) or without DR (n = 15,444) were retrospectively collected from Chinese PLA General Hospital medical records. Statistical analysis of baseline was performed on two groups of people, and the risk factor of DR was analyzed through logic regression. Six machine learning models were built accordingly, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to prediction efficiency. Results: The preliminary analysis of variance showed significant differences in the incidence of DR among patients with lung infection, hyperlipidemia, type 2 diabetes, and kidney disease. There were significant differences in estimated glomerular filtration rate (eGFR) (P < 0.001). In addition, some physical indicators like BMI were different, the laboratory results like mean red blood cell volume or C-reactive protein assay were also significantly different. The optimal classification model indicated that the best cutoff points for risk factors were vein carbon dioxide, 21 mmol/L and 29 mmol/L; total protein, 64 g/L; pro-brain natriuretic peptide (pro-BNP), 7,600 pg/mL; eGFR, 50 mL/(min ∙ 1.73 m 2 ); serum albumin, 33 g/L; hematocrit, 0.32% and 0.56%; red blood cell volume distribution width, 13; and age, 59 years. The optimal area under the curve was 0.9512. The ranked features derived from the model were age, abnormal sodium level, pro-BNP level, serum albumin level, D -dimer level, direct bilirubin level, and eGFR. Conclusions: The DR risk prediction model based on a gradient boosting decision tree created here identified its important risk factors. The model made very accurate predictions using simple indicators and simultaneously calculated cutoff values to help doctors predict the occurrence of DR.

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A 2018 overview of diuretic resistance in heart failure

Cited by 21 publications

References 54 publications

Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

Population Pharmacokinetics and Pharmacodynamics Modeling of Torasemide and Furosemide After Oral Repeated Administration in Healthy Dogs

Diuretic Resistance Prediction and Risk Factor Analysis of Patients with Heart Failure During Hospitalization

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