Six-Month Predictive Value of Diuretic Resistance Formulas in Discharged Heart Failure Patients after an Acute Decompensation

Feola, Mauro; Rossi, Arianna; Testa, M.; Ferreri, Carla; Palazzuoli, Alberto; Pastorini, Guido; Ruocco, Gaetano

doi:10.3390/jcm9092932

Cited by 4 publications

(6 citation statements)

References 28 publications

(40 reference statements)

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“…In a study performed as a post hoc analysis of the PROTECT randomized trial, investigators defined diuretic response as a change in baseline weight at day four per 40mg furosemide equivalent dose based on the total dose of furosemide from days 1 through 3 (6). A poor diuretic response independently predicted mortality and rehospitalization, which was subsequently confirmed in an analysis of the RELAX-AHF trial and in another prospective study in the ICU setting (5,14,(23)(24)(25). More recently, investigators used a natriuretic response prediction equation based on a spot 2h urine sodium post diuretic administration and found for a strong association total 6h natriuresis (26).…”

Section: Discussionmentioning

confidence: 97%

“…Diuretic resistance is increasingly recognized as an independent predictor of poor outcomes, and several recent studies have examined risk factors associated with diuretic resistance. (6,(23)(24)(25)(26). In a study performed as a post hoc analysis of the PROTECT randomized trial, investigators defined diuretic response as a change in baseline weight at day four per 40mg furosemide equivalent dose based on the total dose of furosemide from days 1 through 3 (6).…”

Section: Discussionmentioning

confidence: 99%

“…6 A poor diuretic response independently predicted mortality and rehospitalization, which was subsequently confirmed in an analysis of the RELAX-AHF trial and in another prospective study in the ICU setting. 5,14,[23][24][25] More recently, investigators used a natriuretic response prediction equation on the basis of a 2-hour spot urine sodium postdiuretic administration and found a strong association with a total of 6-hour natriuresis. 26 Unfortunately, none of these previous studies integrated these factors in a risk prediction model that can be used prospectively by clinicians at the bedside.…”

Section: Discussionmentioning

confidence: 99%

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A Machine Learning Model to Predict Diuretic Resistance

2022

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Background: Volume overload is a common complication encountered in hospitalized patients, and the mainstay of therapy is diuresis. Unfortunately, the diuretic response in some individuals is inadequate despite a typical dose of loop diuretics, a phenomenon called diuretic resistance. An accurate prediction model that predicts diuretic resistance using pre-dosing variables could inform the right diuretic dose for a prospective patient. Methods: Two large, deidentified, publicly available and independent ICU databases from the United States were used - The Medical Information Mart for Intensive Case III (MIMIC) and the Philips eICU databases. Loop diuretic resistance was defined as less than 1400cc of urine per 40mg of diuretic dose in 24hrs. Using 24-hour windows throughout admission, commonly accessible were obtained and incorporated into the model. Data imputation was performed using a highly accurate Machine Learning method. Using XGBoost, several models were created using train and test datasets from the eICU database. These were then combined into an ensemble model optimized for increased specificity and then externally validated on the MIMIC database. Results: The final ensemble model was composed of four separate models, each using 21 commonly available variables. The ensemble model outperformed individual models during validation. Higher serum creatinine, lower systolic blood pressure, lower serum chloride, higher age, and female sex were the most important predictors of diuretic resistance (in that order). The specificity of the model on external validation was 92% yielding a positive likelihood ratio of 3.46 while maintaining overall discrimination (C-statistic 0.69). Conclusions: A diuretic resistance prediction model was created using machine learning and was externally validated in ICU populations. The model is easy to use, would provide actionable information at the bedside, and would be ready for implementation in existing electronic medical records. This study also provides a framework for the development of future Machine Learning models.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Machine Learning Model to Predict Diuretic Resistance

2022

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“…Proposed quantitative definitions of diuretic resistance after loop diuretic administration in humans and dogs34,[38][39][40][41][42] …”

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Toward quantification of loop diuretic responsiveness for congestive heart failure

Oyama

Adin

2022

Veterinary Internal Medicne

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Diuretics, such as furosemide, are routinely administered to dogs with congestive heart failure (CHF). Traditionally, dose and determination of efficacy primarily are based on clinical signs rather than quantitative measures of drug action. Treatment of human CHF patients increasingly is guided by quantification of urine sodium concentration (uNa) and urine volume after diuretic administration. Use of these and other measures of diuretic responsiveness is associated with decreased duration of hospitalization, complication rates, future rehospitalization, and mortality. At their core, loop diuretics act through natriuresis, and attention to body sodium (Na) stores and handling offers insight into the pathophysiology of CHF and pharmacology of diuretics beyond what is achievable from clinical signs alone. Human patients with low diuretic responsiveness or diuretic resistance are at risk for difficult or incomplete decongestion that requires diuretic intensification or other remedial strategies. Identification of the specific etiology of resistance in a patient can help tailor personalized interventions.In this review, we advance the concept of loop diuretic responsiveness by highlighting Na and natriuresis. Specifically, we review body water homeostasis and congestion in light of the increasingly recognized role of interstitial Na, propose definitions for diuretic responsiveness and resistance in veterinary subjects, review relevant findings of recent studies, explain how the particular cause of resistance can guide treatment, and identify current knowledge gaps. We believe that a quantitative approach to loop diuretic usage primarily involving natriuresis will advance our understanding and care of dogs with CHF.

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“…There was an error in the original article [ 1 ]. "(a Morinsky scale<1 point)" has been changed to "(medication adherence was assessed using a self-reported measure)".…”

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confidence: 99%