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Related Concept Videos

Heart Failure VI: Adjunct Therapies01:22

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Additional therapies for treating patients with heart failure (HF) may include procedural interventions, supplemental oxygen, the management of sleep disorders, and nutritional therapy.Procedural InterventionsImplantable Cardioverter-Defibrillator: For patients at risk of life-threatening arrhythmias due to severe left ventricular dysfunction, an Implantable Cardioverter-Defibrillator (ICD) can detect and terminate these arrhythmias, preventing sudden cardiac death and improving survival rates.
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Heart Failure II: Pathophysiology01:29

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Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
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Heart Failure IV: Classification and Diagnostic Evaluation01:30

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Heart Failure V: Medical Management01:30

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Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
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The heart's primary function is to pump blood throughout the body, maintaining a balance between blood sent out (cardiac output) and blood returning (venous return). If this balance is disrupted, it can result in congestive heart failure (CHF), a severe condition where the heart becomes an inefficient pump, leading to inadequate blood circulation.
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Related Experiment Video

Updated: Oct 6, 2025

Author Spotlight: Exploring Venous Waveforms in Porcine Models to Tackle Volume Overload in Medicine
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Fluid Overload Phenotypes in Critical Illness-A Machine Learning Approach.

Anna S Messmer1, Michel Moser1, Patrick Zuercher1

  • 1Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.

Journal of Clinical Medicine
|January 21, 2022
PubMed
Summary

Machine learning identified fluid overload (FO) phenotypes in critically ill patients. Patients admitted post-surgery or with sepsis/septic shock, high lactate, and low bicarbonate are at higher risk.

Keywords:
fluid overloadfluid resuscitationintensive carerisk factors

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Area of Science:

  • Critical Care Medicine
  • Machine Learning in Healthcare
  • Patient Phenotyping

Background:

  • Fluid overload (FO) significantly increases intensive care unit (ICU) morbidity and mortality.
  • Identifying specific patient subgroups prone to FO is crucial for targeted interventions.
  • Limited research exists on distinct FO phenotypes in critically ill populations.

Purpose of the Study:

  • To derive and identify "fluid overload phenotypes" in critically ill patients.
  • To apply machine learning techniques for uncovering novel patient subgroups.
  • To enhance understanding of risk factors associated with FO in the ICU.

Main Methods:

  • Retrospective single-center cohort study of adult ICU patients with ≥3 days length of stay.
  • Utilized machine learning models: random forest (RF), logistic regression, fast and frugal trees (FFT), and decision trees (DT).
  • Analyzed patient data including admission lactate, bicarbonate, and admission source (postsurgical, sepsis/septic shock).

Main Results:

  • Out of 1772 patients, 21.8% experienced FO.
  • The RF model demonstrated the highest predictive accuracy for FO (AUC 0.84).
  • Key predictors for FO included admission lactate, bicarbonate, and postsurgical ICU admission; sepsis/septic shock was also a significant risk factor.

Conclusions:

  • Identified distinct FO phenotypes characterized by specific clinical profiles.
  • Patients admitted after surgery or with sepsis/septic shock, presenting with high lactate and low bicarbonate, represent a high-risk FO phenotype.
  • These findings can inform personalized fluid management strategies in critical care.