scholarly journals Validation of the ADHERE model for risk stratification in patients with acute heart failure admitted to the critical care unit

Critical Care ◽  
2005 ◽  
Vol 9 (S2) ◽  
Author(s):  
HCV Rey ◽  
FOD Rangel ◽  
MI Bittencourt ◽  
RM Rocha ◽  
ALC Marins ◽  
...  
Keyword(s):  
Heart Failure ◽  
Critical Care ◽  
Risk Stratification ◽  
Acute Heart Failure ◽  
Critical Care Unit

Characterization of hemodynamically stable acute heart failure patients requiring a critical care unit admission: Derivation, validation, and refinement of a risk score

2017 ◽  
Vol 188 ◽  
pp. 127-135 ◽  
Author(s):  
Ismail R. Raslan ◽  
Paul Brown ◽  
Cynthia M. Westerhout ◽  
Justin A. Ezekowitz ◽  
Adrian F. Hernandez ◽  
...  
Keyword(s):  
Heart Failure ◽  
Critical Care ◽  
Acute Heart Failure ◽  
Risk Score ◽  
Critical Care Unit ◽  
Heart Failure Patients

Acute heart failure

2019 ◽  
pp. 23-34
Author(s):  
Amy Krepska ◽  
Deirdre Murphy
Keyword(s):  
Heart Failure ◽  
Critical Care ◽  
Acute Heart Failure ◽  
Critical Care Unit ◽  
Daily Practice ◽  
Care Staff ◽  
Evidence Based ◽  
Standard Textbook ◽  
Learning Points

This chapter is centred on a case study on acute heart failure. This topic is one of the key challenging areas in critical care medicine and one that all intensive care staff will encounter. The chapter is based on a detailed case history, ensuring clinical relevance, together with relevant images, making this easily relatable to daily practice in the critical care unit. The chapter is punctuated by evidence-based, up-to-date learning points, which highlight key information for the reader. Throughout the chapter, a topic expert provides contextual advice and commentary, adding practical expertise to the standard textbook approach and reinforcing key messages.

scholarly journals Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ashwath Radhachandran ◽  
Anurag Garikipati ◽  
Nicole S. Zelin ◽  
Emily Pellegrini ◽  
Sina Ghandian ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Decision Tree ◽  
Risk Stratification ◽  
Acute Heart Failure ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Electronic Health Record Data ◽  
Test Set ◽  
Patient Disposition

Abstract Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. Conclusions A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.

scholarly journals The associations between direct and delayed critical care unit admission with mortality and readmissions among patients with heart failure

2021 ◽  
Vol 233 ◽  
pp. 20-38
Author(s):  
Ismail R. Raslan ◽  
Heather J. Ross ◽  
Robert A. Fowler ◽  
Damon C. Scales ◽  
Henry T. Stelfox ◽  
...  
Keyword(s):  
Heart Failure ◽  
Critical Care ◽  
Critical Care Unit ◽  
Patients With Heart Failure

scholarly journals ACUTE Heart Failure Risk Stratification

Circulation ◽  
2019 ◽  
Vol 139 (9) ◽  
pp. 1157-1161 ◽  
Author(s):  
Sean P. Collins ◽  
Peter S. Pang
Keyword(s):  
Heart Failure ◽  
Risk Stratification ◽  
Acute Heart Failure ◽  
Failure Risk
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