Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology.
Left ventricular ejection fraction (LVEF) constitutes an important physiological parameter for the assessment of cardiac function, particularly in the settings of coronary artery disease and heart failure. This study explores the use of routinely and easily acquired variables in the intensive care unit (ICU) to predict severely depressed LVEF following ICU admission. A retrospective study was conducted. We extracted clinical physiological variables derived from ICU monitoring and available within the MIMIC II database and developed a fuzzy model using sequential feature selection and compared it with the conventional logistic regression (LR) model. Maximum predictive performance was observed using easily acquired ICU variables within 6 hours after admission and satisfactory predictive performance was achieved using variables acquired as early as one hour after admission. The fuzzy model is able to predict LVEF ≤ 25% with an AUC of 0.71 ± 0.07, outperforming the LR model, with an AUC of 0.67 ± 0.07. To the best of the authors' knowledge, this is the first study predicting severely impaired LVEF using multivariate analysis of routinely collected data in the ICU. We recommend inclusion of these findings into triaged management plans that balance urgency with resources and clinical status, particularly for reducing the time of echocardiographic examination.
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal ; Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Rúben Duarte M A
RD
Pereira
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.
Cátia M
CM
Salgado
Beth Israel Deaconess Medical Center, Cardiology Division and Harvard Medical School, Boston, MA 02215, USA.
Andre
A
Dejam
Beth Israel Deaconess Medical Center, Division of Clinical Informatics and Harvard Medical School, Boston, MA 02215, USA.
Shane R
SR
Reti
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.
Susana M
SM
Vieira
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.
João M C
JM
Sousa
Beth Israel Deaconess Medical Center, Pulmonary Division and Harvard Medical School, Boston, MA 02215, USA.
Leo A
LA
Celi
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA 02139, USA ; Massachusetts Institute of Technology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, MA 02139, USA.
Stan N
SN
Finkelstein
D000465:Algorithms; D015415:Biomarkers; D016208:Databases, Factual; D017143:Fuzzy Logic; D006333:Heart Failure / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology*; D006439:Hemodynamics; D006801:Humans; D007362:Intensive Care Units; D008962:Models, Theoretical; D010343:Patient Admission; D011379:Prognosis; D012189:Retrospective Studies; D012720:Severity of Illness Index; D013318:Stroke Volume; D016277:Ventricular Function, Left
D015415:Biomarkers