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 u...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2015
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| Online Access: | http://hdl.handle.net/1721.1/98087 |
| _version_ | 1811085187039100928 |
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| author | Dejam, Andre Reti, Shane R. Vieira, Susana M. Celi, Leo A. Pereira, Ruben Duarte M. A. Salgado, Catia M. Sousa, Joao M. C. Finkelstein, Stan Neil |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Dejam, Andre Reti, Shane R. Vieira, Susana M. Celi, Leo A. Pereira, Ruben Duarte M. A. Salgado, Catia M. Sousa, Joao M. C. Finkelstein, Stan Neil |
| author_sort | Dejam, Andre |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:05:01Z |
| format | Article |
| id | mit-1721.1/98087 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:05:01Z |
| publishDate | 2015 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | mit-1721.1/980872022-09-28T11:51:06Z Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology Dejam, Andre Reti, Shane R. Vieira, Susana M. Celi, Leo A. Pereira, Ruben Duarte M. A. Salgado, Catia M. Sousa, Joao M. C. Finkelstein, Stan Neil Massachusetts Institute of Technology. Institute for Data, Systems, and Society Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Engineering Systems Division Pereira, Ruben Duarte M. A. Finkelstein, Stan Neil 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. MIT-Portugal Program Portuguese Science and Technology Foundation (Project LAETA 2015-202, Reference UID/EMS/50022/2013) Portuguese Ministry of Education and Science (Portuguese Science and Technology Foundation Grant SFRH/BD/51028/2010) Portuguese Science and Technology Foundation (IC4U-Decision Support System for Preventing ICU Readmissions PTDC/EMS-SIS/3220/2012) 2015-08-18T12:51:34Z 2015-08-18T12:51:34Z 2015 2015-04 2015-08-08T07:00:17Z Article http://purl.org/eprint/type/JournalArticle 2356-6140 1537-744X http://hdl.handle.net/1721.1/98087 Pereira, Ruben Duarte M. A., Catia M. Salgado, Andre Dejam, Shane R. Reti, Susana M. Vieira, Joao M. C. Sousa, Leo A. Celi, and Stan N. Finkelstein. “Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction Following Admission to the Intensive Care Unit Using Clinical Physiology.” The Scientific World Journal 2015 (2015): 1–9. en http://dx.doi.org/10.1155/2015/212703 The Scientific World Journal Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Copyright © 2015 Rúben Duarte M. A. Pereira et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. application/pdf Hindawi Publishing Corporation Hindawi Publishing Corporation |
| spellingShingle | Dejam, Andre Reti, Shane R. Vieira, Susana M. Celi, Leo A. Pereira, Ruben Duarte M. A. Salgado, Catia M. Sousa, Joao M. C. Finkelstein, Stan Neil Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology |
| title | Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology |
| title_full | Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology |
| title_fullStr | Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology |
| title_full_unstemmed | Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology |
| title_short | Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology |
| title_sort | fuzzy modeling to predict severely depressed left ventricular ejection fraction following admission to the intensive care unit using clinical physiology |
| url | http://hdl.handle.net/1721.1/98087 |
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