Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression
This paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments,...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8752052/ |
| _version_ | 1819120269542817792 |
|---|---|
| author | Alfredo Lucas Alexander T. Williams Pedro Cabrales |
| author_facet | Alfredo Lucas Alexander T. Williams Pedro Cabrales |
| author_sort | Alfredo Lucas |
| collection | DOAJ |
| description | This paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments, which encompassed two different HS protocols and two resuscitation protocols using blood stored for short periods using five different techniques. Twenty-seven different systemic hemodynamics, cardiac function, and blood gas parameters were measured in each experiment, of which feature selection deemed only 25% of the them as relevant. The reduced feature set was used to train a final logistic regression model. A final test set accuracy is 84% compared to 74% for a baseline classifier using only MAP and HR measurements. Receiver operating characteristics (ROC) curve analysis and Cohens kappa statistics were also used as measures of performance, with the final reduced model outperforming the model, including all parameters. Our results suggest that LRMs trained with a combination of systemic hemodynamics, cardiac function, and blood gas parameters measured at multiple timepoints during HS can successfully classify HS recovery groups. Our results show the predictive ability of traditional and novel hemodynamic and cardiac function features and their combinations, many of which had not previously been taken into consideration, for monitoring HS. Furthermore, we have devised an effective methodology for feature selection and shown ways in which the performance of such predictive models should be assessed in future studies. |
| first_indexed | 2024-12-22T06:17:59Z |
| format | Article |
| id | doaj.art-f38d7abdac1c4c94a557da4e968312ca |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-12-22T06:17:59Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-f38d7abdac1c4c94a557da4e968312ca2022-12-21T18:36:03ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722019-01-0171910.1109/JTEHM.2019.29240118752052Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic RegressionAlfredo Lucas0https://orcid.org/0000-0001-9439-735XAlexander T. Williams1https://orcid.org/0000-0003-2150-9263Pedro Cabrales2https://orcid.org/0000-0002-8794-2839Department of Bioengineering, University of California at San Diego, La Jolla, CA, USADepartment of Bioengineering, University of California at San Diego, La Jolla, CA, USADepartment of Bioengineering, University of California at San Diego, La Jolla, CA, USAThis paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments, which encompassed two different HS protocols and two resuscitation protocols using blood stored for short periods using five different techniques. Twenty-seven different systemic hemodynamics, cardiac function, and blood gas parameters were measured in each experiment, of which feature selection deemed only 25% of the them as relevant. The reduced feature set was used to train a final logistic regression model. A final test set accuracy is 84% compared to 74% for a baseline classifier using only MAP and HR measurements. Receiver operating characteristics (ROC) curve analysis and Cohens kappa statistics were also used as measures of performance, with the final reduced model outperforming the model, including all parameters. Our results suggest that LRMs trained with a combination of systemic hemodynamics, cardiac function, and blood gas parameters measured at multiple timepoints during HS can successfully classify HS recovery groups. Our results show the predictive ability of traditional and novel hemodynamic and cardiac function features and their combinations, many of which had not previously been taken into consideration, for monitoring HS. Furthermore, we have devised an effective methodology for feature selection and shown ways in which the performance of such predictive models should be assessed in future studies.https://ieeexplore.ieee.org/document/8752052/Hemorrhagic shocklogistic regressioncritical carecardiovascular function |
| spellingShingle | Alfredo Lucas Alexander T. Williams Pedro Cabrales Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression IEEE Journal of Translational Engineering in Health and Medicine Hemorrhagic shock logistic regression critical care cardiovascular function |
| title | Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression |
| title_full | Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression |
| title_fullStr | Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression |
| title_full_unstemmed | Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression |
| title_short | Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression |
| title_sort | prediction of recovery from severe hemorrhagic shock using logistic regression |
| topic | Hemorrhagic shock logistic regression critical care cardiovascular function |
| url | https://ieeexplore.ieee.org/document/8752052/ |
| work_keys_str_mv | AT alfredolucas predictionofrecoveryfromseverehemorrhagicshockusinglogisticregression AT alexandertwilliams predictionofrecoveryfromseverehemorrhagicshockusinglogisticregression AT pedrocabrales predictionofrecoveryfromseverehemorrhagicshockusinglogisticregression |