Septic shock : providing early warnings through multivariate logistic regression models
Includes bibliographical references (leaves 87-89).
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/42338 |
| _version_ | 1811096418157330432 |
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| author | Shavdia, Dewang |
| author2 | Roger G. Mark. |
| author_facet | Roger G. Mark. Shavdia, Dewang |
| author_sort | Shavdia, Dewang |
| collection | MIT |
| description | Includes bibliographical references (leaves 87-89). |
| first_indexed | 2024-09-23T16:43:25Z |
| format | Thesis |
| id | mit-1721.1/42338 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:43:25Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/423382019-04-11T01:19:13Z Septic shock : providing early warnings through multivariate logistic regression models Shavdia, Dewang Roger G. Mark. Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology. Includes bibliographical references (leaves 87-89). Thesis (M. Eng.)--Harvard-MIT Division of Health Sciences and Technology, 2007. (cont.) The EWS models were then tested in a forward, casual manner on a random cohort of 500 ICU patients to mimic the patients' stay in the unit. The model with the highest performance achieved a sensitivity of 0.85 and a positive predictive value (PPV) of 0.70. Of the 35 episodes of hypotension despite fluid resuscitation present in the random patient dataset, the model provided early warnings for 29 episodes with a mean early warning time of 582 ± 355 minutes. Early goal-directed therapy (EGDT) in severe sepsis and septic shock has shown to provide substantial benefits in patient outcomes. However, these preventive therapeutic interventions are contingent upon an early detection or suspicion of the underlying septic etiology. Detection of sepsis in the early stages can be difficult, as the initial pathogenesis can occur while the patient is still displaying normal vital signs. This study focuses on developing an early warning system (EWS) to provide clinicians with a forewarning of an impending hypotensive crisis-thus allowing for EGDT intervention. Research was completed in three main stages: (1) generating an annotated septic shock dataset, (2) constructing multivariate logistic regression EWS models using the annotated dataset, and (3) testing the EWS models in a forward, causal manner on a random cohort of patients to simulate performance in a real-life ICU setting. The annotated septic shock dataset was created using the Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database. Automated pre-annotations were generated using search criteria designed to identify two patient types: (1) sepsis patients who do not progress to septic shock, and (2) sepsis patient who progress to septic shock. Currently, manual review by expert clinicians to verify the pre-annotations has not been completed. Six separate EWS models were constructed using the annotated septic shock dataset. The multivariate logistic regression EWS models were trained to differentiate between 107 high-risk sepsis patients of whom 39 experienced a hypotensive crisis and 68 who remained stable. The models were tested using 7-fold cross validation; the mean area under the receiver operating characteristic (ROC) curve for the best model was 0.940 ± 0.038. by Dewang Shavdia. M.Eng. 2008-09-03T15:23:00Z 2008-09-03T15:23:00Z 2007 2007 Thesis http://hdl.handle.net/1721.1/42338 233639098 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 89 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Harvard University--MIT Division of Health Sciences and Technology. Shavdia, Dewang Septic shock : providing early warnings through multivariate logistic regression models |
| title | Septic shock : providing early warnings through multivariate logistic regression models |
| title_full | Septic shock : providing early warnings through multivariate logistic regression models |
| title_fullStr | Septic shock : providing early warnings through multivariate logistic regression models |
| title_full_unstemmed | Septic shock : providing early warnings through multivariate logistic regression models |
| title_short | Septic shock : providing early warnings through multivariate logistic regression models |
| title_sort | septic shock providing early warnings through multivariate logistic regression models |
| topic | Harvard University--MIT Division of Health Sciences and Technology. |
| url | http://hdl.handle.net/1721.1/42338 |
| work_keys_str_mv | AT shavdiadewang septicshockprovidingearlywarningsthroughmultivariatelogisticregressionmodels |