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2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients
BACKGROUND: Changes in vital signs are frequently the first sign to point to pathology in the postoperative setting. There is no prediction model that exists that evaluates risk of postoperative complication in real-time. We are interested in understanding if we are able to risk stratify patients af...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252617/ http://dx.doi.org/10.1093/ofid/ofy210.1800 |
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author | Bhaimia, Eric Ravichandran, Urmila Baied, Elias Lahrman, Frances Saeed, Huma Kaplar, Katherine Parikh, Ronak Paruch, Jennifer Padman, Rema Grant, Jennifer Shah, Nirav |
author_facet | Bhaimia, Eric Ravichandran, Urmila Baied, Elias Lahrman, Frances Saeed, Huma Kaplar, Katherine Parikh, Ronak Paruch, Jennifer Padman, Rema Grant, Jennifer Shah, Nirav |
author_sort | Bhaimia, Eric |
collection | PubMed |
description | BACKGROUND: Changes in vital signs are frequently the first sign to point to pathology in the postoperative setting. There is no prediction model that exists that evaluates risk of postoperative complication in real-time. We are interested in understanding if we are able to risk stratify patients after surgery using novel predictors, trajectories of the various vital signs and evaluating their ability to risk stratify patients. METHODS: We reviewed patients who underwent pancreatectomy at an academic health system from January 2015 to February 2018. Postoperative complications were abstracted using definitions set by the National Surgical Quality Improvement Program (NSQIP) and vital signs, including pain score, were extracted from the Data Warehouse. Group-based trajectory modeling, a technique used to identify distinct clusters of patients with similar trajectories, was used to group patients with similar temperature, heart rate, blood pressure and pain scores. Postoperative complications were tabulated for each risk group and chi-square test was used to compare categorical variables. RESULTS: A total of 195 patients with pancreatectomy were evaluated and the rate of NSQIP complications was 35.4%. Pancreatectomy patients clustered into two distinct clusters for temperature, heart rate, systolic blood pressure and pain score. All four of these vital signs were able to stratify infectious and inflammatory complications between low- and high-risk groups but only systolic blood pressure was significant in stratifying readmission risk and heart rate and pain score for stratifying sepsis risk (Table 1). CONCLUSION: Trends of vital signs may be important predictors of complications. Some vital signs may be better at predicting distinct complications. More work is required to understand if different covariates within trajectory analysis can be combined to further enhance risk stratification for any and specific postoperative complications. DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-6252617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62526172018-11-28 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients Bhaimia, Eric Ravichandran, Urmila Baied, Elias Lahrman, Frances Saeed, Huma Kaplar, Katherine Parikh, Ronak Paruch, Jennifer Padman, Rema Grant, Jennifer Shah, Nirav Open Forum Infect Dis Abstracts BACKGROUND: Changes in vital signs are frequently the first sign to point to pathology in the postoperative setting. There is no prediction model that exists that evaluates risk of postoperative complication in real-time. We are interested in understanding if we are able to risk stratify patients after surgery using novel predictors, trajectories of the various vital signs and evaluating their ability to risk stratify patients. METHODS: We reviewed patients who underwent pancreatectomy at an academic health system from January 2015 to February 2018. Postoperative complications were abstracted using definitions set by the National Surgical Quality Improvement Program (NSQIP) and vital signs, including pain score, were extracted from the Data Warehouse. Group-based trajectory modeling, a technique used to identify distinct clusters of patients with similar trajectories, was used to group patients with similar temperature, heart rate, blood pressure and pain scores. Postoperative complications were tabulated for each risk group and chi-square test was used to compare categorical variables. RESULTS: A total of 195 patients with pancreatectomy were evaluated and the rate of NSQIP complications was 35.4%. Pancreatectomy patients clustered into two distinct clusters for temperature, heart rate, systolic blood pressure and pain score. All four of these vital signs were able to stratify infectious and inflammatory complications between low- and high-risk groups but only systolic blood pressure was significant in stratifying readmission risk and heart rate and pain score for stratifying sepsis risk (Table 1). CONCLUSION: Trends of vital signs may be important predictors of complications. Some vital signs may be better at predicting distinct complications. More work is required to understand if different covariates within trajectory analysis can be combined to further enhance risk stratification for any and specific postoperative complications. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6252617/ http://dx.doi.org/10.1093/ofid/ofy210.1800 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Bhaimia, Eric Ravichandran, Urmila Baied, Elias Lahrman, Frances Saeed, Huma Kaplar, Katherine Parikh, Ronak Paruch, Jennifer Padman, Rema Grant, Jennifer Shah, Nirav 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients |
title | 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients |
title_full | 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients |
title_fullStr | 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients |
title_full_unstemmed | 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients |
title_short | 2144. Vital Signs Are Vital in Identifying High-Risk Postoperative Patients |
title_sort | 2144. vital signs are vital in identifying high-risk postoperative patients |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252617/ http://dx.doi.org/10.1093/ofid/ofy210.1800 |
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