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Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study

OBJECTIVES: There are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk...

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Autores principales: Faisal, Muhammad, Scally, Andrew J, Jackson, Natalie, Richardson, Donald, Beatson, Kevin, Howes, Robin, Speed, Kevin, Menon, Madhav, Daws, Jeremey, Dyson, Judith, Marsh, Claire, Mohammed, Mohammed A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286481/
https://www.ncbi.nlm.nih.gov/pubmed/30530474
http://dx.doi.org/10.1136/bmjopen-2018-022939
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author Faisal, Muhammad
Scally, Andrew J
Jackson, Natalie
Richardson, Donald
Beatson, Kevin
Howes, Robin
Speed, Kevin
Menon, Madhav
Daws, Jeremey
Dyson, Judith
Marsh, Claire
Mohammed, Mohammed A
author_facet Faisal, Muhammad
Scally, Andrew J
Jackson, Natalie
Richardson, Donald
Beatson, Kevin
Howes, Robin
Speed, Kevin
Menon, Madhav
Daws, Jeremey
Dyson, Judith
Marsh, Claire
Mohammed, Mohammed A
author_sort Faisal, Muhammad
collection PubMed
description OBJECTIVES: There are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions. DESIGN: Logistic regression model development and external validation study. SETTING: Two acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data). PARTICIPANTS: Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission. RESULTS: The risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00). CONCLUSIONS: We have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.
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spelling pubmed-62864812018-12-26 Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study Faisal, Muhammad Scally, Andrew J Jackson, Natalie Richardson, Donald Beatson, Kevin Howes, Robin Speed, Kevin Menon, Madhav Daws, Jeremey Dyson, Judith Marsh, Claire Mohammed, Mohammed A BMJ Open Health Services Research OBJECTIVES: There are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions. DESIGN: Logistic regression model development and external validation study. SETTING: Two acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data). PARTICIPANTS: Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission. RESULTS: The risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00). CONCLUSIONS: We have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. BMJ Publishing Group 2018-12-06 /pmc/articles/PMC6286481/ /pubmed/30530474 http://dx.doi.org/10.1136/bmjopen-2018-022939 Text en © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Health Services Research
Faisal, Muhammad
Scally, Andrew J
Jackson, Natalie
Richardson, Donald
Beatson, Kevin
Howes, Robin
Speed, Kevin
Menon, Madhav
Daws, Jeremey
Dyson, Judith
Marsh, Claire
Mohammed, Mohammed A
Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
title Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
title_full Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
title_fullStr Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
title_full_unstemmed Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
title_short Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
title_sort development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study
topic Health Services Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286481/
https://www.ncbi.nlm.nih.gov/pubmed/30530474
http://dx.doi.org/10.1136/bmjopen-2018-022939
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