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A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure

OBJECTIVE: To develop and validate a clinical risk prediction score for noninvasive ventilation (NIV) failure defined as intubation after a trial of NIV in non-surgical patients. DESIGN: Retrospective cohort study of a multihospital electronic health record database. PATIENTS: Non-surgical adult pat...

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Autores principales: Stefan, Mihaela S., Priya, Aruna, Pekow, Penelope S., Steingrub, Jay S., Hill, Nicholas S., Lagu, Tara, Raghunathan, Karthik, Bhat, Anusha G., Lindenauer, Peter K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863252/
https://www.ncbi.nlm.nih.gov/pubmed/33546651
http://dx.doi.org/10.1186/s12890-021-01421-w
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author Stefan, Mihaela S.
Priya, Aruna
Pekow, Penelope S.
Steingrub, Jay S.
Hill, Nicholas S.
Lagu, Tara
Raghunathan, Karthik
Bhat, Anusha G.
Lindenauer, Peter K.
author_facet Stefan, Mihaela S.
Priya, Aruna
Pekow, Penelope S.
Steingrub, Jay S.
Hill, Nicholas S.
Lagu, Tara
Raghunathan, Karthik
Bhat, Anusha G.
Lindenauer, Peter K.
author_sort Stefan, Mihaela S.
collection PubMed
description OBJECTIVE: To develop and validate a clinical risk prediction score for noninvasive ventilation (NIV) failure defined as intubation after a trial of NIV in non-surgical patients. DESIGN: Retrospective cohort study of a multihospital electronic health record database. PATIENTS: Non-surgical adult patients receiving NIV as the first method of ventilation within two days of hospitalization. MEASUREMENT: Primary outcome was intubation after a trial of NIV. We used a non-random split of the cohort based on year of admission for model development and validation. We included subjects admitted in years 2010–2014 to develop a risk prediction model and built a parsimonious risk scoring model using multivariable logistic regression. We validated the model in the cohort of subjects hospitalized in 2015 and 2016. MAIN RESULTS: Of all the 47,749 patients started on NIV, 11.7% were intubated. Compared with NIV success, those who were intubated had worse mortality (25.2% vs. 8.9%). Strongest independent predictors for intubation were organ failure, principal diagnosis group (substance abuse/psychosis, neurological conditions, pneumonia, and sepsis), use of invasive ventilation in the prior year, low body mass index, and tachypnea. The c-statistic was 0.81, 0.80 and 0.81 respectively, in the derivation, validation and full cohorts. We constructed three risk categories of the scoring system built on the full cohort; the median and interquartile range of risk of intubation was: 2.3% [1.9%–2.8%] for low risk group; 9.3% [6.3%–13.5%] for intermediate risk category; and 35.7% [31.0%–45.8%] for high risk category. CONCLUSIONS: In patients started on NIV, we found that in addition to factors known to be associated with intubation, neurological, substance abuse, or psychiatric diagnoses were highly predictive for intubation. The prognostic score that we have developed may provide quantitative guidance for decision-making in patients who are started on NIV.
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spelling pubmed-78632522021-02-05 A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure Stefan, Mihaela S. Priya, Aruna Pekow, Penelope S. Steingrub, Jay S. Hill, Nicholas S. Lagu, Tara Raghunathan, Karthik Bhat, Anusha G. Lindenauer, Peter K. BMC Pulm Med Article OBJECTIVE: To develop and validate a clinical risk prediction score for noninvasive ventilation (NIV) failure defined as intubation after a trial of NIV in non-surgical patients. DESIGN: Retrospective cohort study of a multihospital electronic health record database. PATIENTS: Non-surgical adult patients receiving NIV as the first method of ventilation within two days of hospitalization. MEASUREMENT: Primary outcome was intubation after a trial of NIV. We used a non-random split of the cohort based on year of admission for model development and validation. We included subjects admitted in years 2010–2014 to develop a risk prediction model and built a parsimonious risk scoring model using multivariable logistic regression. We validated the model in the cohort of subjects hospitalized in 2015 and 2016. MAIN RESULTS: Of all the 47,749 patients started on NIV, 11.7% were intubated. Compared with NIV success, those who were intubated had worse mortality (25.2% vs. 8.9%). Strongest independent predictors for intubation were organ failure, principal diagnosis group (substance abuse/psychosis, neurological conditions, pneumonia, and sepsis), use of invasive ventilation in the prior year, low body mass index, and tachypnea. The c-statistic was 0.81, 0.80 and 0.81 respectively, in the derivation, validation and full cohorts. We constructed three risk categories of the scoring system built on the full cohort; the median and interquartile range of risk of intubation was: 2.3% [1.9%–2.8%] for low risk group; 9.3% [6.3%–13.5%] for intermediate risk category; and 35.7% [31.0%–45.8%] for high risk category. CONCLUSIONS: In patients started on NIV, we found that in addition to factors known to be associated with intubation, neurological, substance abuse, or psychiatric diagnoses were highly predictive for intubation. The prognostic score that we have developed may provide quantitative guidance for decision-making in patients who are started on NIV. BioMed Central 2021-02-05 /pmc/articles/PMC7863252/ /pubmed/33546651 http://dx.doi.org/10.1186/s12890-021-01421-w Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Article
Stefan, Mihaela S.
Priya, Aruna
Pekow, Penelope S.
Steingrub, Jay S.
Hill, Nicholas S.
Lagu, Tara
Raghunathan, Karthik
Bhat, Anusha G.
Lindenauer, Peter K.
A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
title A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
title_full A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
title_fullStr A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
title_full_unstemmed A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
title_short A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
title_sort scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863252/
https://www.ncbi.nlm.nih.gov/pubmed/33546651
http://dx.doi.org/10.1186/s12890-021-01421-w
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