NDLI: Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

Content Provider	Springer Nature : BioMed Central
Author	Seely, Andrew JE Bravi, Andrea Herry, Christophe Green, Geoffrey Longtin, André Ramsay, Tim Fergusson, Dean McIntyre, Lauralyn Kubelik, Dalibor Maziak, Donna E Ferguson, Niall Brown, Samuel M Mehta, Sangeeta Martin, Claudio Rubenfeld, Gordon Jacono, Frank J Clifford, Gari Fazekas, Anna Marshall, John
Abstract	Introduction Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown. Methods We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models. Results Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively. Conclusions Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability. Trial registration ClinicalTrials.gov NCT01237886 . Registered 13 October 2010.
Related Links	https://ccforum.biomedcentral.com/counter/pdf/10.1186/cc13822.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	13648535
DOI	10.1186/cc13822
Journal	Critical Care
Issue Number	2
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2014-04-08
Access Restriction	Open
Subject Keyword	Intensive Critical Care Medicine Emergency Medicine Heart Rate Variability Spontaneous Breathing Trial Recurrence Plot Extubation Failure Recurrence Quantification Analysis
Content Type	Text
Resource Type	Article
Subject	Critical Care and Intensive Care Medicine
Journal Impact Factor	8.8/2023
5-Year Journal Impact Factor	10.4/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Erratum to: Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

Erratum to: Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

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Erratum to: Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

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