@article{
 title = {Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {R65},
 volume = {18},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4057494&tool=pmcentrez&rendertype=abstract},
 month = {1},
 publisher = {BioMed Central Ltd},
 id = {4f843031-422e-3e9f-b5c0-467cd6096ec3},
 created = {2016-03-29T20:10:40.000Z},
 accessed = {2014-09-05},
 file_attached = {false},
 profile_id = {8c4ca2d5-86de-3b5d-86be-8408415f34e0},
 group_id = {d7b44578-07c1-3210-ae74-3bcd7f980767},
 last_modified = {2017-03-14T15:45:25.917Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Seely_CritCare_2014},
 source_type = {article},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Seely, Andrew J E and Bravi, Andrea and Herry, Christophe and Green, Geoffrey and Longtin, André and Ramsay, Tim and Fergusson, Dean and McIntyre, Lauralyn and Kubelik, Dalibor and Maziak, Donna E and Ferguson, Niall and Brown, Samuel M and Mehta, Sangeeta and Martin, Claudio and Rubenfeld, Gordon and Jacono, Frank J and Clifford, Gari D and Fazekas, Anna and Marshall, John and Marshal, John},
 journal = {Critical care},
 number = {2}
}

{"_id":"ahwvuyKpwdMftrjoY","bibbaseid":"seely-bravi-herry-green-longtin-ramsay-fergusson-mcintyre-etal-doheartandrespiratoryratevariabilityimprovepredictionofextubationoutcomesincriticallyillpatients-2014","downloads":0,"creationDate":"2015-12-07T03:05:02.552Z","title":"Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?","author_short":["Seely, A., J., E.","Bravi, A.","Herry, C.","Green, G.","Longtin, A.","Ramsay, T.","Fergusson, D.","McIntyre, L.","Kubelik, D.","Maziak, D., E.","Ferguson, N.","Brown, S., M.","Mehta, S.","Martin, C.","Rubenfeld, G.","Jacono, F., J.","Clifford, G., D.","Fazekas, A.","Marshall, J.","Marshal, J."],"year":2014,"bibtype":"article","biburl":null,"bibdata":{"title":"Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?","type":"article","year":"2014","identifiers":"[object Object]","pages":"R65","volume":"18","websites":"http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4057494&tool=pmcentrez&rendertype=abstract","month":"1","publisher":"BioMed Central Ltd","id":"4f843031-422e-3e9f-b5c0-467cd6096ec3","created":"2016-03-29T20:10:40.000Z","accessed":"2014-09-05","file_attached":false,"profile_id":"8c4ca2d5-86de-3b5d-86be-8408415f34e0","group_id":"d7b44578-07c1-3210-ae74-3bcd7f980767","last_modified":"2017-03-14T15:45:25.917Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Seely_CritCare_2014","source_type":"article","private_publication":false,"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.","bibtype":"article","author":"Seely, Andrew J E and Bravi, Andrea and Herry, Christophe and Green, Geoffrey and Longtin, André and Ramsay, Tim and Fergusson, Dean and McIntyre, Lauralyn and Kubelik, Dalibor and Maziak, Donna E and Ferguson, Niall and Brown, Samuel M and Mehta, Sangeeta and Martin, Claudio and Rubenfeld, Gordon and Jacono, Frank J and Clifford, Gari D and Fazekas, Anna and Marshall, John and Marshal, John","journal":"Critical care","number":"2","bibtex":"@article{\n title = {Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?},\n type = {article},\n year = {2014},\n identifiers = {[object Object]},\n pages = {R65},\n volume = {18},\n websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4057494&tool=pmcentrez&rendertype=abstract},\n month = {1},\n publisher = {BioMed Central Ltd},\n id = {4f843031-422e-3e9f-b5c0-467cd6096ec3},\n created = {2016-03-29T20:10:40.000Z},\n accessed = {2014-09-05},\n file_attached = {false},\n profile_id = {8c4ca2d5-86de-3b5d-86be-8408415f34e0},\n group_id = {d7b44578-07c1-3210-ae74-3bcd7f980767},\n last_modified = {2017-03-14T15:45:25.917Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Seely_CritCare_2014},\n source_type = {article},\n private_publication = {false},\n 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. 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