The Usefulness of the Rapid Shallow Breathing Index in Predicting Successful Extubation

Trivedi, Vatsal; Chaudhuri, Dipayan; Jinah, Rehman; Piticaru, Joshua; Agarwal, Arnav; Liu, Kuan; McArthur, Eric; Sklar, Michael C.; Friedrich, Jan O.; Rochwerg, Bram; Burns, Karen E. A.

doi:10.1016/j.chest.2021.06.030

Cited by 45 publications

(44 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Identifying the risk of extubation failure is clinically important, and RSBI was developed as a simple assessment method for extubation failure ( 40 ). In a previous meta-analysis ( 41 ), the sensitivity and specificity of RSBI in predicting extubation failure were higher than those of our study (sensitivity, 0.58 vs 0.48; and specificity, 0.83 vs 0.65). This is possibly because we included patients at a relatively low risk of extubation failure.…”

Section: Discussioncontrasting

confidence: 91%

The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms

Fukuchi

Osawa

Satake

et al. 2022

Critical Care Explorations

View full text Add to dashboard Cite

To evaluate the contribution of a preextubation chest X-ray (CXR) to identify the risk of extubation failure in mechanically ventilated patients.DESIGN: Retrospective cohort study. SETTINGS:ICUs in a tertiary center (the Medical Information Mart for Intensive Care IV database). PATIENTS:Patients greater than or equal to 18 years old who were mechanically ventilated and extubated after a spontaneous breathing trial. INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: Among 1,066 mechanically ventilated patients, 132 patients (12%) experienced extubation failure, defined as reintubation or death within 48 hours of extubation. To predict extubation failure, we developed the following models based on deep learning (EfficientNet) and machine learning (LightGBM) with the training data: 1) model using only the rapidshallow breathing index (RSBI), 2) model using RSBI and CXR, 3) model using all candidate clinical predictors (i.e., patient demographics, vital signs, laboratory values, and ventilator settings) other than CXR, and 4) model using all candidate clinical predictors with CXR. We compared the predictive abilities between models with the test data to investigate the predictive contribution of CXR. The predictive ability of the model using CXR as well as RSBI was not significantly higher than that of the model using only RSBI (c-statistics, 0.56 vs 0.56; p = 0.95). The predictive ability of the model using clinical predictors with CXR was not significantly higher than that of the model using all clinical predictors other than CXR (c-statistics, 0.71 vs 0.70; p = 0.12). Based on SHapley Additive exPlanations values to interpret the model using all clinical predictors with CXR, CXR was less likely to contribute to the predictive ability than other predictors (e.g., duration of mechanical ventilation, inability to follow commands, and heart rate). CONCLUSIONS:Adding CXR to a set of other clinical predictors in our prediction model did not significantly improve the predictive ability of extubation failure in mechanically ventilated patients.

show abstract

Section: Discussioncontrasting

confidence: 91%

The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms

Fukuchi

Osawa

Satake

et al. 2022

Critical Care Explorations

View full text Add to dashboard Cite

show abstract

“…While rapid shallow breathing index measured at spontaneous breathing trial baseline performed better than peripheral perfusion index at baseline in predicting weaning failure and re-intubation, the peripheral perfusion index ratio between baseline and at end of spontaneous breathing trial performed better regardless of timing of rapid shallow breathing index measurement, with sensitivity and specificity of 94% and 72% for predicting weaning failure and 91% and 72% for predicting reintubation, respectively. While not directly comparable, these results compare favorably to the sensitivity and specificity for prediction of successful extubation reported using rapid shallow breathing index in a recent meta-analysis [ 56 ]. That ratios of peripheral perfusion index performed better than single index measurements is not surprising since the peripheral perfusion index is known to be heavily skewed and to exhibit significant inter-subject variability [ 55 ].…”

Section: Applications Of Pulse Oximetry Peripheral Perfusion Indexsupporting

confidence: 56%

“…Prediction of weaning success and detection of failure to wean from mechanical ventilation are important clinical problems, since weaning attempts that are both too aggressive and conservative have been associated with poor outcomes [ 56 ]. Readiness for liberation from mechanical ventilation is commonly evaluated by performing a spontaneous breathing trial, which consists of a period with reduced or no ventilator support to determine if a patient can cope with breathing on their own.…”

Section: Applications Of Pulse Oximetry Peripheral Perfusion Indexmentioning

confidence: 99%

“…Lotfy et al[ 57 ] reasoned that peripheral perfusion index would detect whether or not a patient has the ability to increase cardiac output sufficiently in response to the increased metabolic demands of a spontaneous breathing trial. This is an interesting alternative to the well-studied rapid shallow breathing index, which focuses entirely on the ventilatory response to an spontaneous breathing trial by relating respiratory rate to tidal volume [ 56 , 58 ]. Eighteen patients out of a group of 43 were considered as weaning failures by Lotfy et al[ 57 ] due to either failing the spontaneous breathing trial (n = 7) or passing the spontaneous breathing trial but requiring reintubation within 48 h (n = 11).…”

Section: Applications Of Pulse Oximetry Peripheral Perfusion Indexmentioning

confidence: 99%

See 1 more Smart Citation

What is new in respiratory monitoring?

Karbing

Leonhardt

Perchiazzi

et al. 2022

J Clin Monit Comput

View full text Add to dashboard Cite

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.

show abstract

“…This limits the generalizability of our results and further studies are warranted in patients who never attempt yet a SBT. Thirdly, RSBI median values in our study were lower than that reported in the princeps study by Tobin et al, but in this study simple-to-wean patients were included, RSBI predicted extubation failure (not SBT failure) and currently median RSBI value in patients who failed the SBT is around 85 [ 37 ]. Fourthly, although MV-RDOS provide standardization in clinical assessment of respiratory distress, assessment of facial expression of fear and paradoxical motion of the abdomen during inspiration may vary between observers.…”

Section: Discussionmentioning

confidence: 55%

Respiratory distress observation scales to predict weaning outcome

et al. 2022

View full text Add to dashboard Cite

Background Whether dyspnea is present before starting a spontaneous breathing trial (SBT) and whether it may affect the outcome of the SBT is unknown. Mechanical Ventilation—Respiratory Distress Observation Scale (MV-RDOS) has been proposed as a reliable surrogate of dyspnea in non-communicative intubated patients. In the present study, we sought (1) to describe the evolution of the MV-RDOS during a SBT and (2) to investigate whether MV-RDOS can predict the outcome of the SBT. Methods Prospective, single-center study in a twenty-two bed ICU in a tertiary center. Patients intubated since more 48 h who had failed a first SBT were eligible if they meet classical readiness to wean criteria. The MV-RDOS was assessed before, at 2-min, 15-min and 30-min (end) of the SBT. The presence of clinically important dyspnea was inferred by a MV-RDOS value ≥ 2.6. Results Fifty-eight patients (age 63 [51–70], SAPS II 66 [51–76]; med [IQR]) were included. Thirty-three (57%) patients failed the SBT, whose 18 (55%) failed before 15-min. Twenty-five (43%) patients successfully passed the SBT. A MV-RDOS ≥ 2.6 was present in ten (17%) patients before to start the SBT. All these ten patients subsequently failed the SBT. A MV-RDOS ≥ 2.6 at 2-min predicted a SBT failure with a 51% sensibility and a 88% specificity (AUC 0.741 95% confidence interval [CI] 0.616–0.866, p = 0.002). Best cut-off value at 2-min was 4.3 and predicted SBT failure with a 27% sensibility and a 96% specificity. Conclusion Despite patients met classical readiness to wean criteria, respiratory distress assessed with the MV-RDOS was frequent at the beginning of SBT. Measuring MV-RDOS before to initiate a SBT could avoid undue procedure and reduce patient’s exposure to unnecessary mechanical ventilation weaning failure and distress.

show abstract

The Usefulness of the Rapid Shallow Breathing Index in Predicting Successful Extubation

Cited by 45 publications

References 47 publications

The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms

The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms

What is new in respiratory monitoring?

Respiratory distress observation scales to predict weaning outcome

Contact Info

Product

Resources

About