AUTHOR=Bansal Vikas , Smischney Nathan J. , Kashyap Rahul , Li Zhuo , Marquez Alberto , Diedrich Daniel A. , Siegel Jason L. , Sen Ayan , Tomlinson Amanda D. , Venegas-Borsellino Carla P. , Freeman William David TITLE=Reintubation Summation Calculation: A Predictive Score for Extubation Failure in Critically Ill Patients JOURNAL=Frontiers in Medicine VOLUME=8 YEAR=2022 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.789440 DOI=10.3389/fmed.2021.789440 ISSN=2296-858X ABSTRACT=Objective

To derive and validate a multivariate risk score for the prediction of respiratory failure after extubation.

Patients and methods

We performed a retrospective cohort study of adult patients admitted to the intensive care unit from January 1, 2006, to December 31, 2015, who received mechanical ventilation for ≥48 h. Extubation failure was defined as the need for reintubation within 72 h after extubation. Multivariate logistic regression model coefficient estimates generated the Re-Intubation Summation Calculation (RISC) score.

Results

The 6,161 included patients were randomly divided into 2 sets: derivation (n = 3,080) and validation (n = 3,081). Predictors of extubation failure in the derivation set included body mass index <18.5 kg/m2 [odds ratio (OR), 1.91; 95% CI, 1.12–3.26; P = 0.02], threshold of Glasgow Coma Scale of at least 10 (OR, 1.68; 95% CI, 1.31–2.16; P < 0.001), mean airway pressure at 1 min of spontaneous breathing trial <10 cmH2O (OR, 2.11; 95% CI, 1.68–2.66; P < 0.001), fluid balance ≥1,500 mL 24 h preceding extubation (OR, 2.36; 95% CI, 1.87–2.96; P < 0.001), and total mechanical ventilation days ≥5 (OR, 3.94; 95% CI 3.04–5.11; P < 0.001). The C-index for the derivation and validation sets were 0.72 (95% CI, 0.70–0.75) and 0.72 (95% CI, 0.69–0.75). Multivariate logistic regression demonstrated that an increase of 1 in RISC score increased odds of extubation failure 1.6-fold (OR, 1.58; 95% CI, 1.47–1.69; P < 0.001).

Conclusion

RISC predicts extubation failure in mechanically ventilated patients in the intensive care unit using several clinically relevant variables available in the electronic medical record but requires a larger validation cohort before widespread clinical implementation.