Alejandro Rodríguez1, Cristina Ferri2, Ignacio Martin-Loeches3, Emili Díaz4, Joan R Masclans5, Federico Gordo6, Jordi Sole-Violán7, María Bodí2, Francesc X Avilés-Jurado8, Sandra Trefler2, Monica Magret2, Gerard Moreno2, Luis F Reyes9, Judith Marin-Corral5, Juan C Yebenes10, Andres Esteban11, Antonio Anzueto9, Stefano Aliberti12, Marcos I Restrepo9. 1. Critical Care Department, Hospital Universitari de Tarragona Joan XXIII/IISPV/URV/CIBERes, Tarragona, Spain. ahr1161@yahoo.es. 2. Critical Care Department, Hospital Universitari de Tarragona Joan XXIII/IISPV/URV/CIBERes, Tarragona, Spain. 3. Multidisciplinary Intensive Care Research Organization (MICRO), Department of Anaesthesia and Critical Care, St James's University Hospital, Trinity Centre for Health Sciences, Dublin, Ireland. 4. Critical Care Department, ParcTaulí Hospital/CIBERes, Sabadell, Spain. 5. Critical Care Department, Hospital del Mar, IMIM/UPF/CIBERes, Barcelona, Spain. 6. Critical Care Department, Hospital del Henares, Madrid, Spain. 7. Critical Care Department, Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain. 8. Otorhinolaryngology Head-Neck Surgery Department, Hospital Universitari de Tarragona Joan XXIII, IISPV/URV, Tarragona, Catalonia, Spain. 9. Division of Pulmonary Diseases and Critical Care Medicine University of Texas Health Science Center at San Antonio and South Texas Veterans Health Care System, San Antonio, Texas. 10. Critical Care Department, Hospital de Mataró, Mataró, Spain. 11. Critical Care Department, Hospital de Getafe, CIBERes, Madrid, Spain. 12. School of Medicine and Surgery University of Milan Bicocca San Gerardo Hospital, Monza, Italy.
Abstract
BACKGROUND: Despite wide use of noninvasive ventilation (NIV) in several clinical settings, the beneficial effects of NIV in patients with hypoxemic acute respiratory failure (ARF) due to influenza infection remain controversial. The aim of this study was to identify the profile of patients with risk factors for NIV failure using chi-square automatic interaction detection (CHAID) analysis and to determine whether NIV failure is associated with ICU mortality. METHODS: This work was a secondary analysis from prospective and observational multi-center analysis in critically ill subjects admitted to the ICU with ARF due to influenza infection requiring mechanical ventilation. Three groups of subjects were compared: (1) subjects who received NIV immediately after ICU admission for ARF and then failed (NIV failure group); (2) subjects who received NIV immediately after ICU admission for ARF and then succeeded (NIV success group); and (3) subjects who received invasive mechanical ventilation immediately after ICU admission for ARF (invasive mechanical ventilation group). Profiles of subjects with risk factors for NIV failure were obtained using CHAID analysis. RESULTS: Of 1,898 subjects, 806 underwent NIV, and 56.8% of them failed. Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, infiltrates in chest radiograph, and ICU mortality (38.4% vs 6.3%) were higher (P < .001) in the NIV failure than in the NIV success group. SOFA score was the variable most associated with NIV failure, and 2 cutoffs were determined. Subjects with SOFA ≥ 5 had a higher risk of NIV failure (odds ratio = 3.3, 95% CI 2.4-4.5). ICU mortality was higher in subjects with NIV failure (38.4%) compared with invasive mechanical ventilation subjects (31.3%, P = .018), and NIV failure was associated with increased ICU mortality (odds ratio = 11.4, 95% CI 6.5-20.1). CONCLUSIONS: An automatic and non-subjective algorithm based on CHAID decision-tree analysis can help to define the profile of patients with different risks of NIV failure, which might be a promising tool to assist in clinical decision making to avoid the possible complications associated with NIV failure.
BACKGROUND: Despite wide use of noninvasive ventilation (NIV) in several clinical settings, the beneficial effects of NIV in patients with hypoxemic acute respiratory failure (ARF) due to influenza infection remain controversial. The aim of this study was to identify the profile of patients with risk factors for NIV failure using chi-square automatic interaction detection (CHAID) analysis and to determine whether NIV failure is associated with ICU mortality. METHODS: This work was a secondary analysis from prospective and observational multi-center analysis in critically ill subjects admitted to the ICU with ARF due to influenza infection requiring mechanical ventilation. Three groups of subjects were compared: (1) subjects who received NIV immediately after ICU admission for ARF and then failed (NIV failure group); (2) subjects who received NIV immediately after ICU admission for ARF and then succeeded (NIV success group); and (3) subjects who received invasive mechanical ventilation immediately after ICU admission for ARF (invasive mechanical ventilation group). Profiles of subjects with risk factors for NIV failure were obtained using CHAID analysis. RESULTS: Of 1,898 subjects, 806 underwent NIV, and 56.8% of them failed. Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, infiltrates in chest radiograph, and ICU mortality (38.4% vs 6.3%) were higher (P < .001) in the NIV failure than in the NIV success group. SOFA score was the variable most associated with NIV failure, and 2 cutoffs were determined. Subjects with SOFA ≥ 5 had a higher risk of NIV failure (odds ratio = 3.3, 95% CI 2.4-4.5). ICU mortality was higher in subjects with NIV failure (38.4%) compared with invasive mechanical ventilation subjects (31.3%, P = .018), and NIV failure was associated with increased ICU mortality (odds ratio = 11.4, 95% CI 6.5-20.1). CONCLUSIONS: An automatic and non-subjective algorithm based on CHAID decision-tree analysis can help to define the profile of patients with different risks of NIV failure, which might be a promising tool to assist in clinical decision making to avoid the possible complications associated with NIV failure.
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Authors: Shadman Aziz; Yaseen M Arabi; Waleed Alhazzani; Laura Evans; Giuseppe Citerio; Katherine Fischkoff; Jorge Salluh; Geert Meyfroidt; Fayez Alshamsi; Simon Oczkowski; Elie Azoulay; Amy Price; Lisa Burry; Amy Dzierba; Andrew Benintende; Jill Morgan; Giacomo Grasselli; Andrew Rhodes; Morten H Møller; Larry Chu; Shelly Schwedhelm; John J Lowe; Du Bin; Michael D Christian Journal: Intensive Care Med Date: 2020-06-08 Impact factor: 41.787