Literature DB >> 31455561

Predictors of success of high-flow nasal cannula in the treatment of acute hypoxemic respiratory failure.

R Artacho Ruiz1, B Artacho Jurado2, F Caballero Güeto3, A Cano Yuste4, I Durbán García5, F García Delgado5, J A Guzmán Pérez5, M López Obispo6, I Quero Del Río7, F Rivera Espinar5, E Del Campo Molina5.   

Abstract

BACKGROUND: High-flow nasal cannula (HFNC) therapy is used in the treatment of acute respiratory failure (ARF) and is both safe and effective in reversing hypoxemia. In order to minimize mortality and clinical complications associated to this practice, a series of tools must be developed to allow early detection of failure. The present study was carried out to: (i)examine the impact of respiratory rate (RR), peripheral oxygen saturation (SpO2), ROX index (ROXI=[SpO2/FiO2]/RR) and oxygen inspired fraction (FiO2) on the success of HFNC in patients with hypoxemic ARF; and (ii)analyze the length of stay and mortality in the ICU, and the need for mechanical ventilation (MV).
METHODS: A retrospective study was carried out in the medical-surgical ICU of Hospital de Montilla (Córdoba, Spain). Patients diagnosed with hypoxemic ARF and treated with HFNC from January 2016 to January 2018 were included.
RESULTS: Out of 27 patients diagnosed with ARF, 19 (70.37%) had hypoxemic ARF. Fifteen of them (78.95%) responded satisfactorily to HFNC, while four (21.05%) failed. After two hours of treatment, RR proved to be the best predictor of success (area under the ROC curve [AUROC] 0.858; 95%CI: 0.63-1.05; P=.035). For this parameter, the optimal cutoff point was 29rpm (sensitivity 75%, specificity 87%). After 8hours of treatment, FiO2 and ROXI were reliable predictors of success (FiO2: AUROC 0.95; 95%CI: 0.85-1.04; P=.007 and ROXI: AUROC 0.967; 95%CI: 0.886-1.047; P=.005). In the case of FiO2 the optimal cutoff point was 0.59 (sensitivity 75%, specificity 93%), while the best cutoff point for ROXI was 5.98 (sensitivity 100%, specificity 75%). Using a Cox regression model, we found RR<29rpm after two hours of treatment, and FiO2<0.59 and ROXI>5.98 after 8hours of treatment, to be associated with a lesser risk of MV (RR: HR 0.103; 95%CI: 0.11-0.99; P=.05; FiO2: HR 0.053; 95%CI: 0.005-0.52; P=.012; and ROXI: HR 0.077; 95%CI: 0.008-0.755; P=.028, respectively).
CONCLUSIONS: RR after two hours of treatment, and FiO2 and ROXI after 8hours of treatment, were the best predictors of success of HFNC. RR<29rpm, FiO2<0.59 and ROXI>5.98 were associated with a lesser risk of MV.
Copyright © 2021. Publicado por Elsevier España, S.L.U.

Entities:  

Keywords:  Acute respiratory failure; Cánula nasal de alto flujo; Fallo respiratorio agudo; High-flow nasal cannula; Hipoxemia; Hypoxemia; Predictores de éxito; Predictors of success; ROX index; Índice ROX

Year:  2019        PMID: 31455561     DOI: 10.1016/j.medin.2019.07.012

Source DB:  PubMed          Journal:  Med Intensiva (Engl Ed)        ISSN: 2173-5727


  4 in total

1.  Prediction of high-flow nasal cannula outcomes at the early phase using the modified respiratory rate oxygenation index.

Authors:  Zhe Li; Chen Chen; Zhangjun Tan; Yulong Yao; Shunpeng Xing; Yan Li; Yuan Gao; Zhanqi Zhao; Yuxiao Deng; Mingli Zhu
Journal:  BMC Pulm Med       Date:  2022-06-13       Impact factor: 3.320

2.  Efficacy and Safety of Ivermectin and Hydroxychloroquine in Patients with Severe COVID-19: A Randomized Controlled Trial.

Authors:  Jose Lenin Beltran Gonzalez; Mario González Gámez; Emanuel Antonio Mendoza Enciso; Ramiro Josue Esparza Maldonado; Daniel Hernández Palacios; Samuel Dueñas Campos; Itzel Ovalle Robles; Mariana Jocelyn Macías Guzmán; Andrea Lucia García Díaz; César Mauricio Gutiérrez Peña; Lucila Martinez Medina; Victor Antonio Monroy Colin; Jose Manuel Arreola Guerra
Journal:  Infect Dis Rep       Date:  2022-03-03

3.  Validity of the ROX index in predicting invasive mechanical ventilation requirement in pneumonia.

Authors:  Luis F Reyes; Alirio Bastidas Goyes; Eduardo Andrés Tuta Quintero; Karen D Pedreros; Yesid F Mantilla; Manuela Herrera; Germán A Carmona; Laura D Saza; Laura E Bello; Carlos A Muñoz; Juan C Chaves; Jennifer C Arias; Paula M Alcaraz; María D Hernández; Alejandra P Nonzoque; Natalia Trujillo; Andrés F Pineda; Gina S Montaño
Journal:  BMJ Open Respir Res       Date:  2022-09

4.  Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19.

Authors:  Anoop R Kulkarni; Ambarish M Athavale; Ashima Sahni; Shashvat Sukhal; Abhimanyu Saini; Mathew Itteera; Sara Zhukovsky; Jane Vernik; Mohan Abraham; Amit Joshi; Amatur Amarah; Juan Ruiz; Peter D Hart; Hemant Kulkarni
Journal:  BMJ Innov       Date:  2021-03-02
  4 in total

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