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Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19.
Maia, Gabriel; Martins, Camila Marinelli; Marques, Victoria; Christovam, Samantha; Prado, Isabela; Moraes, Bruno; Rezoagli, Emanuele; Foti, Giuseppe; Zambelli, Vanessa; Cereda, Maurizio; Berra, Lorenzo; Rocco, Patricia Rieken Macedo; Cruz, Mônica Rodrigues; Samary, Cynthia Dos Santos; Guimarães, Fernando Silva; Silva, Pedro Leme.
Afiliação
  • Maia G; Laboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Centro de Ciências da Saúde, Federal University of Rio de Janeiro, Avenida Carlos Chagas Filho, 273, Bloco G-014, Ilha do Fundão, Rio de Janeiro, 21941-902, RJ, Brazil.
  • Martins CM; Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Marques V; AAC&T Research Consulting LTDA, Curitiba, Brazil.
  • Christovam S; Laboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Centro de Ciências da Saúde, Federal University of Rio de Janeiro, Avenida Carlos Chagas Filho, 273, Bloco G-014, Ilha do Fundão, Rio de Janeiro, 21941-902, RJ, Brazil.
  • Prado I; Department of Cardiorespiratory and Musculoskeletal Physiotherapy, Faculty of Physiotherapy, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Moraes B; Laboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Centro de Ciências da Saúde, Federal University of Rio de Janeiro, Avenida Carlos Chagas Filho, 273, Bloco G-014, Ilha do Fundão, Rio de Janeiro, 21941-902, RJ, Brazil.
  • Rezoagli E; Department of Cardiorespiratory and Musculoskeletal Physiotherapy, Faculty of Physiotherapy, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Foti G; Laboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Centro de Ciências da Saúde, Federal University of Rio de Janeiro, Avenida Carlos Chagas Filho, 273, Bloco G-014, Ilha do Fundão, Rio de Janeiro, 21941-902, RJ, Brazil.
  • Zambelli V; Department of Cardiorespiratory and Musculoskeletal Physiotherapy, Faculty of Physiotherapy, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Cereda M; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Berra L; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.
  • Rocco PRM; School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
  • Cruz MR; Department of Emergency and Intensive Care, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy.
  • Samary CDS; School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
  • Guimarães FS; Department of Emergency and Intensive Care, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy.
  • Silva PL; School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Ann Intensive Care ; 14(1): 129, 2024 Aug 21.
Article em En | MEDLINE | ID: mdl-39167241
ABSTRACT

BACKGROUND:

This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index.

METHODS:

A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong's algorithm. Models were validated externally using an international database.

RESULTS:

Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval) 1.46 (1.07-2.05), 0.81 (0.72-0.90), 9.13 (3.29-28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2-9.8] versus 9.6 [6.8-12.9], p < 0.001, respectively). In the external validation population, the area under the curve (AUC) of the ROX index was 0.683 (accuracy 63%), the AIC model showed an AUC of 0.703 (accuracy 69%), and the lowest AIC model with significant p values had an AUC of 0.725 (accuracy 79%).

CONCLUSIONS:

In the development population of ICU patients with COVID-19, SOFA score, SpO2, and respiratory effort signals predicted the need for IMV better than the ROX index. In the external validation population, although the AUCs did not differ significantly, the accuracy was higher when using SOFA score, SpO2, and respiratory effort signals compared to the ROX index. This suggests that these variables may be more useful in predicting the need for IMV in ICU patients with COVID-19. GOV IDENTIFIER NCT05663528.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Ann Intensive Care Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Ann Intensive Care Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha