A novel intubation prediction model for patients hospitalized with COVID-19: the OTO-COVID-19 scoring model

References

• Damarla M, Zaeh S, Niedermeyer S, et al. Prone positioning of nonintubated patients with COVID-19. Am J Respir Crit Care Med. 2020;202(4):604–606.
   PubMed Web of Science ®Google Scholar
• Caputo ND, Strayer RJ, Levitan R. Early self-proning in awake, non-intubated patients in the emergency department: a single ED's experience during the COVID-19 pandemic. Acad Emerg Med. 2020;27(5):375–378.
   PubMed Web of Science ®Google Scholar
• Jagan N, Morrow LE, Walters RW, et al. The POSITIONED study: prone positioning in nonventilated coronavirus disease 2019 patients—a retrospective analysis. Crit Care Explor. 2020;2(10):e0229.
   PubMedGoogle Scholar
• Thompson AE, Ranard BL, Wei Y, et al. Prone positioning in awake, nonintubated patients with COVID-19 hypoxemic respiratory failure. JAMA Intern Med. 2020;180(11):1537–1539.
   PubMed Web of Science ®Google Scholar
• Parish AJ, West JR, Caputo ND, et al. Early intubation and increased coronavirus disease 2019 mortality: a propensity score-matched retrospective cohort study. Crit Care Explor. 2021;3(6):e0452.
   PubMedGoogle Scholar
• Grasselli G, Zangrillo A, Zanella A, COVID-19 Lombardy ICU Network, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA. 2020;323(16):1574–1581 (Erratum in: JAMA. 2021;325:1581).
   PubMed Web of Science ®Google Scholar
• Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8(5):475–481. (Erratum in: Lancet Respir Med. 2020;8:e26).
   PubMed Web of Science ®Google Scholar
• Richardson S, Hirsch JS, Narasimhan M, The Northwell COVID-19 Research Consortium, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city area. JAMA. 2020;323(20):2052–2059 (Erratum in: JAMA. 2020;323:2098).
   PubMed Web of Science ®Google Scholar
• Arentz M, Yim E, Klaff L, et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington state. JAMA. 2020;323(16):1612–1614.
   PubMed Web of Science ®Google Scholar
• Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel Coronavirus-Infected pneumonia in wuhan, China. JAMA. 2020;323(11):1061–1069 (Erratum in: JAMA. 2021;325:1113).
   PubMed Web of Science ®Google Scholar
• Cummings MJ, Baldwin MR, Abrams D, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York city: a prospective cohort study. Lancet. 2020;395(10239):1763–1770.
   PubMed Web of Science ®Google Scholar
• Brewster DJ, Chrimes N, Do TB, et al. Consensus statement: Safe airway society principles of airway management and tracheal intubation specific to the COVID-19 adult patient group. Med J Aust. 2020;212(10):472–481.
   PubMed Web of Science ®Google Scholar
• Schünemann HJ, Khabsa J, Solo K, et al. Ventilation techniques and risk for transmission of coronavirus disease, including COVID-19: a living systematic review of multiple streams of evidence. Ann Intern Med. 2020;173(3):204–216.
   PubMed Web of Science ®Google Scholar
• Rice TW, Janz DR. In defense of evidence-based medicine for the treatment of COVID-19 acute respiratory distress syndrome. Ann Am Thorac Soc. 2020;17(7):787–789.
   PubMed Web of Science ®Google Scholar
• Roca O, Caralt B, Messika J, et al. An index combining respiratory rate and oxygenation to predict outcome of nasal high-flow therapy. Am J Respir Crit Care Med. 2019;199(11):1368–1376.
   PubMed Web of Science ®Google Scholar
• Bhatraju PK, Ghassemieh BJ, Nichols M, et al. Covid-19 in critically ill patients in the Seattle region – case series. N Engl J Med. 2020;382(21):2012–2022.
   PubMed Web of Science ®Google Scholar
• Hosmer DW, Jr. Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken (NJ): John Wiley & Sons, Inc; 2013.
   Google Scholar
• Hébert M, Collin-Vézina D, Daigneault I, et al. Factors linked to outcomes in sexually abused girls: a regression tree analysis. Compr Psychiatry. 2006;47(6):443–455.
   PubMed Web of Science ®Google Scholar
• Witten H, Frank E. Data mining: Practical machine learning tools and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann Publisher; 2005.
   Google Scholar
• Maimon O, Rokach L. Data mining and knowledge discovery handbook. New York (NY): Springer; 2005.
   Google Scholar
• Gong J, Ou J, Qiu X, et al. A tool for early prediction of severe coronavirus disease 2019 (COVID-19) : a multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin Infect Dis. 2020;71(15):833–840.
   PubMed Web of Science ®Google Scholar
• Patel M, Chowdhury J, Mills N, et al. ROX index predicts intubation in patients with COVID-19 pneumonia and moderate to severe hypoxemic respiratory failure receiving high flow nasal therapy. Am J Respir Crit Care Med. 2021;203:A2624.
   Web of Science ®Google Scholar
• Yang K, Sheng Y, Huang C, et al. Clinical characteristics, outcomes, and risk factors for mortality in patients with cancer and COVID-19 in Hubei, China: a multicentre, retrospective, cohort study. Lancet Oncol. 2020;21(7):904–913.
   PubMed Web of Science ®Google Scholar
• Tian J, Yuan X, Xiao J, et al. Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study. Lancet Oncol. 2020;21:898–903.
   Web of Science ®Google Scholar
• Suliman LA, Abdelgawad TT, Farrag NS, et al. Validity of ROX index in prediction of risk of intubation in patients with COVID-19 pneumonia. Adv Respir Med. 2021;89(1):1–7.
   PubMed Web of Science ®Google Scholar
• Downing J, Cardona S, Alfalasi R, et al. Predictors of intubation in COVID-19 patients undergoing awake proning in the emergency department. Am J Emerg Med. 2021;49:276–286.
   PubMed Web of Science ®Google Scholar
• Arac S, Ozel M. A new parameter for predict the clinical outcome of patients with COVID-19 pneumonia: the direct/total bilirubin ratio. Int J Clin Pract. 2021;75:e14557.
   PubMed Web of Science ®Google Scholar
• Khandelwal A, Singh GP, Rath GP, et al. The "COVID-19 score" can predict the need for tracheal intubation in critically ill COVID-19 patients – a hypothesis. Med Hypotheses. 2020;144:110292.
   PubMed Web of Science ®Google Scholar
• Shang Y, Liu T, Wei Y, et al. Scoring systems for predicting mortality for severe patients with COVID-19. EClinicalMedicine. 2020;24:100426.
   PubMedGoogle Scholar
• Zhang C, Qin L, Li K, et al. A novel scoring system for prediction of disease severity in COVID-19. Front Cell Infect Microbiol. 2020;10(318):318.
   PubMed Web of Science ®Google Scholar
• Dong Y, Zhou H, Li M, et al. A novel simple scoring model for predicting severity of patients with SARS-CoV-2 infections. Transbound Emerg Dis. 2020;67(6):2823–2829.
   PubMed Web of Science ®Google Scholar
• Bellos I, Lourida P, Argyraki A, et al. Development of a novel risk score for the prediction of critical illness amongst COVID-19 patients. Int J Clin Pract. 2021;75(4):e13915.
   PubMed Web of Science ®Google Scholar
• Mejía-Vilet JM, Córdova-Sánchez BM, Fernández-Camargo DA, et al. A risk score to predict admission to the intensive care unit in patients with COVID-19: the ABC-GOALS score. Salud Publica Mex. 2020;63(1, ene-feb):1–11.
   PubMed Web of Science ®Google Scholar
• Ji D, Zhang D, Xu J, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL score. Clin Infect Dis. 2020;71(6):1393–1399.
   PubMed Web of Science ®Google Scholar
• Liang W, Liang H, Ou L, China Medical Treatment Expert Group for COVID-19, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180(8):1081–1089.
   PubMed Web of Science ®Google Scholar
• Garcia-Gordillo JA, Camiro-Zúñiga A, Aguilar-Soto M, ARMII Study Group, et al. COVID-IRS: a novel predictive score for risk of invasive mechanical ventilation in patients with COVID-19. PLOS One. 2021;16(4):e0248357.
   PubMed Web of Science ®Google Scholar
• Qin L, Yang Y, Cao Q, et al. A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19. Eur Radiol. 2020;30(12):6797–6807.
   PubMed Web of Science ®Google Scholar
• Miller JL, Tada M, Goto M, et al. Prediction models for severe manifestations and mortality due to COVID-19: a systematic review. Acad Emerg Med. 2022;29(2):206–216.
   PubMed Web of Science ®Google Scholar
• Bolourani S, Brenner M, Wang P, Northwell COVID-19 Research Consortium, et al. A machine learning prediction model of respiratory failure within 48 hours of patient admission for COVID-19: model development and validation. J Med Internet Res. 2021;23(2):e24246.
   PubMed Web of Science ®Google Scholar