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Esteban, A. et al. Evolution of mortality over time in patients receiving mechanical ventilation. Am. J. Respir. Crit. Care Med. 188, 220230. https://doi.org/10.1164/rccm.201212-2169OC (2013).

Article PubMed Google Scholar

Divo, M. J., Murray, S., Cortopassi, F. & Celli, B. R. Prolonged mechanical ventilation in Massachusetts: The 2006 prevalence survey. Respir. Care 55, 16931698 (2010).

PubMed Google Scholar

Hsu, C. L. et al. Timing of tracheostomy as a determinant of weaning success in critically ill patients: A retrospective study. Crit. Care 9, R46-52. https://doi.org/10.1186/cc3018 (2005).

Article PubMed Google Scholar

Wang, C. H. et al. Predictive factors of in-hospital mortality in ventilated intensive care unit: A prospective cohort study. Medicine (Baltimore) 96, e9165. https://doi.org/10.1097/md.0000000000009165 (2017).

Article MathSciNet PubMed Google Scholar

Clark, P. A. & Lettieri, C. J. Clinical model for predicting prolonged mechanical ventilation. J. Crit. Care 28, 880.e881-880.e887 (2013).

Article Google Scholar

Sheikhbardsiri, H., Esamaeili Abdar, Z., Sheikhasadi, H., Ayoubi Mahani, S. & Sarani, A. Observance of patients rights in emergency department of educational hospitals in south-east Iran. Int. J. Hum. Rights Healthcare. 13, 435444 (2020).

Article Google Scholar

Parreco, J., Hidalgo, A., Parks, J. J., Kozol, R. & Rattan, R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J. Surg. Res. 228, 179187 (2018).

Article PubMed Google Scholar

Agle, S. C. et al. Early predictors of prolonged mechanical ventilation in major torso trauma patients who require resuscitation. Am. J. Surg. 192, 822827 (2006).

Article PubMed Google Scholar

Dimopoulou, I. et al. Prediction of prolonged ventilatory support in blunt thoracic trauma patients. Intensive Care Med. 29, 11011105 (2003).

Article PubMed Google Scholar

Figueroa-Casas, J. B. et al. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J. Crit. Care 30, 502505 (2015).

Article PubMed Google Scholar

Davarani, E. R., Tavan, A., Amiri, H. & Sahebi, A. Response capability of hospitals to an incident caused by mass gatherings in southeast Iran. Injury 53, 17221726 (2022).

Article PubMed Google Scholar

Young, D., Harrison, D. A., Cuthbertson, B. H. & Rowan, K. Effect of early vs late tracheostomy placement on survival in patients receiving mechanical ventilation: The TracMan randomized trial. JAMA 309, 21212129. https://doi.org/10.1001/jama.2013.5154 (2013).

Article CAS PubMed Google Scholar

Gomes Silva, B. N., Andriolo, R. B., Saconato, H., Atallah, A. N. & Valente, O. Early versus late tracheostomy for critically ill patients. Cochrane Database Syst. Rev. 3 144 (2012).

Google Scholar

Rose, L. et al. Variation in definition of prolonged mechanical ventilation. Respir. Care 62, 13241332 (2017).

Article PubMed Google Scholar

Clark, P. A. & Lettieri, C. J. Clinical model for predicting prolonged mechanical ventilation. J. Crit. Care 28, 880-e881 (2013).

Article Google Scholar

Brook, A. D., Sherman, G., Malen, J. & Kollef, M. H. Early versus late tracheostomy in patients who require prolonged mechanical ventilation. Am. J. Crit. Care 9, 352 (2000).

Article CAS PubMed Google Scholar

Chang, Y.-C. et al. Ventilator dependence risk score for the prediction of prolonged mechanical ventilation in patients who survive sepsis/septic shock with respiratory failure. Sci. Rep. 8, 111 (2018).

ADS Google Scholar

Lone, N. I. & Walsh, T. S. Prolonged mechanical ventilation in critically ill patients: Epidemiology, outcomes and modelling the potential cost consequences of establishing a regional weaning unit. Crit. Care 15, 110 (2011).

Article Google Scholar

Dunn, H. et al. Mobilization of prolonged mechanical ventilation patients: An integrative review. Heart Lung 46, 221233. https://doi.org/10.1016/j.hrtlng.2017.04.033 (2017).

Article PubMed PubMed Central Google Scholar

Abujaber, A. et al. Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach. PLoS ONE 15, e0235231 (2020).

Article CAS PubMed PubMed Central Google Scholar

Zolbanin, H. M., Delen, D. & Zadeh, A. H. Predicting overall survivability in comorbidity of cancers: A data mining approach. Decis. Support Syst. 74, 150161 (2015).

Article Google Scholar

Shaikhina, T. et al. Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed. Signal Process. Control 52, 456462 (2019).

Article ADS Google Scholar

Archer, K. J. & Kimes, R. V. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 22492260 (2008).

Article MathSciNet MATH Google Scholar

Dag, A., Oztekin, A., Yucel, A., Bulur, S. & Megahed, F. M. Predicting heart transplantation outcomes through data analytics. Decis. Support Syst. 94, 4252 (2017).

Article Google Scholar

Cui, S., Wang, D., Wang, Y., Yu, P.-W. & Jin, Y. An improved support vector machine-based diabetic readmission prediction. Comput. Methods Programs Biomed. 166, 123135 (2018).

Article PubMed Google Scholar

Hale, A. T. et al. Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury. Neurosurg. Focus 45, E2 (2018).

Article PubMed Google Scholar

Shi, H.-Y., Hwang, S.-L., Lee, K.-T. & Lin, C.-L. In-hospital mortality after traumatic brain injury surgery: A nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models. J. Neurosurg. 118, 746752 (2013).

Article PubMed Google Scholar

Das, A. et al. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: Internal and external validation of a predictive model. Lancet 362, 12611266 (2003).

Article PubMed Google Scholar

Han, J., Kamber, M. & Pei, J. Data Mining Concepts and Techniques 3rd edn. (University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University, 2012).

MATH Google Scholar

Zolbanin, H. M., Delen, D. & Zadeh, A. H. Predicting overall survivability in comorbidity of cancers: A data mining approach. Decis Support Syst 74, 150161 (2015).

Article Google Scholar

Lakshmi, B. N., Indumathi, T. S. & Ravi, N. A study on C.5 decision tree classification algorithm for risk predictions during pregnancy. Procedia Technol. 24, 15421549 (2016).

Article Google Scholar

Rivers, E. et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N. Engl. J. Med. 345, 13681377 (2001).

Article CAS PubMed Google Scholar

Weil, M. H. Functional Hemodynamic Monitoring 917 (Springer, 2005).

Book Google Scholar

Sevransky, J. Clinical assessment of hemodynamically unstable patients. Curr. Opin. Crit. Care 15, 234 (2009).

Article PubMed PubMed Central Google Scholar

Scheeren, T. W. L. et al. Current use of vasopressors in septic shock. Ann. Intensive Care 9, 112 (2019).

Article Google Scholar

Hidalgo, D. C., Patel, J., Masic, D., Park, D. & Rech, M. A. Delayed vasopressor initiation is associated with increased mortality in patients with septic shock. J. Crit. Care 55, 145148 (2020).

Article Google Scholar

Li, Y., Li, H. & Zhang, D. Timing of norepinephrine initiation in patients with septic shock: a systematic review and meta-analysis. Crit. Care 24, 19 (2020).

Article Google Scholar

Sellers, B. J., Davis, B. L., Larkin, P. W., Morris, S. E. & Saffle, J. R. Early prediction of prolonged ventilator dependence in thermally injured patients. J. Trauma 43, 899903 (1997).

Article CAS PubMed Google Scholar

Rachmale, S., Li, G., Wilson, G., Malinchoc, M. & Gajic, O. Practice of excessive FiO2 and effect on pulmonary outcomes in mechanically ventilated patients with acute lung injury. Respir. Care 57, 18871893 (2012).

Article PubMed Google Scholar

de Jonge, E. et al. Association between administered oxygen, arterial partial oxygen pressure and mortality in mechanically ventilated intensive care unit patients. Crit. Care 12, 18 (2008).

Article Google Scholar

Esan, A., Hess, D. R., Raoof, S., George, L. & Sessler, C. N. Severe hypoxemic respiratory failure: Part 1Ventilatory strategies. Chest 137, 12031216 (2010).

Article PubMed Google Scholar

Gajic, O. et al. Prediction of death and prolonged mechanical ventilation in acute lung injury. Crit. Care 11, 17 (2007).

Article Google Scholar

Seeley, E. et al. Predictors of mortality in acute lung injury during the era of lung protective ventilation. Thorax 63, 994998 (2008).

Article CAS PubMed Google Scholar

Nash, G., Blennerhassett, J. B. & Pontoppidan, H. Pulmonary lesions associated with oxygen therapy and artificial ventilation. Laval. Med. 276, 368374 (1967).

CAS Google Scholar

Ghauri, S. K., Javaeed, A., Mustafa, K. J. & Khan, A. S. Predictors of prolonged mechanical ventilation in patients admitted to intensive care units: A systematic review. Int. J. Health Sci. (Qassim) 13, 3138 (2019).

PubMed Google Scholar

Pu, L. et al. Weaning critically ill patients from mechanical ventilation: A prospective cohort study. J. Crit. Care 30, 862.e867813. https://doi.org/10.1016/j.jcrc.2015.04.001 (2015).

Article Google Scholar

Sellares, J. et al. Predictors of prolonged weaning and survival during ventilator weaning in a respiratory ICU. Intensive Care Med. 37, 775784. https://doi.org/10.1007/s00134-011-2179-3 (2011).

Article PubMed Google Scholar

Clark, P. A. & Lettieri, C. J. Clinical model for predicting prolonged mechanical ventilation. J. Crit. Care 28(880), e881-887. https://doi.org/10.1016/j.jcrc.2013.03.013 (2013).

Article Google Scholar

Clark, P. A., Inocencio, R. C. & Lettieri, C. J. I-TRACH: Validating a tool for predicting prolonged mechanical ventilation. J. Intensive Care Med. 33, 567573. https://doi.org/10.1177/0885066616679974 (2018).

Article PubMed Google Scholar

Rojek-Jarmua, A., Hombach, R. & Krzych, J. APACHE II score cannot predict successful weaning from prolonged mechanical ventilation. Chron. Respir. Dis. 14, 270275. https://doi.org/10.1177/1479972316687100 (2017).

Article PubMed PubMed Central Google Scholar

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