Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial
This work is licensed under the Creative Commons Attribution 4.0 International License.
Toussaint M, van Hove O, Leduc D, et al. Invasive versus noninvasive paediatric home mechanical ventilation: review of the international evolution over the past 24 years. Thorax. 2024;79(6):581–588.ToussaintMvan HoveOLeducDInvasive versus noninvasive paediatric home mechanical ventilation: review of the international evolution over the past 24 yearsThorax2024796581588Search in Google Scholar
Szafran JC, Patel BK. Invasive Mechanical Ventilation. Crit Care Clin. 2024;40(2):255–273.SzafranJCPatelBKInvasive Mechanical VentilationCrit Care Clin2024402255273Search in Google Scholar
Dolinay T, Hsu L, Maller A, et al. Ventilator Weaning in Prolonged Mechanical Ventilation-A Narrative Review. J Clin Med. 2024;13(7):1909.DolinayTHsuLMallerAVentilator Weaning in Prolonged Mechanical Ventilation-A Narrative ReviewJ Clin Med20241371909Search in Google Scholar
Marinaki C, Kapadochos T, Katsoulas T, et al. Estimation of the optimal time needed for weaning of Intensive Care Unit tracheostomized patients on mechanical ventilation. A prospective observational study. Acta Biomed. 2023;94(2):e2023103.MarinakiCKapadochosTKatsoulasTEstimation of the optimal time needed for weaning of Intensive Care Unit tracheostomized patients on mechanical ventilation. A prospective observational studyActa Biomed2023942e2023103Search in Google Scholar
Pham T, Heunks L, Bellani G, et al. Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study. Lancet Respir Med. 2023;11(5):465–476.PhamTHeunksLBellaniGWeaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort studyLancet Respir Med2023115465476Search in Google Scholar
Boles JM, Bion J, Connors A, et al. Weaning from mechanical ventilation. Eur Respir J. 2007;29(5):1033–1056.BolesJMBionJConnorsAWeaning from mechanical ventilationEur Respir J200729510331056Search in Google Scholar
Varón-Vega F, Giraldo-Cadavid LF, Uribe AM, et al. Utilization of spontaneous breathing trial, objective cough test, and diaphragmatic ultrasound results to predict extubation success: COBRE-US trial. Crit Care. 2023;27(1):414.Varón-VegaFGiraldo-CadavidLFUribeAMUtilization of spontaneous breathing trial, objective cough test, and diaphragmatic ultrasound results to predict extubation success: COBRE-US trialCrit Care2023271414Search in Google Scholar
Igarashi Y, Ogawa K, Nishimura K, Osawa S, Ohwada H, Yokobori S. Machine learning for predicting successful extubation in patients receiving mechanical ventilation. Front Med (Lausanne). 2022;9:961252.IgarashiYOgawaKNishimuraKOsawaSOhwadaHYokoboriSMachine learning for predicting successful extubation in patients receiving mechanical ventilationFront Med (Lausanne)20229961252Search in Google Scholar
Hur S, Min JY, Yoo J, et al. Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study. J Med Internet Res. 2021;23(8):e23508.HurSMinJYYooJDevelopment and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning StudyJ Med Internet Res2021238e23508Search in Google Scholar
Liu CF, Hung CM, Ko SC, et al. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne). 2022;9:935366.LiuCFHungCMKoSCAn artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approachFront Med (Lausanne)20229935366Search in Google Scholar
Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med. 2024;13(5):1505.StiviTPadawerDDiriniNNachshonABatzofinBMLedotSUsing Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress SyndromeJ Clin Med20241351505Search in Google Scholar
Chen T, Xu J, Ying H, et al. Prediction of extubation failure for intensive care unit patients using light gradient boosting machine. IEEE Access. 2019;7:150960–8.ChenTXuJYingHPrediction of extubation failure for intensive care unit patients using light gradient boosting machineIEEE Access201971509608Search in Google Scholar
Fabregat A, Magret M, Ferré JA, et al. A Machine Learning decision-making tool for extubation in Intensive Care Unit patients. Comput Methods Programs Biomed. 2021;200:105869.FabregatAMagretMFerréJAA Machine Learning decision-making tool for extubation in Intensive Care Unit patientsComput Methods Programs Biomed2021200105869Search in Google Scholar
Otaguro T, Tanaka H, Igarashi Y, et al. Machine learning for prediction of successful extubation of mechanical ventilated patients in an intensive care unit: a retrospective observational study. J Nippon Med Sch. 2021;88:408–17.OtaguroTTanakaHIgarashiYMachine learning for prediction of successful extubation of mechanical ventilated patients in an intensive care unit: a retrospective observational studyJ Nippon Med Sch20218840817Search in Google Scholar
Zhao QY, Wang H, Luo JC, et al. Development and validation of a machine-learning model for prediction of extubation failure in intensive care units. Front Med. 2021;8:676343.ZhaoQYWangHLuoJCDevelopment and validation of a machine-learning model for prediction of extubation failure in intensive care unitsFront Med20218676343Search in Google Scholar
Fleuren LM, Dam TA, Tonutti M, et al. Predictors for extubation failure in COVID-19 patients using a machine learning approach. Crit Care. 2021;25:448.FleurenLMDamTATonuttiMPredictors for extubation failure in COVID-19 patients using a machine learning approachCrit Care202125448Search in Google Scholar
Thille AW, Gacouin A, Coudroy R, et al. Spontaneous-Breathing Trials with Pressure-Support Ventilation or a T-Piece. N Engl J Med. 2022;387(20):1843–1854.ThilleAWGacouinACoudroyRSpontaneous-Breathing Trials with Pressure-Support Ventilation or a T-PieceN Engl J Med20223872018431854Search in Google Scholar
Varón-Vega F, Rincón A, Giraldo-Cadavid LF, et al. Assessing the reproducibility and predictive value of objective cough measurement for successful withdrawal of invasive ventilatory support in adult patients. BMC Pulm Med. 2024;24(1):218.Varón-VegaFRincónAGiraldo-CadavidLFAssessing the reproducibility and predictive value of objective cough measurement for successful withdrawal of invasive ventilatory support in adult patientsBMC Pulm Med2024241218Search in Google Scholar
Goligher EC, Laghi F, Detsky ME, et al. Measuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validity. Intensive Care Med. 2015;41(4):642–649.GoligherECLaghiFDetskyMEMeasuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validityIntensive Care Med2015414642649Search in Google Scholar
Vivier E, Muller M, Putegnat JB, et al. Inability of diaphragm ultrasound to predict extubation failure: a multicenter study. Chest. 2019;155(6):1131–1139.VivierEMullerMPutegnatJBInability of diaphragm ultrasound to predict extubation failure: a multicenter studyChest2019155611311139Search in Google Scholar
Matamis D, Soilemezi E, Tsagourias M, et al. Sonographic evaluation of the diaphragm in critically ill patients. Technique and clinical applications. Intensive Care Med. 2013;39(5):801–810.MatamisDSoilemeziETsagouriasMSonographic evaluation of the diaphragm in critically ill patientsTechnique and clinical applications. Intensive Care Med2013395801810Search in Google Scholar
Berrar D. Cross-validation. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Elsevier; 2018. p. 542–5.BerrarDCross-validationIn:Encyclopedia of Bioinformatics and Computational Biology: ABC of BioinformaticsElsevier20185425Search in Google Scholar
Šimundić AM. Measures of diagnostic accuracy: basic definitions. EJIFCC. 2009;19(4):203–11.ŠimundićAMMeasures of diagnostic accuracy: basic definitionsEJIFCC200919420311Search in Google Scholar
Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression, 3rd edn. Hosmer DW, Lemeshow S, Sturdivant RX, editors. New York: Wiley; 2013; 2013.HosmerDWLemeshowSSturdivantRXApplied logistic regression3rd ednHosmerDWLemeshowSSturdivantRXeditors.New YorkWiley20132013.Search in Google Scholar
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381.HarrisPATaylorRThielkeRPayneJGonzalezNCondeJGResearch electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics supportJ Biomed Inform2009422377381Search in Google Scholar
Chen JH, Asch SM. Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. N Engl J Med. 2017;376(26):2507–2509.ChenJHAschSMMachine Learning and Prediction in Medicine - Beyond the Peak of Inflated ExpectationsN Engl J Med20173762625072509Search in Google Scholar
Shamout F, Zhu T, Clifton DA. Machine Learning for Clinical Outcome Prediction. IEEE Rev Biomed Eng. 2021;14:116–126.ShamoutFZhuTCliftonDAMachine Learning for Clinical Outcome PredictionIEEE Rev Biomed Eng202114116126Search in Google Scholar
Huang Y, Guo J, Chen WH, et al. A scoping review of fair machine learning techniques when using real-world data. J Biomed Inform. 2024;151:104622.HuangYGuoJChenWHA scoping review of fair machine learning techniques when using real-world dataJ Biomed Inform2024151104622Search in Google Scholar
Ono S. Building a better machine learning model of extubation for neurocritical care patients. Intensive Care Med. 2023 Jan;49(1):119–120.OnoSBuilding a better machine learning model of extubation for neurocritical care patientsIntensive Care Med.2023Jan491119120Search in Google Scholar
Park JE, Kim DY, Park JW, et al. Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials. Bioengineering (Basel). 2023;10(10):1163.ParkJEKimDYParkJWDevelopment of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing TrialsBioengineering (Basel)202310101163Search in Google Scholar
Liao KM, Ko SC, Liu CF, et al. Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers. Diagnostics (Basel). 2022;12(4):975.LiaoKMKoSCLiuCFDevelopment of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care CentersDiagnostics (Basel)2022124975Search in Google Scholar
Maldonado-Franco A, Giraldo-Cadavid LF, Tuta-Quintero E, Cagy M, Bastidas Goyes AR, Botero-Rosas DA. Curve-Modelling and Machine Learning for a Better COPD Diagnosis. Int J Chron Obstruct Pulmon Dis. 2024;19:1333–1343.Maldonado-FrancoAGiraldo-CadavidLFTuta-QuinteroECagyMBastidas GoyesARBotero-RosasDACurve-Modelling and Machine Learning for a Better COPD DiagnosisInt J Chron Obstruct Pulmon Dis20241913331343Search in Google Scholar
Hudson DL, Cohen ME. Neural networks and artificial intelligence for biomedical engineering. Nueva York, NY, EE. UU.: IEEE; 2000.HudsonDLCohenMENeural networks and artificial intelligence for biomedical engineeringNueva York, NY, EE. UU.IEEE2000Search in Google Scholar