Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction
<b>Background/Objectives</b>: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV fail...
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2024-12-01
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| author | Maria Vittoria Chiaruttini Giulia Lorenzoni Marco Daverio Luca Marchetto Francesca Izzo Giovanna Chidini Enzo Picconi Claudio Nettuno Elisa Zanonato Raffaella Sagredini Emanuele Rossetti Maria Cristina Mondardini Corrado Cecchetti Pasquale Vitale Nicola Alaimo Denise Colosimo Francesco Sacco Giulia Genoni Daniela Perrotta Camilla Micalizzi Silvia Moggia Giosuè Chisari Immacolata Rulli Andrea Wolfler Angela Amigoni Dario Gregori |
| author_facet | Maria Vittoria Chiaruttini Giulia Lorenzoni Marco Daverio Luca Marchetto Francesca Izzo Giovanna Chidini Enzo Picconi Claudio Nettuno Elisa Zanonato Raffaella Sagredini Emanuele Rossetti Maria Cristina Mondardini Corrado Cecchetti Pasquale Vitale Nicola Alaimo Denise Colosimo Francesco Sacco Giulia Genoni Daniela Perrotta Camilla Micalizzi Silvia Moggia Giosuè Chisari Immacolata Rulli Andrea Wolfler Angela Amigoni Dario Gregori |
| author_sort | Maria Vittoria Chiaruttini |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities. <b>Methods</b>: Data were sourced from the TIPNet registry, which comprised patients admitted to 23 Italian Paediatric Intensive Care Units (PICUs). We selected patients between January 2010 and January 2024 who received non-invasive ventilation (NIV) as their initial approach to respiratory support. The study aimed to develop a predictive model for NIV failure, selecting the best Machine Learning technique, including Generalized Linear Models, Random Forest, Extreme Gradient Boosting, and Neural Networks. Additionally, an ensemble approach was implemented. Model performances were measured using sensitivity, specificity, AUROC, and predictive values. Moreover, the model calibration was evaluated. <b>Results</b>: Out of 43,794 records, 1861 admissions met the inclusion criteria, with 678 complete cases and 97 NIV failures. The RF model demonstrated the highest AUROC and sensitivity equal to 0.83 (0.64, 0.94). Base excess, weight, age, systolic blood pressure, and fraction of inspired oxygen were identified as the most predictive features. A check for model calibration ensured the model’s reliability in predicting NIV failure probabilities. <b>Conclusions</b>: This study identified highly sensitive models for predicting NIV failure in PICU patients, with RF as a robust option. |
| format | Article |
| id | doaj-art-e8d05e30cea548a1ae7cf126da8e14c1 |
| institution | OA Journals |
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| language | English |
| publishDate | 2024-12-01 |
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| spelling | doaj-art-e8d05e30cea548a1ae7cf126da8e14c12025-08-20T02:00:45ZengMDPI AGDiagnostics2075-44182024-12-011424285710.3390/diagnostics14242857Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven PredictionMaria Vittoria Chiaruttini0Giulia Lorenzoni1Marco Daverio2Luca Marchetto3Francesca Izzo4Giovanna Chidini5Enzo Picconi6Claudio Nettuno7Elisa Zanonato8Raffaella Sagredini9Emanuele Rossetti10Maria Cristina Mondardini11Corrado Cecchetti12Pasquale Vitale13Nicola Alaimo14Denise Colosimo15Francesco Sacco16Giulia Genoni17Daniela Perrotta18Camilla Micalizzi19Silvia Moggia20Giosuè Chisari21Immacolata Rulli22Andrea Wolfler23Angela Amigoni24Dario Gregori25Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35131 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35131 Padova, ItalyPediatric Intensive Care Unit, Department of Women’s and Children’s Health, University Hospital of Padova, Via Giustiniani 3, 35128 Padova, ItalyPediatric Intensive Care Unit, Department of Women’s and Children’s Health, University Hospital of Padova, Via Giustiniani 3, 35128 Padova, ItalyPediatric Intensive Care Unit, Buzzi Children’s Hospital, Via Lodovico Castelvetro 32, 20154 Milan, ItalyDepartment of Anesthesia Resuscitation Emergency Care, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, Via Francesco Sforza 35, 20122 Milan, ItalyPediatric Intensive Care Unit, Pediatric Trauma Center, Fondazione IRCCS Policlinico Universitario “A. Gemelli”, Largo Agostino Gemelli 8, 00136 Rome, ItalyAnaesthesia and Pediatric Resuscitation, AOU Alessandria, SS Antonio e Biagio e Cesare Arrigo Hospital, Spalto Marengo 43, 15121 Alessandria, ItalyPediatric Intensive Care Unit, San Bortolo Hospital, Viale Ferdinando Rodolfi 37, 36100 Vicenza, ItalyAnesthesia and Resuscitation Unit, IRCCS Burlo Garofolo, Via dell’Istria 65, 34137 Trieste, ItalyAnaesthesia, Emergency and Pediatric Intensive Care Unit, Bambino Gesu’ Children Hospital IRCCS, Piazza di Sant’Onofrio 4, 00165 Rome, ItalyIRCCS AOU di Bologna, Via Giuseppe Massarenti 9, 40138 Bologna, ItalyDepartment of Emergency Acceptance, Bambino Gesù Children’s Hospital, Piazza di Sant’Onofrio 4, 00165 Rome, ItalyPediatric and Neonatal Intensive Care Unit, Children’s Hospital Regina Margherita, Piazza Polonia 94, 10126 Turin, ItalyARNAS G. di Cristina Hospital, 90127 Palermo, ItalyPediatric Intensive Care Unit, Children’s Hospital Meyer, IRCCS, Viale Gaetano Pieraccini 24, 50139 Florence, ItalyPaediatric Intensive Care Unit, Azienda Ospedaliera Universitaria Integrata di Verona, Piazzale Aristide Stefani 1, 37126 Verona, ItalyNeonatal and Pediatric Intensive Care Unit, Maggiore della Carità University Hospital, L.go Bellini, 28100 Novara, ItalyA.R.C.O. Palidoro, Bambino Gesù Children’s Hospital, Piazza di Sant’Onofrio 4, 00165 Rome, ItalyPediatric and Neonatal Intensive Care Unit, IRCCS G Gaslini, Via Gerolamo Gaslini 5, 16147 Genoa, ItalyPediatric Intensive Care Unit, AORN Santobono-Pausilipon, Via della Croce Rossa 8, 80122 Naples, ItalyUOSD Pediatric Resuscitation, ARNAS Garibaldi PO Nesima, Piazza Santa Maria di Gesù 5, 95124 Catania, ItalyUOC Neonatal Pathology and TIN, AOU G MARTINO, Via Consolare Valeria 1, 98124 Messina, ItalyDepartment of Emergency, Division of Anesthesia IRCCS G Gaslini, Via Gerolamo Gaslini 5, 16147 Genoa, ItalyPediatric Intensive Care Unit, Department of Women’s and Children’s Health, University Hospital of Padova, Via Giustiniani 3, 35128 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, Via Loredan 18, 35131 Padova, Italy<b>Background/Objectives</b>: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities. <b>Methods</b>: Data were sourced from the TIPNet registry, which comprised patients admitted to 23 Italian Paediatric Intensive Care Units (PICUs). We selected patients between January 2010 and January 2024 who received non-invasive ventilation (NIV) as their initial approach to respiratory support. The study aimed to develop a predictive model for NIV failure, selecting the best Machine Learning technique, including Generalized Linear Models, Random Forest, Extreme Gradient Boosting, and Neural Networks. Additionally, an ensemble approach was implemented. Model performances were measured using sensitivity, specificity, AUROC, and predictive values. Moreover, the model calibration was evaluated. <b>Results</b>: Out of 43,794 records, 1861 admissions met the inclusion criteria, with 678 complete cases and 97 NIV failures. The RF model demonstrated the highest AUROC and sensitivity equal to 0.83 (0.64, 0.94). Base excess, weight, age, systolic blood pressure, and fraction of inspired oxygen were identified as the most predictive features. A check for model calibration ensured the model’s reliability in predicting NIV failure probabilities. <b>Conclusions</b>: This study identified highly sensitive models for predicting NIV failure in PICU patients, with RF as a robust option.https://www.mdpi.com/2075-4418/14/24/2857non-invasive ventilationNIVNIV failurePICUTIPNetmachine learning |
| spellingShingle | Maria Vittoria Chiaruttini Giulia Lorenzoni Marco Daverio Luca Marchetto Francesca Izzo Giovanna Chidini Enzo Picconi Claudio Nettuno Elisa Zanonato Raffaella Sagredini Emanuele Rossetti Maria Cristina Mondardini Corrado Cecchetti Pasquale Vitale Nicola Alaimo Denise Colosimo Francesco Sacco Giulia Genoni Daniela Perrotta Camilla Micalizzi Silvia Moggia Giosuè Chisari Immacolata Rulli Andrea Wolfler Angela Amigoni Dario Gregori Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction Diagnostics non-invasive ventilation NIV NIV failure PICU TIPNet machine learning |
| title | Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction |
| title_full | Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction |
| title_fullStr | Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction |
| title_full_unstemmed | Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction |
| title_short | Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction |
| title_sort | non invasive ventilation failure in pediatric icu a machine learning driven prediction |
| topic | non-invasive ventilation NIV NIV failure PICU TIPNet machine learning |
| url | https://www.mdpi.com/2075-4418/14/24/2857 |
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