Chiaruttini Maria Vittoria, Lorenzoni Giulia, Daverio Marco, Marchetto Luca, Izzo Francesca, Chidini Giovanna, Picconi Enzo, Nettuno Claudio, Zanonato Elisa, Sagredini Raffaella, Rossetti Emanuele, Mondardini Maria Cristina, Cecchetti Corrado, Vitale Pasquale, Alaimo Nicola, Colosimo Denise, Sacco Francesco, Genoni Giulia, Perrotta Daniela, Micalizzi Camilla, Moggia Silvia, Chisari Giosuè, Rulli Immacolata, Wolfler Andrea, Amigoni Angela, Gregori Dario
Unit 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.
Pediatric Intensive Care Unit, Department of Women's and Children's Health, University Hospital of Padova, Via Giustiniani 3, 35128 Padova, Italy.
Diagnostics (Basel). 2024 Dec 19;14(24):2857. doi: 10.3390/diagnostics14242857.
: 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. : 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. : 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. : This study identified highly sensitive models for predicting NIV failure in PICU patients, with RF as a robust option.
无创通气（NIV）已成为儿科重症监护病房（PICUs）避免有创插管的一种可能的初始治疗方法，因为它在降低插管相关风险方面具有优势。然而，及时识别NIV失败对于预防不良后果至关重要。本研究旨在通过测试各种机器学习技术并比较它们的预测能力，来识别PICU患者首次尝试NIV失败的预测因素。：数据来源于TIPNet登记处，该登记处包括入住23家意大利儿科重症监护病房（PICUs）的患者。我们选择了2010年1月至2024年1月期间接受无创通气（NIV）作为初始呼吸支持方法的患者。该研究旨在开发一个NIV失败的预测模型，选择最佳的机器学习技术，包括广义线性模型、随机森林、极端梯度提升和神经网络。此外，还实施了一种集成方法。使用敏感性、特异性、AUROC和预测值来衡量模型性能。此外，还评估了模型校准。：在43794条记录中，1861例入院患者符合纳入标准，其中678例为完整病例，97例NIV失败。RF模型显示出最高的AUROC和敏感性，分别为0.83（0.64，0.94）。碱剩余、体重、年龄、收缩压和吸入氧分数被确定为最具预测性的特征。对模型校准的检查确保了模型在预测NIV失败概率方面的可靠性。：本研究确定了预测PICU患者NIV失败的高敏感性模型，RF是一个可靠的选择。
