Mamandipoor Behrooz, Frutos-Vivar Fernando, Peñuelas Oscar, Rezar Richard, Raymondos Konstantinos, Muriel Alfonso, Du Bin, Thille Arnaud W, Ríos Fernando, González Marco, Del-Sorbo Lorenzo, Del Carmen Marín Maria, Pinheiro Bruno Valle, Soares Marco Antonio, Nin Nicolas, Maggiore Salvatore M, Bersten Andrew, Kelm Malte, Bruno Raphael Romano, Amin Pravin, Cakar Nahit, Suh Gee Young, Abroug Fekri, Jibaja Manuel, Matamis Dimitros, Zeggwagh Amine Ali, Sutherasan Yuda, Anzueto Antonio, Wernly Bernhard, Esteban Andrés, Jung Christian, Osmani Venet
Fondazione Bruno Kessler Research Institute, Trento, Italy.
Hospital Universitario de Getafe & Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain.
BMC Med Inform Decis Mak. 2021 May 7;21(1):152. doi: 10.1186/s12911-021-01506-w.
Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid-base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters.
We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders.
Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders.
The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes.
NCT02731898 ( https://clinicaltrials.gov/ct2/show/NCT02731898 ), prospectively registered on April 8, 2016.
机械通气（MV）是危重症患者护理中一个复杂且核心的治疗过程。它会影响酸碱平衡，还可通过产生作用力和释放活性氧引发具有预后相关性的生物创伤，对治疗结果产生负面影响。在本研究中，我们评估了使用循环神经网络（RNN）模型，利用标准机械通气参数来预测机械通气患者的治疗结果。
我们对VENTILA数据集进行分析，这是一项观察性、前瞻性、国际性、多中心研究，旨在调查基线特征和随时间的管理变化对ICU中机械通气患者全因死亡率的影响。我们的队列包括12,596名年龄超过18岁的成年患者，涉及37个国家ICU中的12,755次不同入院，且接受有创和无创机械通气。我们进行了四项不同的分析。最初，我们选择典型的机械通气参数，并在整个队列以及因呼吸系统疾病入院的患者亚组中评估机器学习模型。此外，我们进行敏感性分析，以评估纳入与其他器官功能相关的变量是否能提高模型对整个队列以及呼吸系统疾病患者亚组的预测性能。
与随机森林（RF）和逻辑回归（LR）相比，基于RNN的模型在整个患者数据集中的预测性能更高，受试者工作特征曲线下面积（ROC曲线下面积，AUC）为0.72（±0.01），平均精度（AP）为0.57（±0.01）。在因呼吸系统疾病入院的患者亚组中记录到更高的预测性能，AUC为0.75（±0.02），AP为0.65（±0.03）。纳入其他器官功能进一步将整个患者数据集的性能提高到AUC为0.79（±0.01），AP为0.68（±0.02），将呼吸系统疾病亚组的性能提高到AUC为0.79（±0.01），AP为0.72（±0.02）。
基于RNN的模型在机械通气患者及其因呼吸系统疾病入院的亚组中表现出比RF和LR更好的性能。需要进行临床研究来评估其是否会影响决策和患者治疗结果。
NCT02731898（https://clinicaltrials.gov/ct2/show/NCT02731898），于2016年4月8日进行前瞻性注册。
