Abstract 14410: Predicting Intubation and Ventilation Needs Upon Admission for Intensive Care Patients With Congestive Heart Failure

• Published in: Circulation (New York, N.Y.) N.Y.), 2021-11, Vol.144 (Suppl_1), p.A14410-A14410
• Main Authors: Sangari, Ayush, Sood, Aditya, Sangari, Anish, Sood, Nitish, Maini, Kushagara
• Format: Article
• Language: English
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.144.suppl_1.14410

IntroductionCongestive heart failure (CHF) is one of the most common diagnoses for patients admitted to an intensive care unit (ICU), and intubation or ventilation may be necessary to combat severe associated respiratory symptoms. Current guidelines give high-level recommendations to intubate patients with a Glasgow Coma Scale (GCS) score of 8 or lower. However, prior studies have shown that a large portion of patients who were ultimately intubated had GCS scores greater than 8, demonstrating the need for a better tool to predict the necessity of intubation. The present study sought to build machine-learning (ML) models that could predict the need for intubation or ventilation for CHF patients upon ICU admission. Methods5,623 patients (of which 1,058 were intubated and 2,816 were ventilated) in the eICU Collaborative Research Database who underwent screening using the Acute Physiology and Chronic Health Evaluation and were diagnosed with CHF were used to train (50%) and test (50%) the ML models. Gender, age, treatment status, readmission status, medication status upon admission, GCS motor score upon admission, and GCS eyes score upon admission were used as feature inputs for the ML models, which were classic three-layer neural networks. For intubation, the ML model performance was compared against using the common heuristic of intubating patients with a GCS score of 8 or less. ResultsThe ML model for intubation upon admission prediction had an accuracy of 84.8% and an area under the receiver operating characteristic curve (AUC) of 0.79. Meanwhile, using the aforementioned heuristic for intubation prediction yields an accuracy of 85.7% (AUC n/a). A second ML model was trained to predict the need for ventilation upon admission and had an accuracy of 71.2% and an AUC of 0.80. ConclusionThis newly developed ML model for intubation demonstrates relative success in predicting intubation needs for patients with CHF upon ICU admission. In addition, this model performs similarly to currently accepted guidelines. Further improvement and a larger dataset could better train the model to provide more accurate predictions. Such predictions could help support medical decision-making and resource allocation planning.