A Risk Prediction Model for Efficient Intubation in the Emergency Department: A 4-year Single-center Retrospective Analysis

Overview

Journal J Am Coll Emerg Physicians Open

Publisher Elsevier

Specialty Emergency Medicine

Date 2024 Jun 3

PMID 38827500

Authors

Hongbo Ding

Xue Feng

Qi Yang

Yichang Yang

Siyi Zhu

Xiaozhen Ji

Yangbo Kang

Jiashen Shen

Mei Zhao

Shanxiang Xu

Gangmin Ning

Yongan Xu

Affiliations

Soon will be listed here.

Abstract

Objective: To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms.

Methods: This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs' examination, and status of mechanical ventilation were recorded. These patients were assigned to the intubation and non-intubation groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression, artificial neural network, and random forest. SHapley Additive exPlanations was used to analyze the risk factors of intubated critically ill patients in the ED.

Results: Of 14,589 critically ill patients, 10,212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, mean scores of vital signs, parameters of different organs, and blood oxygen examination results differed significantly between the two groups (< 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest area under the receiver operating characteristic curve (0.84) for predicting ED intubation.

Conclusions: For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.

Citing Articles

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

Moulaei K, Afrash M, Parvin M, Shadnia S, Rahimi M, Mostafazadeh B Sci Rep. 2024; 14(1):15751.

PMID: 38977750 PMC: 11231277. DOI: 10.1038/s41598-024-66481-4.

A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.

Ding H, Feng X, Yang Q, Yang Y, Zhu S, Ji X J Am Coll Emerg Physicians Open. 2024; 5(3):e13190.

PMID: 38827500 PMC: 11142897. DOI: 10.1002/emp2.13190.

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