Nopour Raoof, Shanbezadeh Mostafa, Kazemi-Arpanahi Hadi
Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
J Educ Health Promot. 2023 Jan 31;12:16. doi: 10.4103/jehp.jehp_20_22. eCollection 2023.
Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical care services. This study aimed to design a statistical algorithm to select the best features influencing intubation prediction in coronavirus disease 2019 (COVID-19) hospitalized patients. Then, using selected features, multiple artificial neural network (ANN) configurations were developed to predict intubation risk.
In this retrospective single-center study, a dataset containing 482 COVID-19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First, the Phi correlation coefficient method was performed for selecting the most important features affecting COVID-19 patients' intubation. Then, the different configurations of ANN were developed. Finally, the performance of ANN configurations was assessed using several evaluation metrics, and the best structure was determined for predicting intubation requirements among hospitalized COVID-19 patients.
The ANN models were developed based on 18 validated features. The results indicated that the best performance belongs to the 18-20-1 ANN configuration with positive predictive value (PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and area under curve (AUC) = 0.906.
The results demonstrate the effectiveness of the ANN models for timely and reliable prediction of intubation risk in COVID-19 hospitalized patients. Our models can inform clinicians and those involved in policymaking and decision making for prioritizing restricted mechanical ventilation and other related resources for critically COVID-19 patients.
在入院时准确预测2019冠状病毒病（COVID-19）患者的插管风险对于优化有限的医院资源利用、提供个性化且基于证据的治疗以及提高所提供医疗服务的质量至关重要。本研究旨在设计一种统计算法，以选择影响2019冠状病毒病（COVID-19）住院患者插管预测的最佳特征。然后，利用所选特征开发多种人工神经网络（ANN）配置，以预测插管风险。
在这项回顾性单中心研究中，使用了一个包含482例于2020年2月9日至2021年7月20日期间住院的COVID-19患者的数据集。首先，采用Phi相关系数法选择影响COVID-19患者插管的最重要特征。然后，开发了不同的ANN配置。最后，使用多种评估指标评估ANN配置的性能，并确定用于预测COVID-19住院患者插管需求的最佳结构。
基于18个经过验证的特征开发了ANN模型。结果表明，最佳性能属于18-20-1的ANN配置，其阳性预测值（PPV）=0.907，阴性预测值（NPV）=0.941，灵敏度=0.898，特异性=0.951，曲线下面积（AUC）=0.906。
结果证明了ANN模型在及时可靠地预测COVID-19住院患者插管风险方面的有效性。我们的模型可以为临床医生以及参与政策制定和决策的人员提供信息，以便为重症COVID-19患者优先分配受限的机械通气和其他相关资源。
