VIOSync 败血症预测指数的开发与验证:用于 ICU 病人败血症预测的新型机器学习模型

Sotirios G. Liliopoulos, Alexander Dejaco, Lucas Paseiro-Garcia, Vasileios S. Dimakopoulos, Ioannis A. Gkouzionis
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摘要

背景:败血症是全球第三大死因,也是院内死亡的主要原因。尽管经过几十年的研究,败血症仍是全球患者、临床医生和医疗系统面临的一大挑战。早期识别和预测脓毒症高危患者以及脓毒症相关不良后果至关重要。在这项工作中,我们旨在开发一种能早期预测败血症的人工智能算法。材料和方法:我们利用 Physionet Cardiology Challenge 2019 ICU 数据库中的数据开发了脓毒症预测模型。我们的队列由入住重症监护室的成年患者组成。败血症诊断采用败血症-3 标准。该模型采用 XGBoost 算法建立,目的是在临床症状出现之前预测败血症。使用暂缓测试数据集进行了内部验证,以评估人工智能模型的预测性能。结果我们开发了 VIOSync 败血症预测指数 (SPI),这是一个基于人工智能的预测模型,旨在根据败血症-3 标准的定义,在临床症状出现前 6 小时预测败血症。该人工智能模型是在由约 40,000 名成年患者组成的数据集上训练出来的,整合了生命体征、实验室数据和人口统计学信息等变量。该模型的预测准确率高达 97%,在脓毒症发病前 6 小时内的预测灵敏度为 87%,特异度为 98%。与脓毒症预测特异性为 89% 的 qSOFA 评分相比,我们的 VIOSync SPI 算法显著提高了预测可靠性,可将假阳性率降低 5.5 倍:与目前的脓毒症预警评分和脓毒症发病预测算法相比,VIOSync SPI 的预测性能更胜一筹。要验证我们的方法在不同人群和治疗方案中的通用性,外部验证研究至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and Validation of the VIOSync Sepsis Prediction Index: A Novel Machine Learning Model for Sepsis Prediction in ICU Patients
Background: Sepsis is the third leading cause of death worldwide and the main cause of in-hospital mortality. Despite decades of research, sepsis remains a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. In this work, we aimed to develop an artificial intelligence algorithm that can predict sepsis early. Materials and Methods: We developed a predictive model for sepsis using data from the Physionet Cardiology Challenge 2019 ICU database. Our cohort consisted of adult patients who were admitted to the ICU. Sepsis diagnoses were determined using the Sepsis-3 criteria. The model, built with the XGBoost algorithm, was designed to anticipate sepsis prior to the appearance of clinical symptoms. An internal validation was conducted using a hold-off test dataset to evaluate the AI model's predictive performance. Results: We have developed the VIOSync Sepsis Prediction Index (SPI), an AI-based predictive model designed to forecast sepsis up to six hours before its clinical onset, as defined by Sepsis-3 criteria. The AI model, trained on a dataset comprising approximately 40,000 adult patients, integrates variables such as vital signs, laboratory data, and demographic information. The model demonstrated a high prediction accuracy rate of 97%, with a sensitivity of 87% and a specificity of 98% in predicting sepsis up to 6 hours before the onset. When compared to the established qSOFA score, which has a specificity of 89% for sepsis prediction, our VIOSync SPI algorithm significantly enhances predictive reliability, potentially reducing false positive rates by a factor of 5.5. Conclusions: The VIOSync SPI demonstrated superior prediction performance over current sepsis early warning scores and predictive algorithms for sepsis onset. To validate the generalizability of our method across populations and treatment protocols, external validation studies are essential.
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