Huete-Garcia Alejandro, Rodriguez-Lopez Sara
Servicio de Medicina Intensiva, H.U. Torrejón, Servicio de Salud de Madrid, Madrid, Spain.
Servicio de Urgencias Médicas de Madrid-SUMMA112, Servicio de Salud de Madrid, Madrid, Spain.
Acute Crit Care. 2024 Nov;39(4):488-498. doi: 10.4266/acc.2024.00759. Epub 2024 Nov 18.
Early detection of critical events in hospitalized patients improves clinical outcomes and reduces mortality rates. Traditional early warning score systems, such as the National Early Warning Score 2 (NEWS2), effectively identify at-risk patients. Integrating artificial intelligence (AI) could enhance the predictive accuracy and operational efficiency of such systems. The study describes the development and implementation of an AI-enhanced early warning system based on a modified NEWS2 scale with laboratory parameters (mNEWS2-Lab) and evaluates its ability to improve patient safety in hospital wards.
For this retrospective cohort study of 3,790 adults admitted to hospital wards, data were collected before and after implementing the mNEWS2-Lab protocol with and without AI enhancement. The study used a multivariate prediction model with statistical analyses such as Fisher's chi-square test, relative risk (RR), RR reduction, and various AI models (logistic regression, decision trees, neural networks). The economic cost of the intervention was also analyzed.
The mNEWS2-Lab reduced critical events from 6.15% to 2.15% (RR, 0.35; P<0.001), representing a 65% risk reduction. AI integration further reduced events to 1.59% (RR, 0.26; P<0.001) indicating a 10% additional risk reduction and enhancing early warning accuracy by 15%. The intervention was cost-effective, resulting in substantial savings by reducing critical events in hospitalized patients.
The mNEWS2-Lab scale, particularly when integrated with AI models, is a powerful and cost-effective tool for the early detection and prevention of critical events in hospitalized patients.
早期发现住院患者的危急事件可改善临床结局并降低死亡率。传统的早期预警评分系统，如国家早期预警评分2（NEWS2），能有效识别高危患者。整合人工智能（AI）可提高此类系统的预测准确性和运行效率。本研究描述了基于改良的带有实验室参数的NEWS2量表（mNEWS2-Lab）的人工智能增强早期预警系统的开发与实施，并评估其在医院病房提高患者安全的能力。
在这项对3790名入住医院病房的成年人进行的回顾性队列研究中，收集了在实施有和没有人工智能增强的mNEWS2-Lab方案前后的数据。该研究使用了多变量预测模型，并进行了诸如费舍尔卡方检验、相对风险（RR）、RR降低率等统计分析，以及各种人工智能模型（逻辑回归、决策树、神经网络）。还分析了干预措施的经济成本。
mNEWS2-Lab将危急事件从6.15%降至2.15%（RR，0.35；P<0.001），风险降低了65%。整合人工智能进一步将事件降至1.59%（RR，0.26；P<0.001），表明额外降低了10%的风险，并将早期预警准确性提高了15%。该干预措施具有成本效益，通过减少住院患者的危急事件节省了大量费用。
mNEWS2-Lab量表，特别是与人工智能模型整合时，是早期发现和预防住院患者危急事件的有力且具成本效益的工具。
