Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA; Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Br J Anaesth. 2024 Mar;132(3):607-615. doi: 10.1016/j.bja.2023.11.039. Epub 2024 Jan 6.
Preoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.
We conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and patient characteristics data from the electronic health record which predicts 30-day mortality, 30-day readmission, discharge to long-term care, and hospital length of stay.
Our method, the Flexible Surgical Set Embedding (FLEX) score, achieved state-of-the-art performance to identify comorbidities that significantly contribute to the risk of each adverse outcome. The contributions of comorbidities are weighted based on patient-specific context, yielding personalised risk predictions. Understanding the significant drivers of risk of adverse outcomes for each patient can inform clinicians of potential targets for intervention.
FLEX utilises information from a wider range of medical diagnostic and procedural codes than previously possible and can adapt to different coding practices to accurately predict adverse postoperative outcomes.
术前了解手术风险可以改善围手术期护理和患者预后。然而，需要临床医生检查患者或手动病历审查的评估对于常规使用来说过于繁琐。
我们在美国 MGB 系统的 4 家医院对 243479 名成年非心脏手术患者进行了一项多中心回顾性研究。我们使用电子病历中常规收集的编码和患者特征数据开发了一种机器学习方法，该方法可预测 30 天死亡率、30 天再入院率、长期护理出院和住院时间。
我们的方法，即灵活手术集嵌入（FLEX）评分，在识别对每种不良结局风险有显著贡献的合并症方面达到了最新水平。合并症的贡献基于患者特定的背景进行加权，从而产生个性化的风险预测。了解每个患者不良结局风险的主要驱动因素可以为临床医生提供潜在的干预目标。
FLEX 利用了比以往更广泛的医疗诊断和手术代码信息，并且可以适应不同的编码实践，从而准确预测术后不良结局。
