OBJECTIVE To evaluate machine learning models' performance in predicting acute pancreatitis severity using early-stage variables while excluding laboratory and imaging tests. SUMMARY BACKGROUND DATA Severe acute pancreatitis (SAP) affects approximately 20% of acute pancreatitis (AP) patients and is associated with high mortality rates. Accurate early prediction of SAP and in-hospital mortality is crucial for effective management. Traditional scores such as APACHE-II and BISAP are complex and require laboratory tests, while early predictive models are lacking. Machine learning (ML) has shown promising results in predictive modelling, potentially outperforming traditional methods. METHODS We analysed data from a prospective database of AP patients admitted to Vall d'Hebron Hospital from November 2015 to January 2022. Inclusion criteria were adults diagnosed with AP according to the 2012 Atlanta classification. Data included basal characteristics, current medication, and vital signs. We developed machine learning models to predict SAP, in-hospital mortality, and intensive care unit (ICU) admission. The modelling process included two stages: Stage 0, which used basal characteristics and medication, and Stage 1, which included data from Stage 0 and vital signs. RESULTS Out of 634 cases, 594 were analysed. The Stage 0 model showed AUC values of 0.698 for mortality, 0.721 for ICU admission, and 0.707 for persistent organ failure. The Stage 1 model improved performance with AUC values of 0.849 for mortality, 0.786 for ICU admission, and 0.783 for persistent organ failure. The models demonstrated comparable or superior performance to APACHE-II and BISAP scores. CONCLUSIONS The ML models showed good predictive capacity for SAP, ICU admission, and mortality using early-stage data without laboratory or imaging tests. This approach could revolutionise AP patients' initial triage and management, providing a personalised prediction method based on early clinical data.

中文翻译：

---

使用人工智能技术在疾病的早期预测严重急性胰腺炎，无需实验室数据或影像学检查：胰腺研究。


目的 评估机器学习模型在使用早期变量预测急性胰腺炎严重程度方面的性能，同时排除实验室和影像学检查。摘要 背景数据 严重急性胰腺炎 （SAP） 影响约 20% 的急性胰腺炎 （AP） 患者，并且与高死亡率相关。准确早期预测 SAP 和院内死亡率对于有效管理至关重要。APACHE-II 和 BISAP 等传统评分很复杂，需要实验室测试，而缺乏早期预测模型。机器学习 （ML） 在预测建模方面显示出有希望的结果，可能优于传统方法。方法 我们分析了 2015 年 11 月至 2022 年 1 月在 Vall d'Hebron 医院收治的 AP 患者的前瞻性数据库中的数据。纳入标准是根据 2012 年亚特兰大分类诊断为 AP 的成年人。数据包括基础特征、当前药物和生命体征。我们开发了机器学习模型来预测 SAP、院内死亡率和重症监护病房 （ICU） 收治率。建模过程包括两个阶段：第 0 阶段，使用基础特征和药物，以及第 1 阶段，包括第 0 阶段的数据和生命体征。结果 在 634 例病例中，分析了 594 例。0 期模型显示死亡率的 AUC 值为 0.698，入住 ICU 的 AUC 值为 0.721，持续性器官衰竭的 AUC 值为 0.707。第 1 阶段模型提高了性能，死亡率的 AUC 值为 0.849，入住 ICU 的 AUC 值为 0.786，持续性器官衰竭的 AUC 值为 0.783。这些模型表现出与 APACHE-II 和 BISAP 分数相当或更好的性能。 结论 ML 模型使用没有实验室或影像学检查的早期数据显示对 SAP 、 ICU 收治和死亡率具有良好的预测能力。这种方法可以彻底改变 AP 患者的初始分诊和管理，提供基于早期临床数据的个性化预测方法。