Yamashita Shun, Tago Masaki, Minami Kota, Katsuki Naoko E, Oda Yasutomo, Yamashita Shu-Ichi
Department of General Medicine, Saga University Hospital, Saga, JPN.
Education and Research Center for Community Medicine, Faculty of Medicine, Saga University, Saga, JPN.
Cureus. 2025 Feb 8;17(2):e78754. doi: 10.7759/cureus.78754. eCollection 2025 Feb.
Infective endocarditis (IE) often presents as a fever of unknown origin due to its extremely diverse clinical presentations, requiring diverse advanced medical equipment and tests to make a correct diagnosis. Whether a physician can suspect IE in a clinical setting is dependent on the physician's knowledge and experience. If IE is not suspected, antibiotics are administered without obtaining blood cultures, complicating the clinical course and prognosis. To avoid delayed diagnosis or entering the maze of diagnostic difficulties of IE cases, a prediction model to deduce IE likelihood can be used at an early stage after a patient's arrival at the hospital before blood culture examinations would be invaluable. In this study, we aimed to review the literature on such prediction models for IE diagnosis in existence, discussing their strengths and limitations. A narrative review was conducted by two researchers using PubMed. Comprehensive searches included the index terms "infective endocarditis" or "infectious endocarditis", coupled with "prediction model" or "prediction rule" or "predictive model". Five articles reporting one of the three prediction models were identified. The first model, developed for intravenous drug users (IDUs) admitted to the emergency departments of two to three hospitals showed a good area under the curve (AUC) of 0.8; however, the small sample size and overfitting of the model were a limit. The second model for inpatients in all departments of four hospitals showed an AUC of 0.783 with a shrinkage coefficient of 0.963, indicating high generalizability. Moreover, it featured the highest ease of use because it consisted of only five factors readily available in any hospital. The third model, developed for inpatients admitted to an emergency department at a single center, consisted of 12 factors and achieved the highest AUC (0.881). All models demonstrated fair to good AUC. The second model excelled in generalizability and ease of use, while the third model was superior in performance. To further improve the accuracy of each IE prediction, further high-level evidence studies, such as randomized controlled trials in multiple facilities, are mandatory.
感染性心内膜炎（IE）因其临床表现极为多样，常表现为不明原因发热，需要借助多种先进医疗设备和检查才能做出正确诊断。医生能否在临床环境中怀疑IE取决于其知识和经验。若未怀疑IE，在未进行血培养的情况下就使用抗生素，会使临床病程和预后复杂化。为避免IE病例的诊断延迟或陷入诊断困境，在患者入院早期、血培养检查之前使用能推断IE可能性的预测模型将非常有价值。在本研究中，我们旨在回顾现有关于IE诊断此类预测模型的文献，讨论其优点和局限性。两位研究人员使用PubMed进行了叙述性综述。全面检索包括索引词“感染性心内膜炎”或“感染性心内膜炎”，以及“预测模型”或“预测规则”或“预测模型”。共识别出五篇报告三种预测模型之一的文章。第一个模型是为两到三家医院急诊科收治的静脉药物使用者（IDU）开发的，曲线下面积（AUC）为0.8，表现良好；然而，该模型样本量小且存在过拟合问题，是一个局限。第二个模型是为四家医院所有科室的住院患者开发的，AUC为0.783，收缩系数为0.963，表明具有较高的可推广性。此外，它的易用性最高，因为它仅由任何医院都 readily available（此处原文有误，推测可能是readily obtainable，意为容易获得）的五个因素组成。第三个模型是为单一中心急诊科收治的住院患者开发的，由12个因素组成，AUC最高（0.881）。所有模型的AUC表现均为中等至良好。第二个模型在可推广性和易用性方面表现出色，而第三个模型在性能方面更优。为进一步提高各IE预测的准确性，必须开展更高水平的证据研究，如在多个机构进行的随机对照试验。
