Sakoh Takashi, Kimura Muneyoshi, Takagi Shinsuke, Ogura Sho, Morishima Masayo, Yamamuro Ryosuke, Yamaguchi Kyosuke, Yuasa Mitsuhiro, Kaji Daisuke, Kageyama Kosei, Taya Yuki, Nishida Aya, Ishiwata Kazuya, Yamamoto Hisashi, Yamamoto Go, Asano-Mori Yuki, Wake Atsushi, Uchida Naoyuki, Taniguchi Shuichi, Araoka Hideki
Department of Infectious Diseases, Toranomon Hospital, 2-2-2 Toranomon, Minato-Ku, Tokyo, 105-8470, Japan.
Department of Hematology, Toranomon Hospital, 2-2-2 Toranomon, Minato-Ku, Tokyo, 105-8470, Japan.
Ann Hematol. 2023 May;102(5):1239-1246. doi: 10.1007/s00277-023-05185-7. Epub 2023 Mar 27.
Difficulties in immediately distinguishing Stenotrophomonas maltophilia (SM) bacteremia from Pseudomonas aeruginosa (PA) bacteremia in the clinical setting can lead to treatment delay. We aimed to develop a scoring system to immediately distinguish SM bacteremia from PA bacteremia using clinical indicators. We enrolled cases of SM and PA bacteremia in adult patients with hematological malignancies between January 2011 and June 2018. The patients were randomized into derivation and validation cohorts (2:1), and a clinical prediction tool for SM bacteremia was developed and verified. In total, 88 SM and 85 PA bacteremia cases were identified. In the derivation cohort, the following independent predictors of SM bacteremia were identified: no evidence of PA colonization, antipseudomonal β-lactam breakthrough bacteremia, and central venous catheter insertion. We scored each of the three predictors according to their regression coefficient (2, 2, and 1, respectively). Receiver operating characteristic curve analysis confirmed the score's predictive performance, with an area under the curve of 0.805. The combined sensitivity and specificity (0.655 and 0.821) was highest with a cut-off value of 4 points. Positive and negative predictive values were 79.2% (19/24) and 69.7% (23/33), respectively. This novel predictive scoring system is potentially useful for distinguishing SM bacteremia from PA bacteremia, which would facilitate immediate administration of appropriate antimicrobial therapy.
在临床环境中，难以立即区分嗜麦芽窄食单胞菌（SM）菌血症和铜绿假单胞菌（PA）菌血症，这可能导致治疗延迟。我们旨在开发一种评分系统，利用临床指标立即区分SM菌血症和PA菌血症。我们纳入了2011年1月至2018年6月期间患有血液系统恶性肿瘤的成年患者的SM和PA菌血症病例。患者被随机分为推导队列和验证队列（2:1），并开发和验证了一种用于SM菌血症的临床预测工具。总共确定了88例SM菌血症病例和85例PA菌血症病例。在推导队列中，确定了以下SM菌血症的独立预测因素：无PA定植证据、抗假单胞菌β-内酰胺突破性菌血症和中心静脉导管插入。我们根据三个预测因素各自的回归系数（分别为2、2和1）对其进行评分。受试者工作特征曲线分析证实了该评分的预测性能，曲线下面积为0.805。截断值为4分时，联合敏感性和特异性（分别为0.655和0.821）最高。阳性和阴性预测值分别为79.2%（19/24）和69.7%（23/33）。这种新型预测评分系统可能有助于区分SM菌血症和PA菌血症，从而便于立即给予适当的抗菌治疗。
