Ostrosky-Zeichner Luis, Harrington Rachel, Azie Nkechi, Yang Hongbo, Li Nanxin, Zhao Jing, Koo Valerie, Wu Eric Q
Division of Infectious Diseases, McGovern Medical School, Houston, Texas, USA
Astellas Pharma US, Northbrook, Illinois, USA.
Antimicrob Agents Chemother. 2017 Apr 24;61(5). doi: 10.1128/AAC.02091-16. Print 2017 May.
This study aimed to develop a prediction model to identify patients with candidemia who were at high risk of failing fluconazole treatment. Adult patients in the United States with candidemia who received fluconazole during hospitalization were selected from the Cerner Health Facts Hospital Database (04/2004 to 03/2013). Fluconazole failure was defined as switching/adding another antifungal, positive culture ≥10 days after fluconazole initiation, or death during hospitalization. Patients were randomized into modeling and validation samples. Using the modeling sample, a regression analysis of least absolute shrinkage and selection operator was used to select risk predictors of fluconazole failure (demographics, species, initiation of fluconazole before positive culture and after admission, and comorbidities, procedures, and treatments during the 6 months before admission and fluconazole initiation). The prediction model was evaluated using the validation sample. We found that of 987 identified patients (average age of 61 years, 51% male, 72% Caucasian), 49% failed and 51% did not fail fluconazole treatment. Of those who failed, 70% switched or added another antifungal, 21% had a second positive test, and 42% died during hospitalization. Nine risk factors were included in the prediction model: days to start fluconazole after admission, or infection, hematological malignancy, venous thromboembolism (VTE), enteral nutrition, use of nonoperative intubation/irrigation, and other antifungal use. All but VTE were associated with a higher risk of failure. The model's c-statistic was 0.65, with a Hosmer-Lemeshow test value of 0.23. In summary, this prediction model identified patients with a high risk of fluconazole failure, illustrating the potential value and feasibility of personalizing candidemia treatment.
本研究旨在开发一种预测模型,以识别氟康唑治疗失败风险较高的念珠菌血症患者。从Cerner健康事实医院数据库(2004年4月至2013年3月)中选取美国住院期间接受氟康唑治疗的成年念珠菌血症患者。氟康唑治疗失败定义为更换/加用另一种抗真菌药物、氟康唑开始使用后≥10天培养结果仍为阳性或住院期间死亡。患者被随机分为建模样本和验证样本。利用建模样本,采用最小绝对收缩和选择算子回归分析来选择氟康唑治疗失败的风险预测因素(人口统计学特征、菌种、培养结果阳性前及入院后开始使用氟康唑的情况,以及入院前和氟康唑开始使用前6个月内的合并症、手术操作和治疗)。使用验证样本对预测模型进行评估。我们发现,在987例确诊患者中(平均年龄61岁,51%为男性,72%为白种人),49%的患者氟康唑治疗失败,51%未失败。在治疗失败的患者中,70%更换或加用了另一种抗真菌药物,21%第二次培养结果仍为阳性,42%在住院期间死亡。预测模型纳入了9个风险因素:入院后或感染后开始使用氟康唑的天数、血液系统恶性肿瘤、静脉血栓栓塞(VTE)、肠内营养、非手术插管/冲洗的使用以及其他抗真菌药物的使用。除VTE外,所有因素均与更高的治疗失败风险相关。该模型的c统计量为0.65,Hosmer-Lemeshow检验值为0.23。总之,该预测模型识别出了氟康唑治疗失败风险较高的患者,说明了个性化念珠菌血症治疗的潜在价值和可行性。