Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin, 300060, China.
State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China.
BMC Infect Dis. 2021 Jan 15;21(1):76. doi: 10.1186/s12879-021-05780-x.
Invasive candidiasis is the most common fungal disease among hospitalized patients and continues to be a major cause of mortality. Risk factors for mortality have been studied previously but rarely developed into a predictive nomogram, especially for cancer patients. We constructed a nomogram for mortality prediction based on a retrospective review of 10 years of data for cancer patients with invasive candidiasis.
Clinical data for cancer patients with invasive candidiasis during the period of 2010-2019 were studied; the cases were randomly divided into training and validation cohorts. Variables in the training cohort were subjected to a predictive nomogram based on multivariate logistic regression analysis and a stepwise algorithm. We assessed the performance of the nomogram through the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) in both the training and validation cohorts.
A total of 207 cases of invasive candidiasis were examined, and the crude 30-day mortality was 28.0%. Candida albicans (48.3%) was the predominant species responsible for infection, followed by the Candida glabrata complex (24.2%) and Candida tropicalis (10.1%). The training and validation cohorts contained 147 and 60 cases, respectively. The predictive nomogram consisted of bloodstream infections, intensive care unit (ICU) admitted > 3 days, no prior surgery, metastasis and no source control. The AUCs of the training and validation cohorts were 0.895 (95% confidence interval [CI], 0.846-0.945) and 0.862 (95% CI, 0.770-0.955), respectively. The net benefit of the model performed better than "treatment for all" in DCA and was also better for opting low-risk patients out of treatment than "treatment for none" in opt-out DCA.
Cancer patients with invasive candidiasis exhibit high crude mortality. The predictive nomogram established in this study can provide a probability of mortality for a given patient, which will be beneficial for therapeutic strategies and outcome improvement.
侵袭性念珠菌病是住院患者中最常见的真菌感染，仍是主要的死亡原因。先前已经研究了死亡率的危险因素，但很少将其发展为预测列线图，尤其是针对癌症患者。我们构建了一个基于 10 年侵袭性念珠菌病癌症患者回顾性研究数据的死亡率预测列线图。
研究了 2010 年至 2019 年期间侵袭性念珠菌病癌症患者的临床数据；病例被随机分为训练和验证队列。在训练队列中，使用多元逻辑回归分析和逐步算法对变量进行预测列线图。我们通过在训练和验证队列中评估接收者操作特征曲线（ROC）下面积（AUC）和决策曲线分析（DCA）来评估列线图的性能。
共检查了 207 例侵袭性念珠菌病，粗死亡率为 28.0%。白色念珠菌（48.3%）是主要的感染物种，其次是近平滑念珠菌复合体（24.2%）和热带念珠菌（10.1%）。训练和验证队列分别包含 147 例和 60 例。预测列线图由血流感染、入住重症监护病房（ICU）>3 天、无既往手术、转移和无源头控制组成。训练和验证队列的 AUC 分别为 0.895（95%置信区间 [CI]，0.846-0.945）和 0.862（95% CI，0.770-0.955）。DCA 显示模型的净收益优于“治疗所有”，在 DCA 中选择低风险患者而不是“不治疗所有”的模型也优于“不治疗任何”。
侵袭性念珠菌病癌症患者的死亡率较高。本研究建立的预测列线图可为特定患者提供死亡率概率，这将有助于治疗策略和改善预后。
