1Big Data Center, China Medical University Hospital, Taichung, 404 Taiwan.
2Department of Medical Research, Department of Internal Medicine, China Medical University Hospital, Taichung, 404 Taiwan.
Antimicrob Resist Infect Control. 2019 Nov 12;8:175. doi: 10.1186/s13756-019-0642-z. eCollection 2019.
Current guidelines have unsatisfied performance in predicting severe outcomes after infection (CDI). Our objectives were to develop a risk prediction model for 30-day mortality and to examine its performance among inpatients with CDI.
This retrospective cohort study was conducted at China Medical University Hospital, a 2111-bed tertiary medical center in central Taiwan. We included adult inpatients who had a first positive culture or toxin assay and had diarrhea as the study population. The main exposure of interest was the biochemical profiles of white blood cell count, serum creatinine (SCr), estimated glomerular filtration rate, blood urea nitrogen (BUN), serum albumin, and glucose. The primary outcome was the 30-day all-cause mortality and the secondary outcome was the length of stay in the intensive care units (ICU) following CDI. A multivariable Cox model and a logistic regression model were developed using clinically relevant and statistically significant variables for 30-day mortality and for length of ICU stay, respectively. A risk scoring system was established by standardizing the coefficients. We compared the performance of our models and the guidelines.
Of 401 patients, 23.4% died within 30 days. In the multivariable model, malignancy (hazard ratio [HR] = 1.95), ≥ 1.5-fold rise in SCr (HR = 2.27), BUN-to-SCr ratio > 20 (HR = 2.04), and increased glucose (≥ 193 vs < 142 mg/dL, HR = 2.18) were significant predictors of 30-day mortality. For patients who survived the first 30 days of CDI, BUN-to-SCr ratio > 20 (Odds ratio [OR] = 4.01) was the only significant predictor for prolonged (> 9 days) length of ICU stay following CDI. The Harrell's statistic of our Cox model for 30-day mortality (0.727) was significantly superior to those of SHEA-IDSA 2010 (0.645), SHEA-IDSA 2018 (0.591), and ECSMID (0.650). Similarly, the conventional statistic of our logistic regression model for prolonged ICU stay (0.737) was significantly superior to that of the guidelines (SHEA-IDSA 2010, = 0.600; SHEA-IDSA 2018, = 0.634; ESCMID, = 0.645). Our risk prediction scoring system for 30-day mortality correctly reclassified 20.7, 32.1, and 47.9% of patients, respectively.
Our model that included novel biomarkers of BUN-to-SCr ratio and glucose have a higher predictive performance of 30-day mortality and prolonged ICU stay following CDI than do the guidelines.
目前的指南在预测感染后（CDI）的严重结局方面表现不佳。我们的目的是建立一个预测 30 天死亡率的风险预测模型，并在 CDI 住院患者中检验其性能。
这是一项在中国台湾中部的一家拥有 2111 张床位的三级医疗中心的中国医科大学医院进行的回顾性队列研究。我们纳入了首次阳性白细胞计数、血清肌酐（SCr）、估计肾小球滤过率、血尿素氮（BUN）、血清白蛋白和葡萄糖培养或毒素检测呈阳性且腹泻的成年住院患者作为研究人群。主要暴露因素为白细胞计数、SCr、估计肾小球滤过率、BUN、血清白蛋白和葡萄糖的生化特征。主要结局是 30 天全因死亡率，次要结局是 CDI 后入住重症监护病房（ICU）的时间长度。使用与 30 天死亡率和 ICU 住院时间相关的有临床意义和统计学意义的变量，分别使用多变量 Cox 模型和逻辑回归模型进行分析。通过标准化系数建立风险评分系统。我们比较了我们的模型和指南的性能。
在 401 名患者中，23.4%的患者在 30 天内死亡。在多变量模型中，恶性肿瘤（风险比[HR] = 1.95）、SCr 升高≥1.5 倍（HR = 2.27）、BUN/SCr 比值>20（HR = 2.04）和血糖升高（≥193 与 <142mg/dL，HR = 2.18）是 30 天死亡率的显著预测因素。对于在 CDI 后存活 30 天的患者，BUN/SCr 比值>20（比值比[OR] = 4.01）是 CDI 后 ICU 住院时间延长（>9 天）的唯一显著预测因素。我们的 Cox 模型用于 30 天死亡率的 Harrell's 统计量（0.727）明显优于 SHEA-IDSA 2010（0.645）、SHEA-IDSA 2018（0.591）和 ECSMID（0.650）。同样，我们的逻辑回归模型用于 ICU 住院时间延长的传统统计量（0.737）明显优于指南（SHEA-IDSA 2010， = 0.600；SHEA-IDSA 2018， = 0.634；ESCMID， = 0.645）。我们的 30 天死亡率风险预测评分系统正确地重新分类了 20.7%、32.1%和 47.9%的患者。
与指南相比，纳入 BUN/SCr 比值和血糖等新型生物标志物的模型对 CDI 后 30 天死亡率和 ICU 住院时间延长的预测性能更高。
