背景:严重的细菌感染(SBI)与计划外住院和高死亡率有关。SBI的早期识别在临床实践中至关重要。
目的:本研究旨在建立和验证旨在鉴定感染性发热患者SBI的临床适用模型。
方法:945例感染性发热患者的临床数据,包括人口统计和实验室指标,在2013年1月至2020年12月之间从2200张病床的教学医院进行回顾性收集。数据以7:3的比例随机分为训练集和测试集。各种机器学习(ML)算法,包括Boruta,套索(最小绝对收缩和选择运算符),和递归特征消除,用于特征过滤。随后将所选特征用于构建使用逻辑回归(LR)预测SBI的模型,随机森林(RF),和极端梯度提升(XGBoost)与5倍交叉验证。性能指标,包括受试者工作特征(ROC)曲线和ROC曲线下面积(AUC),准确度,灵敏度,和其他相关参数,用于评估模型性能。考虑到模型性能和临床需求,使用LR分析最终确认了2种临床时序-序列预警模型。然后绘制相应的预测列线图以供临床使用。此外,医生,对这项研究视而不见,在2021年期间,从同一中心收集了涉及164名患者的其他数据。然后将研究中开发的列线图应用于临床实践中以进一步验证其临床实用性。
结果:总计,69.9%(661/945)的患者发生SBIs。年龄,血红蛋白,中性粒细胞与淋巴细胞的比率,纤维蛋白原,通过至少两种ML算法将C反应蛋白水平确定为重要特征。考虑到这些指标的采集顺序和临床需求,相应地构建了2个预测SBI风险的时序模型:提前入院模型(模型1)和入院24小时内模型(模型2)。LR在两个模型中都表现出比RF和XGBoost更好的稳定性,并且在模型2中表现最好,具有AUC,准确度,灵敏度为0.780(95%CI0.720-841),0.754(95%CI0.698-804),和0.776(95%CI0.711-832),分别。XGBoost在AUC方面优于LR(0.708,95%CI0.641-775vs0.686,95%CI0.617-754),与模型1中的其他两种方法相比,RF获得了更好的准确性(0.729,95%CI0.673-780)和灵敏度(0.790,95%CI0.728-844)。基于LR开发了两个SBI风险预测列线图供临床使用,在临床应用中表现出良好的性能,准确度分别为0.707和0.750,灵敏度分别为0.729和0.927。
结论:临床时序预警模型在预测疑似感染性发热患者的SBIs和临床应用方面表现出了有效性,提示临床决策的良好潜力。然而,需要更多的前瞻性和多中心研究来进一步证实其临床效用.
Serious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice.
This study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever.
Clinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility.
In total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application.
The clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.