基于淋巴细胞亚群分型的机器学习预测老年脓毒症患者腹腔内念珠菌病：一项前瞻性队列研究。
Predicting intra-abdominal candidiasis in elderly septic patients using machine learning based on lymphocyte subtyping: a prospective cohort study.
作者信息
Zhang Jiahui, Zhao Guoyu, Lei Xianli, Cui Na
机构信息
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
出版信息
Front Pharmacol. 2024 Dec 12;15:1486346. doi: 10.3389/fphar.2024.1486346. eCollection 2024.
OBJECTIVE
Intra-abdominal candidiasis (IAC) is difficult to predict in elderly septic patients with intra-abdominal infection (IAI). This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of IAC in elderly septic patients.
METHODS
A prospective cohort study of 284 consecutive elderly patients diagnosed with sepsis and IAI was performed. We assessed the clinical characteristics and parameters of lymphocyte subtyping at the onset of IAI. A machine-learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. A nomogram model was constructed, and the discrimination, calibration, and clinical effectiveness of the model were verified.
RESULTS
According to the results of the random forest and multivariate analyses, gastrointestinal perforation, renal replacement therapy (RRT), T-cell count, CD28+CD8+ T-cell count and CD38+CD8+ T-cell count were independent predictors of IAC. Using the above parameters to establish a nomogram, the area under the curve (AUC) values of the nomogram in the training and testing cohorts were 0.840 (95% CI 0.778-0.902) and 0.783 (95% CI 0.682-0.883), respectively. The AUC in the training cohort was greater than the score [0.840 (95% CI 0.778-0.902) vs. 0.539 (95% CI 0.464-0.615), p< 0.001]. The calibration curve showed good predictive values and observed values of the nomogram; the DCA results showed that the nomogram had high clinical value.
CONCLUSION
We established a nomogram based on the T-cell count, CD28+CD8+ T-cell count, CD38+CD8+ T-cell count and clinical risk factors that can help clinical physicians quickly rule out IAC or identify elderly patients at greater risk for IAC at the onset of infection.
CLINICAL TRIAL REGISTRATION
[chictr.org.cn], identifier [ChiCTR2300069020].
目的
腹腔念珠菌病（IAC）在患有腹腔感染（IAI）的老年脓毒症患者中难以预测。本研究旨在开发并验证一种基于淋巴细胞亚群分型和临床因素的列线图，用于早期快速预测老年脓毒症患者的IAC。
方法
对284例连续诊断为脓毒症和IAI的老年患者进行前瞻性队列研究。我们在IAI发病时评估了临床特征和淋巴细胞亚群分型参数。使用机器学习随机森林模型选择重要变量，并采用多因素逻辑回归分析影响IAC的因素。构建列线图模型，并验证该模型的辨别力、校准度和临床有效性。
结果
根据随机森林和多因素分析结果，胃肠道穿孔、肾脏替代治疗（RRT）、T细胞计数、CD28 + CD8 + T细胞计数和CD38 + CD8 + T细胞计数是IAC的独立预测因素。使用上述参数建立列线图，训练队列和测试队列中列线图的曲线下面积（AUC）值分别为0.840（95%CI 0.778 - 0.902）和0.783（95%CI 0.682 - 0.883）。训练队列中的AUC大于 评分[0.840（95%CI 0.778 - 0.902）对0.539（95%CI 0.464 - 0.615），p < 0.001]。校准曲线显示列线图具有良好的预测值和观察值；决策曲线分析（DCA）结果表明列线图具有较高的临床价值。
结论
我们基于T细胞计数、CD28 + CD8 + T细胞计数、CD38 + CD8 + T细胞计数和临床危险因素建立了一种列线图，可帮助临床医生在感染发作时快速排除IAC或识别IAC风险较高的老年患者。
临床试验注册
[chictr.org.cn]，标识符[ChiCTR2300069020]
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