Clustering using unsupervised machine learning to stratify the risk of immune-related liver injury
Clustering using unsupervised machine learning to stratify the risk of immune-related liver injury
复制标题
使用无监督机器学习进行聚类来分层免疫相关肝损伤的风险
DOI：
10.1111/jgh.16038
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Kawashima H.
中科院分区：
文献类型：
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作者：
Yamamoto T;Morooka H;Ito T（Corresponding）;Ishigami M;Mizuno K;Yokoyama S;Yamamoto K;Imai N;Ishizu Y;Honda T;Yokota K;Hase T;Maeda O;Hashimoto N;Ando Y;Akiyama M;Kawashima H.
背景和目的免疫相关性肝损伤（liver-irAE）是一个潜在的预后不良的临床问题。方法我们回顾性收集了2014年9月至2021年12月在名古屋大学医院接受免疫检查点抑制剂治疗的患者的临床数据。使用无监督机器学习方法，高斯混合模型，根据炎症标志物将队列划分为聚类，我们研究了这些聚类中肝脏irAE的累积发生率。其中，492例（70.1%）患者为男性，平均年龄为66.6岁。在平均423天的随访期内，43例患者发生重度肝脏irAE（≥ 3级不良事件通用术语标准）。将患者分为5个聚类（a、B、c、d和e）。聚类c中肝脏irAE的累积发生率高于聚类a（风险比[HR]：13.59，95%可信区间[CI]：1.70- 108.76，P = 0.014），总生存期在c组和d组比a组差（HR：2.83，95%CI：1.77- 4.50，P < 0.001; HR：2.87，95%CI：1.47- 5.60，P = 0.002）。聚类c和聚类d的特征在于高温、C反应蛋白、血小板和低白蛋白。然而，有差异的患病率中性粒细胞计数，中性粒细胞-淋巴细胞比率，和肝转移两个clusters.ConclusionsThe多个标志物和体温的联合评估可能有助于分层的高风险组发展肝irAE。
Background and AimImmune‐related liver injury (liver‐irAE) is a clinical problem with a potentially poor prognosis.MethodsWe retrospectively collected clinical data from patients treated with immune checkpoint inhibitors between September 2014 and December 2021 at the Nagoya University Hospital. Using an unsupervised machine learning method, the Gaussian mixture model, to divide the cohort into clusters based on inflammatory markers, we investigated the cumulative incidence of liver‐irAEs in these clusters.ResultsThis study included a total of 702 patients. Among them, 492 (70.1%) patients were male, and the mean age was 66.6 years. During the mean follow‐up period of 423 days, severe liver‐irAEs (Common Terminology Criteria for Adverse Events grade ≥ 3) occurred in 43 patients. Patients were divided into five clusters (a, b, c, d, and e). The cumulative incidence of liver‐irAE was higher in cluster c than in cluster a (hazard ratio [HR]: 13.59, 95% confidence interval [CI]: 1.70–108.76,P= 0.014), and overall survival was worse in clusters c and d than in cluster a (HR: 2.83, 95% CI: 1.77–4.50,P< 0.001; HR: 2.87, 95% CI: 1.47–5.60,P= 0.002, respectively). Clusters c and d were characterized by high temperature, C‐reactive protein, platelets, and low albumin. However, there were differences in the prevalence of neutrophil count, neutrophil‐to‐lymphocyte ratio, and liver metastases between both clusters.ConclusionsThe combined assessment of multiple markers and body temperature may help stratify high‐risk groups for developing liver‐irAE.