Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery

Shiqin Song, Shixiong Song, Huarong Zhao, Shike Huang, Xinghua Xiao, Xiaobo Lv, Yuehong Deng, Yiyin Tao, Yanlin Liu, Ke Su, Shansha Cheng
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Abstract

Background

The impact of age on the causes of death (CODs) in patients with early-stage intrahepatic cholangiocarcinoma (ICC) who had undergone surgery was analyzed in this study.

Methods

A total of 1555 patients (885 in the older group and 670 in the younger group) were included in this study. Before and after applying inverse probability of treatment weighting (IPTW), the different CODs in the 2 groups were further investigated. Additionally, 7 different machine learning models were used as predictive tools to identify key variables, aiming to evaluate the therapeutic outcome in early ICC patients undergoing surgery.

Results

Before (5.92 vs. 4.08 years, P < 0.001) and after (6.00 vs. 4.08 years, P < 0.001) IPTW, the younger group consistently showed longer overall survival (OS) compared with the older group. Before IPTW, there were no significant differences in cholangiocarcinoma-related deaths (CRDs, P = 0.7) and secondary malignant neoplasms (SMNs, P = 0.78) between the 2 groups. However, the younger group had a lower cumulative incidence of cardiovascular disease (CVD, P = 0.006) and other causes (P < 0.001) compared with the older group. After IPTW, there were no differences between the 2 groups in CRDs (P = 0.2), SMNs (P = 0.7), and CVD (P = 0.1). However, the younger group had a lower cumulative incidence of other CODs compared with the older group (P < 0.001). The random forest (RF) model showed the highest C-index of 0.703. Time-dependent variable importance bar plots showed that age was the most important factor affecting the 2-, 4-, and 6-year survival, followed by stage and size.

Conclusions

Our study confirmed that younger patients have longer OS compared with older patients. Further analysis of the CODs indicated that older patients are more likely to die from CVDs. The RF model demonstrated the best predictive performance and identified age as the most important factor affecting OS in early ICC patients undergoing surgery.

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使用机器学习方法研究年龄对接受手术的早期肝内胆管癌患者死亡原因的影响
背景本研究分析了年龄对接受手术治疗的早期肝内胆管癌(ICC)患者死亡原因(CODs)的影响。方法本研究共纳入 1555 例患者(老年组 885 例,年轻组 670 例)。在应用逆治疗概率加权法(IPTW)前后,进一步研究了两组患者不同的 CODs。此外,还使用了 7 种不同的机器学习模型作为预测工具来确定关键变量,旨在评估接受手术的早期 ICC 患者的治疗结果。结果在 IPTW 之前(5.92 vs. 4.08 岁,P < 0.001)和之后(6.00 vs. 4.08 岁,P < 0.001),年轻组的总生存期(OS)始终长于年长组。在 IPTW 之前,两组之间在胆管癌相关死亡(CRDs,P = 0.7)和继发性恶性肿瘤(SMNs,P = 0.78)方面没有显著差异。然而,与老年组相比,年轻组心血管疾病(CVD,P = 0.006)和其他原因(P < 0.001)的累积发病率较低。IPTW 后,两组在 CRDs(P = 0.2)、SMNs(P = 0.7)和 CVD(P = 0.1)方面没有差异。然而,与老年组相比,年轻组的其他慢性阻塞性肺病累积发病率较低(P <0.001)。随机森林(RF)模型的 C 指数最高,为 0.703。随时间变化的变量重要性柱状图显示,年龄是影响 2 年、4 年和 6 年生存率的最重要因素,其次是分期和体型。对CODs的进一步分析表明,老年患者更有可能死于心血管疾病。RF 模型显示出最佳预测性能,并确定年龄是影响接受手术的早期 ICC 患者 OS 的最重要因素。
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