Kim Woorim, Cho Young Ah, Min Kyung Hyun, Kim Dong-Chul, Lee Kyung-Eun
College of Pharmacy, Kangwon National University, Chuncheon 24341, Republic of Korea.
College of Pharmacy, Gyeongsang National University, Jinju 52828, Republic of Korea.
Pharmaceuticals (Basel). 2023 Aug 3;16(8):1097. doi: 10.3390/ph16081097.
Adrenal insufficiency is a rare, yet life-threatening immune-related adverse event of immune checkpoint inhibitors (ICIs). This study aimed to establish a risk scoring system for adrenal insufficiency in patients receiving anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. Moreover, several machine learning methods were utilized to predict such complications. This study included 209 ICI-treated patients from July 2015 to February 2021, excluding those with prior adrenal insufficiency, previous steroid therapy, or incomplete data to ensure data integrity. Patients were continuously followed up at Gyeongsang National University Hospital, with morning blood samples taken for basal cortisol level measurements, facilitating a comprehensive analysis of their adrenal insufficiency risk. Using a chi-squared test and logistic regression model, we derived the odds ratio and adjusted odds ratio (AOR) through univariate and multivariable analyses. This study utilized machine learning algorithms, such as decision trees, random forests, support vector machines (SVM), and logistic regression to predict adrenal insufficiency in patients treated with ICIs. The performance of each algorithm was evaluated using metrics like accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve (AUROC), ensuring rigorous assessment and reproducibility. A risk scoring system was developed from the multivariable and machine learning analyses. In a multivariable analysis, proton pump inhibitors (PPIs) (AOR 4.5), and α-blockers (AOR 6.0) were significant risk factors for adrenal insufficiency after adjusting for confounders. Among the machine learning models, logistic regression and elastic net showed good predictions, with AUROC values of 0.75 (0.61-0.90) and 0.76 (0.64-0.89), respectively. Based on multivariable and machine learning analyses, females (1 point), age ≥ 65 (1 point), PPIs (1 point), α-blockers (2 points), and antipsychotics (3 points) were integrated into the risk scoring system. From the logistic regression curve, patients with 0, 1, 2, 4, 5, and 6 points showed approximately 1.1%, 2.8%, 7.3%, 17.6%, 36.8%, 61.3%, and 81.2% risk for adrenal insufficiency, respectively. The application of our scoring system could prove beneficial in patient assessment and clinical decision-making while administering PD-1/PD-L1 inhibitors.
肾上腺功能不全是免疫检查点抑制剂（ICI）罕见但危及生命的免疫相关不良事件。本研究旨在建立接受抗程序性细胞死亡蛋白1（PD-1）或抗程序性细胞死亡配体1（PD-L1）药物治疗患者的肾上腺功能不全风险评分系统。此外，还利用了几种机器学习方法来预测此类并发症。本研究纳入了2015年7月至2021年2月期间接受ICI治疗的209例患者，排除了既往有肾上腺功能不全、既往接受过类固醇治疗或数据不完整的患者，以确保数据完整性。患者在庆尚国立大学医院持续接受随访，采集清晨血样测量基础皮质醇水平，以便全面分析其肾上腺功能不全风险。使用卡方检验和逻辑回归模型，通过单变量和多变量分析得出比值比和调整后的比值比（AOR）。本研究利用决策树、随机森林、支持向量机（SVM）和逻辑回归等机器学习算法来预测接受ICI治疗患者的肾上腺功能不全。使用准确率、敏感性、特异性、精确率和受试者工作特征曲线下面积（AUROC）等指标评估每种算法的性能，确保严格评估和可重复性。通过多变量和机器学习分析建立了风险评分系统。在多变量分析中，质子泵抑制剂（PPI）（AOR 4.5）和α受体阻滞剂（AOR 6.0）在调整混杂因素后是肾上腺功能不全的显著危险因素。在机器学习模型中，逻辑回归和弹性网络显示出良好的预测效果，AUROC值分别为0.75（0.61 - 0.90）和0.76（0.64 - 0.89）。基于多变量和机器学习分析，女性（1分）、年龄≥65岁（1分）、PPI（1分）、α受体阻滞剂（2分）和抗精神病药物（3分）被纳入风险评分系统。根据逻辑回归曲线，得0分、1分、2分、4分、5分和6分的患者肾上腺功能不全风险分别约为1.1%、2.8%、7.3%、17.6%、36.8%、61.3%和81.2%。我们的评分系统在应用PD-1/PD-L1抑制剂时对患者评估和临床决策可能有益。
