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使用在线工具对头颈部癌患者的合并症和衰弱指数进行比较。

Comparison of Comorbidity and Frailty Indices in Patients With Head and Neck Cancer Using an Online Tool.

作者信息

Vitzthum Lucas K, Feng Christine H, Noticewala Sonal, Hines Paul J, Nguyen Cammie, Zakeri Kaveh, Sojourner Elena J, Shen Hanjie, Mell Loren K

机构信息

Lucas K. Vitzthum, Christine H. Feng, Sonal Noticewala, Cammie Nguyen, Kaveh Zakeri, Elena J. Sojourner, and Hanjie Shen, University of California San Diego; Loren K. Mell, University of California San Diego; Center for Translational Radiation Medicine and Imaging, La Jolla, CA; and Paul J. Hines, Dose Health, Minneapolis, MN.

出版信息

JCO Clin Cancer Inform. 2018 Dec;2:1-9. doi: 10.1200/CCI.18.00082.

Abstract

PURPOSE

Comorbidity is an independent predictor of mortality and treatment tolerance in head and neck cancer and should be considered with regard to treatment intensification. Multiple previously validated models can be used to evaluate comorbidity and propensity to benefit from intensive treatment, but they have not been directly compared.

MATERIALS AND METHODS

An online tool was developed and used to calculate the Charlson Comorbidity Index (CCI), Adult Comorbidity Evaluation-27 (ACE-27), Cumulative Illness Rating Scale for Geriatrics (CIRS-G), Geriatric 8 (G8), Cancer and Aging Research Group (CARG), and Generalized Competing Event (GCE) scores. To assess interrater variability, five evaluators independently calculated scores on a retrospective cohort of 20 patients. Correlation between models as well as age and performance status were calculated from a cohort of 40 patients.

RESULTS

The GCE and G8 models had an excellent (intraclass correlation coefficient and Fleiss' kappa ≥ 0.75) degree of interrater agreement. The CCI, ACE-27, CIRS-G, and CARG had a good (intraclass correlation coefficient and Fleiss' kappa 0.6-0.74) degree of interrater agreement. There was statistically significant correlation between models, especially with the CCI, ACE-27, and CIRS-G indices. Increased age was correlated with an increased CCI score and having moderate to severe comorbidity was correlated with the ACE-27 model. Except for the G8 model, the comorbidity indices were not associated with Eastern Cooperative Oncology Group performance status.

CONCLUSION

We developed an online tool to calculate indices of comorbidity in patients with head and neck cancer with a high degree of reproducibility. Comorbidity is not strongly correlated with performance status and should be independently evaluated in patients.

摘要

目的

合并症是头颈癌死亡率和治疗耐受性的独立预测因素,在考虑强化治疗时应予以考虑。多个先前经过验证的模型可用于评估合并症以及从强化治疗中获益的倾向,但尚未对它们进行直接比较。

材料与方法

开发了一个在线工具,用于计算查尔森合并症指数(CCI)、成人合并症评估-27(ACE-27)、老年累积疾病评定量表(CIRS-G)、老年8分法(G8)、癌症与衰老研究组(CARG)以及广义竞争事件(GCE)评分。为评估评分者间的变异性,五名评估者对20例患者的回顾性队列独立计算评分。从40例患者的队列中计算模型之间以及年龄与体能状态的相关性。

结果

GCE和G8模型具有出色的(组内相关系数和Fleiss' kappa≥0.75)评分者间一致性程度。CCI、ACE-27、CIRS-G和CARG具有良好的(组内相关系数和Fleiss' kappa 0.6 - 0.74)评分者间一致性程度。模型之间存在统计学上的显著相关性,尤其是与CCI、ACE-27和CIRS-G指数。年龄增加与CCI评分增加相关,合并症为中度至重度与ACE-27模型相关。除G8模型外,合并症指数与东部肿瘤协作组体能状态无关。

结论

我们开发了一个在线工具来计算头颈癌患者的合并症指数,具有高度的可重复性。合并症与体能状态的相关性不强,应在患者中进行独立评估。

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