BackgroundMalnutrition and sarcopenia are highly prevalent in patients with head neck cancer (HNC). An accurate early diagnosis is necessary for starting nutritional support, as both are clearly associated with clinical outcomes and mortality. We aimed to evaluate the applicability and accuracy of body composition analysis using electrical bioimpedance vectorial analysis (BIVA) for diagnosing malnutrition and sarcopenia in patients with HNC cancer undergoing systemic treatment with chemotherapy or radiotherapy.MethodsCross‐sectional, observational study that included 509 HNC patients. A comprehensive nutritional evaluation that included BIVA was performed.ResultsThe prevalence of malnutrition was higher in patients that received treatment with chemotherapy (59.2% vs. 40.8%, P < 0.001); increased mortality was observed in malnourished patients (33.3% vs. 20.1%; P < 0.001); ECOG status (1–4) was also worse in malnourished patients (59.2% vs. 22.8% P < 0.001). Body cell mass (BCM) and fat mass were the most significantly associated parameters with malnutrition [OR 0.88 (0.84–0.93) and 0.98 (0.95–1.01), respectively]; BCM and fat free mass index (FFMI) were associated with several aspects including (1) the patient‐generated subjective global assessment [OR 0.93 (0.84–0.98) and 0.86 (0.76–0.97), respectively], (2) the presence of sarcopenia [OR 0.81 (0.76–0.87) and 0.78 (0.66–0.92), respectively]. A BCM index (BCMI) < 7.8 in combination with other parameters including FFMI and BCM accurately predicted patients with malnutrition [accuracy 95% CI: 0.803 (0.763–0.839); kappa index: 0.486; AUC: 0.618 (P < 0.01)]. A BCMI cutoff of 7.6 was enough for identifying males with malnutrition (P < 0.001), while it should be combined with other parameters in females.ConclusionsBody composition parameters determined by BIVA accurately identify patients with HNC and malnutrition. Phase angle, but other parameters including BCMI, FFMI and BCM provide significant information about nutritional status in patients with HNC.

中文翻译：

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使用生物电阻抗分析改善头颈癌患者的营养评估：不仅相位角很重要


背景营养不良和肌肉减少症在头颈癌 （HNC） 患者中非常普遍。准确的早期诊断对于开始营养支持是必要的，因为两者都与临床结果和死亡率明显相关。我们旨在评估使用电生物阻抗矢量分析 （BIVA） 进行身体成分分析诊断接受化疗或放疗全身治疗的 HNC 癌症患者营养不良和肌肉减少症的适用性和准确性。方法包括 509 名 HNC 患者的横断面观察性研究。进行了包括 BIVA 在内的综合营养评估。结果接受化疗的患者营养不良患病率较高 （59.2% vs. 40.8%，P < 0.001）;在营养不良患者中观察到死亡率增加 （33.3% vs. 20.1%;P < 0.001）;营养不良患者的 ECOG 状态 （1-4） 也较差 （59.2% vs. 22.8% P < 0.001）。体细胞量 （BCM） 和脂肪量是与营养不良最显著相关的参数 [OR 分别为 0.88 （0.84-0.93） 和 0.98 （0.95-1.01]）;BCM 和无脂肪质量指数 （FFMI） 与几个方面相关，包括 （1） 患者生成的主观总体评估 [OR 分别为 0.93 （0.84-0.98） 和 0.86 （0.76-0.97]），（2） 肌肉减少症的存在 [OR 分别为 0.81 （0.76-0.87） 和 0.78 （0.66-0.92]）。BCM 指数 （BCMI） < 7.8 结合包括 FFMI 和 BCM 在内的其他参数准确预测了营养不良患者 [准确性 95% CI：0.803 （0.763–0.839）;kappa 指数：0.486;AUC： 0.618 （P < 0.01）]。BCMI 临界值为 7.6 足以识别营养不良的男性 （P < 0.001），而女性应将其与其他参数相结合。结论BIVA 测定的体成分参数可准确识别 HNC 合并营养不良患者。相位角，但包括 BCMI 、 FFMI 和 BCM 在内的其他参数提供了有关 HNC 患者营养状况的重要信息。