Herrera-Martínez Aura D, Prior-Sánchez Inmaculada, Fernández-Soto María Luisa, García-Olivares María, Novo-Rodríguez Cristina, González-Pacheco María, Martínez-Ramirez María José, Carmona-Llanos Alba, Jiménez-Sánchez Andrés, Muñoz-Jiménez Concepción, Torres-Flores Fátima, Fernández-Jiménez Rocío, Boughanem Hatim, Del Galindo-Gallardo María Carmen, Luengo-Pérez Luis Miguel, Molina-Puerta María Josefa, García-Almeida José Manuel
Maimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.
Endocrinology and Nutrition Service, Reina Sofia University Hospital, Córdoba, Spain.
J Cachexia Sarcopenia Muscle. 2024 Dec;15(6):2426-2436. doi: 10.1002/jcsm.13577. Epub 2024 Oct 24.
Malnutrition 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.
Cross-sectional, observational study that included 509 HNC patients. A comprehensive nutritional evaluation that included BIVA was performed.
The 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.
Body 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.
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很有必要，因为这两者都与临床结局和死亡率明显相关。我们旨在评估使用生物电阻抗矢量分析（BIVA）进行身体成分分析在诊断接受化疗或放疗全身治疗的HNC癌症患者的营养不良和肌肉减少症方面的适用性和准确性。
一项横断面观察性研究，纳入了509例HNC患者。进行了包括BIVA在内的全面营养评估。
接受化疗的患者中营养不良的患病率更高（59.2%对40.8%，P<0.001）；营养不良患者的死亡率增加（33.3%对20.1%；P<0.001）；营养不良患者的ECOG状态（1-4级）也更差（59.2%对22.8%，P<0.001）。身体细胞质量（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和去脂体重指数（FFMI）与几个方面相关，包括（1）患者自评的主观全面评定法[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并结合包括FFMI和BCM在内的其他参数可准确预测营养不良患者[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足以识别男性营养不良患者（P<0.001），而在女性中则应与其他参数结合使用。
通过BIVA确定的身体成分参数可准确识别HNC和营养不良患者。相位角，但包括BCMI、FFMI和BCM在内的其他参数也能提供有关HNC患者营养状况的重要信息。
