Study design and participants
This was a prospective longitudinal study. Subjects who were pathologically diagnosed with HNC and scheduled for RT from March 2017 to December 2019 were recruited from one cancer hospital in Beijing. The other recruitment criteria were: (1) age ≥ 18 years; (2) entirely voluntary participation; (3) ability to communicate clearly. Considering factor analysis, the number of subjects should be five to ten times the number of items on the scale. A total of 17 symptoms were assessed in this study, so 85 to 170 subjects were necessary [20].
All subjects were informed about the study, including the purpose and content of this study prior to the RT. This study had got approval from the Ethics Committee of the author’s University (No. IRB00001052-17002), and the procedure adhered to the principles in the Declaration of Helsinki.
Define the crucial NIS cluster
In this study, we defined a crucial NIS cluster as the cluster that had significantly correlation with both weight loss rate (WLR) and QoL.
Variables and instruments
General and clinical information
We used a questionnaire to record the demographic, sociology, and clinical information at T1 (age, gender, marital status, tumor site, tumor stage, and RT type).
Nutritional risk and nutritional status before RT
Nutritional risk was assessed by the NRS 2002 [21]. This tool comprises of three parts: undernutrition, disease severity, and age. The undernutrition assesses three aspects: patients’ body mass index (BMI), recent weight change, and food intake change. The disease severity is determined by the nutritional requirements. Each aspect will be classified as absent (Score = 0), mild (Score = 1), moderate (Score = 2), and severe (Score = 3). Patients with score ≥ 3 had nutritional risk.
Nutritional status was evaluated by the GLIM which is newly proposed for diagnosing malnutrition [22]. Based on identified nutritional risk, the diagnosis criteria include three phenotypic criteria (non-volitional weight loss, low body mass index, and reduced muscle mass) and two etiologic criteria (reduced food intake or assimilation, and inflammation or disease burden). The muscle mass was measured by the InBody 120 (Biospace Co., Ltd, Seoul, South Korea) based on the bioelectrical impedance analysis. Appendicular skeletal muscle index (ASMI) and fat free mass index (FFMI) were used to define the low body mass index (male, ASMI<7 kg/m2 or FFMI<17 kg/m2; female, ASMI<5.7 kg/m2 or FFMI<15 kg/m2 for Asian) [22]. Patients with at least one phenotypic criterion and one etiologic criterion would be diagnosed with undernutrition.
Occurrence of nutrition impact symptoms
We adopted the Head and neck patient symptom checklist (HNSC) to evaluate 17 NIS (oral mucositis and dry mouth, etc.) [23]. This checklist assesses two dimensions: the severity and interference with dietary of symptoms using a five-point Likert scale (1 = not at all to 5 = a lot). Jin et al. [24] had validated it in Chinese patients with HNC receiving RT. It had good psychometric performance and could be used to assess the NIS of patients with HNC.
Weight loss rate
Patients’ weight was measured by the InBody120 device (Biospace Co., Ltd, Seoul, South Korea) when they wore light clothes and removed shoes. WLR was calculated using the equation: WLR= (baseline weight-present weight)/baseline weight×100%.
Global quality of life
The global QoL was evaluated with the global QoL subscales from the European Organization for Research and Treatment of Cancer (EORTC) Questionnaire-Core 30 (QLQ-C30) [25]. The global QoL dimension includes two items on global health and global life quality with a seven-point Likert scale. Higher scores indicated better global life quality.
Data collection
Trained research fellows collected data by face-to-face interviews with uniform instruction at three follow-up visits in the outpatient. Before RT (baseline, T1), the general information was reported by subjects. Information on disease and treatment characteristics was determined from the medical records. The occurrence of NIS, weight, QoL, nutritional risk, and nutritional status were evaluated. At the third week (T2) and the end of RT (T3), occurrence of NIS, present weight and QoL were reassessed.
Data analysis
All data analysis was conducted using the IBM SPSS Statistics for Windows, version 24.0 (IBM Corp., Armonk, N.Y., USA). The NIS’s interference had several missing values and was filled up by the mean of the nearby two points[26]. Counts and percentages were used to report categorical variables, while mean and standard deviation were used to report continuous variables. Exploratory factor analysis (EFA) with orthogonal rotation and principal component analysis was used to extracted symptom clusters. The individual measure of sampling adequacy (MSA) for each symptom was calculated to evaluate correlations between symptoms. We deleted symptoms with low MSA (< 0.5) as they could result in too many factors [27]. The average score of severity of NIS at T2 and T3 was used to conduct EFA. The number of factors was determined by the principle of eigenvalue > 1. Symptoms with weak factor loadings (< 0.40) were deleted. If symptoms had fair loadings on more than one factor, it was placed with the factor that it was mostly related to conceptually. [27] Symptom clusters were named and interpreted based on analysis results and theoretical considerations. Cronbach’s α coefficient was adopted to assess clusters’ internal consistency. The unweighted mean of symptoms was calculated to get the composite scores of clusters at T2 and T3 [27]. Generalized estimating equations (GEE) were adopted to explore the association between clusters and WLR or global QoL, and the predictive factors of symptom clusters. Variables with P < 0.1 in univariable models were entered into the multivariable models. As the nutritional risk and nutritional status before RT had a closed correlation with each other, so they would be placed in two multivariable GEE models separately (Model 1 and Model 2).