The SARS-CoV-2 pandemic has affected more than 2 million people with a progressively increasing global trend. 1 This new coronavirus is highly contagious and severe forms are more prevalent among elderly individuals with cardiometabolic and respiratory comorbidities such as hypertension, chronic lung disease and diabetes. 1,2 These types of patients have a higher risk to develop acute respiratory syndrome and/or cardiac distress requiring invasive mechanical ventilation (IMV) and prolonged hospital stays. 3 IMV is accepted as a standard of care to treat patients with severe acute respiratory failure (ARF), but this procedure is frequently associated with a high incidence of complications, greater resource consumption and needs intensive care unit (ICU) admission. 4, 5 Use of non-invasive mechanical ventilation (NIMV) has greatly increased in the last decades as a possible and effective alternative to IMV because of its relatively simpler management and the possibility to be applied outside the ICU. 6 Although in ARF NIMV was proven to be associated to both a lower short-term mortality risk compared with standard oxygen therapy or IMV, 7 this procedure might be ineffective for some patients, leading to the need for a delayed invasive respiratory support. The availability of robust criteria to predict the risk of intubation and IMV since hospital admission is therefore pivotal.
The identification of predictive factors for severe course of the novel coronavirus disease (COVID-19) mainly comes from Chinese retrospective cohorts. 8 Recently, Simonnet et al retrospectively showed a high prevalence of obesity among patients admitted in French ICUs and reported IMV need for COVID-19 to be significantly associated with body mass index (BMI > 35 kg/m 2 ) independent of age, diabetes and hypertension. 9 However, these findings are in contrast with the 'obesity paradox' for patients with Adult Respiratory Distress Syndrome (ARDS). 10 In fact, a meta-analysis including 6268 ARDS patients documented that BMI was not associated with a higher mortality rate. 11 Several anthropometric indexes, such as waist-to-hip ratio, BMI, waist circumference and neck circumference (NC) are used to evaluate and estimate the 'adiposity'. 12 Many studies have shown that upper-body obesity has a stronger association with cardiometabolic conditions compared to lower-body obesity. 13, 14 NC, as an index for upper-body subcutaneous adipose tissue distribution, has been shown to be strongly related to insulin resistance, 15 early stage atherosclerosis, 16 diabetes, 17 coronary heart diseases 18 and cardiometabolic syndrome even after adjustment for visceral adipose tissue and BMI. 19 Furthermore, NC has been demonstrated to be more strongly correlated with respiratory functions in children 20 and obstructive sleep apnea in adults when compared to other clinical parameters, such as snoring, sex, age and BMI. 21 As NC is associated with both cardiometabolic and respiratory profile, it might represent an accurate and easy to measure parameter to predict the outcome of COVID-19 patients.
As a prompt recognition of predictive factors for respiratory outcomes is important to target high-risk populations for adequate surveillance and management, we conducted a prospective study to investigate the potential of NC as a predictor of clinical outcomes for adult inpatients with COVID-19.
This multicentric study had an observational prospective design. From 25 March to 7 April 2020, we consecutively enrolled COVID-19 patients admitted to dedicated medical wards of the following centres: Trieste (Azienda Sanitaria Universitaria Giuliano Isontina) and
Latina (Santa Maria Goretti Hospital). Nasopharyngeal swab samples were obtained from all patients and tested using real-time reverse transcriptase-polymerase chain reaction assays. On hospital admission, COVID-19 pneumonia was diagnosed in the whole study population according to World Health Organization guidance. 22 No patient was receiving pre-diagnosis potential treatment (namely antiviral therapies or corticosteroids) to face COVID-19. Following hospital admission, all patients required oxygen therapy. Patients were followed until the hospital discharge, death or at least for 30 days after the COVID-19 diagnosis. We excluded subjects with advanced cancer and end stage renal or liver diseases. The study was conducted in compliance with the Declaration of Helsinki and the International Conference on Harmonization Principles of Good Clinical Practice.
The research protocol was approved by the Ethic Committee. All participants gave informed consent allowing their anonymized information to be used for a data analysis.
The primary outcome was the maximum respiratory support received 
On ward admission, trained physicians collected data on past medical history, chronic medications and clinical history. The Charlson Index was used as a comorbidity score. The following treatments administered during the hospital stay were recorded: use of angiotensin converting enzyme inhibitors, angiotensin II receptor blockers, hydroxychloroquine, azithromycin, lopinavir/ritonavir, heparin, steroids, amiodarone, tocilizumab and remdesivir. Anthropometric measurements were taken in duplicate according to the NHANES III procedures. 25 In cases where the first two measures differed by 0.5 cm, a third measure was recorded, and the average of all measures recorded was computed. NC was measured immediately below the laryngeal prominence and perpendicular to the long axis of the neck. 25 Body weight and height were revealed to calculate the BMI. Fasting blood samples were obtained in the morning (from 8:00 to 8:30 AM) to evaluate complete white blood cell count, platelet count, C-reactive protein (CRP), albumin and D-dimer levels. The maximum Youden index (J) was considered as the optimal cutoff value.
'Survival' analysis was adopted to separate patients with longer from those with shorter intervals between hospital admission and the start of IMV. Observations were right-censored after 30 days of observation, corresponding to the follow-up study period. Unadjusted comparison according to NC subgroups was performed comparing crude Kaplan-Meier curves; differences between groups was assessed with Mantel-Cox log-rank test.
All above analyses were repeated on subgroups of male and female patients, as well as after excluding subjects with a BMI threshold of >30 kg/m 2 .
Statistical analyses were performed using the software IBM SPSS Statistics, version 24.0 (Armonk, New York: IBM Corp.). For all tests, an alpha level of P < .05 was set for statistical significance.
During the enrollment period, 162 patients were admitted to the study wards. Thirty patients were excluded according to the exclusion criteria. Finally, 132 patients constituted the study population.
The main characteristics of the enrolled patients are described in 
In the bivariate analysis, the NC differed significantly (P < .001) according to the highest level of breathing support needed by the patient. Moreover, the NC differed significantly (P < .001) for patients undergoing IMV or VM/HFNC in all sex and BMI threshold subgroup comparisons, while NC differences between the other respiratory support modalities gave uneven results (Figure 1 ).
In multivariable logistic regression analyses, after adjusting the model (#1) for age, sex, presence of diabetes, hypertension and COPD, NC showed to be independently and significantly associated to IMV risk (adjusted OR 1.260; CI:1.120-1.417, P < .001). We confirmed an independent and significant association with IMV risk even in the subgroup of patients with a BMI of ≤30 (adjusted OR 1.526; CI:1.243-1.874, P < .001). NC showed a similar independent association with IMV risk when the regression models (#2) were adjusted for variables (ie, monocytes, eosinophils, albumin, CRP, hydroxychloroquine and steroids) ( Table 2 .
The NC showed a good discrimination power (AUC 0.783; 95% CI: 0.684-0.882; P < .001) at separating patients who needed IMV from those who did not. Compared to the whole population, the discrimination power was accurate for both male (AUC 0.801; 95% CI: 0.693-0.909; P < .001) and female patients (AUC 0.767; 95% CI: 0.581-0.953; P = .027). The optimal cutoff value of NC to predict the need for IMV in the whole study population was 40.5 cm (J: 0.456), while different thresholds were found according to patient's sex (male: 42.5 cm; female 37.5 cm). After ruling out patients with BMI > 30 kg/ m 2 , the NC threshold did not change. When performing ROC analysis in patients with a BMI of ≤30 kg/m 2 , a further improvement in the discrimination power of NC with respect to IMV occurrence was found, particularly in the female population (all pts: AUC 0.799, 95% CI: 0.691-0.910, P < .001; male: AUC 0.816, 95% CI: 0.698-0.934, P < .001; female: AUC 0.816, 95% CI: 0.624-1.000, P = .016). Figure 2 shows the ROC curves for all tested subgroups. Figure 3 shows the Kaplan-Meier curves for the risk of IMV in patients belonging to the NC groups according to the identified risk threshold and to patient's sex. In the whole study population, patients with a NC > 40.5 cm showed a higher and earlier risk for IMV (Logrank test: P < .001) compared to subjects in the lower circumference group ( Figure 3A) . Similar results were found after separately analysing the female and male populations (Log-rank test: P = .028 and P < .001, respectively) according to the respective circumference
Baseline patients demographics, relevant comorbidities, anthropometric parameters, blood test parameters, and administered medications and comparisons according to the higher breathing support thresholds ( Figure 3B,C) , and even better results were confirmed (Log-rank test: P < .001, P = .017 and P < .001, respectively for all, female and male patients) after repeating the same analyses in the subgroup of patients with a BMI of ≤30 kg/m 2 ( Figure 3D -F).
Our findings have demonstrated that NC is an independent predictor for IMV in adult COVID-19 inpatients. In particular, in our study population, the risk of being subjected to IMV increases by 26% for each centimetre increase in NC, increasing up to 53% in patients with a BMI of ≤30 kg/m 2 .
The COVID-19 outbreak has pushed worldwide scientific efforts to identify patients at higher risk of developing critical illness requiring advanced supportive care. Furthermore, NC is a practical and easy to measure anthropometric parameter, especially useful for bedridden patients where traditional measurements may be challenging or not meaningful.
The main strengths of our study are the prospective and multicentric design and the novelty of using a simple, easy to measure and costless anthropometric parameter to predict clinical outcome and therefore potential health care needs for COVID-19 patients. Instead, the main limitation is the relatively small patient sample size.
In conclusion, NC appears to be an easy and valuable tool able to 