Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: A complex relationship.

INTRODUCTION: Delay between symptom onset and access to care is essential to prevent clinical worsening for different infectious diseases. For COVID-19, this delay might be associated with the clinical prognosis, but also with the different characteristics of patients. The objective was to describe characteristics and symptoms of community-acquired (CA) COVID-19 patients at hospital admission according to the delay between symptom onset and hospital admission, and to identify determinants associated with delay of admission.
METHODS: The present work was based on prospective NOSO-COR cohort data, and restricted to patients with laboratory confirmed CA SARS-CoV-2 infection admitted to Lyon hospitals between February 8 and June 30, 2020. Long delay of hospital admission was defined as ≥6 days between symptom onset and hospital admission. Determinants of the delay between symptom onset and hospital admission were identified by univariate and multiple logistic regression analysis.
RESULTS: Data from 827 patients were analysed. Patients with a long delay between symptom onset and hospital admission were younger (p<0.01), had higher body mass index (p<0.01), and were more frequently admitted to intensive care unit (p<0.01). Their plasma levels of C-reactive protein were also significantly higher (p<0.01). The crude in-hospital fatality rate was lower in this group (13.3% versus 27.6%), p<0.01. Multiple analysis with correction for multiple testing showed that age ≥75 years was associated with a short delay between symptom onset and hospital admission (≤5 days) (aOR: 0.47 95% CI (0.34-0.66)) and CRP>100 mg/L at admission was associated with a long delay (aOR: 1.84 95% CI (1.32-2.55)). DISCUSSION: Delay between symptom onset and hospital admission is a major issue regarding prognosis of COVID-19 but can be related to multiple factors such as individual characteristics, organization of care and severe pathogenic processes. Age seems to play a key role in the delay of access to care and the disease prognosis.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogenic agent of coronavirus disease 2019 (COVID-19), is a new coronavirus that emerged from China in December 2019 [1]. The virus causes infections of the lower or upper respiratory tract of varying severity, from the common cold to severe pneumonia, respiratory failure, and death [2]. Male sex, old age, obesity, biological parameters such as biomarkers of inflammation, are known to be associated with severe disease [3-6]. The presence of at least one comorbidity (e.g. cardiovascular or chronic pulmonary disease) has been reported in 60% to 90% of hospitalized COVID-19 patients [7]. Regarding access to care, a positive association between a long time interval between COVID-19 diagnosis or hospital admission and the occurrence of severe disease or death has been described but little data are available [8, 9].

The NOSO-COR study is an international observational, prospective, multicentric study carried out in 13 French hospitals and hospitals affiliated with the GABRIEL network [10] in order to estimate the prevalence and incidence of SARS-CoV-2 infection and to assess the associated characteristics among healthcare workers and patients. Our previous results, based on 417 patients, showed that in COVID-19 patients, age as well as delay between symptom onset and hospital admission were both associated with ICU admission [11].

In order to investigate this observation further in a larger number of patients, this work aimed #1) to describe in detail the characteristics and symptoms of community-acquired (CA) COVID-19 patients at hospital admission according to the delay between symptom onset and hospital admission, #2) to identify determinants associated with the delay of admission.

Methods

The present work was restricted to patients with CA SARS-CoV-2 infection admitted to Lyon University Hospitals (Hospices Civils de Lyon, HCL), between February 8 and June 30, 2020 with complete data at discharge or death. Compared to our previously published work [11], this analysis was based on the same cohort, but with a larger number of patients (n = 827 versus n = 417 in the previously published paper).

The detailed protocol of the NOSO-COR study is available online [10]. Briefly, any patient who presented an infectious syndrome based on the WHO definition of COVID-19 as of March 30, 2020 [12], and was hospitalized for a period of at least 24 hours, was included. Patients with positive real-time Reverse Transcriptase–Polymerase Chain Reaction (RT-PCR) results were defined as laboratory confirmed SARS-CoV-2 infections. A CA SARS-CoV-2 infection was defined as a patient with symptom onset before or at hospital admission.

Demographic characteristics, underlying comorbidities, clinical and biological parameters and patient outcome data were collected. The clinical outcomes were monitored up to hospital discharge or in-hospital death.

Delay between symptom onset and hospital admission was computed as the difference between the two dates. Data higher than the 99th percentile of the distribution of the delay were considered as outliers and removed. We categorized the delay in 2 groups according to the median of its distribution: “Short delay” when occurrence of symptoms was 5 days (included) or less before hospital admission; “long delay” when occurrence of symptoms was ≥6 days before hospital admission. This choice was guided by the distribution of the variable (S1 Fig and S1 Table). Admission to ICU included patients directly admitted to ICU and those hospitalized in a medical ward and subsequently transferred to ICU during their hospitalization. Continuous variables were reported as median and interquartile range (IQR) with comparisons using the Mann-Whitney U test. Qualitative variables were computed as number of individuals (n) and frequency (%) using the χ2 or Fisher exact test as appropriate for comparison. The trend of the delay between symptom onset and hospital admission by age group and the trend of the proportion of patients with ICU hospitalization by age group were assessed using Cuzick’s test. All tests were 2-tailed, with p<0.05 considered statistically significant.

The logistic regression analysis was performed with delay between symptom onset and hospital admission as the dependent variable. Explanatory variables were first tested by univariate regression. Interaction of each variable with the variable age ≥ 75 years and sex was tested 1 by 1. Variables with p<0.10 in univariate analysis were added in a multivariate logistic regression model (i.e. the complete model). Then, using a backward selection technique, variables were removed one by one from the complete model, in order to keep the simplest model to predict the delay (i.e. the parsimonious model). Holmes correction for multiple testing was applied to the final parsimonious model. Statistical analysis was performed using STATA 13® (College Station, TX, USA).

Ethics

The study was approved by the clinical research and ethics committee of Ile-de-France V on March 8, 2020 (NOSO-COR, ClinicalTrials: NCT04290780). This study is an observational study based on patient medical records, and all data were fully anonymized at the time of collection. According to French law, patients or parents/guardians of minors received written information on this observational study, and their non-opposition to the use of their data was obtained.

Results

Between February 8 and June 30, 2020, a total of 1,150 patients hospitalized in Lyon University Hospital were included. Of the 905 (78.7%) patients with a CA SARS-CoV-2 infection, 67 (7.4%) were excluded because of an unknown delay between symptom onset and hospital admission, and 11 (1.2%) were considered as outliers, leaving 827 patients for the final analysis.

The characteristics of the overall study population, according to the delay between symptom onset and hospital admission, are detailed in Table 1. The median age was 73 years (IQR: 61–84), 55.9% of patients were male, 21.9% were admitted to intensive care unit (ICU) or transferred to ICU during their hospitalization. Cardiovascular disease was the most frequent comorbidity (53.2%). Patients with a longer delay were younger (p<0.01); had a higher body mass index (p<0.01); were more frequently admitted to ICU (p<0.01). They presented different characteristics and symptoms at admission more frequently, particularly cough (p<0.01), weakness (p = 0.02), shortness of breath (p = 0.01), pain (p<0.01), ageusia (p<0.01) and anosmia (p<0.01). The frequency of reported confusion was lower in patients with a longer delay (p<0.01), whereas the plasmatic levels of C-reactive protein (CRP) were significantly higher (p<0.01) than in patients with a shorter delay. The crude in-hospital fatality rate was lower in the group of patients with a longer delay (13.3%) than in patients with a short delay (27.6%), p<0.01.

Table 1

Characteristics of the study population according to delay between symptom onset and hospital admission, Lyon University Hospital (NOSO-COR Study), February 8–June 30, 2020.

Characteristics	All patients n = 827	Patient with a short delay between symptom onset and hospital admission (≤5 days): n = 421	Patient with a long delay between symptom onset and hospital admission (≥6 days): n = 406	p-value
Male gender, n (%)	462 (55.9)	219 (52.0)	243 (59.9)	0.02
Age, median (IQR)	73 (61–84)	79 (65–87)	69 (54–78)	<0.01
Age ≥ 75 y., n (%)	384 (46.4)	246 (58.4)	138 (34.0)	<0.01
BMI, median (IQR)	26.2 (23.1–29.7) [664]	25.7 (22.5–29.2) [349]	26.7 (23.7–30.0) [315]	<0.01
BMI ≥ 30, n (%)	153 (23.0)	76 (21.8)	77 (24.4)	0.42
Comorbidities, n (%)
Cardiovascular disease	440 (53.2)	256 (60.8)	184 (45.3)	<0.01
Diabetes	191 (23.1)	106 (25.2)	85 (20.9)	0.15
Malignancy	146 (17.7)	92 (21.9)	54 (13.3)	0.01
Chronic kidney disease	115 (13.9)	72 (17.1)	43 (10.6)	0.07
Chronic lung disease	103 (12.5)	60 (14.3)	43 (10.6)	0.11
Chronic liver disease	55 (7.5) [736]	28 (7.1) [395]	27 (7.9) [341]	0.67
Immunodeficiency	48 (5.8)	27 (6.4)	21 (5.2)	0.45
Smoking status, n (%)	[626]	[304]	[322]	0.30
Current smoker	29 (4.6)	18 (5.9)	11 (3.4)
Ex-smoker	197 (31.5)	97 (31.9)	100 (31.1)
Never smoker	400 (63.9)	189 (62.2)	211 (65.5)
Characteristics	All patients n = 827	Patient with a short delay between symptom onset and hospital admission (≤5 days): n = 421	Patient with a long delay between symptom onset and hospital admission (≥6 days): n = 406	p-value
Temperature at admission, median (IQR)	38.0 (37.1–38.6) [739]	38.0 (37.2–38.5) [383]	38.0 (37.1–38.8) [356]	0.12
Symptoms at admission, n (%)
History of fever/chills	674 (81.5)	330 (78.4)	344 (84.7)	0.02
Weakness	584 (70.6)	282 (67.0)	302 (74.4)	0.02
Cough	560 (67.7)	252 (59.9)	308 (75.9)	<0.01
Shortness of breath	550 (66.5)	257 (61.1)	293 (72.2)	0.01
Diarrhea	229 (27.8)	103 (24.5)	126 (31.0)	0.04
Pain	225 (27.2)	97 (23.0)	128 (31.5)	<0.01
Myalgia	144 (17.4)	60 (14.3)	84 (20.7)	0.02
Abdominal pain	62 (7.5)	36 (8.6)	26 (6.4)	0.24
Chest pain	50 (6.0)	19 (4.5)	31 (7.6)	0.06
Joint pain	12 (1.5)	5 (1.2)	7 (1.7)	0.99
Nausea	107 (12.9)	50 (11.9)	57 (14.0)	0.35
Headache	105 (12.7)	43 (10.2)	62 (15.3)	0.03
Confusion	93 (11.3)	63 (14.5)	30 (7.4)	0.01
Runny nose	72 (8.7)	33 (7.8)	39 (9.6)	0.37
Ageusia	64 (7.7)	14 (3.3)	50 (12.3)	<0.01
Anosmia	58 (7.0)	12 (2.9)	46 (11.3)	<0.01
Sore throat	34 (4.1)	16 (3.8)	18 (4.4)	0.65
Biological parameters, median (IQR)
White blood cells (G/L)	6.36 (4.87–8.59) [769]	6.46 (4.71–8.62) [394]	6.26 (4.95–8.52) [375]	0.76
Neutrophils (G/L)	4.75 (3.28–6.83) [768]	4.73 (3.19–7.07) [393]	4.82 (3.35–6.61) [375]	0.69
Lymphocytes (G/L)	0.95 (0.64–1.31) [767]	0.95 (0.63–1.32) [392]	0.94 (0.64–1.30) [375]	0.78
CRP (mg/L)	71.4 (30.0–135.1) [711]	65.0 (27.3–116.7) [369]	86.6 (32.4–142.2) [342]	<0.01
Admission to ICU, n (%)	181 (21.9)	76 (18.1)	105 (25.9)	<0.01
Admission directly to ICU	136 (16.4)	46 (10.9)	90 (22.2)	<0.01
Admission to general ward and transfer to ICU during hospitalization	45 (5.4)	30 (7.1)	15 (3.6)	0.03
Death during hospitalization, n (%)	170 (20.6)	116 (27.6)	54 (13.3)	<0.01

NOTE: in square brackets []: number of data available for the variable. If no square brackets, there is no missing data for the variable. BMI: Body mass index, CRP: C-reactive protein, IQR: Interquartile range. ereference category: white blood cells ≤ 10 G/L, freference category: Neutrophils ≤ 7.5 G/L,. greference category: Lymphocytes ≥ 1 G/L, hreference category: CRP ≤ 100 mg/L

Median delay between symptom onset and hospital admission was 5 days (IQR: 3–9). This delay was significantly longer in ICU-hospitalized patients compared to those without ICU admission (7 days [IQR: 4–10] vs 5 days [IQR: 2–8], p<0.01).

As shown in Fig 1, the delay between symptom onset and hospital admission and the proportions of patients hospitalized to ICU decreased with age for patients above 60 years of age (p<0.01 and p<0.01, respectively). Patient characteristics according to age are displayed in S2 Table. The results showed that the median delay between symptom onset and hospital admission decreased in older patients (P<0.01). The proportion of ICU admission decreased in older patients (P<0.01), while death during hospitalization increased in this population (P<0.01). Overall, older patients had more comorbidities, such as cardiovascular diseases (P<0.01), malignancy (P<0.01) or chronic kidney diseases (P<0.01) compared to younger patients.

Fig 1

Delays between symptom onset and hospital admission and proportions of admission to ICU according to patient age, Lyon University Hospital (NOSO-COR Study), February 8–June 30, 2020.

Note: Admission to ICU includes patients directly admitted to ICU and patients hospitalized in a medical ward and subsequently transferred to ICU during their hospitalization.

Overall, 711 patients (86.0%) for whom complete data were available, were included in the logistic regression analysis. Table 2 depicts the association between underlying comorbidities, clinical features and biological parameters at admission and the delay between symptom onset and hospitalization. In multivariate analysis, age ≥75 years (p<0.01), and confusion at admission (p = 0.02), were associated with a short delay between symptom onset and hospital admission. On the contrary, weakness (p = 0.02), cough (p = 0.01), ageusia (p = 0.02), anosmia (p = 0.03) and CRP>100 mg/L at admission (p<0.01) were associated with a long delay between symptom onset and hospital admission. After correction for multiple testing, only two variables remained significant in the model: age ≥75 years was associated with a short delay between symptom onset and hospital admission (p<0.01) and CRP>100 mg/L at admission was associated with a long delay between symptom onset and hospital admission (p<0.01).

Table 2

Association between patient characteristics and delay between symptom onset and hospital admission, Lyon University Hospital (NOSO-COR Study), February 8–June 30, 2020.

Characteristics	Crude OR of the risk of long delay between symptom onset and hospital admission (>5 d) (CI 95%) a	p-value	Adjusted OR of the risk of long delay between symptom onset and hospital admission (>5 d) (ie. complete model) (CI 95%) b	p-value	Adjusted OR of the risk of long delay between symptom onset and hospital admission (>5 d) (ie. parsimonious model) (CI 95%) c	p-value	Corrected p-value (Holmes multiple testing correction)
Male gender	1.31 (0.98–1.77)	0.07	0.90 (0.65–1.26)	0.55
Age ≥ 75 y	0.36 (0.26–0.49)	<0.01	0.54 (0.38–0.77)	<0.01	0.47 (0.34–0.66)	<0.01	<0.01
BMI ≥ 30	1.17 (0.79–1.74)	0.43
Comorbidities
Cardiovascular disease	0.53 (0.39–0.72)	<0.01	0.79 (0.56–1.11)	0.18
Diabetes	0.90 (0.63–1.27)	0.55
Malignancy	0.55 (0.37–0.82)	<0.01	0.66 (0.43–1.01)	0.06	0.65 (0.42–0.99)	0.05	0.08
Chronic lung disease	0.73 (0.47–1.14)	0.16
Chronic kidney disease	0.61 (0.39–0.94)	0.03	0.93 (0.57–1.51)	0.76
Chronic liver disease	1.13 (0.63–2.03)	0.69
Immunodeficiency	0.79 (0.41–1.50)	0.47
Smoking status
Current smoking	1 (ref.)
Ex smoker	1.52 (0.67–3.47)	0.32
Never smoking	1.53 (0.69–3.40)	0.29
Characteristics	Crude OR of the risk of long delay between symptom onset and hospital admission (>5 d) (CI 95%) a	p-value	Adjusted OR of the risk of long delay between symptom onset and hospital admission (>5 d) (ie. complete model) (CI 95%) b	p-value	Adjusted OR of the risk of long delay between symptom onset and hospital admission (>5 d) (ie. parsimonious model) (CI 95%) c	p-value	Corrected p-value (Holmes multiple testing correction)
Symptoms at admission
History of fever/chills	1.57 (1.07–2.30)	0.02	1.28 (0.83–1.95)	0.26
Weakness	1.40 (1.01–1.95)	0.04	1.52 (1.06–2.18)	0.02	1.53 (1.07–2.19)	0.02	0.08
Cough	2.13 (1.54–2.96)	<0.01	1.47 (1.03–2.10)	0.03	1.56 (1.10–2.22)	0.01	0.08
Shortness of breath	1.51 (1.10–2.06)	0.01	1.20 (0.84–1.70)	0.31
Diarrhea	1.45 (1.04–2.01)	0.03	1.22 (0.85–1.75)	0.28
Myalgia	1.83 (1.24–2.69)	<0.01	1.38 (0.89–2.13)	0.15
Abdominal pain	0.83 (0.48–1.43)	0.50
Chest pain	1.60 (0.86–2.98)	0.14
Joint pain	1.52 (0.48–4.84)	0.48
Nausea	1.15 (0.75–1.75)	0.53
Headache	1.66 (1.07–2.57)	0.02	1.02 (0.62–1.69)	0.94
Confusion	0.41 (0.25–0.68)	<0.01	0.56 (0.33–0.96)	0.04	0.52 (0.31–0.89)	0.02	0.08
Runny nose	1.39 (0.83–2.34)	0.22
Ageusia	5.06 (2.57–9.94)	<0.01	2.58 (1.17–5.71)	0.02	2.61 (1.20–5.71)	0.02	0.08
Anosmia	5.03 (2.48–10.19)	<0.01	2.28 (0.98–5.29)	0.06	2.50 (1.10–5.70)	0.03	0.08
Sore throat	1.08 (0.51–2.30)	0.84
Biological parameters
White blood cells > 10 G/L	0.86 (0.57–1.30)	0.48
Neutrophils > 7.5 G/L	0.88 (0.60–1.31)	0.54
Lymphocytes < 1 G/L	1.10 (0.82–1.48)	0.51
CRP > 100 mg/L	1.72 (1.27–2.33)	<0.01	1.76 (1.24–2.48)	<0.01	1.84 (1.32–2.55)	<0.01	<0.01

NOTE: BMI: Body mass index, CRP: C-reactive protein

ain univariate analysis,

bin multivariate analysis, the complete model included all variables with p<0.10 in univariate analysis,

cin multivariate analysis, the model included the variables retained after backward selection, ie. age ≥75 y. o., malignancy, weakness, cough, confusion, ageusia, anosmia and CRP > 100 mg/L

Discussion

The results of our study showed that during the course of SARS-CoV-2 infection, older patients (≥75 years) present earlier to the hospital. An association between younger age and longer delay to hospital admission was also found in Brazil [13]. Higher prevalence of some infectious diseases (e.g. bacteremia) together with more severe presentation of other infectious diseases such as influenza in the elderly could explain, at least partly, the observed shorter delay between symptom onset and hospital admission in this population [14]. Aging plays a role in the pathogenic process and is also an independent determinant of outcome [11, 13]. However, age could also be a determinant of behavior and perception of risk and severity. A recent review of the literature showed that risk perception increases with age [15]. Some researchers even postulate the existence of “a unique integrated compensatory biological/behavioral immune system” by reasoning that the weakened immune system in older adults could be compensated by more prudent behavior [16, 17]. Age would therefore play a key role in the delay of access to care and the prognosis of COVID-19. It suggests also that young people without comorbidities may neglect the first clinical signs of COVID-19 and the need to consult health care facilities. Indeed, due to the widespread information communicated by social media on the low COVID-19 morbidity and mortality in this population; adolescents and young adults may have a lower risk perception of COVID-19 for themselves compared to older people, thus delaying the seeking of care [18-20].

A recent study carried out in China showed a positive association between severity of COVID-19 and the interval between symptom onset and diagnosis [9]. Similarly, the low fatality rate from COVID-19 in South Korea was attributed to a rapid presentation to health care facilities as soon as symptoms appeared [21]. This observation has also been reported for other viral respiratory diseases, such as influenza, where more severe clinical presentation with admission to ICU have been associated to late laboratory diagnosis [22]. This work showed that a long delay between symptom onset and hospital admission was associated with an increase in ICU admission. However, it showed also that patient characteristics (e.g. age, comorbidities such as cardiovascular diseases) changed according to the delay between symptom onset and hospital admission, and could act as confounding factors. Indeed, the differences in patient characteristics, biological signs or imaging procedures, in particular age, inflammatory levels, or computed tomography detected lung lesions according to the delay between symptom onset and hospital admission have been reported to be closely linked to the clinical course of the disease and the prognosis [23, 24]. Investigating the association between delay of hospital admission and ICU admission is complex in the context of our study because of the heterogeneity of the population, the close relationship between ICU admission, age and patient health conditions. In addition, it is known that the first wave of COVID-19 created overloads in the healthcare system due to the limited number of beds in ICU [25, 26]. Our data did not allow to investigate whether the lower proportion of ICU admission in older patients was the result of or the presence of a large proportion of patients with do-not-resuscitate decisions.

Plasmatic level of CRP>100 mg/L was found to be associated with a long delay between symptom onset and hospital admission. It is known that hyper inflammation can occur during the clinical course of the disease [27] and that elevated plasmatic level of inflammatory biomarkers (such as CRP) were associated with COVID-19 severity [28]. Our results showed that clinical presentation at admission could be associated with the delay between symptom onset and hospital admission, even if the variables did not reach significance after correction for multiple testing (p = 0.08). Confusion at admission tended to be associated with a short delay between symptom onset and hospital admission, as already reported in the literature [29]. This observation could be explained by an early COVID-19 diagnosis and hospital admission in cognitively impaired persons, for example institutionalized persons or individuals with dementia [30]. Ageusia and anosmia tended to be associated with a long delay between symptom onset and hospital admission, whilst they are typically early symptoms [31]. A classification bias might explain this observation, as these two symptoms have a long time of recovery, up to several weeks [31].

None of the studied comorbidities was associated with the delay between symptom onset and hospital admission, despite a statistical significance of cardiovascular diseases, malignancy, chronic kidney diseases in univariate analysis. These factors, described as risk factors for COVID-19 severity in other studies [32-34] are closely associated with age, which seems to be the major determinant of the delay between symptom onset and hospital admission.

The prospective character of the study design and data collection using a standardized protocol are the main strengths of the study implemented early in the pandemic. The study could bring additional findings that will inspire future exploration of the complex link between patient characteristics, delay of hospital admission and outcome for this emerging infection.

This study has however some limitations. We did not collect parameters such as socioeconomic status, known to be associated with access to care for other diseases [21]. Also, a bias might exist as governmental guidelines changed from March 2020 and during the entire COVID-19 pandemic, the French government recommended the general population to first call their general practitioner (GP) in case of suspicion of COVID-19. GPs had to prescribe nasopharyngeal swabs for COVID-19 confirmation, to assess the clinical severity of the COVID-19 confirmed patients and to call the emergency department to inform the hospital for those requiring hospitalization. Finally, the statistical power of the study could be too low to detect an independent effect between delay of hospital admission and patient characteristics.

In conclusion, delay between symptom onset and hospital admission is a key issue regarding prognosis. It can be related to individual characteristics, organization of care and/or a pathogenic process increasing the need for healthcare (e.g. exacerbation of a chronic disease). The respective contribution of these factors remains challenging to determine. Improving access to diagnosis, clinical surveillance and access to care after symptom onset is essential to avoid severe clinical symptoms in patients with high risk of severe disease. It could also reduce the rate of transmission during the interval between symptom onset and hospital admission, particularly in patients with a delayed diagnosis (e.g. young patients), by informing them about the prevention measures to apply. In this context, patient surveillance using a telemedicine system could be an innovative approach [35].

Distribution of the variable “delay between symptom onset and hospital admission”.

(DOCX)

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Characteristics of the study population according to the quartiles of delay between symptom onset and hospital admission, Lyon University Hospital (NOSO-COR Study), February 8–June 30, 2020.

(DOCX)

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Characteristics of the study population according to age group, Lyon University Hospital (NOSO-COR Study), February 8–June 30, 2020.

(DOCX)

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Minimal underlying dataset.

(XLSX)

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10 Sep 2021

PONE-D-21-19254Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: a complex relationshipPLOS ONE

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Please provide an amended statement that declares *all* the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now.  Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

4. Thank you for stating the following financial disclosure:

This work was partially supported by REACTing (Research and ACTion targeting emerging infectious diseases), Institut national de la santé et de la recherche médicale (INSERM), France and Fondation AnBer (http://fondationanber.fr), France.

Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is interesting study, however, I have several important comments.

Major comment)

1. The standard value of the day, 5 day, is reliable? Actually, that day is the mean value of them in this study. If authors divide them to 1Q, 2-3Q, and 4Q (quartile), then they might have statistically significant results. To define proper standard value of the day (the definition of the 'deyal'), authors need to confirm the distribution of the day (spot distribution figure?).

2. Some specific clinical characteristics (age, gender, symptoms...) can induce delay of the admision, as authors said. However, I think admission to ICU is not attributable factor to delay of the admission, but it can be the results of the dalay of the admission. Then, the data of the ICU admission can be descrbied in the bottom of the table. In addition, in multiple analysis, that variable should be deleted.

3. Authors can find significant factors (including, age, delay of the admission...) to induce admission to ICU (this might be the outcome variables).

Minor comments)

1. the name of the group can be modified as general form (such as 'group 1 and group 2' or 'control and delayed group')

2. In table 1. please describe the symbol of the [ ]

Reviewer #2: This paper focused on the delay of hospital admission in COVID-19 and stated that age appeared to be a key determinant of it. The delay of hospital admission is of great interest, since it might affect the clinical outcome as the authors pointed out.

I have several comments:

1. The authors should separate the cause of delay from its effect on the clinical outcome. In the present analysis, ICU admission was adopted as an explanatory variable for delay, but it could not be the determinant of delay since ICU admission occurred after the admission. Authors should exclude the ICU admission from the variables to assess the determinant of delay. Plus, in order to assess the impact of delay on the clinical consequence, outcome (ICU admission or death) should be the dependent variable and delay should be one of the explanatory variables along with other clinical characteristics and biomarkers.

2. The authors pointed out in figure 1 that the proportion of patients with ICU admission decreased with age for patients above 60 years old. Is this because older patients’ condition was milder? Or any other reason other than severity, such as increased proportion of patients with the do-not-resuscitate (DNR) decisions? If the latter is the case, caution should be needed in discussing about clinical outcome based on ICU admission.

3. As authors pointed out, government guideline for the hospital visits presumably affected the interval between onset and hospital visit. I believe it would be beneficial to incorporate the data concerning the government guideline (e.g., whether the admission of a given patient was before or after the change of guideline).

4. Authors should confirm whether the reference is appropriate for a given statement. For example, reference [2] and [3] is a “clinical practice” article, which might be inappropriate in L59–L60, L61–L62, respectively.

5. In L64–L66, authors stated previous report was questionable. The reason should be clarified.

6. The statement in L109–L110 “Overall, 711 patients (86.0%) for whom complete data were available” should be in the Result section.

7. Renaming the groups into more easy ones e.g., “Long delay” / “Short delay” instead of Gr#1/Gr#2 might be more reader friendly.

8. Decimal places should be consistent for p values in the tables.

9. In L176, authors stated that incidence of infectious diseases increases dramatically with age, but it’s not true. It’s variable according to kind of infectious diseases.

10. In the X axis of Figure 1, using en dash (e.g., 0–60) might be better label for descripting age rage.

Reviewer #3: The manuscript studies the association between the delay between symptom-onset date and hospital-admission date and clinical and demographic factors in 827 COVID-19 patients. The patients were divided into two groups, based on the delay in their admission to hospital, with the long (short) delay group having a delay higher (lower) than the median (which is 6 days). In the abstract, it is stated that factors associated with the delay are identified by means of multivariate logistic regression. In fact, univariate logistic regression and comparisons of factors between groups were also performed.

The main results are based on the multivariate logistic regression, which shows that age, confusion at admission, and subsequent transfer to ICU were positively associated with a short delay while weakness, cough, ageusia, anosmia, and CRP>100 mg/L at admission were negatively associated (although their statistical significance was not properly addressed, see below).

I recommend the publication of this manuscript provided that the following issues are addressed.

-The abstract reports a comparison of the characteristics of the two groups and provides p-values. It is not clear which statistical tests were actually used and this should be explained in details. The statement in the main-text methods section it is too vague as it only states "Mann-Whitney U test and Chi-square or Fisher exact test were used when appropriate and that trends were assessed using Cuzick's test or Spearman's rank correlation coefficient". Which test was used exactly for each comparison?

-It appears that a correction for multiple testing was not performed in the multivariable regression, while that should be included, especially given that many p-values are borderline (too close to the standard threshold at 0.05) and some feature may be strongly correlated. Performing a multiple-testing correction will show whether the association found in this study are statistically significant.

-I was surprised to see that, In table 2, the adjusted ORs of many features (e.g., comorbidities) for the multivariable regression are not included, while the crude ORs are always included. As the multivariable regression is a better tool than the univariate regression to study the associations, all adjusted ORs must be included.

minor comment:

- It is not clear to me what "prospectively collected" means in the following context:

"Demographic characteristics, underlying comorbidities, clinical and biological parameters and patient outcome data were collected prospectively" and I would appreciate a clear definition of its meaning.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: Yes: Hiroaki Sasaki

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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25 Oct 2021

October 25, 2021

Answer letter

Reference : PONE-D-21-19254.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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R: Thank you, we have modified the article as requested.

2. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

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R We have added the requested information in the ethics statement of the revised manuscript.

3. Thank you for stating in your Funding Statement:

R: We have amended the statement as requested. Please see the revised manuscript.

Please provide an amended statement that declares *all* the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

R: We have amended the statement as requested. Please see the revised manuscript.

4. Thank you for stating the following financial disclosure:

This work was partially supported by REACTing (Research and ACTion targeting emerging infectious diseases), Institut national de la santé et de la recherche médicale (INSERM), France and Fondation AnBer (http://fondationanber.fr), France.

Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

R: We have added the above-mentioned statement in the revised manuscript.

5. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

R: As requested, we have added the minimal data set as a Supporting Information file (S1 File).

6. Please amend either the abstract on the online submission form (via Edit Submission) or the abstract in the manuscript so that they are identical.

R: We have modified the abstracts so that they are now identical.

Reviewer #1: This is interesting study; however, I have several important comments.

Major comments:

1. The standard value of the day, 5 day, is reliable? Actually, that day is the mean value of them in this study. If authors divide them to 1Q, 2-3Q, and 4Q (quartile), then they might have statistically significant results. To define proper standard value of the day (the definition of the 'deyal'), authors need to confirm the distribution of the day (spot distribution figure?).

R: Thank you for this comment. The value of 5 days used to define short delay and long delay is in fact the median value of the distribution, and not the mean value. This value appears to be more relevant than the mean value as the distribution of the delays is not a normal distribution. Using the median value of the delay is useful to divide the population into 2 groups of the same size.

The parameters of the distribution are the following: number of included patients n=827, median delay between symptom onset and hospital admission = 5 days, Q1=3 days, Q3=9 days, mean delay between symptom onset and hospital admission = 6.06 days, standard deviation = 4.56 days, Shapiro-Wilk test for normality: p<0.01.

The graph of the distribution of patients according to the delay between symptom onset and hospital admission is reported in Figure S1 of the revised manuscript.

We described also the data in 4 groups according to the quartiles of the distribution (Q1-median-Q3), as suggested. Please see Table S1 in the revised version. No major differences were observed compared to the description with 2 groups, with a threshold at the median value. We concluded that using the median value of the distribution as a threshold was suitable for the description of our dataset and our analyses.

2. Some specific clinical characteristics (age, gender, symptoms...) can induce delay of the admission, as authors said. However, I think admission to ICU is not attributable factor to delay of the admission, but it can be the results of the delay of the admission. Then, the data of the ICU admission can be described in the bottom of the table. In addition, in multiple analysis, that variable should be deleted.

R: Indeed, ICU admission occurred after or at hospital admission.

The objective of the addition of this variable in the model was to determine if ICU admission was associated with the delay between symptom onset and hospital admission; not as an explanatory variable, but as a consequence of the delay.

However, we understand the comment of the reviewer and as suggested, we reported this variable at the bottom of the table and removed it from the multiple regression model.

3. Authors can find significant factors (including, age, delay of the admission...) to induce admission to ICU (this might be the outcome variables).

R: Our previously published paper (Vanhems P, Gustin M-P, Elias C, Henaff L, Dananché C, Grisi B, et al. Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France. PLoS One. 2021;16: e0243709. doi:10.1371/journal.pone.0243709) aimed to study the associations between different factors and ICU admission. In this paper, ICU admission was the dependent variable and the delay between symptom onset and hospital admission, one of the explanatory variables. The results showed that the delay between symptom onset and hospital admission was associated with ICU admission.

This observation motivated us to better characterize the determinants of the delay between symptom onset and hospital admission.

According to the above-mentioned publication, and even with a larger population size (n=827 versus n=417 in the previously published paper), we did choose to not present a model with ICU admission as the outcome variable.

Minor comments:

1. the name of the group can be modified as general form (such as 'group 1 and group 2' or 'control and delayed group')

R: As suggested, we have changed the name of the groups to facilitate reading.

2. In table 1. please describe the symbol of the [ ]

R: We have specified the meaning of the symbol in the Table legend.

Reviewer #2:

This paper focused on the delay of hospital admission in COVID-19 and stated that age appeared to be a key determinant of it. The delay of hospital admission is of great interest, since it might affect the clinical outcome as the authors pointed out.

I have several comments:

1. The authors should separate the cause of delay from its effect on the clinical outcome. In the present analysis, ICU admission was adopted as an explanatory variable for delay, but it could not be the determinant of delay since ICU admission occurred after the admission. Authors should exclude the ICU admission from the variables to assess the determinant of delay. Plus, in order to assess the impact of delay on the clinical consequence, outcome (ICU admission or death) should be the dependent variable and delay should be one of the explanatory variables along with other clinical characteristics and biomarkers.

R: Thank you for this comment. Indeed, ICU admission occurred after or at hospital admission.

The objective of the addition of this variable in the model was to determine if ICU admission was associated with the delay between symptom onset and hospital admission; not as an explanatory variable, but as a consequence of the delay.

However, we understand the comment of the reviewer and removed the variable ICU admission of the multiple regression analysis.

Our previously published paper (Vanhems P, Gustin M-P, Elias C, Henaff L, Dananché C, Grisi B, et al. Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France. PLoS One. 2021;16: e0243709. doi:10.1371/journal.pone.0243709) aimed to study the associations between different factors and ICU admission. In this paper, ICU admission was the dependent variable and the delay between symptom onset and hospital admission, one of the explanatory variables. The results showed that the delay between symptom onset and hospital admission was associated with ICU admission.

This observation motivated us to better characterize the determinants of the delay between symptom onset and hospital admission.

According to the above-mentioned publication, and even with a larger population size (n=827 versus n=417 in the previously published paper), we did choose to not present a model with ICU admission as the outcome variable.

2. The authors pointed out in figure 1 that the proportion of patients with ICU admission decreased with age for patients above 60 years old. Is this because older patients’ condition was milder? Or any other reason other than severity, such as increased proportion of patients with the do-not-resuscitate (DNR) decisions? If the latter is the case, caution should be needed in discussing about clinical outcome based on ICU admission.

R: We compared patient characteristics according to age group in S2 Table of the revised manuscript. We also provided description of the results in the Results section of the revised manuscript lines 164-168.

Investigating the association between delay of hospital admission and ICU admission is complex in the context of our study because of the heterogeneity of the population, the close relationship between ICU admission, age and patient health conditions. In addition, it is known that the first wave of COVID-19 created overloads in the healthcare system due to the limited number of beds in ICU. Our data did not allow to investigate whether the lower proportion of ICU admission in older patients was the result of or the presence of a large proportion of patients with do-not-resuscitate decisions.

We have added these details regarding limitations in the Discussion section lines 238-245.

3. As authors pointed out, government guideline for the hospital visits presumably affected the interval between onset and hospital visit. I believe it would be beneficial to incorporate the data concerning the government guideline (e.g., whether the admission of a given patient was before or after the change of guideline).

A: Indeed, governmental guidelines were changed from March 2020 and were in effect during the duration of the pandemic. The French government recommended the general population to first call their general practitioner (GP) in case of suspicion of COVID-19. GPs had to prescribe nasopharyngeal swabs for COVID-19 confirmation, to assess the clinical severity of the COVID-19 confirmed patients and to call the emergency department to inform the hospital for those requiring hospitalization. We have added these details to the Discussion section lines 272-278.

4. Authors should confirm whether the reference is appropriate for a given statement. For example, reference [2] and [3] is a “clinical practice” article, which might be inappropriate in L59–L60, L61–L62, respectively.

R: We have replaced these 2 references by more appropriate articles:

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11.

Gao YD, Ding M, Dong X, Zhang JJ, Kursat Azkur A, Azkur D, Gan H, Sun YL, Fu W, Li W, Liang HL, Cao YY, Yan Q, Cao C, Gao HY, Brüggen MC, van de Veen W, Sokolowska M, Akdis M, Akdis CA. Risk factors for severe and critically ill COVID-19 patients: A review. Allergy. 2021 Feb;76(2):428-455. doi: 10.1111/all.14657. Epub 2020 Dec 4. PMID: 33185910

5. In L64–L66, authors stated previous report was questionable. The reason should be clarified.

R: We have reworded the sentence: “Regarding access to care, a positive association between a long time interval between COVID-19 diagnosis or hospital admission and the occurrence of severe disease or death has been described but little data are available”. Please see the revised manuscript lines 64-66.

6. The statement in L109–L110 “Overall, 711 patients (86.0%) for whom complete data were available” should be in the Result section.

R: As suggested, this statement has been included in the Results section. Please see the revised manuscript lines 178-179.

7. Renaming the groups into more easy ones e.g., “Long delay” / “Short delay” instead of Gr#1/Gr#2 might be more reader friendly.

R: We have changed the name of the groups to facilitate reading.

8. Decimal places should be consistent for p values in the tables.

R: We have harmonized the number of decimals for p-values in the revised version of the manuscript.

9. In L176, authors stated that incidence of infectious diseases increases dramatically with age, but it’s not true. It’s variable according to kind of infectious diseases.

R: We agree with the reviewer and we have changed the sentence. Please see the revised manuscript lines 209-212.

10. In the X axis of Figure 1, using en dash (e.g., 0–60) might be better label for descripting age range.

R: Thank you, we have corrected the Figure 1 accordingly.

Reviewer #3: The manuscript studies the association between the delay between symptom-onset date and hospital-admission date and clinical and demographic factors in 827 COVID-19 patients. The patients were divided into two groups, based on the delay in their admission to hospital, with the long (short) delay group having a delay higher (lower) than the median (which is 6 days). In the abstract, it is stated that factors associated with the delay are identified by means of multivariate logistic regression. In fact, univariate logistic regression and comparisons of factors between groups were also performed.

R: We have changed the Abstract to mention: “Determinants of the delay between symptom onset and hospital admission were identified by univariate and multiple logistic regression”.

The main results are based on the multivariate logistic regression, which shows that age, confusion at admission, and subsequent transfer to ICU were positively associated with a short delay while weakness, cough, ageusia, anosmia, and CRP>100 mg/L at admission were negatively associated (although their statistical significance was not properly addressed, see below).

I recommend the publication of this manuscript provided that the following issues are addressed.

-The abstract reports a comparison of the characteristics of the two groups and provides p-values. It is not clear which statistical tests were actually used and this should be explained in details. The statement in the main-text methods section it is too vague as it only states "Mann-Whitney U test and Chi-square or Fisher exact test were used when appropriate and that trends were assessed using Cuzick's test or Spearman's rank correlation coefficient". Which test was used exactly for each comparison?

R: We provided more detail about the statistical analysis in the Methods section. Please see the revised manuscript lines 103-109: “Continuous variables were reported as median and interquartile range (IQR) with comparisons using the Mann-Whitney U test. Qualitative variables were computed as number of individuals (n) and frequency (%) using the χ2 or Fisher exact test as appropriate for comparison. The trend of the delay between symptom onset and hospital admission by age group and the trend of the proportion of patients with ICU hospitalization by age group were assessed using Cuzick’s test. All tests were 2-tailed, with p<0.05 considered statistically significant.”

-It appears that a correction for multiple testing was not performed in the multivariable regression, while that should be included, especially given that many p-values are borderline (too close to the standard threshold at 0.05) and some feature may be strongly correlated. Performing a multiple-testing correction will show whether the association found in this study are statistically significant.

R: As suggested, in the final multiple regression model (ie. the parsimonious model), we performed a multiple-testing correction using Holmes correction. Please see the revised manuscript Table 2. Results showed that plasmatic level of CRP >100 mg/L was significantly associated with a longer delay between symptom onset and hospital admission (p<0.01). Conversely, old age (≥75 years) was significantly associated with a shorter delay between symptom onset and hospital admission (p<0.01).

Confusion at admission tended to be associated with a short delay between symptom onset and hospital admission; whereas weakness, cough, ageusia and anosmia at admission tended to be associated with a longer delay between symptom onset and hospital admission, despite a lack of significance (p=0.08 for each variable). Please see the revised manuscript lines 185-188. The Discussion section was also changed accordingly (lines 249-257).

-I was surprised to see that, In table 2, the adjusted ORs of many features (e.g., comorbidities) for the multivariable regression are not included, while the crude ORs are always included. As the multivariable regression is a better tool than the univariate regression to study the associations, all adjusted ORs must be included.

R: We have rewritten the Methods section in order to clarify the analyses. Please see the revised manuscript lines 110-118: “The logistic regression analysis was performed with delay between symptom onset and hospital admission as the dependent variable. Explanatory variables were first tested by univariate regression. Interaction of each variable with the variable age ≥ 75 years and sex was tested 1 by 1. Variables with p<0.10 in univariate analysis were added in a multivariate logistic regression model (i.e. the complete model). Then, using a backward selection technique, variables were removed one by one from the complete model, in order to keep the simplest model to predict the delay (i.e. the parsimonious model). Holmes correction for multiple testing was applied to the final parsimonious model. Statistical analysis was performed using STATA 13® (College Station, TX, USA).”

Moreover, in the Table 2, we mentioned all adjusted OR in the complete model and in the parsimonious model.

minor comment:

- It is not clear to me what "prospectively collected" means in the following context:

"Demographic characteristics, underlying comorbidities, clinical and biological parameters and patient outcome data were collected prospectively" and I would appreciate a clear definition of its meaning.

R: This study is a prospective study according to the definition found in TL Lash, TJ VanderWeele, S Haneuse, KJ Rothman. Wolters Kluwer, 2021. Modern Epidemiology, 4th edition, chapter 6 Study design, page 116: “When the person-time accumulates after the study begins, it is said to be a prospective study. In this situation, the exposure status is ordinarily recorded before disease occurrence, although there are exceptions.”. In our study, participants were enrolled as soon as they were diagnosed as infected with SARS-CoV-2. In the present analysis, they were included in the study at hospital admission; as the population was restricted to patients with community-acquired SARS-CoV-2 infections.

For more clarity, we have removed the word “prospectively”, as the Introduction section mentions that the NOSO-COR study is an observational, prospective study.

Submitted filename: Answer letter_V2.docx

Click here for additional data file.

15 Nov 2021

PONE-D-21-19254R1Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: a complex relationshipPLOS ONE

Dear Dr. Dananché,

Thank you for submitting your manuscript to PLOS ONE. Following re-evaluation of your revised manuscript, the reviewers conveyed that most of the issues were clarified. Still, on minor issue remains. Therefore, we invite you to submit a revised version of the manuscript that addresses the point raised during the review process.

Please submit your revised manuscript by Dec 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Reviewer #3: All comments have been addressed

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Reviewer #2: (No Response)

Reviewer #3: The authors have addressed all of my previous concerns and clarified the details of the statistical analyses. I now also noticed that the sentence of line 78 "Our previous results showed that in COVID-19 patients, age as well as delay between symptom onset and hospital admission were both associated with ICU admission [11]" requires further clarifications. The authors should really include the number of patients in the cited study or state whether the cohort of reference [11] is the same as that of the submitted paper.

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Reviewer #2: Yes: Hiroaki Sasaki

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16 Nov 2021

Reviewer #3: The authors have addressed all of my previous concerns and clarified the details of the statistical analyses. I now also noticed that the sentence of line 78 "Our previous results showed that in COVID-19 patients, age as well as delay between symptom onset and hospital admission were both associated with ICU admission [11]" requires further clarifications. The authors should really include the number of patients in the cited study or state whether the cohort of reference [11] is the same as that of the submitted paper.

R : Thank you for this comment. Our previous results were based on 417 patients. In the present work, we used the same cohort of reference, but with a larger number of patients. We have modified the Introduction section lines 70-71 and the Methods section line 84 accordingly.

Submitted filename: Response to reviewers V3.docx

Click here for additional data file.

2 Dec 2021

Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: a complex relationship

PONE-D-21-19254R2

Dear Dr. Dananché,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Itamar Ashkenazi

Academic Editor

PLOS ONE

31 Dec 2021

PONE-D-21-19254R2

Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: a complex relationship

Dear Dr. Dananché:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Itamar Ashkenazi

Academic Editor

PLOS ONE