Gender differences in predictors of intensive care units admission among COVID-19 patients: The results of the SARS-RAS study of the Italian Society of Hypertension.

BACKGROUND: The global rate of intensive care unit (ICU) admission during the COVID-19 pandemic varies within countries and is among the main challenges for health care systems worldwide. Conflicting results have been reported about the response to coronavirus infection and COVID-19 outcomes in men and women. Understanding predictors of intensive care unit admission might be of help for future planning and management of the disease. METHODS AND
FINDINGS: We designed a cross-sectional observational multicenter nationwide survey in Italy to understand gender-related clinical predictors of ICU admission in patients with COVID-19. We analyzed information from 2378 charts of Italian patients certified for COVID-19 admitted in 26 hospitals. Three hundred ninety-five patients (16.6%) required ICU admission due to COVID19 infection, more frequently men (74%), with a higher prevalence of comorbidities (1,78±0,06 vs 1,54±0,03 p<0.05). In multivariable regression model main predictors of admission to ICU are male gender (OR 1,74 95% CI 1,36-2,22 p<0.0001) and presence of obesity (OR 2,88 95% CI 2,03-4,07 p<0.0001), chronic kidney disease (OR: 1,588; 95%, 1,036-2,434 p<0,05) and hypertension (OR: 1,314; 95% 1,039-1,662; p<0,05). In gender specific analysis, obesity, chronic kidney disease and hypertension are associated with higher rate of admission to ICU among men, whereas in women, obesity (OR: 2,564; 95% CI 1,336-4.920 p<0.0001) and heart failure (OR: 1,775 95% CI: 1,030-3,057) are associated with higher rate of ICU admission.
CONCLUSIONS: Our study demonstrates that gender is the primary determinant of the disease's severity among COVID-19. Obesity is the condition more often observed among those admitted to ICU within both genders. TRIAL REGISTRATION: Clinicaltrials.gov: NCT04331574.

Background

The global rate of intensive care unit (ICU) admission during the COVID-19 pandemic varies within countries and is among the main challenges for health care systems worldwide. Understanding predictors of ICU admission might be of help for future planning and management of the disease. The clinical response to coronavirus infection in men and women appears to be different according to scientific reports [1]. A gender-specific analysis has not yet been carried out, while most recently, researchers' attention focused on pregnant women infected by SARS-Cov2 infection [2].

In China, the gender distribution was equal in a group of 140 patients with COVID-19 [3], and in a subsequent report of 1099 patients with COVID-19 from 552 hospitals in 30 Chinese provinces, 58% of the patients were men [4]. Some more recent data from the New York City area show a lower prevalence of women, and confirm lower mortality for different age range [5]. Female gender seems to represent a protective factor for in-hospital mortality [odds ratio 0.79 (CI 0.65–0.95)] in an extensive observational database collecting patients from Asia, Europe, and the United States [6].

Among critically ill patients, fewer women were affected than men in China [7] and Italy [8] (33 and 18% respectively). The analysis of gender-disaggregated data has shown a similar number of men and women affected by the disease, although mortality seems to be higher in men. It has been suggested that the observed male gender predisposition could be explained by the higher proportion of smokers in men than in women [9]. In addition to lifestyle habits, other gender-related aspects, including enzymatic activity, metabolism and immunology, and drug response, have been evoked as potential explanations for these observations [10].

In the last few months, it has become clear that Age and multimorbidity are the significant determinants of more severe clinical manifestation of the disease [11-13]. Some authors also hypothesize that ACE inhibitors and Angiotensin Receptor Antagonist (ARB) can induce protection from COVID-19 [14].

To the best of our knowledge, whether gender-related differences in clinical characteristics, comorbidities, and treatment may affect an adverse outcome is still not established. Nevertheless, such a piece of information is very much expected and called for [15]. Accordingly, we explored the influence of gender-related differences, comorbidities, and treatment on clinical predictors of ICU admission in patients with COVID-19.

Methods and findings

Study population

The SARS-RAS is a cross-sectional, multicenter, observational study conducted in 26 hospitals and centers in Italy [13]. The centers were distributed in 13 regions, each contributing according to the detailed geographical distribution of the disease, most of the patients being located in Northern regions, especially Lombardy, compared to Southern regions. The patients’ cohort included 2378 patients aged 18 to 101 years with confirmed COVID-19, according to World Health Organization interim guidance [16]. The observation period started on March 9th and ended on April 29th, 2020. The study is performed under the article 89 of the General Data Protection and Regulation, which allows the processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes, provided that technical and organizational measures are in place to ensure the principle of data minimization (https://gdpr-info.eu). The SARS-RAS study is registered in Clinicaltrials.gov at the accession number NCT04331574.

Procedures

An online questionnaire was distributed among the centers to collect reviewed epidemiological, clinical, and outcomes data from hospital emergency rooms, regular and intensive care wards. Each center designated at least one physician that was instructed to the acquisition and review of the requested information. Patients were pseudonymized by assigning a deidentified identification code. The questionnaire collected information regarding the center and the Age, gender, nationality (Italian or other), and city of origin of the patient. From the anamnesis, we collected whether the patient had a known diagnosis of hypertension with prescribed antihypertensive drugs, coronary artery disease (history of myocardial Infarction, PCI or CABG), heart failure (based on clinical history), diabetes (with prescribed antidiabetic drugs), chronic kidney disease [based on anamnestic estimated glomerular filtration rate (eGFR) below 60 ml/min/kg], chronic obstructive pulmonary disease (based on the presence of signs and symptoms according to GOLD 2019), obesity (body mass index ≥30 Kg/m2 according to the Center for Disease Control and Prevention; https://www.cdc.gov/obesity/adult/defining.html), history of blood and solid tumors, liver disease, and other comorbidities; pharmacological treatment as regards the use of RAS inhibitors (ACE inhibitors, ARBs) and other antihypertensives; the degree of the severity of COVID-19 [17]. The electronic data was transmitted with the modern cryptography systems over the web and stored in a locked, password-protected computer. All collected records were then quality checked by two authors (G.I and C.M.).

COVID-19 diagnosis was confirmed in all patients by RT-PCR performed on nasopharyngeal or throat swab samples [18] in each center by the designated Institutions.

We also collected the outcomes (hospital dismission or death) if available at the time point of the survey. All patients for which the course of the disease was in an active state, were classified as such [16].

Statistical analysis

Descriptive analyses of the variables were expressed as mean and standard errors (S.E.) or frequencies expressed in absolute numbers and percentages. We used ANOVA to analyze continuous variables, and the χ2 test or the Kruskal Wallis test as appropriate to compare categorical data. We tested regression analyses, odds ratio, and confidence intervals on the interest variables grouped by admission or non-admission to the intensive care unit (ICU). We performed multivariable regression analyses on the significant and clinically relevant continuous and categorical variables to assess the primary determinant of ICU admission. We applied the same model after grouping the study population by gender. p<0.05 was considered statistically significant.

Results

Answers to all questions of the questionnaire were mandatory before the online form could be validated. We collected 2409 questionnaires. After a quality check, 2378 were used for the analysis. Reasons for discharge were incongruences between the responses or duplication. The average Age was 68,21±0,38 years, and 1489 (62.6%) were men; patients were prevalent of Italian nationality (94%). Patients presented with several comorbidities including hypertension, diabetes, chronic obstructive pulmonary disease, coronary artery disease, heart failure, obesity, chronic kidney disease; the prevalence of diabetes, coronary artery disease, and chronic kidney disease was lower in women than in men while, as expected, thyroid diseases were more prevalent in women (Table 1). Women took fewer ACE inhibitors and alpha-blockers and more diuretics than men (Table 2).

Table 1

Demographic characteristics of the study population.

	Total study population (n = 2378)	Males (n = 1489)	Females (n = 889)	p	Admitted to ICU (n = 395)	Not admitted to ICU (n = 1983)	p
Age (years)	68,2141±0,38	66,9±0,39	70,3±0,77	0,0005	68.9±0.70	68.1±0.43	0,418
Men (%)	62.6				73.7	60.4	0.0005
Hypertension (%)	58.5	58.8	58.0	0,689	65,3	57.2	0.003
Obesity (%)	6,6	6,7	6,5	0.861	12.4	5.5	0.0001
Diabetes (%)	18,2	19,8	15,5	0.009	22,8	17,3	0.01
COPD (%)	8,5	8,9	7,5	0.280	10.4	8.1	0.132
CKD (%)	6.1	6,4	4.3	0.031	8.6	5.0	0.004
Coronary artery disease (%)	14.3	16,9	10.0	0.0005	15,7	14,1	0.153
Heart Failure (%)	12.1	11,9	12,4	0.716	15.2	11,6	0.05
Thyroid Disease (%)	0,2	0,1	0,5	0,049	0,0	0,3	0,318

COPD: Chronic obstructive pulmonary disease; CKD: Chronic kidney disease; p-value for Age was calculated by unpaired t-test; for categorical variables, the χ2 test was used.

Table 2

Cardiovascular active drugs in the study population.

	Total study population (n = 2378)	Males (n = 1489)	Females (n = 889)	p	Admitted to ICU (n = 395)	Not admitted to ICU (n = 1983)	p
ACE-inhibitors (%)	22.2	24,3	18,6	0,001	24,6	21.7	0.211
ARBs (%)	18.6	17,7	20,2	0,141	16,7	19.1	0.281
β-blockers (%)	23.6	24,2	22,5	0,358	24.3	23.4	0.703
Ca-Antagonists (%)	8,1	8,2	8,0	0,864	7,8	8.2	0.829
Diuretics (%)	15,4	14,2	17,4	0,042	14,7	15,5	0.667
α-blockers	1,9	2,9	0,2	0,001	1,8	1,9	0,847

ACE: Angiotensin-converting enzyme; ARB: Angiotensin II Receptor 1 Blockers; β-Adrenergic receptor blockers p values for categorical variables, were calculated by the χ2 test.

Of the total study population, 395 patients entered ICUs. These patients were more frequently men, with a higher prevalence of obesity, hypertension, diabetes, chronic kidney disease, and heart failure (all p<0.05, Table 1).

The use of ACE inhibitors or ARBs medications did not differ among patients admitted or not admitted to ICU. Interestingly, ACE inhibitors were less used, while diuretics were more often prescribed in women admitted to ICUs (Table 2).

The rate of admission in ICU was higher for males, compared to females (19,5 vs 11.7%, p<0.0001). Compared to men, women admitted to ICU were older, with a more considerable prevalence of heart failure. (Table 3, Fig 1, panel A and B).

Table 3

Characteristics of the ICU population.

	Admitted to ICU (n = 395)	Males (n = 291)	Females (n = 104)	p
Age (years)	68.9±0.70	67,4±0,81	73,0±1,4	0,001
Men (%)	73.7
Hypertension (%)	65,3	65,3	65,4	0,986
Obesity (%)	12,4	12,0	13,5	0,703
Diabetes (%)	22,8	23,4	21,2	0,644
COPD (%)	10.4	9,6	12,5	0.409
CKD (%)	8.6	9,3	6,7	0,427
Coronary artery disease (%)	15,7	16,8	12,5	0.297
Heart Failure (%)	14,4	12,4	23,1	0,001
Thyroid Disease (%)	0,0	0	0	1

COPD: Chronic obstructive pulmonary disease; CKD: Chronic kidney disease; p-value for continuous variable Age was calculated by unpaired t-test; for categorical variables, the χ2 test was used.

Fig 1

Panel A Patients admitted to ICU were 395: Women (n = 104) were older than men (n = 291); * p<0,05, unpaired t test. Panel B Men and women admitted at the ICU presented a similar number of comorbidities (hypertension, coronary artery disease, heart failure, diabetes, chronic kidney disease, chronic obstructive pulmonary disease, obesity, history of blood and solid tumors, liver disease).

In the multivariable regression model, the main predictors of admission to ICU were male gender, obesity, hypertension, and chronic kidney disease (Table 4, Fig 2, panel A).

Table 4

Multivariable logistic regression analysis for ICU admission in the total study population.

	Univariate Analysis		Multivariable Analysis
			Model		95% CI
	p	Pearson	p	Beta
Age	0.425	0.016
Gender (M/F)	0.001	-0.102	0.0001	1.876	1.415–2.304
Hypertension (y/n)	0.003	0,061	0.023	1.314	1.039–1.662
Diabetes (y/n)	0.01	0,053	0.493	1.174	0.891–1.547
CKD (y/n)	0.005	0,058	0.034	1.588	1.036–2.434
Heart failure (y/n)	0.05	0,040	0.554	1.117	0.804–1.551
CAD (y/n)	0.400	0.017
Obesity (y/n)	0.001	0,103	0.0005	2.476	1.724–3,555

ICU: intensive care unit; CKD: Chronic kidney disease; CAD: Coronary Artery Disease

Fig 2

Panel A: Forrest plot of the odds ratio and confidence intervals calculated for each variable found to be independently associated with ICU admission in the total study population. Panel B: Forrest plot of the odds ratio and confidence intervals calculated for each variable found to be independently associated with ICU admission among male patients. Panel C Forrest plot of the odds ratio and confidence intervals calculated for each variable found to be independently associated with ICU admission among females. ICU: Intensive Care Units; In red, p<0,05.

In the gender-specific analysis, among men, obesity, hypertension, and chronic kidney disease were associated with a higher rate of admission to ICU (Fig 2, panel B). Similarly, among women, obesity and heart failure were associated with a higher rate of ICU admission (Fig 2, panel C).

Discussion

Admission to ICU has been a significant challenge for all health care systems, fighting the COVID-19 pandemic. The percentage of patients admitted to ICU differs among countries, ranging from 5 to 32% in China and 5 to 10% in Italy. Knowledge of clinical characteristics of patients admitted to ICU is of crucial importance for future management of the disease, with specific regards to the prevention and surveillance of active patients with mild to moderate disease. Our study demonstrates that the main determinants of ICU admission are male gender and obesity and that patients admitted to ICU have more comorbidities than those not-admitted to ICU. Furthermore, a gender-specific phenotype of patients admitted to ICU exists since men are more often obese, hypertensive, and affected by chronic kidney disease, while women are older than men and present with obesity and heart failure.

Clinical characteristics and outcomes of COVID-19 patients differ by gender: males are more prevalent, especially among those admitted to ICU [19] and associated with increased disease severity [20]. A recent study from Meng et al. identified gender-specific differences in disease incidence and fatality rate, related to more severe kidney and liver function abnormalities in male patients [21]. A recent meta-analysis, including more than 59.000 patients identified male gender and older Age as the main predictors of mortality [22]. All these findings, in line with our results, strongly suggest that a gender-specific susceptibility to the infection and disease progression exists. Women—even in postmenopausal Age—seem to be protected against COVID-19 as well as against worse outcomes. A biological mechanism may explain the higher risk of men. The ability of androgens to regulate transmembrane protease serine 2 [23], i.e. the enzyme allowing the final interaction between ACE2 and SARS-CoV-2 and viral RNA entry into the cell, might take part in this mechanism [24]. Also, data from animal studies with SARS-CoV infection have identified more enhanced virus replication and alveolar damage in male mice, mainly due to enhanced and ineffective cytokine response [25].

Recent reports from different countries have highlighted the importance of obesity as primary comorbidity in COVID19 patients leading to ICU admission and deaths [26]. During H1N1 influenza in 2010, different reports identified obesity as the main risk factors for hospitalization and deaths [27]. Obesity is associated with reduced respiratory function due to decreased expiratory reserve, functional capacity, and respiratory system compliance [28]. Furthermore, adipose tissue is involved in complex interactions with the immune system. Release of inflammatory adipokines from visceral fat depots can affect the immune response and contribute to the imbalance between anti and pro-inflammatory adipokines secretion from thoracic visceral fat depots [29]. There is also evidence that obesity impaired adaptive immune response to seasonal influenza virus [30]. This complex scenario can partially explain the cytokine storm described in patients with severe SARS-CoV infection.

The coexistence of male gender and obesity may trigger a maladaptive, exaggerated immune response, leading to the development of acute respiratory distress syndrome, and an increased frequency of ICU admission. Also, among women, admission to ICU was associated with obesity, supporting the role of decreased inflammatory protection caused by the accumulation of adipose tissue and by increasing Age. With older Age, females develop stronger chronic immune reactions in the myocardium [31]. Heart failure in women, by raising left ventricle filling pressures, could cause a higher frequency of pulmonary complications and hypoxic respiratory failure requiring mechanical ventilation in ICU. Also, sex hormones may affect many components of the renin-angiotensin-aldosterone system, including ACE2. A very recent report shows that women with heart failure have lower plasma ACE2 levels than men [32], although the evidence of the relationship among circulating and tissue concentrations of ACE2 is still missing. In our population, women received fewer ACE inhibitors and more diuretics than men. ACE-inhibitors might exert protection by reducing the production of Angiotensin II, which in turn sustains the pro-inflammatory response [33, 34]. It is possible, therefore, to speculate that reduced use of ACE inhibitors in women also provides reduced protection from COVID-19; at the same time, greater exposure to diuretics could increase the risk of hypokalemia.

Our study has limitations. Firstly, the use of questionnaires might allow for some level of uncertainty. The online questionnaire was designed with closed answers, all necessary for final validation of the form, therefore reducing the risk of incomplete answers. Secondly, the present study includes only symptomatic, hospitalized patients. We excluded subjects with not confirmed disease by nasal or pharyngeal swabs. Therefore, we might have missed those patients with early infection and negative tests. Also, the cross-sectional and not prospective design of our study does not allow us to identify a causative role for the reported parameters on the outcomes.

Nevertheless, our study is compelling in generating hypotheses to be further demonstrated in extensive prospective studies.

Conclusions

Our study demonstrates a gender effect for women in COVID-19 that are protected from more severe clinical presentations of the disease. In women, heart failure and obesity are more strictly associated with ICU admission.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

22 Jun 2020

PONE-D-20-14687

GENDER DIFFERENCES IN PREDICTORS OF INTENSIVE CARE UNITS ADMISSION AMONG  COVID-19 PATIENTS:The results of the SARS-RAS study of the Italian Society of Hypertension

PLOS ONE

Dear Dr. Iaccarino,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Tatsuo Shimosawa, M.D., Ph.D.

Senior Academic Editor

PLOS ONE

6 Jul 2020

Reviewer #1.

Reviewer #1: The study "GENDER DIFFERENCES IN PREDICTORS OF INTENSIVE CARE UNITS ADMISSION AMONG COVID-19 PATIENTS: The results of the SARS-RAS study of the Italian Society of Hypertension" by Guido Iaccarino, et al. showed the importance of sex difference as the main determinant of the disease’s severity among COVID-19 patients. The study is interesting and well written. My comments are below.

We thank this Reviewer for the favorable comment finding our paper “interesting and well written”.

Major comments

#1. The predictors of ICU admission have been centrally discussed, and its relation to sex differences is an important and interesting point. However, most of the discussions have been limited to the general risk factors for ICU admission in COVID-19 patients, such as sex or obesity, and there is little mentioned about the difference in risk factors by sex. The authors should discuss more deeply about this issue with objective data presentation, including multiple analyses of other previous studies, as well as reference to the results of basic research as necessary.

We agree with this comment and have expanded the characterization of the phenotypes of patients admitted to ICU admission according to gender. It is now clear from the new table that women admitted to ICU are older and with increased frequency of HF diagnosis , but not of CAD when compared to men. This is in agreement with the epidemiology of these conditions, for which at older ages more females than males are affected by HFpEF. We have added a new table 3, which is presented in the results at lines 1-3, page 9 and discussed at lines 17-25 of page 10.

#2. There is serious drawback of the study due to nature of data collection through questionnaire. It is not clear whether comorbidities, including diabetes and hypertension, were properly controlled. Moreover, the rationale for defining a BMI≥ 30 as obese should be given (BMI ≥ 25 or BMI ≥ 35 as the predictor for ICU admission is also interesting). This information is inevitable to discuss obesity or hypertension as the risk factor for ICU admission.

We agree that the use of a questionnaire represents a limitation of the study, however many of the data collected were verifiable. For example, we verified that the presence of a positive history for hypertension or coronary disease corresponded to the prescription of cardiovascular drugs. Similarly, heart failure and diabetes were also controlled. We used the BMI >30 as a cutoff, according to the classification of the Center of Disease Control. We did not collect weight and height, and BMI were calculated at the hospital premises. Obesity and hypertension were independently analyzed in univariate and multivariable analysis.

#3. Mortality data and length of ICU stay should be evaluated.

We do not have data regarding the length of stay in ICU, given the cross-sectional nature of our survey. Regarding mortality, the determinants of death in our population was the objective of a recently published paper, which, by the way, shows that sex is not relevant to this outcome (doi/10.1161/HYPERTENSIONAHA.120.15324)

Minor comments

#1. The quality of the figure can be improved.

We have increased the quality to 600 DPI

#2. In Table2, the number of patients who admitted to ICU or not is wrong?

We have updated the correct numbers of Table 2, thanks for pointing it out

Reviewer #2

1. The manuscript is technically sound and data support the conclusion

We thank this Reviewer for the favorable comment.

2. The statistical analysis has been performed appropriately. However I want to make the following comments:

a. The authors do nowhere describe the level of data completeness. Out of the 2378 questionnaires (NB Table 1 states 2377!) that were collected was each and every one of these questionnaires completely filled out with unambiguous information of all variables asked for? If no, how was missing data treated in the statistical analyses?

We now better indicate that indeed, the number of questionnaires collected was larger than that used for the statistical analysis. We collected 2409 questionnaires, of which 2378 were used for analysis, as they contained all data. The questionnaire had closed answers, to be chosen between a limited number of responses, mostly only 2. We verified that the data were complete, entered correctly and that there were no duplications.

b. Since authors refer to validation-procedures (p 10, row 6) with laboratory findings at diagnostic laboratories at each participating institution there is presumably a possibility to check and account for how the coverage (and coverage in males vs females) of covid-19 sick hospitalized patients the questionnaire survey accounted for.

We had agreement with individual wards or departments, therefore we do not have the whole hospital number of admissions for COVID-19. Therefore, we cannot point out the coverage of covid-19 patients that the hospital accounted for. We have for sure, full coverage of the COVID-19 patients admitted with the indicated departments in the time frame indicated in the manuscript.

c. It is somehow unclear how the author reasoned when including and excluding potential explanatory variables in the multivariate logistic analysis presented in Table 3. This is mentioned briefly, but not to satisfactory extent, on page 10, row 16-17. When reviewing the observational demographics statics in Table 1 it appears as if there are variables that to me are both “significant and clinically relevant” to include in the modelling. For example, why include heart failure (where there seem to be no difference between groups in Table 1) but not include coronary artery disease where there appear to be a clear gender difference? One cannot help asking one selves if the “significant beta” for gender just reflects a “significant beta” for coronary artery disease between men and women admitted to the ICU?

We calculated the Pearson correlation of each variable (age, sex, CAD, HF, CKD, COPD, hypertension, Diabetes, obesity) and ICU admission. Afterwords, we included in the multivariable analysis those variables that had a statistical significant Pearson correlation with the ICU admission outcome. We now show these parameters in two columns added in the new table 4.

d. Was data completeness and questionaries design of such quality that it was possible to collect and present reliable Charlson Co-morbidity index? I could only access one of two supplementary questionnaires and these were written in Italian. If the index was complete and of high quality for most subjects – why was it not included as a potential explanatory variable in the multivariate logistic regression analysis? If data on the index was poor – the authors should consider omitting it.

We take advantage of this Reviewer suggestion and remove data regarding the Charlson index, which is the objective of a recently published paper (https://www.ahajournals.org/doi/10.1161/HYPERTENSIONAHA.120.15324)

e. In Figure 1, panel B, the authors referrer to “number of co-morbidities” in males and females admitted to the ICU. However it is not clear exactly how many possible potential co-morbiditites that actually were includes and accounted for in Figure 1, panel B. Is it “only” the conditions presented as potential explanatory variables in Table 3 (i.e. Hypertension, Diabetes, CKD, Heart failure and Obesity) or is the vast range of comorbidities that are presented in the method sections (page 9, row 21 to page 10, row 3).

Thanks for pointing this out. We have now clarified in the text that the means reflect the whole number of comorbidities listed in the methods.

f. I do not really understand the rational of including observational cardiovascular medication treatment data (Table 2). To present this kind of data is not stated as an objective of the paper. The results in this table are also not commented in the results section. There is however a section in the Discussion (page 13, row 17) about ACE-inhibitors but the language in this section is particularly challenging to understand.

We have now added a comment in the Background (lines 22-24 of page 5) regarding the use of ACE inhibitors and AT1 antagonists as a putative mechanism of COVID-19, and the aim to discriminate a sex dependent effect of this class of drugs. We have also reworded the related Discussion to make it clearer.

3. See comments under bullet #2.

See reply to bullet #2

4. The language and wording in the manuscript is at times not eloquent and challenging to understand. I advise the authors to consult a language-editor.

We checked the manuscript with an English mother-tongue editor.

5. In the methods section (p 10, row 8-9) the authors state that they did collect outcome (alive hospital dismission or in-hospital mortality) data for the studied population. If this data was collected and is available - why not present this data in the paper to give the reader an idea of whether there are gender differences in the population admitted to the ICU and those not admitted to the ICU with respect to outcome?

We thank the Reviewer for pointing out this important aspect. ICU admission increases the risk of death in the overall population, with an OR[IC] 4,189[3,184-5,511]. Among patients admitted to the ICU, the majority were men. The OR[IC] of death among ICU patients was affected by the female gender (1,784[1,103-2,885]), older age (1,094[1,068-1,120]), and number of comorbidities (2,243[1,667-3,017]). When we ran the multivariable analysis, comorbidities (1,801[1,310-2,476]) and age (1,082[1,044-1,121]) remained significant modifiers of the OR for death, while female sex was no longer statistically significant (1,535[0,657-3,586]).

We propose this subanalysis to this Reviewer for perusal, nevertheless, we rather prefer not to include it in the present manuscript, since a more extensive analysis of the hard outcome death is the objective of a recently published paper (doi/10.1161/HYPERTENSIONAHA.120.15324).

Submitted filename: Rebuttal.docx

Click here for additional data file.

21 Jul 2020

PONE-D-20-14687R1

GENDER DIFFERENCES IN PREDICTORS OF INTENSIVE CARE UNITS ADMISSION AMONG  COVID-19 PATIENTS:The results of the SARS-RAS study of the Italian Society of Hypertension

PLOS ONE

Dear Dr. Iaccarino,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 04 2020 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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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Tatsuo Shimosawa, M.D., Ph.D.

Academic Editor

PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. 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: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

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Reviewer #1: Yes

Reviewer #2: Yes

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

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Reviewer #1: Yes

Reviewer #2: No

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6. 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: The manuscript has been much improved and is in a nice condition now.

The authors have answered my concerns as much as possible.

Reviewer #2: Thank you for the revised version of the manuscript where I also enjoyed to learn about the input from my reviewer-colleague. Most of my input has been accounted for in the revised version. Thank you indeed. The current manuscript-version is of better quality. However, despite the authors claiming that a native-English speaker is responsible of the wording, a range of linguistic, syntax and grammar errors still occur in the manuscript. It is readable, but it is sometimes hardly intelligible. I therefore still recommend an additional language review but leave the final decision about this to the PLOS one editor since the standard of the accepted paper reflects upon the journal. I do not need to see the paper again but am happy to reccommend that it is accepted for pubilication.

Below I list some EXAMPLES that I encountered in the text, just to illustrate that there are room for language and editiing-improvements:

P3.

R19: exchange “on the opposite, among women” to “whereas in women”

R21: omit “significantly”. In an abstract only significant findings should be described.

R23: omit “biological”. Authors have not provided evidence enough to state that there are biological factors behind the observed results.

Sometimes reference-brackets appear after the punctuation symbol “.” and sometimes before it. Overall there are several small errors and omitted spaces in association with reference-brackets.

P5

R18: exchange “smoke” with “smokers”. Or re-phrase the sentence completely.

P6

R3: Consider omitting “an”

R24: exchange “one or more physicians” with “at least one physician”

P8

R15: Decide and be consistent of whether to write “Ace” och “ACE”. You are not consistent now.

R20: exchange “or” at the end of the row with “and patients”

P9

R1 For consistency exchange “female patients” with “females” since that is the formed used for “males”

R8 Same comment as for abstract. Why using “significantly” here?

P11

R16: A punctuation is lacking.

R20: poor spacing

R21/22. Poor wording of sentence.

R22/23 Poor wording of sentence.

R25 Wrong use of capitalisation

P12

R1: Exchange “some limitations” to “limitations” Exchange “First” to “Firstly”

R5: Exchange “Second” to “Secondly”.

P13

R:21: omit the first “of” and insert a “the” in front of “Italian..”

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Reviewer #1: No

Reviewer #2: 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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22 Jul 2020

Reviewer #1.

Thank you.

Reviewer #2

Thank you for your revision. We have included the suggested changes and reworded throughout the manuscript the less fluent parts.

Submitted filename: Rebuttal2.docx

Click here for additional data file.

24 Jul 2020

GENDER DIFFERENCES IN PREDICTORS OF INTENSIVE CARE UNITS ADMISSION AMONG  COVID-19 PATIENTS:The results of the SARS-RAS study of the Italian Society of Hypertension

PONE-D-20-14687R2

Dear Dr. Iaccarino,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Tatsuo Shimosawa, M.D., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

28 Jul 2020

PONE-D-20-14687R2

GENDER DIFFERENCES IN PREDICTORS OF INTENSIVE CARE UNITS ADMISSION AMONG  COVID-19 PATIENTS:The results of the SARS-RAS study of the Italian Society of Hypertension

Dear Dr. Iaccarino:

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.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Tatsuo Shimosawa

Academic Editor

PLOS ONE