Smoking is associated with increased risk of cardiovascular events, disease severity, and mortality among patients hospitalized for SARS-CoV-2 infections.

The clinical sequalae of SARS-CoV-2 infection are in part dependent upon age and pre-existing health conditions. Although the use of tobacco products decreases cardiorespiratory fitness while increasing susceptibility to microbial infections, limited information is available on how smoking affects COVID-19 severity. Therefore, we examined whether smokers hospitalized for COVID-19 are at a greater risk for developing severe complications than non-smokers. Data were from all hospitalized adults with SARS-CoV-2 infection from the American Heart Association's Get-With-The-Guidelines COVID-19 Registry, from January 2020 to March 2021, which is a hospital-based voluntary national registry initiated in 2019 with 122 participating hospitals across the United States. Patients who reported smoking at the time of admission were classified as smokers. Severe outcome was defined as either death or the use of mechanical ventilation. Of the 31,545 patients in the cohort, 6,717 patients were 1:2 propensity matched (for age, sex, race, medical history, medications, and time-frame of hospital admission) and classified as current smokers or non-smokers according to admission data. In multivariable analyses, after adjusting for sociodemographic characteristics, medical history, medication use, and the time of hospital admission, patients self-identified as current smokers had higher adjusted odds of death (adjusted odds ratio [aOR], 1.41; 95% CI, 1.21-1.64), the use of mechanical ventilation (aOR 1.15; 95% CI 1.01-1.32), and increased risk of major adverse cardiovascular events (aOR, 1.27; 95% CI 1.05-1.52). Independent of sociodemographic characteristics and medical history, smoking was associated with a higher risk of severe COVID-19, including death.

Introduction

Prior research has demonstrated that smoking and the use of other tobacco products is associated with cardiorespiratory injury [1, 2], which is characterized by endothelial dysfunction [3], autonomic dysregulation [4], alveolar injury, and decreased lung capacity [5]. Over time, these changes accumulate and lead to an increase in the risk of developing lung cancer, emphysema, chronic obstructive pulmonary disease (COPD), as well as cardiovascular disease. In comparison with non-smokers, smokers are more vulnerable to respiratory infections such as influenza [6]. Nevertheless, whether smoking exacerbates the adverse consequences of SARS-CoV-2 infection remains unclear.

In addition to pneumonia and acute respiratory distress syndrome, SARS-CoV-2 triggers extensive extra-pulmonary injury [7]. Individuals with pre-existing conditions such as hypertension and diabetes experience a higher frequency of COVID-19 respiratory and extra-pulmonary adverse outcomes than those with low cardiovascular disease risk or without diabetes [8], but the effects of smoking on COVID-19 severity remain poorly understood.

Previous assessments of the impact of smoking on COVID-19 severity have yielded uncertain results. Early data from COVID-19 patients from China identified current smoking as a risk factor for disease progression [9]. Subsequently, some studies reported that smoking was an independent predictor of mortality [10], and that tobacco use predicts mortality [11]. However, other studies have reported that smoking was not associated with COVID-19 [12] or that that current smoking rates among COVID-19 patients were below the general population [13]. A recent meta-analysis of 32 studies concluded that the data for an association between current smoking and greater COVID severity or mortality were inconclusive, and favored no important associations with hospitalization and mortality [14]. Many methodological differences can account for such disparate observations including differences in sample size, the range of facilities examined, varying comparator groups, and not controlling for confounders such as age. Therefore, the purpose of the present study was to examine the effects of smoking history on the severity of COVID-19 among a large cohort of patients hospitalized for COVID-19 from a broad group of hospitals across the United States.

Methods

Data sources and collection (get with the guidelines cohort)

The data for this study were collected from the American Heart Association (AHA) COVID-19 Cardiovascular Disease (CVD) registry. Details of the AHA COVID-19 CVD Registry have been published previously [15]. The AHA COVID-19 CVD Registry was implemented in 2020 to gather data specific to all patients hospitalized with COVID-19 as part of the Get-With-The-Guidelines (GWTG) quality improvement program. This registry was provided free to all U.S. hospitals caring for adults with active COVID-19 and with the infrastructure to support accurate data collection. The GWTG program is a voluntary, in-hospital quality improvement initiative by the AHA. The data collection and coordination for GTWG program are managed by IQVIA (Parsippany, New Jersey). The AHA COVID-19 CVD registry collects more than 200 data elements of patients using case report forms (CRFs) [16]. (https://www.heart.org/en/professional/quality-improvement/covid-19-cvd-registry).

Study period, population, and patient selection

We included all patients 18 years and older admitted to one of the 122 AHA COVID-19 CVD Registry participating hospitals from January 14, 2020 to March 31, 2021. Patients who left the hospital against medical advice, and those with unknown discharge status were excluded. Patients discharged from the hospital with valid data for admission and discharge dates, sex, age, and medical history were included in the study. Those who self-reported smoking at the time of admission were classified as current smokers (hereafter referred to as smokers). No other information on smoking history (duration or intensity) was collected. Former smokers were not identified. Demographic characteristics of the patients are listed in Supporting Information (S1 Table in S1 File).

Propensity score matching

As shown in Supporting Information (S2 Table in S1 File), demographic characteristics and medical history variables of the 2,239 individuals self-identified as smokers differed significantly from the 29,306 non-smokers, making it difficult to compare smokers with non-smokers. Therefore, we used propensity score matching to limit confounding when examining the association of smoking to study outcome measures. Propensity score was obtained from logistic regression with “nearest” method and “logit” distance where smoking status was the dependent variable and medical history, demographics, medications, and time of hospital admission were independent variables. The final analytic sample was comprised of 6,717 patients with a 1:2 ratio of smokers (2,239) to non-smokers (4,478). As shown in Table 1, there were no statistically significant differences between smokers and non-smokers in age, sex, race, medical history, or medication use after propensity matching, indicating that the two groups were well-matched and balanced.

Table 1

Univariate analysis of the propensity-matched study population in the AHA COVID-19 CVD Registry from December 2020 to March 2021 stratified by smoking status.

	Overall	Non-Smokers	Smokers	Standardized Mean Difference
	(N = 6,717)	(N = 4,478)	(N = 2,239)
Age (years)
Mean (SD)	59.6 (17.8)	59.4 (18.3)	60.0 (16.8)	0.0398
Median [Min, Max]	61.0 [18.0, 100]	61.0 [18.0, 100.0]	62.0 [18.0, 99]
Sex
Male, n (%)	4,201 (62.5)	2,818 (62.9)	1,383 (61.8)	-0.0239
Female, n (%)	2,516 (37.5)	1,660 (37.1)	856 (38.2)	-0.0239
Race/Ethnicity
NH-White, n (%)	3,465 (51.6)	2,323 (51.9)	1,142 (51.0)	-0.0174
Black, n (%)	1,801 (26.8)	1,193 (26.6)	608 (27.2)	0.0115
Hispanic, n (%)	922 (13.7)	615 (13.7)	307 (13.7)	-0.0006
Asian/Pacific Islanders, n (%)	191 (2.8)	128 (2.9)	63 (2.8)	-0.0027
Other, n (%)	338 (5.0)	219 (4.9)	119 (5.3)	0.0189
Medical History
Obesity, n (%)	2,887 (43.0)	1,934 (43.2)	953 (42.6)	-0.0126
Diabetes mellitus, n (%)	2,342 (34.9)	1,543 (34.5)	799 (35.7)	0.0123
Hypertension, n (%)	4,494 (66.9)	2,964 (66.2)	1,530 (68.3)	0.0461
Dyslipidemia, n (%)	3,331 (49.6)	2,189 (48.9)	1,142 (51.0)	0.0424
Deep venous/pulmonary embolus, n (%)	413 (6.1)	268 (6.0)	145 (6.5)	0.0200
Coronary artery disease, n (%)	1,025 (15.3)	670 (15.0)	355 (15.9)	0.0245
Peripheral artery disease, n (%)	313 (4.7)	203 (4.5)	110 (4.9)	0.0176
Stroke, n (%)	716 (10.7)	457 (10.2)	259 (11.6)	0.0426
Heart Failure, n (%)	1,049 (15.6)	677 (15.1)	372 (16.6)	0.0402
Chronic kidney disease, n (%)	992 (14.8)	641 (14.3)	351 (15.7)	0.0375
Medications Use
Anti-platelet therapy, n (%)	2,190 (32.6)	1,440 (32.2)	750 (33.5)	0.0284
Anti-coagulant, n (%)	810 (12.1)	524 (11.7)	286 (12.8)	0.0321
Time of Admission
First quarter, 2020, n (%)	1,142 (17.0)	767 (17.1)	375 (16.7)	-0.0102
Second quarter, 2020, n (%)	2,240 (33.3)	1,496 (33.4)	744 (33.2)	-0.0038
Third quarter, 2020, n (%)	1,099 (16.4)	723 (16.1)	376 (16.8)	0.0173
Fourth quarter, 2020, n (%)	1,980 (29.5)	1,323 (29.5)	657 (29.3)	-0.0044
First quarter, 2021, n (%)	252 (3.8)	166 (3.7)	86 (3.8)	0.0070

NH: Non-Hispanic; SD: Standard deviation; AHA: American Heart Association; CVD: Cardiovascular disease

Dependent and independent variables and covariates

The primary outcome was severe COVID-19, defined as in-hospital death or the use of mechanical ventilation. The secondary outcome was major adverse cardiac events (MACE), defined as one of these events occurred to patients during hospitalization: acute myocardial infarction, heart failure, cardiogenic shock, ischemic stroke or intracranial hemorrhage, myocarditis, or death by acute myocarditis, heart failure, or stroke. Patients who reported smoking or the use of e-cigarettes (vaping) were categorized as smokers. Covariates included age, sex, race/ethnicity, risk factors and past medical history (see below), medication use, and time-frame of admission. Race/ethnicity was categorized using hierarchical, mutually exclusive categories including Hispanic, non-Hispanic White, non-Hispanic Black, Asian/ Pacific Islander, and Others. Risk factors included obesity (defined as body mass index [BMI] ≥30 kg/m2), diabetes mellitus, hypertension, and dyslipidemia. Diabetes mellitus and dyslipidemia were defined as a reported history or use of medications to control these metabolic risk factors. Since data on BMI for nearly 9% of the patients were missing, we imputed an obesity variable applying multiple imputation by chained equations (MICE) using logistic regression models. Past medical history included venous thromboembolism (VTE), coronary artery disease, peripheral artery disease, stroke, heart failure, and chronic kidney disease. Medications prior to admissions that were considered included anti-platelet therapy and anticoagulants. Time-frame of admission for COVID-19 treatment was broken down into five quarters from first to fourth quarter of 2020 and first quarter of 2021 to account for changes in COVID-19 management over time.

Statistical analysis

Percentages were calculated for categorical variables and compared using Pearson’s χ2- test with Yates’ continuity correction. Means and standard deviations were calculated for continuous variables and compared using Student’s t-test. We generated two multivariable logistic regression models to calculate odds ratios (ORs) to estimate the likelihood of death and use of mechanical ventilation. Odds ratios were estimated by the probabilistic framework of maximum likelihood estimation. Models were adjusted for the indicated demographics, risk factors, medications, and time of admission. We also examined the interaction of smoking with diabetes mellitus, hypertension, race/ethnicity, sex, and age. Statistical significance was assessed at α = 0.05. Data analysis was performed using the open-source software R (R Foundation for Statistical Computing, Vienna, Austria).

Results

Univariable analysis

The demographic characteristics and medical history variables of the patients who met the inclusion criteria of the study (n = 31,545) are shown in S1 Table in S1 File. After 1:2 propensity matching on age, sex, race, medical history, and time of hospital admission the final propensity-matched cohort included 6,717 patients (Table 1). Characteristics of the matched sample stratified by mortality status, are listed in Table 2. In this comparison, the percent of smokers in the survivor group was significantly lower (32%) than in the group that died (40%). Those who died were also older than those who survived (70 ± 14 years vs 58 ± 18 years, p<0.05). In addition, those who died were more likely to be male (67% vs 62%, p<0.05). Survival also varied by race. The percent of individuals who were self-reported Non-Hispanic Whites and Asians/Pacific Islanders was higher among those who died, while there were higher numbers of Hispanics in the survivor group. As reported previously, several characteristics were related to adverse outcomes even in the matched cohort. Those who died also were more likely to have a history of diabetes mellitus, hypertension, dyslipidemia, VTE, coronary artery disease, peripheral artery disease, cerebrovascular disease, and chronic kidney disease. Surprisingly obesity was less prevalent among those who died. The proportions of patients who had used either antiplatelet therapy or anticoagulant medications were significantly higher among non-survivors. In comparison with the first quarter of 2020, patients admitted later during 2020 were less likely to die.

Table 2

Characteristics of the propensity-matched study population of the AHA COVID-19 CVD Registry from December 2020 to March 2021 by survival status.

	Overall	Survivors	Death
	(N = 6,717)	(N = 5,825)	(N = 892)
Smoking Status
Smokers, n (%)	2,239 (33.3)	1,883 (32.3)	356 (39.9)*
Age (years)
Mean (SD)	59.6 (17.8)	58.1 (17.8)	69.6 (14.3)*
Median [Min, Max]	61.0 [18.0, 100]	60.0 [18.0, 100]	71.0 [18.0, 98.0]
Sex
Male, n (%)	4,201 (62.5)	3,602 (61.8)	599 (67.2)*
Female, n (%)	2,516 (37.5)	2,223 (38.2)	293 (32.8)*
Race/Ethnicity
NH-White, n (%)	3,465 (51.6)	3,006 (51.6)	459 (51.5)
Black, n (%)	1,801 (26.8)	1,559 (26.8)	242 (27.1)
Hispanic, n (%)	922 (13.7)	824 (14.1)	98 (11.0)*
Asian/Pacific Islanders, n (%)	191 (2.8)	165 (2.8)	26 (2.9)
Other, n (%)	338 (5.0)	271 (4.7)	67 (7.5)*
Medical History
Obesity, n (%)	2,887 (43.0)	1,934 (43.2)	953 (42.6)*
Diabetes mellitus, n (%)	2,342 (34.9)	1,936 (33.2)	406 (45.5)*
Hypertension, n (%)	4,494 (66.9)	3,767 (64.7)	727 (81.5)*
Dyslipidemia, n (%)	3,331 (49.6)	2,765 (47.5)	566 (63.5)*
Deep venous/pulmonary embolus, n (%)	413 (6.1)	339 (5.8)	74 (8.3)*
Coronary artery disease, n (%)	1,025 (15.3)	811 (13.9)	214 (24.0)*
Peripheral artery disease, n (%)	313 (4.7)	241 (4.1)	72 (8.1)*
Cerebrovascular disease, n (%)	716 (10.7)	578 (9.9)	138 (15.5)*
Heart failure, n (%)	1,049 (15.6)	829 (14.2)	220 (24.7)*
Chronic kidney disease, n (%)	992 (14.8)	758 (13.0)	234 (26.2)*
Medications Use
Anti-platelet therapy, n (%)	2,190 (32.6)	1,786 (30.7)	404 (45.3)*
Anti-coagulant, n (%)	810 (12.1)	638 (11.0)	172 (19.3)*
Time of Admission
First quarter, 2020, n (%)	1,142 (17.0)	901 (15.5)	241 (27.0)*
Second quarter, 2020, n (%)	2,240 (33.3)	1,943 (33.4)	297 (33.3)
Third quarter, 2020, n (%)	1,099 (16.4)	981 (16.8)	118 (13.2)*
Fourth quarter, 2020, n (%)	1,980 (29.5)	1,772 (30.4)	208 (23.2)*
First quarter, 2021, n (%)	252 (3.8)	224 (3.8)	28 (3.1)

NH: Non-Hispanic; SD: Standard deviation; AHA: American Heart Association; CVD: Cardiovascular disease

* P<0.05 vs survivors

A similar risk profile was seen when the population was stratified by those not receiving mechanical ventilation (n = 5,535) and those receiving respiratory assistance (n = 1,182). Those receiving mechanical ventilation were more likely to be smokers, men, Non-Hispanic Whites, obese, and with a history of diabetes mellitus, hypertension, dyslipidemia, heart failure, chronic kidney disease, and using anti-platelet therapy at the time of admission. Moreover, in comparison with those admitted in the first quarter, those in the fourth quarter were less likely to receive mechanical ventilation (S2 Table in S1 File).

Multivariable analysis

To estimate the odds of severe outcomes among smokers and non-smokers we examined both adjusted and non-adjusted relationships between smoking status and death or mechanical ventilation in the propensity matched study cohort. As shown in Table 3, smokers had a significantly higher odds of death or mechanical ventilator use (OR, 1.39; 95% CI, 1.20–1.61; and OR 1.16; 95% CI 1.01–1.32, respectively). After adjusting for sociodemographic factors, medical history, medications, and time of admission, smokers had higher adjusted odds of death (adjusted odds ratio [aOR], 1.41; 95% CI, 1.21–1.64) or mechanical ventilator use (aOR, 1.15; 95% CI, 1.01–1.32).

Table 3

Multivariate analysis of associations between characteristics and outcomes among the propensity-matched study population of the AHA COVID-19 CVD Registry from December 2020 to March 2021.

Exposure, demographics, and risk factors	Death*	Mechanical ventilator use†
Smoking status1
Unadjusted	1.39 (1.20–1.61)	1.16 (1.01–1.32)
Adjusted2	1.41 (1.21–1.64)	1.15 (1.01–1.32)
Age, 5 years	1.23 (1.19–1.27)	1.01 (0.99–1.04)
Sex
Male	1.0 (Reference)	1.0 (Reference)
Female	0.80 (0.68–0.93)	0.68 (0.59–0.78)
Race/ethnicity
White	1.0 (Ref)	1.0 (Ref)
Black	1.12 (0.93–1.35)	1.17 (0.99–1.37)
Hispanic	1.04 (0.80–1.34)	1.17 (0.95–1.43)
NH- Asian/Pacific Islander	1.09 (0.68–1.69)	1.05 (0.70–1.54)
NH- Other	1.81 (1.33–2.46)	1.74 (1.33–2.27)
Medical history
Obesity	1.07 (0.91–1.26)	1.36 (1.18–1.56)
Diabetes mellitus	1.26 (1.07–1.48)	1.31 (1.13–1.52)
Hypertension	1.06 (0.86–1.32)	1.30 (1.08–1.56)
Dyslipidemia	0.88 (0.73–1.07)	1.01 (0.86–1.19)
Deep vein thrombosis/
Pulmonary embolism	1.05 (0.79–1.40)	0.86 (0.65–1.14)
Coronary artery disease	1.09 (0.89–1.33)	0.97 (0.79–1.18)
Peripheral artery disease	1.08 (0.80–1.44)	0.99 (0.72–1.33)
Cerebrovascular disease	1.08 (0.87–1.34)	0.89 (0.71–1.10)
Heart failure	1.08 (0.89–1.31)	0.95 (0.79–1.16)
Chronic kidney disease	1.56 (1.29–1.89)	1.09 (0.90–1.31)
Medications
Anti-platelet	1.15 (0.96–1.36)	1.01 (0.86–1.18)
Anti-coagulant	1.40 (1.13–1.72)	1.23 (1.00–1.51)
Time of admission
First quarter, 2020	1.0 (Ref)	1.0 (Ref)
Second quarter, 2000	0.52 (0.43–0.64)	0.56 (0.47–0.67)
Third quarter, 2020	0.45 (0.35–0.58)	0.51 (0.41–0.62)
Fourth quarter, 2020	0.40 (0.32–0.49)	0.35 (0.28–0.42)
First quarter, 2021	0.42 (0.27–0.65)	0.38 (0.25–0.57)

1Smoking status is defined as smoking or e-cigarette (vaping) use. The OR ratios are from a comparison between smokers with a matched group of non-smokers.

2Multivariate models were adjusted for age, sex, race/ethnicity, risk factors, medical history, medication use, and the time of hospital admission

NH, non-Hispanic; Obesity is defined as BMI > = 30 kg/m2; Bolded OR ratio are statistically significant (P<0.05).

Cerebrovascular disease includes stroke and transient ischemic attack (TIA).

* Patient’s disposition status is “Expired” at the time of discharge.

† During the hospitalization, intubated or placed on mechanical ventilation.

The study population was matched on medical history, demographics, medications, and time of medications

Subgroup analysis indicated that smoking was a stronger risk factor for death in patients between the age of 18–59 years than those more than 60 years of age (Fig 1). Females and males had overlapping risks, although smoking conferred a slightly higher risk in females. Smoking also was associated with higher risks (ORs) among those who were White, obese, with diabetes mellitus, hypertension, chronic kidney disease, who received anticoagulant therapy before hospital admission, or who were admitted second quarter of 2020. Similarly, smoking was associated with elevated risk of mechanical ventilator use in patients who were female, Hispanic, or admitted in the first quarter of 2020 (Fig 2). The point estimate of the association between smoking and death or mechanical ventilator use varied across time intervals of the COVID-19 pandemic, but confidence intervals of these point estimates overlapped considerably.

Fig 1

Multivariate analysis of associations between smoking and death in subpopulations among the propensity-matched study population of the AHA COVID-19 CVD Registry from December 2020 to March 2021.

Death is defined as patient’s disposition status “Expired” at the time of discharge. Obesity is defined as BMI > = 30 kg/m2. OR, Odds ratio; CI, Confidence interval.

Fig 2

Multivariate analysis of associations between smoking and mechanical ventilator use in subpopulations among the propensity-matched study population of the AHA COVID-19 CVD Registry from December 2020 to March 2021.

Mechanical ventilation use is defined as the hospitalization, intubated, or placed on mechanical ventilation. Obesity is defined as BMI > = 30 kg/m2. OR, Odds ratio; CI, Confidence interval.

In analysis of the secondary outcome, we also examined the association between smoking and MACE. Overall, smokers had a significantly higher odds of MACE (OR, 1.29; 95% CI 1.01–1.32). In adjusted analyses, smoking was associated with an increased risk of MACE (aOR 1.27; 95% CI 1.05–1.52). As shown in Fig 3, smoking was associated with increased odds of MACE specifically in those who were less than 60 years of age, female, White, or obese. Smoking was also associated with increased odds of MACE among those admitted during the first quarter of 2020.

Fig 3

Multivariate analysis of associations between smoking and major adverse cardiac events (MACE) in subpopulations among the propensity-matched study population of the AHA COVID-19 CVD Registry from December 2020 to March 2021.

Major adverse cardiac events (MACE) is defined as the hospitalization, intubated, or placed on mechanical ventilation. Obesity is defined as BMI > = 30 kg/m2. OR, Odds ratio; CI, Confidence interval.

Discussion

The major finding of this study is that in a well-characterized national registry from many different hospitals across the U.S., COVID-19 patients who were identified as current smokers were more likely to die or receive mechanical ventilation than those who were identified as non-smokers. These analyses provide the most extensive and robust evidence to date that smokers have a higher risk of developing severe COVID-19 and dying as a result of SARS-CoV-2 infection. The relationship between smoking and more severe outcomes was significant even when the population of smokers was compared with a population of non-smokers with a similar distribution of age, sex, race, and medical history. Moreover, the relationship remained significant after adjusting for demographic and medical history variables, indicating that smoking was associated with more severe COVID-19, independent of age, sex, race, and medical history. Nevertheless, in our subgroup analysis, smoking had a greater impact on outcomes amount younger patients, perhaps because the baseline risk of death in the population is low or because in comparison with the older age group, younger individuals have fewer co-morbidities, which makes smoking a much more significant risk factor. We also found that smokers who were female, White, or were obese, diabetic, or who had chronic kidney disease were more likely to die, which may be indicative of an additive effect of smoking on existing vulnerabilities or comorbidities. Overall, our findings support the notion that smoking is a risk factor for severe outcomes among COVID-19 patients.

Several previous studies have examined the role of pre-existing conditions on susceptibility to SARS-CoV-2 infections and the risk of developing severe COVID-19. In addition to age and obesity, smoking was reported to be a predictor of mortality in studies from Northern Italy (OR = 2.7, SE = 0.46) [10], and the U.S. (RR = 2.25, CI = 1.39–3.10), and this association was independent of other risk factors [11]. Early data from China identified current smoking as a risk factor for disease progression (OR = 2.51, CI = 1.39–3.32) [9]. However, no association of smoking with COVID-19 was reported in a study from the University Hospital in Padova [12] and subsequent reports from Italy [12] and New York City [13] found that current smoking rates in COVID-19 patients were below those of their respective general populations. In a meta-analysis of data from China, an unusually low prevalence of current smoking was observed, which was approximately one-fourth of the population smoking prevalence [17]. In a risk factor analysis from Oxford, active smoking was linked to decreased odds of a positive SARS-CoV-2 test results [18]. A low prevalence of current smokers among COVID-19 cases (1.3%) compared with the population smoking prevalence in the U.S. (16%) has also been reported by the CDC [19]. Some of these early results may be due to misclassification due to failure to capture a complete and/or accurate smoking history during hospital admissions in the early and hectic days of the pandemic.

The reduced risk in smokers in some studies is in contrast with a report from England, which found that current smokers and long-term ex-smokers (but not those using nicotine replacement therapy or e-cigarettes) have higher odds of self-reported COVID-19 compared with never smokers [20]. However, in a recent meta-analysis of 32 studies, in comparison with never smokers, current smokers appeared to be at a reduced risk of SARS-CoV-2 infection (RR = 0.74, CI = 0.58–0.93) [21]. A similar pooled meta-analysis of data from over 6,500 patients reported a low prevalence of current smoking among hospitalized patients with COVID-19 [22]. Likewise, data from 7 Italian non-intensive care wards showed an unexpected low (4%) prevalence of current smokers among COVID-19 patients compared to patients admitted for non-COVID-19 disease (16%). It was reported that current smokers were significantly less likely to be hospitalized for COVID-19 compared with non-smokers, even after adjusting for age and gender (OR = 0.14, CI = 0.06–0.31). Hence, the contribution of smoking to risk of SARS-CoV-2 infection remains unclear, and further systematic work is required to elucidate the differential risk of infection among smokers.

In addition to infection susceptibility, smoking has also been reported to be independently associated with hospitalization for COVID-19 [23]. In a meta-analysis of 22 studies, smoking was found to increase the risk of severe disease in hospitalized COVID-19 patients [24]. In a similar meta-analysis of 10 studies, mortality among smokers was 29% compared with 17% among non-smokers (RR = 2.07, CI = 1.59–2.69) [25]. Another meta-analysis reported that both a history of smoking and current smoking were associated with severe COVID-19 cases (OR = 1.51, 95% CI = 1.12–2.05) [26]. However, in contrast to these reports, in an analysis of 10,131 veterans, mortality was associated with older age, male sex and comorbidities, but not smoking [27]. Likewise in a study of 4,353 individuals from Israel, smoking did not significantly increase the risk of COVID-19 complications [28]. Many methodological differences can account for the disparate results among studies, particularly those relating to the sample population, the selection of the comparator group, the diversity of outcomes and the population examined. In this regard, our analysis of data from a wide range of hospitals across the U.S., comparing only hospitalized patients, and following only “hard” outcomes (death, ventilator use, MACE) provides clear and unambiguous evidence that the risk of severe outcomes is higher in COVID-19 patients who smoke when compared with a closely-matched group of non-smokers.

Although the adverse health effects of smoking are well known, the results of this study further reinforce the view that smoking creates a susceptibility state that increases the risk of severe adverse outcomes after SARS-COV-2 infection. Cigarette smoke damages the epithelial barrier which results in increased permeability to inhaled pathogens. It also disrupts the epithelial barrier decreasing mucociliary clearance, leading to the accumulation of inflammatory mucous exudates in small airway lumen [29]. On the other hand, smoking suppresses innate immune response; and nicotine, by binding to the α7nACh receptor, could exert an anti-inflammatory effect by inhibiting NF-κB activation [30]. How these opposing effects of nicotine or smoking affect susceptibility to SARS-CoV-2 infection, immune responses to the virus or progression to severe disease remains unclear. Nonetheless, the robust and significant increase in the risk of severe COVID-19 seen in our study, particularly among young individuals, underscores the urgent need for extensive public health interventions such as anti-smoking campaigns and increased access to cessation therapy, especially in the age of COVID.

Limitations

Although our study has many strengths, it has significant limitations. Complete smoking history was not available, so we could not distinguish between never smokers and former smokers. Moreover, smoking status was identified by self-report and could not be independently verified, and we had no information on duration (pack years) and intensity (cigarettes smoked per day) of smoking. However, such exposure misclassification is likely to diminish the effect size as such differences regress to the mean. In addition, we have limited data on biomarkers of inflammation or coagulation so we could not assess whether smokers had higher rates of inflammation or thrombosis. Because we only examined those admitted to the hospital, we could not assess how smoking affects susceptibility to SARS-CoV-2 infection. Finally, although we utilized propensity matching and multivariable logistic modeling to account for a wide variety of variables that are potentially associated with smoking and/or the outcome of death or mechanical ventilation, residual confounding is always possible in observational studies.

Conclusion

Among a large population of patients admitted for COVID-19, smoking was associated with a higher risk of severe COVID-19, including death or mechanical ventilation, independent of sociodemographic characteristics and medical history.

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25 Jan 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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We look forward to receiving your revised manuscript.

Kind regards,

Gaetano Santulli, MD

Academic Editor

PLOS ONE

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The strengths and limitations of the study should be deeply addressed, taking into account sources of potential bias or imprecision: Discuss both direction and magnitude of any potential bias.

The fundamental role of endothelial dysfunction in the systemic manifestations of COVID-19 should be better discussed:

-J Clin Med. 2020;9(5):1417; doi: 10.3390/jcm9051417

-Expert Opin Ther Targets. 2020 Jun 27:1-8. doi: 10.1080/14728222.2020.1783243

-Cells. 2020 Jul 9;9(7):E1652. doi: 10.3390/cells9071652

-Eur Heart J. 2021 May 23;7(3):e2-e3. doi: 10.1093/ehjcvp/pvaa145.

-Atherosclerosis. 2021;322:39-50. doi: 10.1016/j.atherosclerosis.2021.02.009.

-EClinicalMedicine. 2021 Sep 9:101125. doi: 10.1016/j.eclinm.2021.101125.

-Oxid Med Cell Longev. 2021 Aug 21;2021:8671713

-Theranostics. 2021;11(16):8076-8091.

-Crit Care. 2021 Aug 25;25(1):306.

-Front Physiol. 2020 Aug 4;11:989. doi:10.3389/fphys.2020.00989 (chronological appraisal of the publications on COVID-19 and endothelial dysfunction).

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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: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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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: The paper aimed to investigate the association of smoking and Covid-19 in hospitalized patients. The idea is interesting and the study has been presented in a scientific manner. I have just few concerns:

- I see that data come from previously published AHA registries. Howevere, did you obtain the ethical approval from your committe? If no, please clarify the reason.

- Please improve the discussion and the reference section discussing and citing the following PMID:

34655476

35027590

33540664

32464099

33246296

32341101

Reviewer #2: The data and analysis in this paper are fine, but there are several issues that need to be resolved before the paper can be accepted.

1. The association between smoking and COVID-19 disease severity among people diagnosed with COVID is not "limited" or "unclear." There have been several meta-analyses published on this question (beginning with Patanavanich R, Glantz SA. Smoking Is Associated With COVID-19 Progression: A Meta-analysis. Nicotine Tob Res. 2020 Aug 24;22(9):1653-1656. doi: 10.1093/ntr/ntaa082. PMID: 32399563; PMCID: PMC7239135) which show a positive association using a variety of approaches. Thus, the authors overstate to marginal value of their paper. The actual contribution is to confirm this association in a well-characterized group of patients in the US (which is more than enough to warrant publication in PLOS One). This language throughout the paper needs to be toned down accordingly.

2. This situation is different from the question of how smoking affects risk of COVID infection which has been more equivocal, as illustrated in Ref 14 of the manuscript. Because the present manuscript deals only with hospitalized patients, it does not provide any information about the relationship between smoking can COVID risk. This distinction needs to be made clear throughout the paper. Beyond making this distinction and commenting on the lack of consensus on the effect of smoking on risk of COVID infection, discussion of this question should be dropped from the paper, including the Discussion section (pages 7 and * because the data and analysis in this paper does not contribute anything to the discussion of smoking and risk of COVID infection.

3. The authors lump e-cigarette use (vaping) in with smoking in their analysis. They need to justify this approach explicitly, both in terms of biology and by doing a sensitivity analysis showing that doing so does not affect the results. The fact that smoking and vaping are combined needs to be indicated in the abstract. Indeed, if smoking and vaping have similar increases in risk, that would be an important result on its own.

4. The finding that the risks associated with smoking are higher in younger patients is consistent with a recent meta-analysis that reached this conclusion based on data pooled across studies (Patanavanich R, Glantz SA. Smoking is associated with worse outcomes of COVID-19 particularly among younger adults: a systematic review and meta-analysis. BMC Public Health. 2021 Aug 16;21(1):1554. doi: 10.1186/s12889-021-11579-x. PMID: 34399729; PMCID: PMC8366155). The fact that the authors found this result in a single dataset is an important contribution of this paper.

5. The authors state that the data are available but do not say where it is deposited so that people can access it.

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

11 Jun 2022

Response to Reviewers’ Comments

Reviewer # 1:

1. “I see the data come from previously published AHA registries. However, did you obtain ethical approval from your committee?”

Get-With-The-Guidelines® (GWTG) is a continuous quality improvement program registry, overseen by the American Heart Association (AHA) and an Oversight Committee of clinician leaders in quality improvement, that retrospectively collects patient-level adherence data within 3,000 U.S. hospitals. To participate, hospital sites directly and retrospectively abstract or upload quality of care and outcomes data from hospitalized patients’ electronic health records into the registry. Demographic data, clinical data, and test results obtained as part of routine care for CVD patients, as well as pertinent treatments and in-hospital outcomes, are captured. Patient identities (e.g. name, social security number, medical record number, etc.) are not collected. Sites are responsible for assigning a unique study identifier to each submitted record, which is not shared outside the site so the patient cannot be re-identified. The data are collected for quality improvement purposes, and therefore patients are neither recruited nor consented. All research completed using analyses of the AHA GWTG program has been determined to be exempt from IRB oversight by Advarra (see attached). This has been added to the revised text (see page 3).

2. “Please improve the discussion and the reference section discussing and citing the following PMID”

Thank you for pointing us to the relevant literature. To the extent possible, we have cited these references both in the introduction and the discussion.

Reviewer # 2:

1. “The association between smoking and COVID-19 disease severity among people diagnosed with COVID is not "limited" or "unclear." There have been several meta-analyses published…... This language throughout the paper needs to be toned down accordingly.”

The discussion has been “toned down” as suggested. The “limited” and “unclear” qualifications have been removed, although we do note that not all studies have led to consistent results.

2. “This situation is different from the question of how smoking affects risk of COVID infection which has been more equivocal, as illustrated in Ref 14 of the manuscript. Because the present manuscript deals only with hospitalized patients, it does not provide any information about the relationship between smoking can COVID risk. This distinction needs to be made clear throughout the paper. Beyond making this distinction and commenting on the lack of consensus on the effect of smoking on risk of COVID infection, discussion of this question should be dropped from the paper, including the Discussion section (pages 7 and * because the data and analysis in this paper does not contribute anything to the discussion of smoking and risk of COVID infection.”

We have removed the discussion of the relationship between smoking and the risk of COVID-19 infection.

3. “The authors lump e-cigarette use (vaping) in with smoking in their analysis. They need to justify this approach explicitly, both in terms of biology and by doing a sensitivity analysis showing that doing so does not affect the results. The fact that smoking and vaping are combined needs to be indicated in the abstract. Indeed, if smoking and vaping have similar increases in risk, that would be an important result on its own.”

The number of individuals in our registry who used e-cigarettes is very small – 41 patients reported e-cig only versus 2198 who reported smoking only without e-cigarettes. In our sensitivity analysis, removal of e-cigarette users from the dataset did not significantly affect the odds ratio of in-hospital death or mechanical ventilator use. This has been stated in the revised manuscript.

4. “The finding that the risks associated with smoking are higher in younger patients is consistent with a recent meta-analysis that reached this conclusion based on data pooled across studies … The fact that the authors found this result in a single dataset is an important contribution of this paper.”

Thank you for pointing this out. We have cited the paper in our revised manuscript.

5. “The authors state that the data are available but do not say where it is deposited so that people can access it.”

The American Heart Association has a strict policy regarding the use and integrity of its Get-With-The-Guidelines® data and is unable to provide public access to the COVID-19 dataset, in full or in part without other agreements in place. However, researchers are able to go to www.precision.heart.org , navigate to data, documentation, and COVID-19 to see data documentation, dictionary and coding information and explore the variables, descriptions, source, type, missingness and distribution of the data.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

17 Jun 2022

Smoking is Associated with Increased Risk of Cardiovascular Events, Disease Severity, and Mortality Among Patients Hospitalized for SARS-CoV-2 Infections

PONE-D-22-00192R1

Dear Dr. Bhatnagar,

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,

Gaetano Santulli, MD

Academic Editor

PLOS ONE

7 Jul 2022

PONE-D-22-00192R1

Smoking is Associated with Increased Risk of Cardiovascular Events, Disease Severity, and Mortality Among Patients Hospitalized for SARS-CoV-2 Infections

Dear Dr. Bhatnagar:

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

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on behalf of

Professor Gaetano Santulli

Academic Editor

PLOS ONE