Smoking Predictor for Covid-19 Severity and Mortality- A Systematic Review Based on Evidence for Clinical Practice

doi:10.21203/rs.3.rs-3298200/v1

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Smoking Predictor for Covid-19 Severity and Mortality- A Systematic Review Based on Evidence for Clinical Practice

https://doi.org/10.21203/rs.3.rs-3298200/v1

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Background

The objective of the study is to review the available data and investigate whether smoking is a risk factor for severe Covid-19, ICU admission, and mortality. Studies have shown conflicting data linking smoking to serious Covid-19 outcomes, necessitating updating knowledge.

Methods

A systematic review was conducted using PubMed, Cochrane, Scopus, and Web of Science databases until June 8, 2022. All original articles described the risk of smoking as a predictor for hospitalization and ICU admission among Covid-19 patients, and studies that assessed the association between smoking and Covid-19 severity and/or mortality in a multivariable analysis in a cohort study design were eligible. Outcomes were severity and mortality from Covid-19 infection.

Results

A total of 3085 articles were identified through the systematic search, and 34 deemed eligible were included after consensus. The selected articles were all published between 2020 and 2022. The majority of the studies originated in Asia and the United States. Most of patients were non-smokers. 12 articles were developed for predicting severe Covid-19 infection among tobacco users. However, the results did not examine depending on the smoking classification in most studies, despite the fact that the majority of the studies (n = 22) showed a substantial link between smoking and Covid-19 severity/mortality. Mortality (n = 32), ICU admission (n = 17), severe or hospitalization (n = 11), and mechanical ventilation (n = 10) studies were the outcomes presented. The nomogram study including smoking predictors may be useful for the early detection of Covid-19 patients who will be admitted to the ICU.

Conclusions

Smoking history significantly increased the probability of Covid-19 hospitalization, ICU admission, and mortality in patients. Healthcare professionals must assist smokers in giving up and changing their attitude toward smoking.

Covid-19

ICU admission

Mortality

Smoking predictor

Systematic review

Covid-19 is a novel pneumonia viral disease that originated early in Wuhan China at the end of 2019, it is responsible for acute respiratory tract infections (upper and lower) among hospitalized patients [1–2]. The disease continuously accelerated and caused serious health conditions resulting in an epidemic that then rapidly spread to other countries and even across all continents, which was declared a pandemic by WHO on January 30, 2020[3–8]. The number of Covid-19-related mortality and morbidity increased worldwide and reached almost 6,948,764 deaths and 767,726,861 confirmed cases until 23 December 2022 [9]. Clinical manifestations of Covid-19 identified in epidemiological studies as a mild infection (81%-95%); fever, dry cough, dyspnoea, fatigue, and lymphopenia in most infected patients however, some hospitalized patients (5%-19%) tend to develop severe Covid-19 requiring a supportive treatment in the intensive care unit (ICU), these clinical features include acute pneumonia, acute respiratory distress syndrome (ARDS), presence of shock, multi-organ dysfunction syndrome, and thrombotic complication hence, mortality [10–14]. Previous epidemiological studies have linked some prognostic factors to Covid-19 progression, these factors encompassing older age, male sex, smoking, and patients with underlying health conditions such as a history of hypertension, diabetes mellitus, chronic pulmonary disease, cardiovascular disease, coronary heart disease, and cancer [15–22].

Smoking is one of the leading causes of morbidity and premature mortality of preventable diseases including Covid-19 [23]. The harm of smoking is well-known worldwide [24]. Most smokers are susceptible to serious smoking-related illnesses including chronic obstructive pulmonary disease (a condition of irreversible airway obstruction), cardiovascular disease, and malignancy (lung and epithelial cell tumors) [25–27]. Additionally, smoking is linked to the development of pathogenies (bacteria, and viral), the release of harmful cytokines, the mediation of oxidative effects, and the activation of signalling pathways that depress the immune system [28]. Evidence-based smoking induces pulmonary dysfunction increased substantially [26]. Evidence-based smoking is associated with loss of lung function or causes pulmonary impairment significantly increased [27–30]. Smoking may be linked to an increase in the severity and mortality among Covid-19 patients, and studies have yielded conflicting results. Furthermore, whether smoking is a predisposition for Covid-19 severity or mortality remains uncertain. Previous studies identified that smoking history is an independent predictor for ICU admission and hospitalization from Covid-19 infection [31–36]. In contrast, several studies found a significantly lower proportion of light, moderate and heavy smokers among those confirmed positive for Covid-19 disease and ICU admission, implying that smoking is a protective factor against Covid-19 death and hospitalization [37–38].

To predict Covid-19 progression in hospitalized patients, it’s important to estimate the risk factors that may contribute to the disease severity and mortality among the high-risk groups based on the hypothesized risk factors for severe illness such as age, gender, smoking, and pre-existing comorbidities.

To fill this gap, a systematic review was conducted to provide a summary of multivariable prediction studies developed to predict the risk of smoking and ICU admission in Covid-19 patients. A summary of evidence on this topic is essential to guide physicians’ therapeutic decisions and individual behaviours therefore cost-effectiveness of care, and for a better understanding of the pathophysiological process. The study hypotheses that smoking might be a risk factor for ICU admission among hospitalized patients with covid-19.

A protocol was registered in PROSPERO under the ID: CRD42022301404. This review was conducted based on the updated guideline for reporting a systematic review of PRISMA 2020[39].

Eligibility Criteria for Study Selection

All original articles investigated the smoking risk for Covid-19 severity and/or fatality, epidemiological studies evaluated the association between smoking status and Covid-19 progression (severity, need for mechanical ventilation, the need for ICU hospitalization, and mortality) among adult’s patients were included. Qualitative and intervention studies, and some other irrelevant articles such as systematic review and/or meta-analysis, cross-sectional, case-control, and not available full-text articles were excluded.

Sources of Data and Search Strategy

A comprehensive systematic search was performed using PubMed, Cochrane, Scopus, and Web of Science databases applying the human filter from the inception date June 8, 2022. The PICO elements included P: Covid-19 patients, P: Predictor, I: Smoker, C: Non-smoker, and O: Mortality or Severity (ICU or ventilator) of Covid-19 disease. We performed the search using the following key terms; (Smoke* OR tobacco* OR cigar* OR tobacco* OR cigar*) AND (" coronavirus disease 2019" OR "SARS-COV-2" OR "severe acute respiratory syndrome" OR COVID-19 OR "COVID 19" AND "severity" OR "ICU" OR hospital* AND ("causation" OR "predictor")) to capture all available, relevant, and original articles describing a smoking predictor for hospitalization and ICU admission among Covid-19 patients, and studies assessed the association between smoking and Covid-19 severity and/or mortality. The Newcastle Ottawa Scale (NOS) [40] and strengthening the reporting of observational studies in epidemiology (STROBE) [41] guidelines were used for assessing the methodological quality of the articles besides the CHARM checklist. Studies published in the English language from January 2020 to June 2022, in cohort study design were included to minimize the risk of bias.

Selection process

In general, three reviewers (N.M.G, D.L, and M.H.N) independently screened the titles and abstracts based on the inclusion and exclusion criteria, after exporting them into the Endnote X7 library and removing the duplicates. Harmonization was done for matching the outcomes. After consensus, the full text of the potentially eligible articles was subsequently obtained and examined for quality. If there was a disagreement regarding the inclusion of the article, a four-party evaluation will be performed to reach a consensus and methodological quality.

Quality assessment

Two independent reviewers (D.L, and M.H.N) performed the methodological assessments of the included articles, using Newcastle-Ottawa Scale (NOS) [40], and strengthening the reporting of observational studies in epidemiology (STROBE) guidelines [41]. According to the objective, NOS was ranked based on selection, comparability, and outcomes scores ranging from (0–4) for poor quality, (4–6) for medium, and (7–10) for good quality; studies rated 5 or higher were included, and STORBE (yes/no) results were also considered. No statistical consideration for meta-analysis was presented in this study due to the heterogeneity in the included studies.

Data extraction

Three reviewers (N.N, D.L, and M.H.N) independently extracted the following data from the 34 included records. Data were extracted based on the recently published guideline of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement developed in 2020 [39]. The eligible articles were then categorized into three different groups based on the study’s expected outcomes which evolved severity (if the researcher assessed the affect outcomes of smoking status among hospitalized patients without mortality measurement), mortality (if the researcher examined the association between tobacco use and Covid-19 fatality), and the composite endpoint of severity and mortality as a third party.

3085 potentially relevant articles were yielded from the electronic search, and 40 duplicates were then removed automatically. Out of 3045 articles, 2039 irrelevant articles were excluded through the title and abstract screening process, leaving 559 articles for full-text eligibility screening in accordance with the inclusion criteria. After the exclusion of 525 articles describing; qualitative studies (n = 6), narrative review(n = 81), editorials (n = 22), systematic reviews and meta-analysis (n = 47), intervention and treatment (n = 20), case series and report (n = 205), irrelevant studies (n = 80) (patients with substance use disorders, bronchial asthma, chronic cardiac, and cardiometabolic disease), case-control (n = 28), and cross-sectional (n = 36) studies, a final sample of 34 articles were eligible [42–75]. These studies are intended for symptomatic patients admitted to the hospital or ICU unit. Most of the included studies were aims to investigate the risk factors associated with Covid-19 progressions (n = 32,94%), however (n = 2, 6%) articles seek to develop and validate score for early identification of patients who will admitted to ICU. The identified studies were then divided into three subgroups including a composite endpoint (n = 24,70%) if the articles investigated the association between smoking habits associated with severity and mortality (need for mechanical ventilation, ICU admission, and deaths) of Covid-19 among hospitalized patients, severity (n = 5, 15%) if the researcher assessed the affect outcomes of smoking status among Covid-19 patients without mortality measurement, and mortality(n = 5,15%) if the researcher examined the affect outcomes of smoking status among Covid-19 patients without mortality measurement. The PRISMA flow diagram (Fig. 1) described the identified studies and selection process. Categories of the included articles as well as the publication years were presented in Fig. 2. Most of the articles had composite endpoints.

Characteristics of studies population

Table 1 shows the baseline characteristics of the study populations as well as the PICO-based selection technique. It included a summary of patients' information (targeted population, total sample size, age, sexes, and periods of recruitment), study design, intervention and comparator groups that included smokers (current/former) studies, hospitalization endpoint of Covid-19, and the relationship between smoking and Covid-19 severity and/or mortality in multivariate analyses. In general, 34 studies that described the impact of smoking behaviour on Covid-19 progression were included [42–75]. All of the selected records were published between 2020 to 2022 (Fig. 2) and were generated using cohort study. The majority of the publications (n = 19, 56%) were retrospective, (n = 8, 23%) prospective, (n = 1) ambispective, and (n = 6, 18%) did not specify a cohort (see Table 1). The majority of the articles originated in Asia and the United States. For the geographical countries location: Most of the originated countries included the USA (n = 10, 29%), Asia (n = 11, 32%) from China (n = 6), Turkey (n = 2), Saudi Arabia (n = 1), South Korea (n = 1), and Iran (n = 1), the European countries (n = 9, 27%) from the UK (n = 6), and Scotland (n = 1), Netherlands (n = 1), France (n = 1), and Spain (n = 1), African countries (n = 4, 12%) included Egypt (n = 3), and Kenya (n = 1). All of the reviewed studies were conducted during the Covid-19 outbreak (Table 1). Most of the studies(n = 22) intend to examine risk factors associated with Covid-19 and all-cause mortality and severity on Covid-19 patients, with only (n = 12) articles developed to examine the impact of tobacco use status and Covid-19 severity (ICU admission, and mechanical ventilation) or mortality.

Regarding the inclusion criteria, patients were selected consecutively in (n = 25) studies. Covid-19 infection was confirmed using RT-PCR in (n = 23) articles, one study reported RNA test, and one confirmed Covid-19 according to the interim guidance from the World Health Organization and National Health Commission of China, and (n = 9) studies with no specification. The age distribution (range, means ± SD, average or median years, and interquartile range) varied among included studies; (n = 14) stated that they included adults aged ≥ 18 years, (n = 1) aged ranged (40–69) years, (n = 1) aged ranged (49–86) years, (n = 1) aged ranged (< 52->77) years, (n = 1) aged ranged (0- ≥80), (n = 1) aged 20 years and older, (n = 1) age ranged (10–85) years and (n = 2) with no information. Most studies (n = 33) have no gender restrictions (both males and females) and one record has no sex information [58]. The total sample size of included subjects was ranged between 78 to 7869534. Male and female proportions were ranged from (39 to 4111200), and (39 to 3758334) respectively. Our review identified (n = 3) articles focus on specific populations, including diabetic (n = 1), healthcare workers (n = 1), and veterans (n = 1) patients with Covid-19 infection (see Table 1 for more details).

Supplementary Table 1 provides a systematic summary of study populations (objectives, number of centers, methodology, selection criteria, data collection methods, and outcome information). Most of the studies (n = 20) included patients recruited from multicentre hospitals. Data was collected using electronic medical records (n = 26), a questionnaire (n = 5), and an interview or survey (n = 3). Logistic regression analysis (OR and/or aOR) was utilized in (n = 26) reports, cox proportional hazard analysis (HR/ aHR) was used in seven research, and one study was generated using Poisson articles (IRR). Most (n = 24) of studies were developed to predict smoking and severe Covid-19 as a composite endpoint (severity and mortality).

Table 1

PICOTS elements of smoking and Covid-19 severity and mortality
Authors	Study design	Population information (Targeted population, sample size, and sexes, age (Range, means ± SD, average or median, IQR) years, and recruitment)				groups	Intervention & Comparator	Outcome or Endpoint: ICU admission or Death	Multivariable analyses results of smoking predictors (OR, p-value)
Authors	Study design	Targeted population	Sample-size & sexs: (M = male, F = female)	Age	Recruitment	groups	Intervention & Comparator	Outcome or Endpoint: ICU admission or Death
Elliott et.al [42]	Cohort	UK biobank population.	Total = 473550 M = 211846 F = 261704	1.Alive: 67.86 ± 8.09 2.Covid-19 death: 74.50 ± 5.96 3.Non-Covid-19 death: 73.82 ± 6.26	31/1/2020_ 21/9/2020	1. Alive, 2.Covid-19 death, 3.Non-Covid-19 death.	1. Covid-19 death: Current = 71 Former = 211 Never = 171 2. Alive: Current = 46745 Former = 159706 Never = 261361 3.Non-Covid-19 death: Current = 440 Former = 1052 Never = 1111	Covid-19 death = 459: Current = 71 Former = 211	Mortality: Current smoker: OR = 1.25, p = < 0.001. *Current smoking was associated with Covid-19 mortality.
Amanat et.al [43]	Cohort prospective	Iranian from Tehran province	Total = 873 M = 556 F = 317	60.71 ± 18.14	7/4/2020_ 18/12/2020	Severity: 1.Non-severe 2.Severe Mortality: 1.No Yes	Study 1: 1.Severe: Smokers = 60 Never = 239 2.Nonsevere: Smokers = 77 Never = 497 Study 2: 1. Death: Smokers = 36 Never = 86 2.No death: Smokers = 101 Never = 650	Severe = 299: Smokers = 60 Death = 122: Smokers = 36	Combined endpoints: Severity: OR = 1.62 Mortality: OR = 2.69 *Smoking was associated with severity and mortality
Mostafa et.al [44]	Cohort prospective	Healthcare facilities in Cairo, Egypt	Total = 4040 M = 1554 F = 2486	18-≥50	21/4/2020_ 10/6/2020	1.Negative Covid-19 = 2.Positive Covid-19	1.Positve Covid-19: Current = 23, Former = 4 Never = 243 2.Negative Covid-19: Current = 456 Former = 75 Never = 3239	Severity: 1.Positve Covid-19 = 109 2.Negative Covid-19 = 1115	Severity: Current smoker: (aOR:0.74, p = 0.58. Former smoker: (OR:0.68, p = 0.14. *No association.
Lohia et.al [45]	Cohort retrospective	USA from Michigan in	Total = 1871 M = 965 F = 906	64.11 ± 16.0	10/3/2020_ 30/6/2020	All patients in one group	1. Current/former = 704 2.Never = 1167	Ventilation = 489 ICU = 592 Death = 613	Combined endpoint: 1.Mortality: OR = 1.26; p = 0.02. aOR = 1.14, p = 0.25 2.ICU admission: OR = 1.33; p = 0.005 aOR = 1.25, p = 0.03 3.Mechanical ventilation: OR = 1.23, p = 0.05 aOR = 1.15, p = 0.21. *Smoking associated with mortality, and ICU admission, but after adjusting ICU only.
Espejo-Paeres et.al [46]	Cohort ambispective	Spanish	Total = 5224 M = 3060 F = 2164	66.0(52.0–77.0)	23/3/2020_ 5/5/2020	Smoker: 1.Current, 2.Former 3.Non-smoker	1.Current = 307 2.Former = 934 3. Never = 3983	Death = 343: Current = 61 Former = 275 ICU = 348: Current = 30 Former = 82 Prone position = 532: Current = 22 Former = 134	Combined endpoint 1.Mortality: Current smoker: OR = 1.77, p = 0.017 Former smoker: OR = 1.32, p = 0.049 2.Severity: Current smoker: OR = 1.68, p = 0.006 Former smoker: OR = 1.09, p = 0.467. *Currently smoking was associated with severity and mortality
Prats-Uribe et.al [47]	Cohort prospective	England, UK biobank	Total = 404569 M = 180188 F = 222790	Below and above the median age 69	01/02/202_ 28/06/2020	1.Covid-19 Naive: I. Current, II. Former III. Non-smoker 2.Covid-19 Infection: I. Current, II. Former III. Non-smoker	1.Covid-19 Naive: Current = 39471 Former = 139056 Never = 224451 2.Covid-19 Infection: Current = 192 Former = 628 Never = 771	Covid-19 Infection: Death = 372	Combined endpoint 1. Severity: Current smokers: 1.<69 years: RR = 1.88 2.≥69 years RR = 1.05. Former smoker: 1.<69 years: RR = 1.46. 2.≥69 years RR = 1.09. 2.Mortality: Current smoker: 1.<69 years RR:1.22. 2.≥69 years RR = 2.15. Former smoker: 1.<69 years: RR = 1.03. 2.≥69 years RR = 1.60. *Smoking (current and previous) was associated with Covid-19 infection and death.
Dai et.al [48]	Cohort retrospective	Wuhan, China	Total = 1547 M = 802 F = 745	57.31 ± 16.09	01/2/2020 _ 10/4/2020.	Smoking history: 1. Yes 2.No	1.Smoker = 145 2. Never = 1429	Severe = 390 Death = 257	Combined endpoint 1.Severity: aOR = 1.91, p = 0.001. 2.Mortality: aHR = 1.83, p = 0.001. * Currently smoking was associated with severity and mortality.
Alharthy et.al [49]	Cohort retrospective	Riyadh-Saudi Arabia	Total = 352 M = 307 F = 45	50.63 ± 13.3	20/3/2020_ 31/5/ 2020	1. Survivor 2.Non-survivor	1.Non survivor: Active smokers = 65 Never = 48 2.Survivor: Active smokers = 109 Never = 130	ICU = 200: Active smoker = 86 Death = 113: Active smoker = 65	Mortality: Active smoker: OR = 3.00, p = 0.025 *Smoking was associated with mortality.
Saadatian-Elahi et. al [50]	Cohort prospective	Lyon- France	Total = 645 M = 388 F = 257	71 (24)	8/3/2020_ 30/5/2020	1. Current, 2.Former 3. Non-smoker	1.Current = 34 2.Former = 207 3.Never = 404	ICU = 214: Current = 7 Former = 82 Death = 122: Current = 3 Former = 61	Severity: Former smoker: aOR = 1.71, p = 0.013 Current smoker: aOR = 0.54, p = 0.225 * Smoking formerly was associated with risk of severity.
Kelly et. al [51]	Cohort prospective	USA, Veterans; California	Total = 27640 M = 24496 F = 3144	57.2 (16.6)	2/3/2020 _ 30/9/2020	All patients	1.Current = 4910 2.Never = 22730	Hospitalization = 7663 Death = 1230	Mortality: Current smoker: OR = 0.63, p < 0.001, aOR = 0.85, p = 0.06. * No association
McGurnaghan et.al [52]	Cohort	Scotland diabetic	Total= 319349 M = 180486 F = 138863	66·7(56.3–75.8)	1/3/2020_ 31/7/2020	1. Without fatal or critical care unit-treated COVID-19 2. Fatal or critical care unit-treated COVID-19	1. Fatal: Current = 111 Former = 679 Never = 287 2. Without fatal: Current = 50734 Former = 153181 Never = 111292	Death = 963 ICU = 1082	Combined endpoint Fatal or ICU: Former smoker: OR = 1.30, p = 0.0003 Current smoker: OR = 1.13, p = 0.27 * Smoking formerly was a risk of fatal or ICU
Suk Noh et. al [53]	Cohort retrospective	Korean	Total = 265 M = 127 F = 138	46 (30–60)	27/2/2020_ 28/6/ 2020	1.Oxygen therapy 2.No-oxygen therapy	1.Oxygen: Current = 5 Never = 21 2. No-oxygen: Current = 22 Never = 217	Oxygen therapy = 26 Mechanical ventilation = 3 Death = 3	Severity: Need for oxygen: Current smoker: OR = 7.64, p = 0.008. * Smoking currently was associated with oxygen therapy
Adrish et al [54]	Cohort retrospective	USA, South and central Bronix,	Total = 1173 M = 720 F = 453	smokers= 64 (54–73), never = 62 (52–73)	9/3/2020_ 18/5/ 2020	1. Smokers 2. Never smokers	1.Smokers = 336 2. Never = 837	Severe = 545: Smokers = 141 ICU = 466: Smokers = 159 Death = 390 Smokers = 131	Did not included in the multivariate p-value > 0.1.
Gao et al [55]	Cohort	England	Total = 7869534 M = 4111200 F = 3758334	48.2 ± 18.6	24/1/2020_30/4/2020	1. Hospitalization with Covid-19 2.Admitted to ICU with Covid-19 3.Death from Covid-19	Hospitalization 1. Current = 1114 2.Former = 5006 3.Never = 8133 Admitted to ICU 1. Current = 72 2.Former = 518 3.Never = 937 Death 1. Current = 367 2.Former = 2342 3.Never = 3108	Hospitalization = 14253: Current = 1114 Former = 5006 ICU = 1527: Current = 72 Former = 518 Death = 5817: Current = 367 Former = 2342	Combined endpoint Current smoking: Severity: aHR = 0.64 ICU admission: aHR = 0.31. Mortality: aHR = 0.79. Former smokers: Severity: aHR = 1.07. ICU admission: aHR = 1.17. Mortality: aHR = 1.17 * Smoking formerly was associated with hospitalization and ICU admission,
Poudel et al [56]	Cohort	USA	Total = 6717 M = 4201 F = 2516	59.6 ± 17.8	14/1/ 2020_ 31/3/ 2021	1.Smokers 2.Never smoker	Study 1: Smoking history: 1.Smoker = 2239 2.Never = 4478 Study 2: Survivors: Smoker = 1883 Deaths: Smokers = 356	Mechanical ventilation = 1182: Smokers = 426 Death = 892: Smokers = 356	Combined endpoint 1.Mortality: Smokers: OR = 1.39 aOR = 1.41 2.Ventilator use: OR = 1.16 aOR = 1.15 * Smoking associated with death or mechanical ventilator use.
Bruce et al [57]	Cohort	UK, COPE study	Total = 1584 M = 907 F = 676	74 (19–101)	27/2/2020_ 10/6/2020	1.Alive 2.Dead	1.Dead: Current = 27 Former = 224 Never = 237 2.Alive: Current = 85 Former = 381 Never = 587	Death = 502 Current = 27 Former = 224	Mortality: Former smoker: HR = 1.26, p = 0.012 aHR = 1.00, p = 0.867 Current smoker: HR = 0.76, p = 0.169 aHR = 0.93, p = 0.725. * Smoking formerly was associated with mortality
Caliskan et. al [58]	Cohort retrospective	Turkish	Total = 813 M=? F=?	Covid-19: 48.0 ± 19.7 Non Covid-19: 52.0 ± 21.1.	15/3/2020_ 10/5/ 2020	1.Covid-19 2.Non-Covid-19	1.Covid-19: Current = 118 Former = 79 Never = 368 2.Non-Covid-19: Current = 47 Former = 33 Never = 169	ICU = 91: Current = 22 Former = 34 Death = 75: Current = 21 Former = 31	Combined endpoint 1.ICU admission: Former smoker: OR = 3.79, p = < 0.0001 Current smoker: OR = 5.10, p = < 0.0001 2.Mortality: Former smoker: OR = 6.51, p = < 0.0001 Current smoker: OR = 13.01,p = < 0.0001 * Smoking currently and formerly was associated with risk factors for ICU admission, and mortality.
Puebla et.al [59]	Cohort retrospective	USA, Texas	Total = 10216 M = 3584 F = 6632	45.81 ± 18.11	1/3/2020_ 31/1/2021	1. Current 2.Former 3. Never	1.Current = 398 2.Former = 917 3. Never = 8901	Hospitalization = 1150: Current = 29 2.Former = 194 Death = 188: Current = 3 2.Former = 47	Combined endpoint Current OR = 0.55 Former OR = 1.05 Former smoking was associated with severity and death
Tortolero et.al [60]	Cohort retrospective	USA, Texas	Total = 8874 M = 4017 F = 4855	50.3 ± 19.64	1/4/2020_ 12/6/2020	1. No death 2. Death	1.Death: Current/former = 130 Never = 161 2. No-death: Current/former = 1257 Never = 3808	Death = 475: Current/former = 130	Mortality: Current or Former: OR = 2.45, p = < 0.001 aOR = 1.73, p = < 0.001 *Current or Former was associated with Covid-19 mortality.
Meijs et.al [61]	Cohort retrospective	Belgium, Netherlands, and German populations	Total = 551 M = 392 F = 159	65.4 ± 11.2	2/3/2020_ 12/8/ 2020	1.Male 2.Female	1.Male: Smokers = 88 Never = 304 2.Female: Smokers = 24 Never = 135	Death = 196 Mechanical ventilation = 343	Smoking was adjusted in the model.
Zhou et.al [62]	Cohort retrospective	Jinyntan and Wuhan	Total = 191 M = 119 F = 72	56.0 (46.0–76.0)	29/12/2019_ 31/1/2020	1.Non-survivor 2.Survivor	1.Non-survivor: Current = 5 Never = 49 2. Survivor: Current = 6 Never = 131	Death = 54: Current = 5 ICU = 50: Current = 39	Did not included in the multivariate p-value > 0.1.
Fouad et.al [63]	Cohort retrospective	Egyptian	Total = 323 M = 171 F = 152	46.6 ± 16.0	20/4/2020_ 30/7/ 2020	Admission to 1. ICU 2.Ward	1.ICU: Smokers = 9 Never = 53 2.Ward: Smokers = 12 Never = 249	ICU = 62: Current = 9	OR = 3.33, p = 0.017 *Smoking associated with ICU admission
Elijah et.al [64]	Cohort retrospective	Kenyan	Total = 1792 M = 1137 F = 655	47.15 ± 15.13	13/3/2020_ 30/4/ 2021	ICU admission 1.Admitted 2.Not admitted Mortality status 1.Non-survivors 2.Survivors	1. Admitted: Smokers = 21 Never = 325 2.Not admitted: Smokers = 36 Never = 1410	ICU = 346: Smokers = 21 Death = 271: Smokers = 35	Combined endpoint Severity aOR = 1.15, p > 0.05. Mortality: HR = 2.21, P < 0.0001 *Smoking was associated with Covid-19 mortality.
Liu et.al [65]	Cohort	Chinese, Wuhan	Total = 78 M = 39 F = 39	38.0 (33–57)	30/12/2019_15/1/2020	1.Progression 2.Improvement/stabilization	1. Progression: Smokers = 3 Never = 8 2. Improvement: Smokers = 2 Never = 65	Death = 3 Severe = 8	OR = 14.29, P = 0.018 *Smoking was a risk factor for severity.
Petrilli et.al [66]	Cohort prospective	USA	Total = 5279 M = 2615 F = 2664	54.0(38–66)	1/3/ 2020 _ 8/4/ 2020	Hospital admission 1. Not admitted 2. Admitted	1.Admitted: Current = 14 Former = 565 Never = 1590 2.Not admitted: Former = 337 Current = 147 Never = 1678	Death = 391 ICU = 102 Ventilation = 647	Former OR = 1.77 aOR = 0.69 Current OR = 1.01 aOR = 0.59 *No association
Chen et.al [67]	Cohort retrospective	Chinese	Total = 1590 M = 795 F = 647	48.0(36.00–61.00	11/12/201_31/1/ 202	Time from symptom onset to first medical visit: 1. ≤ 4 days 2. > 4 days Severity: 1.Non-severe 2.Severe	1. ≤4 days: Current/former= 63 2. >4 days: Current/former= 39	Severe = 237: Current/former = 24 Death = 50 ICU = 102	Study 1: Smoking status: HR = 0.71 Study 2: Smoking status HR = 0.63 * No association
Oliveira et.al [68]	Cohort retrospective	USA	Total = 131 M = 46 F = 85	61.0 (49.5–71.5)	11/3/2020_ 18/3/2020	1.Alive 2. Deceased	1.Deceased: Smokers = 7 Never = 20 2.Alive: Smokers = 16 Never = 88	Death = 27: Smokers = 7 Ventilation = 109	Did not included in the multivariate p-value > 0.1.
Nassar et.al [69]	Cohort retrospective	Egyptian	Total = 160 M = 108 F = 52	60.0 ± 14.0 years	28/4/202_ 29/6/ 2020	1.Non-survivors 2.Survivors	1.Non-survivors: Smokers = 8 Never = 31 2.Survivors: Smokers = 9 Never = 112	Death = 39: Smokers = 8 Ventilation= 44	Smoking OR = 5.28, p = 0.029 *Smoking was associated with in-hospital death
Martínez et.al [70]	Cohort retrospective	Mexican, USA	Total= 1435316 M = 725289 F = 710027	43.9 ± 16.9	23/12/2020_ 7/6/2020	1.Non-severe 2.Severe	1.Severe Smokers = 23777 Never = 287413 2.Non-severe: Smokers= 83621 Never = 1040505	Death= 147180	Did not included in the multivariate p-value > 0.1.
Zhou et.al [71]	Cohort retrospective	Chinese in Sichuan and Wuhan	Total = 1087 M = 525 F = 562	Training = 51.0(37–65) Validation = 50.0(38–64)	2/1/2020_ 28/2/ 2020	Training 1.ICU 2.Non-ICU Validation 1.ICU 2.Non-ICU	Training: 1.ICUs Current/former = 49 Never = 19 2.Non-ICU: Current/former = = 596 Never = 99 Validation: 1.ICU Current/former = = 26 Never = 3 2.Non-ICU Current/former = = 253 Never = 42	ICU = 97: Current/former = 75	Severity OR = 2.65, p = 0.006 *Smoking was associated with ICU admission
Docherty et.al [72]	Cohort prospective	UK, England, Scotland, and Wales	Total = 20133 M = 12068 F = 8065	73.0 (58.0–82.0)	9/2/2020_ 19/4/2020	1.Male 2.Female	1.Male: Current = 549 Former = 2972 Never = 5030 2.Female: Current = 303 Former = 1392 Never = 3938	ICU = 3001 Death = 5165 Ventilation = 1658	Did not included in the multivariate p-value > 0.1.
ven Gerwen et.al [73]	Cohort retrospective	USA	Total = 3703 M = 2049 F = 1654	56.8 ± 18.2	1/3/2020_ 13/5/2020	1.Non-hospitalized = 1688 2.Hospitalized = 2015	1.Hospitalized: Current = 107 Former = 472 Never = 1164 2.Non- hospitalized: Current = 86 Former = 241 Never = 980	Death = 616: Current = 31 Former = 246 Ventilation= 525: Current = 28 Former = 116	Hospitalization Current aOR = 0.87 OR = 1.05 Former aOR = 0.72 OR = 1.65 *No association
Guan et.al [74]	Cohort retrospective	Chinese	Total = 1590 M = 686 F = 904	48.9 ± 16.3	11/12/2019- 31/1/2020	Any comorbidity 1.No 2.Yes	1.Yes: Current = 399 Former = 47 Never = 352 2.No: Current = 1191 Former = 64 Never = 1127	Death = 50 ICU = 99 Ventilation = 50	Smoking was adjusted in model.
Emecen et.al [75]	Cohort prospective	Turkish, Izmir from Dokuz Eylul University	Total = 5610 M = 2702 F = 2908	18-≥65	1/11/ 2020_ 31/5/2021.	Presence of symptoms 1. At 1st month 2. In 3rd month 3. At 6th month	1.Current = 1440 2.Former = 455 3. Never = 3701	Death = 233 ICU = 68	Former smoking aOR = 1.05 Current smoking aOR = 1.15 Smoking was associated with the presence of symptoms after Covid-19 infection.

IQR = interquartile range, M = male, F = female. p = p value, ICU = intensive care unit.

History of smoking among study subjects

All the studies (n = 34) reported the tobacco use among Covid-19 patients. The majority of patients were non-smokers. Most of the studies (n = 22, 65%) developed to identify risk factors for ICU admission and in-hospital mortality among Covid-19 patients generally [42–43, 49, 51–53, 57, 61–75], while the remaining articles (n = 12, 35%) focus on examining the relationship between smoking and Covid-19 severity and/or fatality in multivariate analysis [44–48, 50, 54–56, 58–60]. Smoking status was most common reported as current, former, and never smoked (n = 15, 44%) studies or active smokers/none-smokers (n = 14, 41%) studies or combined former and current versus never (n = 15) studies. One study examined the amount of daily packs rate of cigarettes smoked divided as (1–9), (10–19), and ≥ 20 cigarettes/day) [55]. Current or Active smokers varied from (5 to 1402133), former smokers ranged from (79 to 1748966), and current or former smokers ranged from (23 to 1387). The intervention and comparator groups, as well as the proportion of smokers (current or former) were demonstrated in Table 1.

Outcomes of smoking predictor for Covid-19 severity and/or mortality

Notably, among hospitalized patients, the primary outcomes following Covid-19 infection include mortality and severity depending on the studies endpoint. In this studies, patients who smoke (current/previous users) experience less negative outcomes than non-smokers, which is particularly remarkable (see Table 1 for more details). Mortality was the most common outcome used to assess the effect of smoking use on Covid-19 infection. Of 34 articles, (n = 32,94%) studies examine the association between smoking and death. The death outcome ranged between (3 to 147180). Of (n = 12, 35%) smoking perdition studies [44–48, 50, 54–56, 58–60], ten studies reported death as the primary endpoint of Covid-19 among hospitalized patients. Seven studies revealed a statistically significant association between smoking (current and former) and Covid-19 fatality [45–48, 56, 58, 60], one study found no significant association [56], one articles did not include smoking predictor in the multivariate analysis due to a p-value > 0.1[54], and one study reported a decreased risk of death [55]. Of the non-smoking perdition studies (n = 22, 65%); there were (n = 17) mortality prediction studies. Out of these articles, seven studies confirmed tobacco use was linked to higher mortality due to Covid-19 infection [42–43, 49, 52, 57, 64, 69], four found no significant association [51, 66–67, 73], (n = 4) reports did not include smoking predictor in the multivariate analysis due to p-value > 0.1[62, 68, 70, 72], two studies reported that smoking was adjusted [61, 74]. Mortality was tended to be a higher among former smokers [42, 46, 50, 55, 57–59, 73] compared to the current smokers however two records revealed a very small number of deaths among current smokers [59, 62]. Some studies two reported 3 deaths within the included subjects [53, 65] (Table 1).

Heterogeneity was observed based on severity outcomes as impact of Covid-19 infection. Furthermore, severity outcomes were defined as ICU admission, mechanical ventilation, or oxygen therapy, and severe or hospitalization. ICU admission was the most common measure, which reported in (n = 17) studies, severe or hospitalization reported in (n = 11) studies, and mechanical ventilation in (n = 10) studies (Table 1 and Supplementary Table 1). One article reported a prone position as severe outcome due to Covid-19 infection [46]. The ICU admissions were ranged between (50 to 3001), mechanical ventilation ranged between (3 to 1658), and severe or hospitalization was ranged (8 to 1425). Most (n = 15) of the studies revealed a statistically significant association between smoking (Current or former) and severity Covid-19 outcomes [43, 45–46, 48, 50, 52–53, 55–56, 58–59, 63, 65, 71, 75], while, four studies revealed no statistically significant association [44, 66–67, 73], and four articles did not include smoking as a predictor in the multivariate analysis due to p-value > 0.1[54, 62, 68, 72], and two reports reported that smoking was adjusted [61, 74] (see Table 1). Among the smoking prediction studies (n = 12) [44–48, 50, 54–56, 58–60], eleven articles revealed a severity endpoint due to Covid-19 infection, out of these, (n = 9) studies found a statistically significant association between smoking (current/former) and Covid-19 severity [45, 46–48, 50, 55–56, 58–59], one report found no statistically significant association [44], and one study [54] stated that smoking was not included in the multivariate (p-value > 0.1). Out of 22 non-smoking studies [42–43, 49, 51–53, 57, 61–75], there were (n = 17) reports indicated that the clinical endpoint for Covid-19 infection was severity; Seven articles found a statistically significant association between smoking (current/former) and Covid-19 severity or mechanical ventilation or ICU admission [43, 52, 53, 63, 65, 71, 75], four studies did not include smoking in the multivariate (p-value > 0.1)[62, 68, 70, 72], four studies were also reported no statistically significant association [51, 66–67, 73], and two articles with smoking history adjustments [61, 74] (See Table 1, and supplementary Table 2 for more details).

Despite the fact that several studies have been developed to predict smoking risk linked to Covid-19 severity and fatality, there is no consensus on whether smoking is associated with Covid-19 progression. Furthermore, there is no agreement on why some Covid-19 patients seem to experience more severe sickness than other individuals, we hypnotized that smoking may be interact in Covid-19 patients. As a result, we conducted this systematic investigation to provide an overview and bridge the gap between smoking risk and Covid-19 severity and mortality.

Overall, 34 studies were included, and only 22 articles revealed a significant association between smoking and Covid-19 outcomes. In general, smoking history increases the risk of Covid-19 infection. Here we found that; smoking increased the need for ICU admission, mechanical ventilation, prone position, and Covid-19 death [47, 74, 77–84]. Several smoking prediction studies did not indicate the outcomes based on the smoking classifications [44, 45, 47–48]. When compared to non-smokers, smoking proportions were substantially lower among Covid-19 hospitalized patients, and the outcomes were similarly lower based on the reviewed articles. Even though, former smokers had a higher rate of ICU hospitalization and mortality compared to current smokers patients [46, 50, 55, 57–59, 73]. The most common outcome used to assess how smoking (current or previous use) influenced Covid-19 infection was death. Death was found to be a significant increase among former smokers. The considerable increase in mortality among ex-smokers suggested that tobacco use may stimulate lung maturation/ malfunction or respiratory disease over time. Although smoking is linked to higher mortality and a more severe Covid-19 infection [22–26, 34–36], it is currently unclear how smoking history directly influences hospitalized patients with Covid-19 infection, those who need to be admitted to the ICU, those who need to use a ventilator or mortality in several studies [47, 51, 54, 61–62, 66–78, 70, 72–74]. A previous cohort study of 23 Covid-19 patients examined the cellular environment and spatial organization of acute lung injury (anatomical manifestations of lung disease progression), including injuries caused by Covid-19 infection, results showed that, in addition to age, gender, and smoking status risk factors, the progression of Covid-19 was not strongly correlated with any of these factors, which suggested that Covid-19 primarily infects alveolar epithelial cells and causes localized hyper inflammation of the cells, which is linked to lung damage[76]. Interestingly, a previous study [55], with a large number of Covid-19 patients was also developed to assess smoking association with severe Covid-19, with sequential adjustment for confounding factors; by classifying smoking status as current (which described as light, moderate and heavy), former and never, then analysed Covid-19 outcomes linked to tobacco use by the number of packs smoked per day which divided as (1–9), (10–19), and ≥ 20 cigarettes/day), the results show that tobacco use was a protective factor among current smokers, and only former smokers were at higher risk of developing Covid-19 hospitalization, ICU admission and fatality which suggest that other factors may interact (a disease with many facets). Understanding the smoking risk implicated in Covid-19 severity and mortality is still critical despite assessment efforts, rapid responses to the Covid-19 outbreak by social distancing, diagnostic tests (RT-PCR), and vaccination efforts (for a 6-month limit), but which higher risk group is still unknown aside from the comorbid subjects why some tobacco users tend to develop a severe illness or died from Covid-19 compare to other smokers is still not fully understood. The clinical manifestation of Covid-19 was varied by geographical location, however, research suggested that the Asia and American continents were the most originated countries. Most studies did not properly address the times of follow-up for cohort studies, and few studies indicated 28–30 days.

Even though just a few studies were developed to investigate if these outcomes were altered by smoking status, several studies revealed a statistically significant association between smoking and severe Covid-19 outcomes (mortality and/or ICU admission) [42–43, 45–58, 60, 63–65, 69, 71, 75]. This indicated that when cigarette smokers become infected, they are more likely to need hospitalization, and ICU admission, consequently, death happens among subsequent groups in complicated situations. However, still there has been a lot of debate regarding smoking (current or previous user’s classification) and the severity and lethality of Covid-19 infection [44, 66–67–68, 70, 72–73]. Ongoing research between smoking status and severe Covid-19 in hospitalized patients is essential.

In general, age, male gender, and having pre-existing medical comorbidities such as respiratory disease diabetes mellitus, CVD, and CKD were found to be significantly associated with higher mortality, greater need for ICU admission, and increased need for mechanical ventilation among formers Covid-19 patients [42–45, 59, 62]. Tobacco users at higher risk of developing severe Covid-19 outcomes, some risk factors were then interacted, these conditions may be modified by elder age, male gender, and underlying medical comorbidities (e.g., lungs maturation or malfunction or respiratory disease, hypertension, diabetes, and cardiovascular diseases), as results death was then occurred because of complicated interactions.

Two developmental and validation studies were identified [71–72]: The first one [71] was developed and validated score to predict the severity of Covid-19 among hospitalized patients using 14 predictors including smoking status(yes/no), this study in general show significant improvement in performance in both developed and validated scores which were described as discrimination (measured by AUC curve) and calibration (measured using calibration plots); unfortunately, this study was retrospective, smoking is not well tolerated, no complete smoking data on (missing = 4418), self-reported information and researchers used the same population for developing (selected 70% to derive the logistic regression) and validating scores (selected the remaining 30% of the patients to validate the m score) using Backward selection of variables. The second study was the nomogram score; a developed and validated score for early identification and prediction of which patients will be admitted to the intensive care unit (ICU) using 6 predictors including smoking status (former and/or current and never), this study in general, show significant improvement in performance described as discrimination Harrell’s concordance index, and calibration (measured using calibration plots), unfortunately, this study was also retrospective, smoking is not adequately characterized in this study, the same population were also randomly assigned into training (70%), and validation (30%) set by using a simple random splitting method, and a bootstrapping (1000 bootstrap replicates).

Despite campaigns to raise awareness, several studies yielded insignificant results because they lacked enough data on smoking status. Future research is necessary to verify the scores that give meaningful findings. Although most of the patients were drawn from various institutions, it is unknown whether the evaluated studies are generalizable. The nomogram research could be a helpful tool for the early identification of Covid-19 patients who will be admitted to the intensive care unit; nevertheless, external validation for generated studies is necessary to establish the model's efficacy and to ensure the generalizability in a variety of demographics [85].

Even when cohort studies were chosen, heterogeneity was seen in the included studies, their objectives, appraisal of smoking history, and the multivariate analysis studies (logistic/Cox proportional, Passion) that were used to assess the risk of smoking among hospitalized patients with Covid-19 infection. Because the majority of the studies were retrospective cohorts, there could have been selection and recall bias during data collection [86].

When confounding factors were adjusted in several studies, the findings gave reliable information on smoking risk as an independent predictor of severe Covid-19 outcomes, ICU admission, and mortality [46, 87]. For studies that found no significant association between smoking history (current or former) and severe illness (ICU admission and mortality) under adjusting conditions [44,], the results could be attributed to a small sample size (smokers) or methodological issues. Adjusting for confounding factors should have no effect on the correlation, implying that more evidence is required for an accurate result about smoking and Covid-19 outcomes.

When considering studying smoking status (current, former, and never) that may be associated with Covid-19 severity and mortality, researchers should focus on addressing the significant questions, matching the pre-existing comorbidities, and confounding factors that may affect their studies (ages, gender, BMI). Controlling such confounding factors will aid in the reduction of bias and the maintenance of transparency, clarity, and impartiality. Other information such as cigarettes (amount, frequency, and duration), which indicated the patient's smoking condition (light/heavy), smoking duration (how long), and whether he/she had a lung malfunction previously should also be provided.

Age has a significant impact on lung health and function in general. Lung dysfunction among the elderly is caused by lung maturation and environmental risk factors (smoking) [88]. In numerous investigations, smoking has been linked to chronic illnesses and has been shown to impair pulmonary function [89]. Thus, smoking is a major contributor to lung damage and impairment of function in patients with Covid-19 besides some other factors. These function abnormalities were more prevalent in the elderly population, men, and those with pre-existing comorbidities. Our study revealed important information on smoking risk as an independent risk factor for Covid-19 severity and/or fatality and included studies show a link between Covid-19 illness and smoking.

Covid-19 mortality is highest in elderly patients with a history of previous smoking [89–91], and the course of Covid-19 sickness is faster among younger smokers hospitalized with Covid-19 infection. Covid-19 severity and mortality predispose to a composite of outcomes caused by high inflammation, advanced age, smoking, and comorbidities. Covid-19 is still on the rise in several nations, and if not contained, it threatens to overwhelm healthcare systems.

Before beginning the intervention, a physician should emphasize the importance of patient characteristics such as history, comorbidities, pulmonary function testing, physical examinations, and smoking history. This information should accurately collect, risk estimated, recorded, and provided with categories in the databases. Prevention of Covid-19 should not be limited to the treatment of visible symptoms but should be extended to tobacco users.

Indeed, smoking history (current or former) was associated with increased severity, ICU admission, and mortality among Covid-19 Patients. Smoker’s patients with pre-existing diseases such as lung dysfunction or comorbidities were at the higher risk of developing severe illness due to Covid-19 infections. These conditions will be more problematic in elderly patients, who may require ICU hospitalization and death might be occur due to a compromised immune system or matured lungs. The nomogram prediction study may be a useful tool for early detection of Covid-19 patients who will be hospitalized to the ICU. Health care provider should help smokers to quit and improve their attitude toward smoking.

This study was conducted by searching PubMed, Cochrane, Scopus, and Web of Science databases, enrolling large groups of authors in the work uniformly, and having three independent reviewers for data extraction. Despite the high number of research describing Covid-19 severity and mortality, we only included cohort-designed studies published in English.

PRISMA Preferred Reporting Items for Systematic reviews and Meta-Analyses

ICU Intensive Care Unit

HCWs Health care workers

SD Standard deviation

IQR Interquartile

DM Diabetes Meletus

OR Odd Ratio

aOR adjusted Odd Ratio

IRR Incidence Risk Ratios

HR Hazard Ratio

aHR adjusted Hazard Ratio

UK United Kingdom

USA United State of America

RT-PCR Reverse Transcription Polymerase Chain Reaction

Acknowledgments

We gratefully thank the contribution of Devinafarica Lukitaruna and Mahdiyyah Husna Nihar for assisting in collecting the data, screening process, and assessing the quality of the eligible articles.

Authors Contributions

S.M performed the conceptualization, writing, original draft preparation, and funding acquisition; A.H and K.A.D, assisted in data collection, analysis, interpretation, and writing the manuscript, and were involved in the interpretation; J.S reviewed and editing; A.K, B.B, C.K, and C.Y performed the literature search, N.N. performed the literature search, assist in data collection, analysis, interpretation and guided the writing of the manuscript, submission the manuscript. The authors read and approved the final manuscript.

Funding

This research is funded by Universitas Airlangga (Airlangga University), grant number 1287/UN3.15/PT/2021.

Data availability and materials

All important data and materials were available. The correspondent author can be contacted for specific data sources.

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Competing interests: The authors stated that there was no conflict of interest in this study.

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Smoking Predictor for Covid-19 Severity and Mortality- A Systematic Review Based on Evidence for Clinical Practice

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Abstract

Background

Methods

Results

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Introduction

Methods

Eligibility Criteria for Study Selection

Sources of Data and Search Strategy

Selection process

Quality assessment

Data extraction

Results

Characteristics of studies population

History of smoking among study subjects

Outcomes of smoking predictor for Covid-19 severity and/or mortality

Discussion

Conclusion

Strengths and limitations of our study

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